Effect of Haemolysis on Result Quality in Biochemistry Laboratory
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Transcript of Effect of Haemolysis on Result Quality in Biochemistry Laboratory
Determination of acceptable levels of haemolysis when accepting blood samples for
Trace mineral analysis.
by
Bronwyn Claudia CloeteStudent Number: 209214511
To be submitted in fulfilment of the requirements for the degree
BACCALAUREUS TECHNOLOGIAE: DISCIPLINE QUALITY
in the
Faculty of Engineering
CAPE PENINSULA UNIVERSITY OF TECHNOLOGY
Supervisor: M Arderne
DECLARATION
“I hereby declare that this dissertation submitted for the degree Baccalaureus Technologiae
at Cape Peninsula University of Technology, is my own original unaided work and has not
previously been submitted to any other institution or higher education. I further declare that
all sources cited or quoted are indicated and acknowledged by means of a comprehensive list
of references”.
Bronwyn Cloete
Copyright © Cape Peninsula University of Technology
2
DEDICATION
This study is dedicated with much love and appreciation to my friends and colleagues on the
staff of the Western Cape Provincial Veterinary Laboratory. It would have been impossible
to complete this project without you.
3
ACKNOWLEDGEMENTS
WCPVL Staff in Biochemistry Section for their eager support and unconditional assistance,
patience and being so accommodating
Nomphilo Zuma: Technologist Biochemistry
Kuthale Funeka: Technologist Biochemistry
Sarah Groenewald: Biochemistry Housekeeping staff:
WCPVL Staff in supportive role for their assistance and time
Ditsimai Banda: Quality Control and Assurance Manager
Renee Pietersen: Technologist PCR Lab
WCPVL Ground staff for their willingness and helpfulness collecting samples
Chrisjan Jantjies
Paul Slingers
WCPVL Laboratory Management for their support and making human and capital resources
available for this project
Dr. Tertius Gous: Head of Laboratory
Dr. Sophette Gers: Head of Biochemistry section
Dr. Jacob Stroebel
Last but certainly not least, my supervisor Meagan Arderne for her tireless support and
understanding throughout the process of compiling the project proposal and report
4
ABSTRACT
Author: Bronwyn Cloete
Degree: B Tech: Quality
Title: Determination of acceptable levels of haemolysis when accepting blood
samples for Trace mineral analysis.
University: Cape Peninsula University of Technology
Department: Industrial Engineering.
Internal Supervisor: M. Arderne
Date: 21 October 2010
Key Words: Haemolysis; Accurate Results; Quality
The outcome of any process can only be as good as the income provided for that process.
Even if all processes themselves, conducted in any quality environment, are sound, it seems
logical that regardless of the quality maintained during processing, if input quality is not
present namely the sample quality is not sound, then any quality efforts thereafter can be
considered futile.
This applies to all industries, including practices of the Western Cape Provincial Veterinary
laboratory (WCPVL). This paper aims to specifically research the effect of the quality of the
blood samples submitted to the biochemistry section of the WCPVL, on the service they
provide to their clients regarding the outcome in the form of results.
Despite haemolysis known to be a major adversary in clinical laboratories, at present, little
consideration is given to the quality of the results outcomes of the tests carried out in the
section, regardless of samples arriving in a condition suspected of being too haemolysed to
provide accurate results.
This research aims to determine the actual critical level of haemolysis which will actually
invalidate results of analysis carried out on the haemolysed blood. Based hereupon, certain
recommendations can then be made to WCPVL in an attempt to improve the quality of the
service to their clients and the greater agricultural community at large. The imperative aspect
which this paper sets out to demonstrate is to what degree “result quality” would be impacted
by “sample quality” and furthermore exactly how dependent laboratory quality maintained
5
depends on whether or not a sample would be deemed suitable enough to provide a quality
result.
6
TABLE OF CONTENTS
Page
DECLARATION 2
DEDICATION 3
ACKNOWLEDGEMENTS 4
ABSTRACT 5
TABLE OF CONTENTS 7
LIST OF TABLES 10
LIST OF FIGURES 11
GLOSSARY OF TERMS 13
CHAPTER 1: SCOPE OF THE RESEARCH
1.1 INTRODUCTION AND BACKGROUND 15
1.2 RESEARCH PROCESS 15
1.3 BACKGROUND TO RESEARCH PROBLEM 16
1.4 THE RESEARCH PROBLEM STATEMENT 17
1.5 THE RESEARCH QUESTION 18
1.5.1 Primary Research Question 18
1.5.2 Investigative Questions 18
1.6 KEY RESEARCH OBJECTIVES 18
1.7 CHAPTER OUTLINE 19
CHAPTER 2: A HOLISTIC PERSPECTIVE OF A RESEARCH 20
ENVIRONMENT
CHAPTER 3: LITERATURE REVIEW 31
CHAPTER 4: DETERMINATION OF THE EXTENT OF HAEMOLYSIS
ON BLOOD RESULTS IN ORDER TO ELLIMINATE INADEQUATE SAMPLES
4.1 THE SURVEY ENVIRONMENT 38
7
4.2 AIM OF THIS CHAPTER 39
4.3 RESEARCH DESIGN AND METHODOLOGY 39
4.4 DATA COLLECTION 42
4.4.1 Data Collection per protocol analysis methodology 44
4.4.2 Data Collection per laboratory experiments methodology 45
4.4.3 Data Collection per observation methodology 47
4.5 MEASURMENT SCALES 49
4.6 VALIDATION OF DATA 50
4.7 CONCLUSION 50
CHAPTER 5: DATA ANALYSIS AND INTEPRETATION OF RESULTS
5.1 INTRODUCTION 52
5.2 DATA ANALYSIS APPROACHES 52
5.2.1 Haemolysis in relation to time 52
5.2.2 Haemolysis effect in groups 53
5.2.3 Haemolysis effect between groups 53
5.2.4 Demonstration of normal ranges 54
5.2.5 Observation Analysis 54
5.2.6 Protocol Analysis 54
5.3 RAW DATA FINDINGS 55
5.3.1 Haemolysis in relation to time 55
5.3.2 Haemolysis effect in groups 57
5.3.3 Haemolysis effect between groups 66
5.3.4 Demonstration of normal ranges 78
5.3.5 Observation Analysis 84
5.3.6 Protocol Analysis 86
5.4 INTERPRETATION OF STATISTICAL ANALYSIS 87
5.4.1 Haemolysis in relation to time 87
5.4.2 Haemolysis effect in groups 87
5.4.3 Haemolysis effect between groups 89
5.4.4 Demonstration of normal ranges 89
5.4.5 Observation Analysis 90
5.4.6 Protocol Analysis 90
5.5 PROBLEMS ENCOUNTERED DURING RESEARCH 91
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5.6 KEY RESEARCH FINDINGS 91
CHAPTER 6: CONCLUSION AND RECOMMENDATIONS
6.1 BACKGROUND 93
6.2 THE RESEARCH PROBLEM RE-VISITED 93
6.3 RESEARCH QUESTIONS RE-VISITED 93
6.4 INVESTIGATIVE QUESTIONS RE-VISITED 93
6.5 KEY RESEARCH OBJECTIVES RE-VISITED 94
6.6 RECOMMENDATIONS 95
6.7 CONCLUSION 97
BIBLIOGRAPHY 98
ANNEXURE A: Survey questionnaire to Laboratory Staff 102
ANNEXURE B: Protocol Analysis Checklist 104
ANNEXURE C: Modified Flowchart of operations in Biochemistry 105
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LIST OF TABLES
Page
Table 2.1: Mean Monthly Specimens received in Biochemistry 21
Table 2.2: Mean Monthly Specimens processed in Biochemistry 21
Table 2.3: Inter-laboratory Comparisons 29
Table 4.1: Demonstration of Observation Method 48
Table 5.1: Comparative Table: Average Haemolysis Readings 55
Table 5.2: Regression Table: Time and Haemolysis 56
Table 5.3: Comparative Table: Average Copper Readings 57
Table 5.4: Regression Table: Haemolysis and Copper 57
Table 5.5: Comparative Table: Average Zinc Readings 58
Table 5.6: Regression Table: Haemolysis and Zinc 59
Table 5.7: Comparative Table: Average Calcium Readings 59
Table 5.8: Regression Table: Haemolysis and Calcium 60
Table 5.9: Comparative Table: Average Phosphorous Readings 61
Table 5.10: Regression Table: Haemolysis and Phosphorous 62
Table 5.11: Comparative Table: Average Magnesium Readings 63
Table 5.12: Regression Table: Haemolysis and Magnesium 64
Table 5.13: Comparative Table: Average Iron Readings 64
Table 5.14: Regression Table: Haemolysis and Iron 65
Table 5.15: Anova Table: Copper 66
Table 5.16: Anova Table: Zinc 68
Table 5.17: Anova Table: Calcium 70
Table 5.18: Anova Table: Phosphorous 72
Table 5.19: Anova Table: Magnesium 73
Table 5.20: Anova Table: Iron 75
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LIST OF FIGURES
Page
Figure 2.1: Pie Chart: Mean specimens processed in Biochemistry 22
Figure 2.2: Quality Management System Training Procedure 24
Figure 2.3 Quality Management System Procurement Procedure 25
Figure 2.4: Flowchart of operations in Biochemistry 27
Figure 3.1: Illustration of operation of a spectrophotometer 35
Figure4.1: Action Research 43
Figure 4.2: Demonstration of application of Protocol Analysis Methodology 45
Figure 5.1: Overview: Average Haemolysis Readings 55
Figure 5.2: Comparative Chart: Haemolysis Readings in groups 55
Figure 5.3: Regression Chart: Time and Haemolysis 56
Figure 5.4: Comparative Chart: Average Copper Readings 57
Figure 5.5: Regression Chart: Haemolysis and Copper 58
Figure 5.6: Comparative Chart: Average Zinc Readings 58
Figure 5.7: Regression Chart: Haemolysis and Zinc 59
Figure: 5.8: Comparative Chart: Average Calcium Readings 60
Figure 5.9: Regression Chart: Haemolysis and Calcium 61
Figure 5.10: Comparative Chart: Average Phosphorous Readings 62
Figure 5.11: Regression Chart: Haemolysis and Phosphorous 63
Figure 5.12: Comparative Chart: Average Magnesium Reading 63
Figure 5.13: Regression Chart: Haemolysis ad Magnesium 64
Figure 5.14: Comparative Chart: Average Iron Reading 65
Figure 5.15: Regression Chart: Haemolysis and Iron 66
Figure 5.16: Effect of Haemolysis on Copper: Fresh 78
Figure 5.17: Effect of Haemolysis on Copper: 3 Days 78
Figure 5.18: Effect of Haemolysis on Copper: 6 Days 78
Figure 5.19: Effect of Haemolysis on Copper: 9 Days 78
Figure 5.20: Effect of Haemolysis on Zinc: Fresh 79
Figure 5.21: Effect of Haemolysis on Zinc: 3 Days 79
Figure 5.22: Effect of Haemolysis on Zinc: 6 Days 79
Figure 5.23: Effect of Haemolysis on Zinc: 9 Days 79
Figure 5.24: Effect of Haemolysis on Calcium: Fresh 80
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Page
Figure 5.25: Effect of Haemolysis on Calcium: 3 Days 80
Figure 5.26: Effect of Haemolysis on Calcium: 6 Days 80
Figure 5.27: Effect of Haemolysis on Calcium: 9 Days 80
Figure 5.28: Effect of Haemolysis on Phosphorous: Fresh 81
Figure 5.29: Effect of Haemolysis on Phosphorous: 3 Days 81
Figure 5.30: Effect of Haemolysis on Phosphorous: 6 Days 81
Figure 5.31: Effect of Haemolysis on Phosphorous: 9 Days 81
Figure 5.32: Effect of Haemolysis on Magnesium: Fresh 82
Figure 5.33: Effect of Haemolysis on Magnesium: 3 Days 82
Figure 5.34: Effect of Haemolysis on Magnesium: 6 Days 82
Figure 5.35: Effect of Haemolysis on Magnesium: 9 Days 82
Figure 5.36: Effect of Haemolysis on Iron: Fresh 83
Figure 5.37: Effect of Haemolysis on Iron: 3 Days 83
Figure 5.38: Effect of Haemolysis on Iron: 6 Days 83
Figure 5.39: Effect of Haemolysis on Iron: 9 Days 83
Figure 5.40: Graphic Representation of Results of Observation Data collected: 84
Pie Charts: Shortfalls
Figure 5.41: Graphic Representation of Results of Observation Data collected: 85
Pie Charts: Improvement
Figure 5.42: Graphic Representation of Results of Observation Data collected: 86
Pie Charts: Haemolysis Grading
Figure 5.43: Spiderchart: Results of Protocol Analysis 87
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GLOSSARY OF TERMS
Case – A group of specimens submitted to the laboratory for testing purposes, from the same
sender, usually all specimens derived from the same owner or farm, all having the same
submission number, although each specimen has it’s own unique identification number.
Centrifuge – A device for separating components of different densities in a liquid, using
centrifugal force. The liquid is placed in special containers that are spun at high speed
around a central axis. (Concise Oxford Veterinary Dictionary, 1988, pg 153-154)
Control (Group) – The part of a study or experiment against which an experimental
procedure can be compared and it’s effects judged. (Concise Oxford Veterinary Dictionary,
1988, pg 193)
Cuvette – a small glass tube used in spectrophotometry (http://www.your
dictionary.com/cuvette n.d.)
Fibrinogen – Protein precursor from which the insoluable component of blood clots is
formed in the final stage of coagulation. (Concise Oxford Veterinary Dictionary, 1988, pg
314)
Haemolysis – Haemolysis (alternate spelling Haemolysis) is the destruction of red blood
cells (erythrocytes) (Concise Oxford Veterinary Dictionary, 1988, pg 366)
Haemoglobin – One of a group of proteins that occur widely in animals and function as
oxygen carriers in the blood. Alternative names Hgb; Hb (Concise Oxford Veterinary
Dictionary, 1988, pg 365)
In Vitro – Latin: describing biological phenomena that are made to occur outside the living
body (traditionally in a test tube). (Concise Oxford Veterinary Dictionary, 1988, pg 444)
LIMS – Acronym for Laboratory Information System. LIMS is a software system used in
laboratories. This system enables the electronic management of samples, laboratory users,
standards and other laboratory functions such as QA/QC Quality Assurance and Quality
13
Control, as well as the integration of all laboratory softwares, and instruments. It facilitates
workflow automation, sample planning, and invoicing.
Normal Reference Values – Reference ranges for blood tests are a set of values used by a
health professional to interpret a set of medical test results from blood samples.
Plasma – The fluid component of blood in which blood cells and platelets are suspended.
Plasma is obtained as a clear yellow-to-white liquid when blood is collected into an
anticoagulant and cells are removed by centrifugation. (Concise Oxford Veterinary
Dictionary, 1988, pg 650)
Process Approach – “The process approach is a management strategy. When managers use a
process approach, it means that they manage the processes that make up their organization,
the interaction between these processes, and the inputs and outputs that tie these processes
together” ( http://www.praxiom.com/iso-definition.htm 5 September 2010)
Purposive sampling - This process is the selection of a particular sample on purpose.
Popular with qualitative research, the variables to which the sample is drawn up are
analytically and theoretically linked to the research questions.
(http://www.marketresearchterms.com/p.php 2010)
Serum – The fluid that separates from clotted blood or plasma that has been allowed to stand.
It differs from plasma in lacking coagulation factors. (Concise Oxford Veterinary Dictionary,
1988, pg 747)
Spectrophotometer – Device or instrument measuring light intensity or optical properties of
solutions or surfaces, specifically measuring the intensity as a function of colour or
wavelength.
14
CHAPTER 1: SCOPE OF THE RESEARCH
1.1 INTRODUCTION AND BACKGROUND
Quality is critical in diagnostic laboratories, not only due to common economical reasons
shared by most organisations, but the service rendered by a diagnostic laboratory has
transience implications as well. All quality practitioners are aware of the fact that a quality
output of any process can only be obtained from quality input. Therefore in order to obtain
what can be considered a quality standard of results, certain requirements are essential, such
as adequate equipment, QMS, quality reagents, etc. None of these above-mentioned will
make any difference however if the integrity of the actual sample submitted for testing
purposes is not suitable for the particular trace mineral test that is requested by the client.
Thus logically, the quality of all the blood samples submitted for trace mineral analysis in the
Biochemistry Section of the Western Cape Provincial Veterinary Laboratory must be
screened in order to determine if it meets minimum acceptance criteria needed to carry out
the type of analysis requested, without jeopardizing the quality of the results of the analysis.
1.2 RESEARCH PROCESS
Initiation of the Research process began with the identification of the aspect requiring
investigation in the form of research, namely the effect of haemolysis on sample quality in
the laboratory environment at WCPVL Biochemistry Section. The Research process
proceeded by the drafting a problem statement in relation to this topic and the preparation and
submission of a research proposal to address the topic.
The preparation of the research proposal was approached by conducting comprehensive
literature review surrounding the topic of haemolysis and it’s effect on sample and result
outcome. Appropriate quality project management tools were identified and selected for use,
to plan the actual research process. A flowchart is used to graphically illustrate and map the
approach to the research process. Research was conducted in order to ascertain the type of
research needed to address the problem statement, as well as identification of which research
paradigm research, research would fall in. An affinity diagram was used to highlight and
identify all aspects surrounding research, as well as a PERT chart outlining the framework of
15
research in the context of a timeframe. All factors not forming part of the actual research but
playing a role in the research process was also given due consideration in the proposal stage.
Following the submission and acceptance of the research proposal, the actual research
commenced with the data collection as indicated in the research proposal, followed by the
analysis of the raw research data, the acquisition of findings based on raw data relating to all
branches of research from this, and thereafter interpretation of the findings. Appropriate
quality tools were also utilised during this stage. Following this, drafting of the Research
Report was able to proceed.
Drafting the Research report involved the re-consideration Research problem statement with
more in-depth consideration given to the research environment. The necessary additional
literature review identified, was conducted and incorporated into the report at this stage.
Finally, the data findings and interpretation thereof, was contextualised and documented for
the purpose of addressing the research problem, coming to a conclusions and making
recommendations on the research problem.
1.3 BACKGROUND TO RESEARCH PROBLEM
Quality control is not only for larger, sophisticated laboratories but necessary for any
laboratory providing test results. For diagnostic laboratories as is the case for WCPVL,
Accuracy, Precision and Reliability thus play critical roles. Accuracy can be defined as the
extent to which measurements agree with the true value of the quantity being measured.
Precision is the reproducibility of measurement and Reliability is the ability of a method to be
both accurate and precise.
As it is practicality impossible for each sample being tested in duplicate, to obtain the true
value, thus methods must be employed to ensure that results obtained by testing a sample
once is statistically near to true value (accuracy). It must also be ensured that a statistically
similar can be obtained on any given specimen if repeated (precision), lastly in addition to the
above-mentioned, methods must be employed to ensure that the above-mentioned are ensured
(reliability). These are the premises of any quality control or assurance program.
16
The above-mentioned relates to the problem at hand in the sense that it applies to all samples
submitted for testing purposes to the Biochemistry Section of WCPVL. Although there is an
extensive range of trace minerals to select from, for the purpose of this project, the decision
was taken to investigate the effect of haemolysis on Calcium, Magnesium, Iron, Phosphate,
Copper and Zinc in Sheep blood.
Normal Ranges or Normal Values are the average normal values of a particular trace element
at any given time in the system of an animal, and these play a critical role during the duration
of our project. Normal values for chemistry tests are subject to innumerable variables, such
as geographic area, season, species, breed, sex, age, husbandry practices, level of feeding,
sample handling, interval between sampling and testing, test method used, person performing
the test and quality control practices. Published normal values can be used as guidelines, but
true normal values must be established for each laboratory by repeated testing of normal
animals. As this is impractical for most laboratories it must be decided which set of
published normal values to use. Most sets are fairly consistent with each other.
Calcium: Previous studies indicate that haemolysis results in a slight decrease in serum
calcium levels as the fluid from ruptured cells dilutes the serum. Normal levels in sheep,
12,16mg/dl+-0.28mg (Pratt, 1985: (15))
Magnesium: Previous studies indicate haemolysis elevates results as magnesium is released
from ruptured cells. Normal levels in sheep, 2.5mg/dl +-0.3mg (Pratt, 1985: (15))
Iron: Normal levels in sheep 29.73ug/100ml -39.76ug/100ml (Puls, 1989: (16))
Phosphate (Inorganic Phosphorous): Previous literature indicates that haemolysis should be
avoided as the organic phosphorus within RBC may be hydrolyzed to inorganic phosphorous
resulting in increased serum levels. Normal values in sheep: 5.0-7.3mg/dl (Pratt, 1985: (15))
Copper: Normal values in sheep 0.70-2.00 ppm wet weight (Puls, 1989: (16))
Zinc: Normal values in sheep 20-40 ppm wet weight (Puls, 1989: (16))
1.4 THE RESEARCH PROBLEM STATEMENT
Trace Mineral Results from analysis carried out in the Biochemistry laboratory of WCPVL
are possibly invalid or of poor quality due to levels of sample haemolysis going unscreened
in the section.
17
1.5 THE RESEARCH QUESTION
1.5.1 Primary Question
What is the maximum haemolysis level acceptable, as measured in terms of optical density
using a spectrophotometer at 540nm wavelength, in order to accept samples for Trace
Mineral Analysis?
1.5.2 Investigative Questions
Is there a difference in the haemolysis level of samples read on Day 0, Day 3, Day 6, Day
9 and Frozen.
Are unacceptable samples being accepted as suitable in the current system?
What are the shortfalls of the current system?
What are the practical considerations or recommendations that can be made to manage
acceptance procedures of samples?
1.6 KEY RESEARCH OBJECTIVES
The primary objective of our research is:
To determine exact values of acceptable levels of haemolysis when accepting blood samples
for trace mineral analysis in terms of the concentrations of trace minerals present in serum
measured in nanometre (nm) units when read on a spectrophotometer.
Secondary objectives evolving from the primary objective:
To determine practical measurable methods available to identify unacceptable samples.
To determine the effects of haemolysis levels of blood samples at WCPVL in terms of the
quality system of the laboratory and implications thereof.
18
To identify preventative measures required to be implemented within biochemistry laboratory
to prevent future acceptance of unsuitable samples.
1.7 CHAPTER OUTLINE
Thus in conclusion, in order to achieve the essential quality components of reliability,
precision and accuracy to WCPVL to uphold their reputation as a quality service provider and
thereby delivering superior customer satisfaction, research into the effect of haemolysis on
quality output is embarked upon.
Chapter 1 identifies the problem by exploring preliminary indicators and surrounding issues,
solicit ting the need for research into the identified problem to be carried out.
Chapter 2 considers the Biochemistry Section of WCPVL holistically, exploring all
contributing factors which play a potential role in the research in order to direct the research
process.
Chapter 3 comprises of a comprehensive literature review on the factor known as haemolysis
including the definition and effects of the factor. It explores the potential implications of
haemolysis as well as what steps can be taken in the form of quality tools to address the
factor.
Chapter 4 extrapolates on the type of research needed to address the haemolysis factor. It
outlines the type of data needed and methodologies needed, so that research can have a
valuable impact and achieve it’s objectives.
Chapter 5 involves data analysis based on the raw data collected. Data findings are attained
and interpreted with the aid of quality tools.
Chapter 6 is considered a conclusive summary, resulting from perusal of the research process,
in which final recommendations are made in order to resolve the initial problem statement.
19
CHAPTER 2: A HOLISTIC PERSPECTIVE OF THE RESEARCH ENVIRONMENT
A research environment is defined as a set of tools, systems and processes interoperating, to
facilitate and enhance the research process, within and without institutional boundaries. It
can therefore be said that a research environment includes a combination of the “nature and
the culture” of a particular environment, as well as tangible aspects relating to the
environment, in which any research is conducted.
Viewing the research environment from a holistic perspective, provides the researcher a
opportunity to identify environmental factors impacting on the quality output of the
Biochemistry section at the Western Cape Provincial Veterinary Laboratory, enabling the
researcher to determine the root problem. The factors identified for consideration are those
suspected of having critical influence on the operational side of the research environment.
These independent, yet interrelated components and can be broadly defined as “invalidated
input factors”, providing “inconsequential processing” leading to “unsubstantiated output” of
the research environment. In an attempt to substantiate whether the output process is indeed
of consequence and adds value to the service delivered by the laboratory, the input, process
and output components of the research environment system must analysed. If there are
further subsystems are found to be present, the critical examination continues till within those
until investigations determine if there is a relation between them and their sub-factors which
ultimately influence the service offered by the laboratory.
In general terms “Input” refers to the all requirements necessary for the research
environment. Input can however been segmented into primary input and secondary input.
The primary input of research environment being the samples, leads to the identification of
the “samples”, as the initial focus point when considering the environment holistically.
Over the time period that the project was conducted it was found that an average of 317
sample specimens per month were received and handled by the biochemistry laboratory.
Specimens include blood/serum samples, liver samples (biopsies or larger pieces of tissue,
bone and other biological fluids such as eye fluid or urine)
20
Upon further investigation, it was found that on average, 66,56% of the total number of
specimens were blood/serum specimens. Furthermore, Trace mineral analysis and/or Cu and
Zn testing was requested on 67,78% of the total amount of blood samples sent into the
laboratory. It is important to note that this percentage value does not take into consideration
of large amount of organ tissue samples, which also requested that trace mineral analysis be
conducted on them. As this project only involved analysis on blood samples, information
regarding the same testing procedures performed on different sample types is omitted and
excluded for statistical purposes.
It appears evident, considering the proportion of samples on which trace mineral analysis is
required, represents a substantially large amount of the total incoming samples and thus
accurate results needs to be assured. It poses a critical quality problem for any diagnostic
laboratory with reputable quality practices, should a problem be suspected but not addressed.
This could logically lead to potential, proportional and severe consequences. As illustrated
the amount of samples arriving at the section in an unverified condition requires that further
investigation essential, as such a large percentage cannot be ignored or considered isolated
cases.
It was deemed essential to thus determine the following:
Table 2.1: Mean Monthly Specimens received in Biochemistry
Mean Monthly totals in Biochemistry
Mean number of Cases received per month 70
Mean number of Specimens per Case per month 5
Mean number of Blood/Serum samples (specimens) per month 211
Mean Trace Mineral Requests per month (including Cu and Zn) 143
Total number of requests for only Cu and/or Zn 66
Total number of requests for Ca, Phos, Mg, and/or Fe 108
Table 2.2: Mean Monthly Specimens processed in Biochemistry
Mean Sample Processing per month
Amount PercentageType of sample Testing Required
Liver samples Se, Cu, Zn, Fe and Mg testing 62 19.56%
21
Blood/Serum
samples
Trace mineral analysis (Ca, Phos, Mg, Fe
etc)211 66.56%
Bone/ Hair, feed or
Multimin samples
Se, Cu, Zn and Trace mineral analysis7 2.2%
Other samples Various 37 11.67%
Figure 2.1 A graphic representation of the tables above is illustrated in the Pie chart
Continuing the consideration of the primary input of the research environment, the type of
sample being suitable for testing purposes required, as well as the quantity of sample required
for testing were deemed the important issues arising. It was determined however that the
most critical factor with potential impact and influence on result output was sample
condition, and all other further investigations focus surrounded this.
Thus following was the determination of are additional input factors related to samples which
potentially influence validity of the process and thus the outcome of results. This has to be
addressed in an adequately scientific manner in order to maintain the validity of all results
stemming from this project. It is found that the best approach in order to contextualise and
gain perspective would be by the employment a “process-based approach" of looking at
system inputs. This approach is based on the knowledge that processes in system are all
interlinked (whether parallel or sequential), it is important that they are all successful and
lastly they cannot be adequately evaluated on a stand-alone basis without regard of their
relationship to each other, as the output of one process feeds the input of the next process in
22
some of other way. The use of a decision chart was thus used to extrapolate issues in able to
contextualise them holistically
The nature and culture of any research environment is directly related to personnel within the
research environment. Personnel forms the backbone of the research environment, must
therefore be also considered a critical component also due for scrutiny. Recognizing the
inconsistency of human behaviour, stresses the important role the institution where the
research is being conducted, has to play in providing an integrity-rich environment. Research
institutions need to provide personnel with education and skills training, guiding procedure
and policies, as well as support systems and tools required to conduct the research.
An essential question which arises is, “Does the research environment makes provision for,
and provide facilitates and practices which characterize integrity, such as peer reviews and
the promotion of self-evaluation efforts among staff in terms of the research being
conducted.” Prompted by this a further question which then presents itself is “Does the
research environment make provision for an ethical working environment framework with a
staff component able to provide a service of the highest standard when measured in terms of
empathy, assurance, reliability, responsiveness (indicators of service quality) in the
laboratory.”
In an attempt to gain perspective and understanding of these issues, focus is shifted to the
current Quality Management System operations employed in the laboratory. Thus, leading to
the examination of QMS documentation with reference to the Human Resource component of
the laboratory. A standard operating procedure was found to be in place with regard to
training. This procedure forms part of the validation component that is in operation in this
research environment.
23
Figure 2.2: Quality Management System Training Procedure
Yet another important secondary input factor identified, involves considering tangible aspects
of the research environment. This included infrastructure such as equipment (fridges to
preserve samples and reagents before actual testing can be carried out, centrifuge to spin
blood down immediately, pipettes and pipette tips, Testing equipment such the Atomic
Absorption Spectrophotometer and Vitros blood chemistry apparatus used to perform reliable
tests, balances and infrastructure of perform quality test. It is found that these are integral
components of the research environment and it needs to be ensured that they are available
and in adequate working order.
Similarly, another essential tangible input factor of the environment is quality reagents are
just as critical in the maintenance of a good research environment. Quality reagents should
thus always be available and of suitable grade for testing purpose. It is found that both
procurement as well as maintenance of equipment in addition to procurement and testing of
quality reagents used to perform testing procedures needs to be confirmed. Thus an
overlapping occurs yet again between laboratory function and QMS function. Quality
Reagents however remains a laboratory function, despite overlapping and interrelation with a
24
QMS. A QMS procedure is in place however to guarantee the procurement of necessary
system input in terms of quality reagents and employs the use of a critical supplier’s list.
Figure 2.3 Quality Management System Procurement Procedure
Quality Management System practices are explored within the context of Biochemistry
research environment and attention is focussed on such as the quality control measures and
quality assurance systems. The following was deemed necessary to be established regarding
the environment:
If all the necessary Standard Operating Procedures (SOP’s) for analysis/testing
procedures have been developed and are available.
If all necessary support documentation as well as records available for Standard
Operating Procedures were available
If adequate customer feedback procedures and mechanisms, relating to all inputs
leading to results in place?
Does environment allow for optimal staff functioning e.g. troubleshooting, can staff
decide sample unsuitable for quality result.
Frontline Activity
Quality Management System Procurement Procedure
Laboratory Function: Track and monitor stock Requisition necessary requirements: use Z15
form Requisition on basis of
Certificate of analysisPrice CompetitivenessSANNAS AccreditationTransport and delivery
Obtain 3 written quotes Confirm stock upon arrival at section Identify Suppliers Schedule Calibration and Service of
instruments
Administrative staff follow Procurement Procedure:
Procedure available at Admin Function
Administration Clerk issues order numbers
Stock Received by Administrative function
Evaluation of suppliers and service providers
Suppliers must be able to meet requirements
Service Providers must be accredited and be able to
demonstrate this
Suppliers on List of Critical Suppliers
Procedural requirements: Minimum of 3 Written Quotes Needed Records must be maintained Sole Agent must provide evidence Orders over R10 000 must be linked to
Tradeworld Orders over R50 000 must go out on
Tender
Hidden Factory Activity
25
The final consideration regarded as important when considering Input Factors in
understanding the research environment, is whether Management demonstrates support to the
Biochemistry section in order to achieve their objectives. It was found to be demonstrated by
the manner management ensures requirements for the section are provided to fulfil function
and objectives. A further indication of management support can be said to be demonstrated
by them showing full support to the initiation, preparation for, as well as the carrying out of
the research for this project in order to make quality improvement to the laboratory.
The secondary part of the exploration into the research environment involves consideration of
the actual Testing Processes involved. The testing processes refer to the main Critical
processes conducted for which the previous system inputs feed into, and which in turn deliver
the results or final product required by the customer.
A process, otherwise known as a “value-added activity” transforms an input to an output, and
can be defined as “A sequence of interdependent and linked procedures which, at every
stage, consume one or more resources (employee time, energy, machines, money) to convert
inputs (data, material, parts, etc.) into outputs. These outputs then serve as inputs for the next
stage until a known goal or end result is reached” (Businessdictionary.com, 2010: online)
Within the Process system of the research environment it is also found that there are at least
two subsystems, namely Primary processes which involve main processes providing the
results required by the customers and also secondary processes, which are those support
processes required with the preparation of samples and administrative processes associated to
the actual testing procedures.
The complete set of processes employed by the Biochemistry research environment can be
said to be called a system or process system. A system is defined as a set of detailed
methods, procedures, and routine established or formulated to carry out a specific act,
perform a duty or solve a problem. (Businessdictionary.com, 2010: online)
The systems employed for the purposes of this research are considered to be the following:
A) Sample registration and preparation
B) Sample processing
C) Validation and Issuing of Results
26
D) QMS related systems
The schematic Fig. 2.4 gives a general overview of the processes involved. Minor processes
are performed which make up the different processes indicated on the schematic, which in
turn make up the system required to achieve section objectives, and ultimately management
and customer satisfaction.
Fig 2.4: Flowchart of operations in Biochemistry
It can be said that the most important aspect to address when exploring this part of the
research environment is the validity of all the processes identified. It is thus important to
identify confirmed methods whereby validity can be established.
Validate is defined as “To declare or make legally valid; To establish the soundness of;
corroborate” (thefreedictionary.com, 2010: online). Validation provides assurance that
results are accurate, thus of good quality. Thus contextualising definition in terms of the
Quality Management SystemAll systems in Biochemistry
Reception: Samples arrive at LabSample information captured
Sample Analysis:Verifiable analytic method according to SOP.
Controlled conditionsUse of Controls and Standards
Critical Suppliers
Quality Management Documents and Records
Satisfied Service Customer
Technologist reviews resultIssues it for release from Biochem
Validation: Checks performed to see if SOP followedControls and Standards in spec
Veterinarian reviewsCompiles with results from other labs
Issues report
Samples delivered to Biochemistry Section
Sample Reception at BiochemistrySamples information recorded.
Biochemistry lab number assigned.Test Allocation.
Samples stored under ideal conditions until testing
Record keeping
YES
NO: Corrective action involves
redo
27
research conducted refers to establishing documented evidence which provides a high degree
of assurance that a specific process will consistently produce a result product meeting its pre-
determined specifications and quality attributes.
Thus validation can also be seen as a function to ensure accurate results as well as process
capability in a system. Juran defines process capability as "the measured, inherent
reproducibility of the product turned out by a process."(Juran, 1988: page 158) The procedure
of validation thus involves determining a set of methods or tools to be used to evaluate if the
expected results of a process conforms with a set of pre-determined acceptance criteria, and
using these to verify the outcome of a process. A validated process is generally considered to
be a stable process, and validation approaches for processes can range from an “auditing
type” approach to utilization of simple statistical testing or tools approach or even
comparative studies with a previously validated party.
It was determined that no formal auditing process was employed at the WCPVL, however an
informal audit of procedures at the WCPVL had been conducted in preparation for planned
accreditation. The results of this audit, reflected non-conformances indirectly related to the
processes involved in this study, such as stock room procedures, which would not have a
direct bearing or effect on the actual processes or resulting outcome thereof. However,
attempts to procure documented evidence of process validation or process soundness from the
general laboratory QMS were unsuccessful, despite having a seemingly good quality system
instituted and in operation.
The Biochemistry section, had however participated in inter-laboratory testing with three
other laboratories, two of which (Nutrilab: University of Pretoria and CSIR) are reputed
laboratories maintaining SANNAS compliant procedures. The results of the inter-laboratory
testing, was found to be very successful in terms of their comparisons.
28
Table 2.3: Inter-laboratory Comparisons
WCPVL internal positive control – Inter-laboratory Testing Data
Sample ID: LC2009
Cu ( mg/kg) Fe (mg/kg) Zn (mg/kg) Mn (mg/kg)
WCPVL values 266 543 202 9.64
Nutrilab (UP) 254 584 221 10.3
Elsenburg Soil lab 215 528.5 180 8.83
CSIR 293.71 550.15 220.37 9.58
Furthermore the following positive evidence was found regarding the quality of the
operations carried out in the biochemistry section namely:
A comprehensive Work Instructions in the form of SOP’s (Standard Operating
Procedures) were in place for all core processes and well as support processes in the
Biochemistry section.
Support Documentation in the form of worksheets, forms and records were in place
and effectively used.
The system made provision for effective identification and traceability of all samples
processed in the system, including the use of an IT based LIMs (Laboratory
Information Management System)
A Robust system of Controls and well as Reference Standards were included with
each testing process/procedure carried out.
The residual considerations of the Process System as a factor in understanding the
Biochemistry research environment from a holistic perspective involved exploring how
Corrective and Preventive issues with regard to processes would be addressed. It was found
to be noteworthy that although detailed procedures with regard to all other requirements were
in place and working effectively, there was not a specific procedure dealing with issue of
corrective and preventive action in the research environment in the system. Thus despite
having comprehensive QMS procedures effectively operating with regard to the operational
processes in the system, no definite way was identified to give guidance and direction in the
event of unforeseeable or unavoidable errors which could potentially occur in the system,
such as equipment breakdown despite maintenance or human error. On the whole the system
appears to be sound however.
29
Evolving from all the above-mentioned considerations, the holistic perspective and
understanding of the research environment aims or directs the researcher to the Output
Factors. Output Factors which form the third and last crucial component in the critical
examination of the research environment, are thus directly impacted upon, and directly
dependent on Input and Process Factors.
Explorations into this last factor reveals that adequate support processes for the
documentation and the timeous distribution of result were in place, in terms of policy,
procedure and infrastructure. A SOP for issuing results from the section adequately covers
all significant issues involved including the relevant authorities needed for issuing results, as
well as timelines in which results should be issued. In addition the utilisation of the LIMS
system facilitates results issuing and provides the necessary back-up required, should there be
a re-issue required.
Thus in conclusion, it can be seen that the most essential and most relevant aspect for
consideration relating to the Output factor is whether the output can be assured or whether it
cannot be assured, and ultimately it “almost demands”, unsubstantiated output is a critical
issue and needs to be addressed
All factors as examined in this holistic manner are found to form part of the research
environment known as Biochemistry. The factors not only lead to, but provides perspective
and insight as well, into the most pertinent and relevant statement about the research
environment, which is Trace Mineral Results from analysis carried out in the Biochemistry
laboratory of WCPVL are possibly invalid or of poor quality due to levels of sample
haemolysis going unscreened in the section.
30
CHAPTER 3: LITERATURE REVIEW
Saibaba, Vijaya Bhaskar, Srinivasa Rao, Ramana and Dakshinamurty (1998: online) has
stated that a number of interferences affect the analytical accuracy when conducting analysis
of body fluids in a clinical chemistry laboratory. For the purposes of diagnostic laboratories
it becomes critically important for the technologist or chemical analyst to be constantly aware
of this factor. As an integral part of the quality assurance program of the laboratory, it is
recommended that such factors be corrected. Furthermore they defined an interference
(1998: online), as being when a substance present in a sample has an effect which changes or
alters the correct value of the result of the analyte. (Saibaba et al., 1998: online)
Thomas (2010: online) contends that haemolysis is an important interference factor with
regard to determination of the normal trace mineral levels in serum. The influence of
haemolysis therefore cannot be ignored and must be considered when accepting samples and
the issuing of outgoing results. (Thomas, 2010: online)
Grafmeyer, Bondon, Manchon, and Levillain (1995: online) concluded that the most common
interference factor effecting validity of results was found in 34.5% of cases was haemolysis,
followed by total bilirubin interfering in 21.7% of cases while direct bilirubin and turbidity
seem to interfere less at around 17% (The influence of bilirubin, haemolysis and turbidity on
20 analytical tests performed on automatic analysers. results of an inter-laboratory study.
(Grafmeyer et al., 1995: online)
Hidiroglou (1983: online) states that average normal levels in sheep with regard to Calcium is
12.11 +-0.69mg/100ml (Hidiroglou, 1983: online) and similarly Pratt, Paul W (1985: (15)),
states that average normal levels in sheep for mineral calcium is 12,16mg/dl+-0.28mg . (1985:
(15)). Thus supporting the view that although more than one set of published normal values
are available for laboratories to use, and most sets are fairly consistent with each other,
however published normal values should only be used as a guideline considering normal
values are subject to variables such as species, breed, age etc. (Pratt, 1985: (15))
Haemolysis is defined as the breakdown of red blood cells and the release of haemoglobin
and intracellular contents into the plasma, and according to Guder. (1986: online)
31
Furthermore Guder states that at any preanalytical stage, the release of blood cell constituents
into plasma (serum) i.e. haemolysis can occur. Haemolysis occurring in vivo may be as a
result of disease (1986: online) whereas physical (mechanical destruction, freezing,
hyperosmotic shock), chemical (detergents), or metabolic causes (increased fragility due to
inherited diseases, depletion of glucose in specimen, metabolic inhibitors of enzymes) may
cause haemolysis occurring in vitro. Plasma concentrations exceeding 300mg/L, results in
the haemolysis of red blood cells being observable to the naked eye. (Gruder, 1986: online)
According to Arzoumanian (2004: online), “Hemolysis is the breakage of the red blood cell’s
(RBC’s) membrane, causing the release of the hemoglobin and other internal components
into the surrounding fluid. Hemolysis is visually detected by showing a pink to red tinge in
serum or plasma.1 Hemolysis is a common occurrence seen in serum samples and may
compromise the laboratory’s test parameters. (2004: online) Hemolysis can occur from two
sources:
• In-vivo hemolysis may be due to pathological conditions, such as autoimmune hemolytic
anemia or transfusion reaction.
•In-vitro hemolysis may be due to improper specimen collection, specimen processing, or
specimen transport.” (Arzoumanian, 2004: online)
From literature available, it was found that Thomas (2010: online) concurs with saying when
haemolysis is present in a sample, the possibility exists of the discharge of intracellular
constituents into the plasma/serum that may of occurred, consequently analytical results are
often false possible according to Thomas. (2010: online)
Thus it can be said that the presence of haemolysis in samples will have a direct effect and
impact of the quality standards in the laboratory where the tests are being conducted, by
being the reason for erroneous results being produced by analysis carried out on inadequate
samples. (Thomas, 2010: online)
Erroneous results have detrimental implications to clinical laboratories in terms of quality.
Lippi,, (2009: online) states that a major worldwide concern for all clinical laboratories is in
vitro haemolysis as through affecting test results it seriously impacts on patient care and the
laboratory’s reputation. (Lippi, 2009: online)
32
Furthermore it can be said that haemolysis also poses a problem in terms improvement of
quality relating to monetary benefit. In a study conducted by Ong, Chan, Lim (2009: online)
it was found that a cost saving occurred with a reduction in sample hemolysis from 19.8%
(before) to 4.9% (after) (P <.001). This further translated into a cost savings of SGD$834.40
(USD$556.30) per day at the emergency department and SGD$304,556 (USD$203,037) per
year. (Ong et al., 2009: online)
Spencer, and Rogers (1995: online) suggests that between quality improvement and
haemolysis a direct link exists. Although it is physically possible to produce results at a
remarkable speed and accurately within decimal point ad infinitum, it becomes redundant if
the specimen is unsuitable.
Trying to eliminate unsuitable specimens such as haemolysed specimens can thus be seen to
be part of a Quality Improvement Process (QIP) and Spencer and Rodgers (1995: online)
proposed a 4 step system in order to do so.
Step 1 involves defining the problem. Step 2 involves gleaning root causes of the problem.
Step 3 involves implementing countermeasures and Step 4 Checking results and maintaining
gains. Such a laboratory quality assurance plan includes several monitors which would assist
a Quality team track the effects of its recommendations. (Spencer et al., 1995: online)
Johnson and Besselsen states the outcome of studies performed on animals can be influenced
by a plethora of variable (2002: online) e.g. genetic variables, environmental variables,
infectious agents etc. Thus the critical need arises, in order to recognize the presence of
unwanted variables as well as minimize the impact of extraneous variables, to use control
animal groups, which are animals sharing the same or similar characteristics. (Johnson et al.,
2002: online)
Similarly a control group as defined by Oxford Concise Veterinary Dictionary is (Oxford
Concise Veterinary Dictionary, 1988, page 193) “The part of a study or experiment against
which an experimental procedure can be compared and it’s effects judged. In animal
experimentation, control animals are subjected to exactly the same conditions” In other
words to conclusively demonstrate a particular factor, the animals used as part of a control
group should be of the same breed, same age, gender and kept under similar conditions
(Oxford Concise Veterinary Dictionary, 1988, page 193)
33
Rising levels of free hemoglobin in serum indicates ongoing haemolysis and thus a typical
measure of hemolysis is the level of free hemoglobin in serum as stated by Na Na, Jin
Ouyang, Youri, Taes and Joris, Delanghe (2005: online) whereby they contend that
hemoglobin is a marker which can be used to indicate hemolysis is free hemoglobin in serum.
(Na Na et al., 2005: online)
Walters, Williams, Hazer, and Kameneva, (2007: online) argue that the baseline degree of
hemolysis present in blood is determined by the level of free hemoglobin in plasma/serum.
(Walters et al, 2007: online)
Studies conducted by Yucel, and Dalva (1992: online), on the effect of In Vitro Hemolysis on
25 Common Biochemical tests also contends that the concentration of free hemoglobin in
serum measured spectrophotemetrically indicates the level of haemolysis present in blood
(Yucel et al, 1992: online)
Henry, Cannon, and Winkelman, asserts that Spectrophotomic methods can be used to read
hemoglobin levels. (Henry et al., 1974 (6))
Spectrophotometers are standard research tools, used in chemistry laboratories, utilizing the
relationship absorption of light and colour as principle for the way it works. (Hoydt, n.d.:
online)
Raphael states that for analytical purposes a spectrophotometer is used to identify and
quantify a substance by determine the extent of the absorption of light energy.
Spectrophotometry investigates a particular substance’s unique pattern of absorption of light
energy, that is, which sections of the available range of wavelengths are most strongly
absorbed as a means of identification. Furthermore Raphael states that oxyhemoglobin
absorbs light strongly at 540 and 578nm. (Raphael, 1983: (17))
Rieser (1997: online) contends the main components of the modern spectrophotometer are:
A light bulb, to produce white light
Defraction grating, to “break-up” white light
Slit, to allow only narrow wavelength bands to enter
34
(Rotation of grating selects portion of spectrum illuminating sample)
Sample chamber, holds cuvette
Display mechanism
Figure 3.1: Illustration of the operation of a spectrophotometer. (Reiser, 1997: online)
This is an instrument for measuring the wavelengths in light. It is also used to compare wavelengths. Using a light source, shine the light into a sample. The sample then absorbs the light.
A detector (the machine that is used in this practice) measures how much light is absorbed. The detector converts that amount into a number and plotted on the corresponding chart for the
experiment
Fraser (n.d.: online) proposes that a research environment is defined as a set of tools, systems
and processes interoperating, to facilitate and enhance the research process, within and
without institutional boundaries. (Fraser, n.d.: online) Thus a research environment includes
a combination of the “nature and the culture” of a particular environment, as well as tangible
aspects relating to the environment, in which any research is conducted (Fraser, n.d.: online)
In order to achieve the most valuable impact out of research being conducted and obtain
optimal outcomes the research environment should be thoroughly examined in a holistic
perspective to ascertain all factors potentially playing a role. Ruiz-Marrero (2009: online)
contends that the holistic view, also known as holism, is an “interdisciplinary vision which
conceives every natural system as an integrated whole, which cannot be understood if broken
down into its constituent components” (Ruiz-Marrero, 2009: online)
Furthermore Ruiz-Marrero states that the sum of all parts is not the same as the whole in the
holistic view. (Ruiz-Marrero, 2009: online). Wikipedia defines holism (from ὅλος holos, a
Greek word meaning all, whole, entire, total) is the “idea that all the properties of a given
system (Wikipedia, 2010: online) It refers to physical, biological, chemical, social, economic,
mental, linguistic, etc. components and cannot be determined or explained by its component
parts alone. Instead, the system as a whole determines in an important way how the parts
behave.” (Wikipedia, 2010: online)
35
In additional to holistically considering the research environment Jiju Mike, Andreas, (1998:
169 - 176) contends the use of statistical quality control techniques is an essential part of the
search for effective quality control and can lead to quality improvement id applied correctly.
(Jiju et al, 1998: 169 - 176)
Arsham (1994: online) purports that statistical skills enable the user to intelligently collect,
analyze and interpret data relevant to their decision-making. (Arsham, 1994: Online)
Statistical concepts and statistical thinking allows:
solve problems in a diversity of contexts.
add substance to decisions.
reduce guesswork.
(Arsham, 1994: online)
Inferential statistics has two goals as stated by Allpsych Online (2004: online). The first goal
of inferential statistics, which is often referred to as an estimation, is to determine what might
be happening in a population based on a sample of the population. (Allpsych Online, 2004:
online). The second goal is known as a prediction and is stated to be to determine what might
happen in the future based on the sample population response. (Allpsych Online, 2004:
online). Thus illustrating that only a sample population is needed with the use of inferential
statistics, instead of the entire population, which is required for descriptive statistics.
(Allpsych Online, 2004: online).
According to The Quality Assurance Project,(QAP), (n.d.: online) quality improvement
involves applying methods most appropriate in order to close the gap between expected
levels of quality and current levels of quality. (The Quality Assurance Project, n.d.: online)
Thus to understand and address system deficiencies, quality management principles and tools
are applied to enhance strengths and improve processes. (The Quality Assurance Project, n.d.:
online)
Gate to Quality (n.d.: online) asserts that “Any tool or technique that can be used for
improving the process/product quality, help in analyzing the current situation, help in
gathering information or help in bringing small or big change (towards improvement) in the
organization can be called a Quality tool or technique.” (Gate to Quality, n.d.: online)
36
Furthermore, Statistical Process Control, (or the use of statistical tools/techniques,), control
charts, scatter diagrams, regression, check lists and check sheets, flowcharts, pie charts,
affinity diagraphs, spider charts and brainstorming are all examples of quality tools. (Gate to
Quality, n.d.: online)
Based on the quality assurance project’s (QAP) above-mentioned contention relating to the
use of quality tools, QAP also states that a flowchart is one of the important tools used to
improve quality (The Quality Assurance Project, n.d.: online), QAP defines a flowchart is a
graphic representation of how a process works, showing, at a minimum, the sequence of
steps. A flowchart helps to clarify how things are currently working and how they could be
improved.
A flowchart, also known as a process map, helps organisations improve the efficiency of their
systems asserts Snow (2005: (1)). According to Snow, process maps can be used in a number
of ways to analyse performance, including the evaluation of the current situation, the
identification of break-downs in the current system such as duplication of effort, gaps,
bottlenecks etc. Thus “a process map can be utilised to identify strengths and weaknesses of
a system, in carrying out it’s purpose” leading to the satisfaction of customers and
stakeholders and ultimately quality improvement. (Snow, 2005: (2))
Sayer, and Williams defines Spider charts, (also known as radar charts,) as being radial plots
of several performance measures in a single display. (Sayer, et al, 2007: (192)) Spider charts
are effective contends Sayer et al, at showing the performance characteristics in a single
graph. As they communicate information rapidly because of their graphical and visual
nature, spider charts are popular. (Sayer et al, 2007: (192)) Performance can be graphically
observed, as well as the observation the relative value of different inputs is enabled by spider
charts, thus logical comparisons can be made between strategies or approaches and instantly
see strengths and weaknesses between alternatives. (Sayer et al, 2007: (192))
37
CHAPTER 4: DETERMINATION OF THE EXTENT OF HAEMOLYSIS ON
BLOOD RESULTS IN ORDER TO ELLIMINATE INADEQUATE SAMPLES
4.1. THE SURVEY ENVIRONMENT
In the context of this project the survey environment refers where sampling data was obtained
and includes the animal testing groups identified to form the foundation on which the
research is carried out in the project.
The predetermined animal control group of one year old ewes husbanded at the WCPVL
were selected for the purpose of obtaining samples. The ewes make up a sub-population of
the entire population of control animals kept for the purpose of laboratory testing. This
specific sub-population was selected for research to be conducted on them due to them
sharing similar characteristics in terms of gender, age and general health condition. Due to
the fact that they are not used for breeding purposes as yet, it was determined that their trace
mineral and Cu and Zn levels would stay relatively stable as they wouldn’t be undergoing
any extraordinary stresses due to a gestation. Furthermore the treatment of these ewes in all
other aspects of husbandry would be consistent with each other in terms of their housing,
diets, anthelminthic treatment, other supplementary treatments and general handling.
Normal Reference values are noted to be a guideline, and thus differ from animal group to
animal group. The laboratory uses a general set of accepted normal reference values.
Interestingly though it was found that the normal values of the sheep husbanded at WCPVL,
was out of range of the normal values used for sheep in the wider area of the Western Cape.
More extensive testing would be required to determine the precise normal range; however the
deviation from the set of normal values does not deviate to the extent that it can be
considered unsuitable.
Reasons for the deviation are believed to be related to feed conditions (diet), climate in the
Stellenbosch region, nutritional supplements as well as anthelminthic medications and others
supplements given to all the sheep husbanded at WCPVL. In addition, it is a known fact that
the copper soil levels in the Stellenbosch region are typically low. For this reason a
procedure is in place at WCPVL, whereby laboratory sheep husbanded at the laboratory, are
dosed with a multimin supplement every 6-8 weeks. Thus ultimately a decision was taken to
38
accept to the current accepted normal range values used by the laboratory for the purposes of
this project as it did not have a significant influence on the actual outcome of the results
obtained for the research.
4.2. AIM OF THIS CHAPTER
Scientific research being conducted thus was necessary to select research methodology which
will best suit and facilitate this type of research required. In the initial stages of this project it
was established that the research conducted in both research paradigms would be employed,
as well as which methods would be employed.
To obtain the most comprehensive understanding however, regarding why the particular
research design and methodology selected, is deemed to be most effective for the purposes of
this project, can be best demonstrated by tracing backwards. The process of tracing back
provides a rationalization instrument for the researcher to be able to illustrate why the
research methodology selected is the most appropriate to address the research problem. The
research question and problem statement are first examined in order to ascertain what the
exact research requirements are. Once requirements to address the research question and
problem statement are adequately established, the investigation sets out to provide guidance
as to which of the available data collection methodologies are most appropriate to gather
information as project data. These data collection methodologies are seen to be analytical
approaches to obtain evidence. They are in turn, classified according to paradigm in which
they are used and associated to the type of research (in the form of a methodology) they are
utilised in. The outcome of this is identification of the Research Methodologies they can be
classified as in as well as in which paradigm they fall in, either Quantitative, or Qualitative or
both as in the case of this project.
4.3. RESEARCH DESIGN AND METHODOLOGY
The process of tracing backward is initiated by initially identifying the primary objective of
the research question based on the research problem statement. This is done in order to be
able to resolve the research question in the most effective manner. The methodology (group
of methods) most suited to collect data in order to achieve the research objective is hereby
identified. Support data collection methodologies are explored and the determination of the
39
type of research methodology these data collection methodologies would be classified as.
This leads to the further establishment whether the research methodology in turn would be
defined as, being either Qualitative or Quantitative or both depending the context in which it
is used for research.
It appeared evident from the objective that laboratory experiments or testing would form a
crucial component of the research and thus it was established on the outset that any research
methodology selected for use had to be selected or designed around the laboratory testing.
Laboratory testing as a data collection methodology is considered to fall within the
quantitative paradigm and used to gauge the extent of the manipulation of one variable affects
another variable. This is also known as casual research whereby controlled laboratory
analysis provide quantitative data used to investigate the relationship the independent
variable (namely haemolysis) on dependent variables (namely time and trace mineral levels).
This primary quantitative data is directly analysed using various statistical methods.
The resultant data collected using the laboratory testing method would have a futile impact on
research however, if laboratory testing was utilised as a stand-alone method. To add value to
the data collected via laboratory testing it is essential to incorporate the use of support
methodologies. The methodologies identified to provide significance to the data collected via
laboratory testing are Protocol analysis and Observation
Data collected utilising the Protocol Analysis Method provides the framework of the research
design and enables the data to be coherent and significant. Protocol analysis involves
identifying the mental processes in problem solving, with the objective of ascertaining
behaviour and thinking processes involved in a particular situation. (Watkins, 2008: (23)).
This definition is most relevant when protocol analysis is conducted involving human
participants in the research environment. This research project involves sample participants
and thus although the same principles apply as when obtaining data from human participants,
the means of obtaining the data differs. Focus of the data (or types of information) obtained
however, needs to remain consistent in terms of relevance to the study. Data collected via
this methodology is purely qualitative and in this phase of data collection, the relevant factors
influencing the research conducted in the research environment were extrapolated and then
expounded. The importance and weight of factors was determined and this data is used.
40
Furthermore supportive data was collected through Observation enables a quality impact in
the industry and research environment that it was conducted, through the research. The
particular type of observation method employed namely participant observation provides a
manner in which the researcher is fully involved (Watkins, 2008: (23)) with analysis
revolving around the haemolysis effect, in order to understand the outcome and ultimately
this is key to the interpretation of data as well in ultimately addressing the research problem.
Observation involves the researcher associating the dependent variables to the independent
variation and then collecting data by evaluating and making observations on the different
relationships or interactions detected. It is notable that this methodology involves gathering
data in both qualitative and quantitative form, involving witnessing and measurements.
The extension evolving from identifying these data collection methodologies is the
classification of the data collection methodologies according to the type of research that they
fall under as well as the appropriate research paradigms that they are components of. When
using data collection methodologies in the manner as formerly described are considered to be
the Action Research Method. Action Research is described by Dick as a methodology which
has dual aims of action (to bring about change in some organisation) and research (to increase
understanding on the part of the researcher) (Dick, 1993: online) or in other words “a way of
doing research and working to solve a problem at the same time” This method was
developed to allow researcher and participants of the research, to work together and analyse
systems with the view to changing them, in other words, to achieve specific goals. (Dick,
1993: online)
Considering the above-mentioned it is thus considered that specific to this project, the Action
research methodology used is considered to fall neither precisely within the qualitative nor
quantitative paradigm, arguing that it is more a tool for change than true research. Thus
although Action Research method being used cannot be considered pure to only one
paradigm, it demonstrates however the use techniques involved in both research paradigms.
As an illustration of how this is done, it can be said Quantitative research tending to be
destructive in nature set out to test theory proposed by this project while Qualitative research
tending to be inductive in nature, is used simultaneously to generate theory.
Furthermore as it was found the pinnacle point the research question revolved around the
actual effect of independent variable, or factor of haemolysis, as well the extent of the
41
relationship of various potential dependent variables (results) on the independent variable, the
data collection methods of Protocol analysis and Observation (to an extent) are classified or
are found to be encompassed by Field Experiments Research type and Experimental Studies
Research Type classification. Both of these research methodologies involve the manipulation
of variables in a controlled laboratory environment in order to obtain a result and are
qualitative in nature.
Thus in conclusion, by tracing back it is demonstrated that this research both Quantitative, as
well as the Qualitative. Quantitative research is objective, deductive, generalised able and
data is found mainly in the form of numbers, while Qualitative research is subjective,
inductive, not generalised able and data is commonly in the form of words. Both are
systematic in nature is a defining principle of any research however, and both effectively
used to achieve research objectives.
4.4 DATA COLLECTION
In addition to understanding why each data collection methodology identified was selected as
most suitable for this research, it is also deemed important to demonstrate the application of
the methodologies in the context of the research.
In order for this to be done, an understanding of the following concepts is essential:
Purposive sampling is the technique used to identify the samples which would be required.
In this instance of purposive sampling, samples were identified for selection not by reference
to sample type or size, rather however in reference to the Animal Control Group identified as
sample targets due to their similar characteristics. A control group, or Animal control group
with reference to this research is defined as “The part of a study or experiment against which
an experimental procedure can be compared and it’s effects judged.” (Concise Oxford
Veterinary Dictionary, 1988, pg 193)
A sample is defined as a limited quantity of something which is intended to be similar to and
represent the larger amount of that thing(s) or population. The things could be countable
objects such as people, animals or individual items available as units for sale. (Wikipedia,
2010: online)
42
Unit of Analysis has been defined by the Research Methods Knowledge Base as “The major
entity being analysed in a particular study” (Research Methods Knowledge Base, 2006:
online). Thus, the unit of analysis of a particular study is said to be the “what” or the
“whom” being studied and from which data is obtained. Thus it is considered that the
selection of what the unit of analysis will be for a particular study is determined by the
interest of that particular study in exploring or explaining a certain phenomenon, namely the
subject of that study.
A variable is defined as a symbolic name associated with a value and whose value may be
changed. (Wikipedia, 2010: Online) The terms “dependent variable” and “independent
variable” are used to distinguish between two types of quantities being considered, separating
them into those available at the start of a process and those being created by it, where the
latter (dependent variables) are dependent on the former (independent variables) (Wikipedia,
2010: Online). A positivistic study calls for an independent and dependent variable to be
stated. The independent is the variable that can be manipulated to predict the values of the
dependent variable, thus the dependent variable is the one whose values can be predicted by
the independent variable. (Watkins, 2008: (54))
The application of the Data collection methods in context of the research process is
demonstrated with the aid of the following schematic:
Figure 4.1: Action Research (Ross et al., 1999: online)
This illustration of the research process provides a means to view the research process from a
perspective whereby it can be seen where and how data collection is inaugurated.
1. Identify the problem: Project proposal written
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2. Discussion of the problem: Protocol Analysis Data Collection Method
3. Review Literature: Holistic Perspective of the Research Environment, Project
Proposal Data Collection level, however could also be required after Protocol
Analysis complete
4. Re-define the problem: Investigative questions addressed
5. Select Method: Laboratory Testing and Observation Data Collection Method
6. Implement change: Involving conducting research analysis and conclusions to guide
the direction of the change
7. Cyclical: Return to step one if necessary
This entire process is cyclical and can be seen as a modification of Deming’s PDCA Cycle
4.4.1 Data collected per Protocol Analysis Methodology:
As the unit of analysis in this project are the serum samples thus, protocol analysis
methodology takes the form of collecting data surrounding them, thus it is understood the use
of this method enables the research to obtain data involving the analysis of the procedures
entailing the samples.
These results of the analysis of these procedures provide an insight into the research
environment as well as the guiding direction as to how to aspects of the research are to be
approached
The protocols employed by the section are investigated with reference to their relation to the
research environment. Thus ultimately protocol analysis involved collecting data in the form
of qualitative or phenomenological informative on situational issues involving the research
environment and how if at all, it impacts on samples e.g. sample quality, treatment of samples
and the consequences that can thus be anticipated from samples.
The situational issues involving the research environment were identified as the Process
factors in the research environment (as discussed in Chapter 2) and were listed as the systems
employed for the purposes of this research being the following:
A) Sample registration and preparation
B) Sample processing
C) Validation and Issuing of Results
D) QMS related systems
44
Protocol Analysis done on the process factors is demonstrated in the schematic Fig 4.2, and
broken down into categories associated to, and addressing each of the 4 above-mentioned
systems
Figure 4.2: Demonstration of application of Protocol Analysis Methodology
Through the process of validation of the systems, relevant information on the systems being
employed was selected, gathered, and then evaluated as part of addressing research objectives
4.4.2 Data collected per Laboratory Experiments Methodology:
The bulk of the data collected for this research was collected via Laboratory experiment data
collection methodology. This involves the collection of pure quantitative data, in the form of
results from actual laboratory testing procedures (analysis) carried out on the unit of analysis,
for the purpose of testing theory proposed by this project. The quantitative nature of data
enabled the theory proposed by the project to be tested by statistical means which is
understood to be one the most credible methods by which to test theory for quality purposes
45
The procedure for obtaining the data involved actual laboratory testing procedures including:
Optical Wavelength Spectrophotometer Readings
Vitros Blood Chemistry Analysis readings
Atomic Absorption Spectrophotometer readings
Support testing involves use of a blood stirrer
The type of data collected via this methodology, provides the core from which primary
research question, as well as the first two investigative questions, are directly addressed by
the research conducted. Thereafter the two remaining investigative questions are addressed
by evaluating the same data and considering it against data collected using other data
collection methodologies.
The application of this data involves adopting systematic approach in addressing the research
questions. Thus the primary research question is the first to be addressed. A range of
haemolysis level values is established for each pre-determined sample group by analysis of
each sample in the group. Thereafter a range of trace mineral values is established, also by
the analysis each sample in the different sample groups. Finally it must be established
whether or whether not, correlation exists between haemolysis level and trace mineral range.
Using this data gathered by this collection method, in addition to data collected via other
methodologies, a minimum acceptance level can be established.
The approach to addressing the first investigative question, involves establishing via
statistical means establishing whether or not a quantitative difference exists in results
obtained between the different groups based on the dependent variable. The approach to
addressing the second investigative question involves the establishment of an Upper Control
Limit based on further statistical analysis of the data collected.
The final two research questions are addressed with primary consideration to the information
supplied through data collected using the two other data collection methods identified, based
however on the results provided by data collected by Laboratory Experiments Data collection
method.
46
4.4.3 Data collection per observation method:
Watkins states that “Observation serves as a data collection methodology for research
methods falling within the context of either the positivistic or phenomenological research
paradigm”. (Watkins, 2008 (23)) A challenge posed for this research was presented in an
attempt to contextualize it for the purpose of the project. Ultimately it was understood that
data collection per observation method, provided complementary data which enabled
practical value to be inserted into the research. Thus the observation data methodology
primarily said to fit more suitability in the qualitative research paradigm for this project,
however it was also found to have a quantitative aspect to it. The data collected via this
methodology provides the cohesion required to be able to perceive the research as a network
from which the resolution to the research problem statement could be extracted.
Data collection per this method, takes place in two phases, namely initial data was collected
to ascertain irresolution associated around the factor of haemolysis in the unit of analysis i.e.
Observation method used to collect data determining relationship to the impact of time on
the samples. This data collected via observation method forms the basis on which further
data later collected via laboratory experiments methods was analysed (i.e. analysis to Trace
mineral levels in relation to the impact of haemolysis in the unit of analysis.) The initial data
collected by this observation provided a reflection of dependencies and interactions between
factors playing a role in the research environment.
The first phase of observation data collection is done by manipulating variables and recording
the outcome of this. In the first instance the independent (phenomenological) variable is
identified as “duration in days” or time and the dependent (positivistic) variable being level
of haemolysis. The result or data obtained in the form of observation was made of the
appearance of the sample. As the data was collected for a scientific project, the manner in
which this data was validated was by means of laboratory testing which provided a
qualitative measurement to be associated to the data.
The two sets of data collected via laboratory experiments methodology and observation
methodology was then evaluated to determine whether a correlation existed, and the result of
this is used to address the last investigative question and to a lesser extent the second
investigative question. It also would form the basis for the development of a colour index for
the laboratory to be used as reference and a quality tool.
47
The procedural steps involved, during which data was collected via this methodology are as
follows:
Table 4.1: Demonstration of Observation Methodology
STEPS DAY DETAIL OF PROCEDURE
1 0 50 Fresh blood samples drawn on, and allowed to clot for approximately 2 hours
2 0 10 Fresh blood samples shaken on a blood stirrer for 10 minutes and then
centrifuges a 3000rpm for 10 minutes. Serum drawn off and stored in a 4oC
refrigerator until testing began
3 0 10 Blood samples were placed in -20oC freezer, allowed to freeze, removed and
thawed, then shaken on a blood stirrer for 10 minutes and then centrifuges a
3000rpm for 10 minutes. Serum drawn off and stored in a 4 degree refrigerator
until testing began
4 3 10 Blood samples allowed to stand and haemolyse at 25oC incubator, to maintain
consistence and imitate room temperature for 3 days. Samples removed from
incubator, shaken on a blood stirrer for 10 minutes before being centrifuged at
3000rpm for 10 minutes. Serum drawn off and stored in a 4oC refrigerator until
testing began
5 6 10 Blood samples allowed to stand and haemolyse at 25oC incubator, to maintain
consistence and imitate room temperature for 6 days. Samples removed from
incubator, shaken on a blood stirrer for 10 minutes before being centrifuged at
3000rpm for 10 minutes. Serum drawn off and stored in a 4oC refrigerator until
testing began
6 9 10 Blood samples allowed to stand and haemolyse at 25oC incubator, to maintain
consistence and imitate room temperature for 9 days. Samples removed from
incubator, shaken on a blood stirrer for 10 minutes before being centrifuged at
3000rpm for 10 minutes. Serum drawn off and stored in a 4oC refrigerator until
testing began.
Repeated 10 times
Conditions for the above-strictly monitored and controlled within the laboratory
The second phase of observation data collection involved data gathered through the means of
a survey conducted on a panel of staff. The method was used in this phase in order to obtain
information which will drive any change or quality improvement in the laboratory. The
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survey involved obtaining their opinions with regard to input and process factors in the
system. The data collected from this is evaluated and applied when addressing the 3 rd and 4th
investigative question.
4.5 MEASUREMENT SCALES
In order to analysis the data, it is important to determine the appropriate measurement scales
according to which the data will be evaluated. The qualities or characteristics of the collected
data i.e. magnitude, equal intervals, absolute zero, determine what scale of measurement is
being used and therefore point towards statistical procedure which would best to use to
analyse the data.
Supported by knowledge that the objective of this research would be to result in a quality
improvement to the research environment, it is understood that inferential statistics can be
employed as a tool in this regard. The rationale behind using inferential statistics lies in the
actuality that the “conclusions from inferential statistics extend beyond the immediate data”
(Watkins, 2008 (164)), as well as inferential statistics can be used to “make judgement calls
of the probability that an observed difference between groups is a dependable observation, or
an observation that may have happened by change during study”.(Watkins, 2008 (164))
Thus to execute the statistical analysis required, it was determined the following scales of
measurement would be used:
Ratio Scale: This scale contains all three data qualities, and therefore it was
determined to use this scale for the analysis of data collected via Laboratory
Experiments Methodology. This scale was also used on the data collected by the first
phase of the Observation Method.
Ordinal Scale: This level, or scale, has magnitude only. Data at this level can be
looked at as any set of data that can be placed in order from greatest to lowest, but
where there is no absolute zero and no equal intervals. Thus this scale was used in the
analysis of data collected by the second phase of Observation Method. A popular
application of this type of scale is known as the Lickert’s scale.
Nominal Scale: The lowest level or scale of measurement, representing only names
(list of words) and therefore has none of the three qualities. This measurement scale
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is primarily employed during the analysis of data collected via Protocol data
collection method.
It is important that the level of measurements for the variables involved during research be
considered when deciding upon which statistical analysis procedure to use during data
analysis. In addition, the researcher should also base the choice of the statistical analysis
procedure selected on the assumptions of that procedure. Lastly the researcher needs to
consider the interpretability and substantive meaning of the statistics being computed, as it
can be said that no substitute exists for informed sound judgement when choosing a statistical
test for analysing data. (Virginia Agricultural and Mechanical College, n.d.: Online)
4.6 VALIDATION OF DATA
The process of validation provides credibility to the research conducted therefore reputable
validation techniques have to be identified and applied in order to the research the be
acceptable as a means to resolving the research problem
Thus in association to data, it is essential to establish that:
The actual data collected during the research process is validated
The application of the methods used in order to collect the data is validated
The application of the methods used in order to analyse the data collected is validated
Validation took place by investigating and extracted information in the operational system,
which is seen as a means to verify or confirm technical correctness of procedures used to
obtain data, surrounding the samples. In this was the data was validated
4.7 CONCLUSION
Thus in summary, this chapter emphasizes the importance of most applicable research
methods (including data collection methods), being selected for application for this project.
Extrapolation of research requirements leads to the selection of Action Research as the
research methodology of choice falling within both the quantitative and qualitative research
paradigm. The most suitable data collection methodologies were found to be Laboratory
Experiments, which would form the base or foundation for the research being carried out,
50
Protocol Analysis, which would provide the research with a guiding direction, and lastly
Observation analysis which could be said to be the component providing the research with
the cohesion necessary to insert practical value in to all other components of the research,
with the objective of ultimately addressing the research question.
The following chapter demonstrates how the application of the above-mentioned data
methodologies, is successfully utilised to gather data, and analyse the data, in order to
develop logical and practical conclusions for ultimate and definitive quality improvement
purposes
51
CHAPTER 5: DATA ANALYSIS AND INTEPRETATION OF RESULTS
5.1 INTRODUCTION
This chapter outlines analysis and interpretation of results, providing a description of which
methods were selected to be able to determine the following:
The 10 Animals were identified by numbers and the animal ID’s were:
103 114 119 123 129
104 117 120 124 141
There were 8 duplications (replications) of the same testing procedure carried out on each of
the laboratory animals in the sample group. Four batches of blood (tubes of blood) were
obtained from each animal at one given sampling, and all treated in the same way except for
the independent variable i.e. time. Thus the only difference between four samples from the
same sample animal were centrifuged at different times
The battery of laboratory tests conducted on each serum sample consisted of:
Spectrophotometric Reading to determine Haemolysis
Copper (Cu) Analysis
Zinc (Zn) Analysis
Calcium (Ca) Analysis
Phosphorous (Phos) Analysis
Magnesium (Mg) Analysis
Iron (Fe) Analysis
The mean result values from each animal obtained from the results of the above-mentioned
analysis
5.2 DATA ANALYSIS APPROACHES
5.2.1 Haemolysis in relation to time
52
It was critical to establish is a relationship between haemolysis and time existed, and if so,
what the nature of the relationship was deemed to be, as it known that the most common
cause of haemolysis. The mean value of haemolysis per animals and for all replications was
calculated per group. Control charts are best used to demonstrate the range of haemolysis
and regression and correlation is the tool selected to establish if, and what type of relationship
exists.
5.2.2 Haemolysis effect in groups
It was necessary to establish if the haemolysis level in samples in a particular time group had
an effect on the trace element levels of that particular time group. For each of the 4 groups,
data was collected per animal, per pre-determined trace element (Cu, Zn, Ca, Phos, Mg and
Fe). It was deemed meaningful observe the difference in the trace mineral level value in
question, knowing that all other aspects of the sample, conditions were maintained the same.
To demonstrate what the results found, control charts were developed for each element using
the means obtained throughout the duration of the project. Since these chart are not
demonstrating a process with the aim of detecting variation in the process but rather intended
to reflect a range of biological values being researched, it was decided that Normal Control
Values being used as UCL and LCL would add more value. The control charts demonstrated
the effect of haemolysis (and time) on each of the trace elements.
Furthermore it was established if it could be said that a relationship exists between that
independent variable (haemolysis/time) and the independent variable (trace element
involved). The statistical tool of regression and correlation was done to demonstrate this
5.2.3 Haemolysis effect between groups
Research then set out to determine if it could be statistically proven that a difference exists
between the Time (Days) Groups. This was done by means of hypothesis testing between the
groups of a particular trace element, for every element. With consideration for all the
variables, as well as the replication involved during the process of data collection, it was
determined that the use of 2 Factor Anova with replication would be the most suitable
statistical tool to employ. It was decided to state the Null hypothesis as No difference exists
between the Time Groups, and with hypothesis testing determine whether this was actually
the case
53
5.2.4 Demonstration of normal ranges
For completeness of research project, it is found necessary to illustrate the effect of
haemolysis on the samples by comparing it’s effect on the groups. This is determined to be
best illustrated by means of charts reflecting the mean results obtained in each group per trace
element analysed.
5.2.5 Observation Analysis
First phase: This involved gathering phenomenological data and positivistic data on the
initial raw samples and probing the relationship of the two factors. The colour intensity of
each sample was observed and rated according on an interval scale of plusses, ranging from
zero or none to 5 plusses (+ + + + +). The simple light intensity test using a
spectrophotometer set at wavelength 540nm, was then conducted on the samples and the
results of these compared in order to ascertain whether an association could be drawn.
Second phase: In order to attach additional quality value and further substantiate the
laboratory results obtained, data was collected from sources deemed competent in the field.
Their opinions on factors influencing the research environment was obtained and statistically
evaluated to attach importance to the factors. Statistical evaluation took the form of
developing pie charts to illustrate relative weights. Furthermore, opinions were also gathered
on potential course of modifications that can be made to the system in order to implement a
quality improvement. The results of this analysis, was then evaluated against the results
obtained from the laboratory experiments phase of the research project. See Annexure A:
Survey to Laboratory Staff
5.2.6 Protocol Analysis
Protocol analysis data collected analysed by means verification and validation of the data
within the operational system of the Biochemistry Research Environment.
Data was collected from the pertinent sources identified in the laboratory, as a means to test
the system in addition to gathering data on expected sample behaviour. This use of checklist
of key questions was employed to do this. See Annexure B: Protocol Analysis Checklist.
Findings were evaluated by means of charting in order to determine the effectiveness of the
systems in place and furthermore determine their strengths and weaknesses
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5.3 RAW DATA FINDINGS
5.3.1 Haemolysis in relation to time
Figure 5.1: Overview Chart: Average Haemolysis Readings
This chart reflects the average haemolysis readings of samples obtained from each individual animal in the
animal control group. The different lines reflect the different manipulation investigated,, i.e. Fresh, Day 3, Day
6 and Day 9
Figure 5.2: Comparative Chart: Haemolysis Readings in groups
This chart reflects the upward trend found when comparing the combined average haemolysis readings of the
different manipulations investigated, i.e. Fresh, Day 3, Day 6 and Day 9
55
Table 5.1: Comparative Table: Average Haemolysis Readings
Day Average Haemolysis Reading
0 0.377038
3 0.561425
6 0.795138
9 1.116775
Table 5.2: Regression Table: Time and Haemolysis
Relationship between Haemolysis as a dependent variable of the independent variable Time
Regression Statistics
Multiple R 0.992143
R Square 0.984347
Adjusted R Square 0.976521
Standard Error 0.048907
Observations 4
ANOVA
df SS MS FSignificance
F
Regression 1 0.300842 0.300842 125.7731 0.007857
Residual 2 0.004784 0.002392
Total 3 0.305626
Coefficients Standard Error t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 0.344655 0.040919 8.422885 0.013804 0.168595 0.520715 0.168595 0.520715
X Variable 1 0.081764 0.007291 11.21486 0.007857 0.050395 0.113133 0.050395 0.113133
Figure 5.3: Regression Chart: Time and Haemolysis
The chart below graphically demonstrate the regressive relationship between the independent variable time and
the dependence of the variable haemolysis on it
56
5.3.2 Haemolysis effect in groups
5.3.2.1 Effect of Haemolysis on Copper (Cu) Readings (Group Comparison)
Table 5.3: Comparative Table: Average Copper Readings
Figure 5.4: Comparative Chart: Average Copper Readings
This chart reflects the average copper readings obtained from samples in the different sample groups, Fresh,
Day 3, Day 6 and Day 9
Table 5.4: Regression Table: Haemolysis and Copper
Relationship between Copper as a dependent variable of the independent variable Haemolysis
Regression Statistics
Multiple R 0.985948
R Square 0.972093
Adjusted R Square 0.958139
Standard Error 0.013896
Observations 4
ANOVA
df SS MS FSignificance
F
Regression 1 0.013452 0.013452 69.66601 0.014052
Residual 2 0.000386 0.000193
Total 3 0.013838
Coefficients Standard Error t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 0.426462 0.019212 22.19775 0.002023 0.343799 0.509124 0.343799 0.509124
X Variable 1 0.209798 0.025136 8.346617 0.014052 0.101648 0.317949 0.101648 0.317949
Averages Fresh Day 3 Day 6 Day 9
Ave Readings 0.513663 0.528525 0.6018 0.659863
LCL 0.8 0.8 0.8 0.8
UCL 1.3 1.3 1.3 1.3
Normal Range Cu: 0.8 1.3
57
Figure 5.5: Regression Chart: Haemolysis and Copper
The chart below graphically demonstrate the regressive relationship between the independent variable
haemolysis and the dependence of the variable copper on it
5.3.2.2 Effect of Haemolysis on Zn (Zn) Readings (Group Comparison)
Table 5.5: Comparative Table: Average Zinc Readings
Average Fresh Day 3 Day 6 Day 9
Ave Zn Readings 0.9613 0.926363 1.185413 1.137988
LCL 0.7 0.7 0.7 0.7
UCL 1.3 1.3 1.3 1.3
Normal Range Zn: 0.7 1.3
Figure 5.6: Comparative Chart: Average Zinc Readings
This chart reflects the average zinc readings obtained from samples in the different sample groups, Fresh, Day 3,
Day 6 and Day 9
58
Table 5.6: Regression Table: Haemolysis and Zinc
Relationship between Zinc as a dependent variable of the independent variable Haemolysis
Regression Statistics
Multiple R 0.985948
R Square 0.972093
Adjusted R Square 0.958139
Standard Error 0.013896
Observations 4
ANOVA
df SS MS FSignificance
F
Regression 1 0.013452 0.013452 69.66601 0.014052
Residual 2 0.000386 0.000193
Total 3 0.013838
Coefficients Standard Error t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 0.426462 0.019212 22.19775 0.002023 0.343799 0.509124 0.343799 0.509124
X Variable 1 0.209798 0.025136 8.346617 0.014052 0.101648 0.317949 0.101648 0.317949
Figure 5.7: Regression Chart: Haemolysis and Zinc
The chart below graphically demonstrate the regressive relationship between the independent variable
haemolysis and the dependence of the variable zinc on it
59
5.3.3.3 Effect of Haemolysis on Calcium (Ca) Readings (Group Comparison)
Table 5.7: Comparative Table: Average Calcium Readings
Averages Fresh Day 3 Day 6 Day 9
Ave Ca Reading 2.571375 2.4785 2.177625 2.009125
LCL 2 2 2 2
UCL 3 3 3 3
Normal Range: Ca: 2 3
Figure: 5.8: Comparative Chart: Average Calcium Readings
This chart reflects the average calcium readings obtained from samples in the different sample groups, Fresh,
Day 3, Day 6 and Day 9
Table 5.8: Regression Table: Haemolysis and Calcium
Relationship between Calcium as a dependent variable of the independent variable Haemolysis
Regression Statistics
Multiple R 0.982234
R Square 0.964784
Adjusted R Square 0.947176
Standard Error 0.060044
Observations 4
ANOVA
df SS MS FSignificance
F
Regression 1 0.197545 0.197545 54.79284 0.017766
Residual 2 0.007211 0.003605
60
Total 3 0.204755
Coefficients Standard Error t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 2.882058 0.083015 34.71742 0.000829 2.524874 3.239241 2.524874 3.239241
X Variable 1 -0.80397 0.108611 -7.40222 0.017766 -1.27128 -0.33665 -1.27128 -0.33665
Figure 5.9: Regression Chart: Haemolysis and Calcium
The chart below graphically demonstrate the regressive relationship between the independent variable
haemolysis and the dependence of the variable calcium on it
5.3.2.4 Effect of Haemolysis on Phosphorous (Phos) Readings (Group Comparison)
Table 5.9: Comparative Table: Average Phosphorous Readings
Average Fresh Day 3 Day 6 Day 9
Ave Phos readings 2.4985 3.0575 3.393375 3.501375
LCL 0.9 0.9 0.9 0.9
UCL 2.55 2.55 2.55 2.55
Normal Range Phos: 0.9 2.55
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Figure 5.10: Comparative Chart: Average Phosphorous Readings
This chart reflects the average phosphorous readings obtained from samples in the different sample groups,
Fresh, Day 3, Day 6 and Day 9
Table 5.10: Regression Table: Haemolysis and Phosphorous
Relationship between Phosphorous as a dependent variable of the independent variable Haemolysis
Regression Statistics
Multiple R 0.914041
R Square 0.835471
Adjusted R Square 0.753207
Standard Error 0.224036
Observations 4
ANOVA
df SS MS FSignificance
F
Regression 1 0.509751 0.509751 10.15595 0.085959
Residual 2 0.100385 0.050192
Total 3 0.610135
Coefficients Standard Error t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 2.192394 0.309744 7.078079 0.019382 0.859672 3.525117 0.859672 3.525117
X Variable 1 1.291469 0.405251 3.18684 0.085959 -0.45218 3.035122 -0.45218 3.035122
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Figure 5.11: Regression Chart: Haemolysis and Phosphorous
The chart below graphically demonstrate the regressive relationship between the independent variable
haemolysis and the dependence of the variable phosphorous on it
5.3.2.5 Effect of Haemolysis on Magnesium (Mg) Readings (Group Comparison)
Table 5.11: Comparative Table: Average Magnesium Readings
Averages Fresh Day 3 Day 6 Day 9
Ave Mg Reading 0.8475 0.898375 0.915125 0.917125
LCL 0.7 0.7 0.7 0.7
UCL 1.23 1.23 1.23 1.23
Normal Range Mg: 0.7 1.23
Figure 5.12: Comparative Chart: Average Magnesium Reading
This chart reflects the average magnesium readings obtained from samples in the different sample groups, Fresh,
Day 3, Day 6 and Day 9
63
Table 5.12: Regression Table: Haemolysis and Magnesium
Relationship between Magnesium as a dependent variable of the independent variable Haemolysis
Regression Statistics
Multiple R 0.837505
R Square 0.701414
Adjusted R Square 0.552121
Standard Error 0.021725
Observations 4
ANOVA
df SS MS FSignificance
F
Regression 1 0.002217 0.002217 4.698236 0.162495
Residual 2 0.000944 0.000472
Total 3 0.003161
Coefficients Standard Error t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 0.833834 0.030036 27.76151 0.001295 0.704601 0.963067 0.704601 0.963067
X Variable 1 0.085177 0.039297 2.167542 0.162495 -0.0839 0.254258 -0.0839 0.254258
Figure 5.13: Regression Chart: Haemolysis and Magnesium
The chart below graphically demonstrate the regressive relationship between the independent variable
haemolysis and the dependence of the variable magnesium on it
5.3.2.6 Effect of Haemolysis on Iron (Fe) Readings (Group Comparison)
Table 5.13: Comparative Table: Average Iron Readings
Averages of all Fresh Day 3 Day 6 Day 9
Ave Fe Readings 41.04 46.70813 53.34225 61.244
LCL 29.73 29.73 29.73 29.73
64
UCL 39.76 39.76 39.76 39.76
Normal Range: Fe: 29.73 39.76
Figure 5.14: Comparative Chart: Average Iron Reading
This chart reflects the average iron readings obtained from samples in the different sample groups, Fresh, Day 3,
Day 6 and Day 9
Table 5.14: Regression Table: Haemolysis and Iron
Relationship between Iron as a dependent variable of the independent variable Haemolysis
Regression Statistics
Multiple R 0.998672
R Square 0.997345
Adjusted R Square 0.996018
Standard Error 0.549361
Observations 4
ANOVA
df SS MS FSignificance
F
Regression 1 226.7503 226.7503 751.3324 0.001328
Residual 2 0.603595 0.301798
Total 3 227.3538
Coefficients Standard Error t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 31.17379 0.759526 41.04377 0.000593 27.90582 34.44177 27.90582 34.44177
X Variable 1 27.23823 0.993717 27.41044 0.001328 22.96261 31.51386 22.96261 31.51386
65
Figure 5.15: Regression Chart: Haemolysis and Iron
The chart below graphically demonstrate the regressive relationship between the independent variable
haemolysis and the dependence of the variable iron on it
5.3.3 Haemolysis effect between groups
5.3.3.1 Haemolysis and Copper
Table 5.15: Anova Table: Copper
Copper (Cu) Anova: Two-Factor With Replication
SUMMARY Fresh 3Days 6 Days 9 Days Total
103
Count 8 8 8 8 32
Sum 5.141 5.653 5.372 8.005 24.171
Average 0.642625 0.706625 0.6715 1.000625 0.755344
Variance 0.036672 0.043833 0.104031 0.143967 0.09541
104
Count 8 8 8 8 32
Sum 3.713 3.359 4.387 4.631 16.09
Average 0.464125 0.419875 0.548375 0.578875 0.502813
Variance 0.055808 0.066352 0.120976 0.075361 0.076109
114
Count 8 8 8 8 32
Sum 4.564 4.554 5.485 5.821 20.424
Average 0.5705 0.56925 0.685625 0.727625 0.63825
Variance 0.042544 0.052616 0.075783 0.107636 0.067959
117
Count 8 8 8 8 32
Sum 4.212 4.534 4.731 5.112 18.589
66
Average 0.5265 0.56675 0.591375 0.639 0.580906
Variance 0.042044 0.055746 0.039459 0.059391 0.046117
119
Count 8 8 8 8 32
Sum 4.393 4.297 4.867 5.17 18.727
Average 0.549125 0.537125 0.608375 0.64625 0.585219
Variance 0.093026 0.07422 0.078053 0.163625 0.09437
120
Count 8 8 8 8 32
Sum 4.269 4.514 5.202 5.896 19.881
Average 0.533625 0.56425 0.65025 0.737 0.621281
Variance 0.099381 0.041934 0.035165 0.080208 0.064456
123
Count 8 8 8 8 32
Sum 3.404 3.099 3.847 3.61 13.96
Average 0.4255 0.387375 0.480875 0.45125 0.43625
Variance 0.049953 0.041805 0.069811 0.053079 0.049687
124
Count 8 8 8 8 32
Sum 3.149 3.236 4.017 3.792 14.194
Average 0.393625 0.4045 0.502125 0.474 0.443563
Variance 0.055768 0.051434 0.106872 0.060655 0.064197
129
Count 8 8 8 8 32
Sum 4.549 5.178 5.733 5.695 21.155
Average 0.568625 0.64725 0.716625 0.711875 0.661094
Variance 0.071468 0.046857 0.062313 0.088174 0.064417
141
Count 8 8 8 8 32
Sum 3.699 3.858 4.503 5.057 17.117
Average 0.462375 0.48225 0.562875 0.632125 0.534906
Variance 0.058469 0.033155 0.090226 0.083118 0.064546
Total
Count 80 80 80 80
Sum 41.093 42.282 48.144 52.789
Average 0.513663 0.528525 0.6018 0.659863
Variance 0.058882 0.055088 0.075073 0.102906
ANOVA
Source of Variation SS df MS F P-value F crit
Sample 2.865743 9 0.318416 4.531019 1.39E-05 1.913399
Columns 1.107071 3 0.369024 5.251162 0.001538 2.636845
Interaction 0.521312 27 0.019308 0.274748 0.999907 1.525695
Within 19.67691 280 0.070275
Total 24.17104 319
67
Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Copper (Cu) between Groups: Fresh, 3 Days, 6 Days, 9 Days
Null Hypothesis: µ1 = µ2 = µ3 = µ4
With Alternate Hypothesis: µ1 ≠ µ2 ≠ µ3 ≠ µ4
Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis stating that the effect of haemolysis on the trace mineral Copper (Cu) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Copper (Cu) is different between groups.
5.3.3.2 Haemolysis and Zinc
Table 5.16: Anova Table: Zinc
Zinc (Zn) Anova: Two-Factor With Replication
SUMMARY Fresh 3Days 6 Days 9 Days Total
103
Count 8 8 8 8 32
Sum 8.664 8.006 8.745 10.115 35.53
Average 1.083 1.00075 1.093125 1.264375 1.110313
Variance 0.522766 0.13822 0.453 0.367628 0.34405
104
Count 8 8 8 8 32
Sum 8.393 7.823 9.144 8.906 34.266
Average 1.049125 0.977875 1.143 1.11325 1.070813
Variance 0.732534 0.218973 0.497457 0.270829 0.3925
114
Count 8 8 8 8 32
Sum 6.829 6.807 8.239 8.346 30.221
Average 0.853625 0.850875 1.029875 1.04325 0.944406
Variance 0.282059 0.167603 0.386919 0.286294 0.262343
117
Count 8 8 8 8 32
Sum 9.193 8.935 10.545 10.477 39.15
Average 1.149125 1.116875 1.318125 1.309625 1.223438
Variance 0.49744 0.24205 0.504953 0.482653 0.398575
119
Count 8 8 8 8 32
Sum 7.214 7.837 9.726 8.445 33.222
Average 0.90175 0.979625 1.21575 1.055625 1.038188
Variance 0.239446 0.214143 0.57448 0.349583 0.324987
120
Count 8 8 8 8 32
68
Sum 7.91 8.176 11.606 9.457 37.149
Average 0.98875 1.022 1.45075 1.182125 1.160906
Variance 0.231943 0.339421 1.111483 0.481089 0.523055
123
Count 8 8 8 8 32
Sum 7.645 6.221 9.403 9.358 32.627
Average 0.955625 0.777625 1.175375 1.16975 1.019594
Variance 0.386027 0.160835 0.153893 0.382036 0.272747
124
Count 8 8 8 8 32
Sum 7.014 5.992 9.086 8.55 30.642
Average 0.87675 0.749 1.13575 1.06875 0.957563
Variance 0.289164 0.171681 0.461036 0.323181 0.305438
129
Count 8 8 8 8 32
Sum 7.211 5.834 9.753 8.414 31.212
Average 0.901375 0.72925 1.219125 1.05175 0.975375
Variance 0.321768 0.256084 0.854584 0.372641 0.441482
141
Count 8 8 8 8 32
Sum 6.831 8.478 8.586 8.971 32.866
Average 0.853875 1.05975 1.07325 1.121375 1.027063
Variance 0.238468 0.36862 0.303739 0.577476 0.34693
Total
Count 80 80 80 80
Sum 76.904 74.109 94.833 91.039
Average 0.9613 0.926363 1.185413 1.137988
Variance 0.341258 0.219272 0.483907 0.35281
ANOVA
Source of Variation SS df MS F P-value F crit
Sample 2.343324 9 0.260369 0.684543 0.722803 1.913399
Columns 3.936134 3 1.312045 3.449527 0.017096 2.636845
Interaction 1.539829 27 0.057031 0.149941 1 1.525695
Within 106.4994 280 0.380355
Total 114.3187 319
Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Zinc (Zn) between Groups: Fresh, 3 Days, 6 Days, 9 Days
Null Hypothesis: µ1 = µ2 = µ3 = µ4
With Alternate Hypothesis: µ1 ≠ µ2 ≠ µ3 ≠ µ4
Result: Since the calculated F test statistic does not exceed the F critical value obtained, it can therefore be said that there is significant evidence to accept the Null hypothesis, stating that the effect of haemolysis on the trace
69
mineral Zinc (Zn) in all the groups is equal. The alternate hypothesis , is to be rejected, stating that the effect of haemolysis on the level of trace mineral Zinc (Zn) is different between groups.
5.3.3.3 Haemolysis and Calcium
Table 5.17: Anova Table: Calcium
Calcium (Ca) Anova: Two-Factor With Replication
SUMMARY Fresh 3Days 6 Days 9 Days Total
103
Count 8 8 8 8 32
Sum 21.91 20.97 18.33 17.41 78.62
Average 2.73875 2.62125 2.29125 2.17625 2.456875
Variance 0.010555 0.035555 0.050098 0.189198 0.119325
104
Count 8 8 8 8 32
Sum 19.71 18.66 16.53 15.23 70.13
Average 2.46375 2.3325 2.06625 1.90375 2.191563
Variance 0.008341 0.019279 0.022227 0.049284 0.072059
114
Count 8 8 8 8 32
Sum 20.96 20.34 16.95 15.98 74.23
Average 2.62 2.5425 2.11875 1.9975 2.319688
Variance 0.004571 0.009021 0.087698 0.108879 0.120752
117
Count 8 8 8 8 32
Sum 21.68 20.61 17.79 15.46 75.54
Average 2.71 2.57625 2.22375 1.9325 2.360625
Variance 0.013514 0.027084 0.048884 0.670279 0.267193
119
Count 8 8 8 8 32
Sum 19.79 18.86 16.51 14.31 69.47
Average 2.47375 2.3575 2.06375 1.78875 2.170938
Variance 0.017541 0.015279 0.058141 0.049041 0.104918
120
Count 8 8 8 8 32
Sum 20.3 19.49 15.55 14.03 69.37
Average 2.5375 2.43625 1.94375 1.75375 2.167813
Variance 0.010936 0.017255 0.058055 0.121227 0.157914
123
Count 8 8 8 8 32
Sum 21.34 20.9 19.12 18.55 79.91
Average 2.6675 2.6125 2.39 2.31875 2.497188
Variance 0.006621 0.013193 0.022629 0.041955 0.041156
124
Count 8 8 8 8 32
70
Sum 18.56 17.99 16.1 15.33 67.98
Average 2.32 2.24875 2.0125 1.91625 2.124375
Variance 0.0092 0.011012 0.019964 0.065712 0.052187
129
Count 8 8 8 8 32
Sum 21.12 20.39 18.78 17.64 77.93
Average 2.64 2.54875 2.3475 2.205 2.435313
Variance 0.0144 0.009984 0.02245 0.077229 0.057826
141
Count 8 8 8 8 32
Sum 20.34 20.07 18.55 16.79 75.75
Average 2.5425 2.50875 2.31875 2.09875 2.367188
Variance 0.006879 0.013527 0.029784 0.01287 0.046543
Total
Count 80 80 80 80
Sum 205.71 198.28 174.21 160.73
Average 2.571375 2.4785 2.177625 2.009125
Variance 0.02426 0.030193 0.059459 0.154145
ANOVA
Source of Variation SS df MS F P-value F crit
Sample 5.320782 9 0.591198 11.37274 3.58E-15 1.913399
Columns 16.38042 3 5.460139 105.0354 1.41E-45 2.636845
Interaction 1.300212 27 0.048156 0.926366 0.573893 1.525695
Within 14.55546 280 0.051984
Total 37.55687 319
Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Calcium (Ca) between Groups: Fresh, 3 Days, 6 Days, 9 Days
Null Hypothesis: µ1 = µ2 = µ3 = µ4
With Alternate Hypothesis: µ1 ≠ µ2 ≠ µ3 ≠ µ4
Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis, stating that the effect of haemolysis on the trace mineral Calcium (Ca) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Calcium (Ca) is different between groups.
5.3.3.4 Haemolysis and Phosphorous
Table 5.18: Anova Table: Phosphorous
Phosphorous (Phos) Anova: Two-Factor with Replication
71
SUMMARY Fresh 3Days 6 Days 9 Days Total
103
Count 8 8 8 8 32
Sum 17.34 22.22 25.13 26.16 90.85
Average 2.1675 2.7775 3.14125 3.27 2.839063
Variance 0.195421 0.25825 0.100813 0.134829 0.344506
104
Count 8 8 8 8 32
Sum 19.59 26.39 27.14 30.1 103.22
Average 2.44875 3.29875 3.3925 3.7625 3.225625
Variance 0.085841 0.137155 0.152336 0.21265 0.371471
114
Count 8 8 8 8 32
Sum 17.9 22.97 26.5 27.12 94.49
Average 2.2375 2.87125 3.3125 3.39 2.952813
Variance 0.103364 0.090755 0.148221 0.083514 0.312634
117
Count 8 8 8 8 32
Sum 18.48 23.21 25.05 23.03 89.77
Average 2.31 2.90125 3.13125 2.87875 2.805313
Variance 0.097571 0.025184 0.108527 1.417727 0.466852
119
Count 8 8 8 8 32
Sum 26.66 31.21 32.84 34.66 125.37
Average 3.3325 3.90125 4.105 4.3325 3.917813
Variance 0.51745 0.421784 0.271371 0.164307 0.452366
120
Count 8 8 8 8 32
Sum 20.73 26.59 28.9 30.04 106.26
Average 2.59125 3.32375 3.6125 3.755 3.320625
Variance 0.30167 0.244398 0.176879 0.111629 0.396419
123
Count 8 8 8 8 32
Sum 17.07 20.28 23.06 22.76 83.17
Average 2.13375 2.535 2.8825 2.845 2.599063
Variance 0.07477 0.072943 0.056079 0.019 0.143583
124
Count 8 8 8 8 32
Sum 25.56 29.16 31.57 34.38 120.67
Average 3.195 3.645 3.94625 4.2975 3.770938
Variance 0.082057 0.180029 0.159227 0.138936 0.295686
129
Count 8 8 8 8 32
Sum 17.44 20.77 25.69 26.46 90.36
Average 2.18 2.59625 3.21125 3.3075 2.82375
Variance 0.083229 0.064084 0.251241 0.069536 0.32514
72
141
Count 8 8 8 8 32
Sum 19.11 21.8 25.59 25.4 91.9
Average 2.38875 2.725 3.19875 3.175 2.871875
Variance 0.068127 0.079543 0.035441 0.036029 0.166571
Total
Count 80 80 80 80
Sum 199.88 244.6 271.47 280.11
Average 2.4985 3.0575 3.393375 3.501375
Variance 0.309853 0.333908 0.264159 0.463318
ANOVA
Source of Variation SS df MS F P-value F crit
Sample 55.60667 9 6.178519 35.14559 3.56E-41 1.913399
Columns 48.81083 3 16.27028 92.55105 1.22E-41 2.636845
Interaction 3.497787 27 0.129548 0.736913 0.827906 1.525695
Within 49.2234 280 0.175798
Total 157.1387 319
Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Phosphorous (Phos) between Groups: Fresh, 3 Days, 6 Days, 9 Days
Null Hypothesis: µ1 = µ2 = µ3 = µ4
With Alternate Hypothesis: µ1 ≠ µ2 ≠ µ3 ≠ µ4
Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis, stating that the effect of haemolysis on the trace mineral Phosphorous (Phos) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Phosphorous (Phos) is different between groups.
5.3.3.5 Haemolysis and Magnesium
Table 5.19: Anova Table: Magnesium
Magnesium (Mg) Anova: Two-Factor With Replication
SUMMARY Fresh 3Days 6 Days 9 Days Total
103
Count 8 8 8 8 32
Sum 6.81 7.15 7.35 7.69 29
Average 0.85125 0.89375 0.91875 0.96125 0.90625
Variance 0.004384 0.007484 0.004584 0.006927 0.006921
104
Count 8 8 8 8 32
73
Sum 6.64 7.19 7.35 7.32 28.5
Average 0.83 0.89875 0.91875 0.915 0.890625
Variance 0.002114 0.002755 0.004327 0.0028 0.004032
114
Count 8 8 8 8 32
Sum 6.67 7.1 7.19 7.39 28.35
Average 0.83375 0.8875 0.89875 0.92375 0.885938
Variance 0.00237 0.003993 0.005727 0.00897 0.00587
117
Count 8 8 8 8 32
Sum 7.46 7.84 7.73 7.16 30.19
Average 0.9325 0.98 0.96625 0.895 0.943438
Variance 0.001021 0.001514 0.001027 0.135057 0.032417
119
Count 8 8 8 8 32
Sum 6.87 7.27 7.53 7.49 29.16
Average 0.85875 0.90875 0.94125 0.93625 0.91125
Variance 0.006013 0.006127 0.011184 0.010741 0.008798
120
Count 8 8 8 8 32
Sum 6.96 7.47 7.43 7.61 29.47
Average 0.87 0.93375 0.92875 0.95125 0.920938
Variance 0.003371 0.00437 0.004927 0.006127 0.005209
123
Count 8 8 8 8 32
Sum 6.44 6.82 7.19 6.97 27.42
Average 0.805 0.8525 0.89875 0.87125 0.856875
Variance 0.000943 0.001621 0.008641 0.00447 0.004745
124
Count 8 8 8 8 32
Sum 6.41 6.8 6.95 7.1 27.26
Average 0.80125 0.85 0.86875 0.8875 0.851875
Variance 0.001355 0.004686 0.005612 0.008336 0.005577
129
Count 8 8 8 8 32
Sum 6.8 7.12 7.3 7.41 28.63
Average 0.85 0.89 0.9125 0.92625 0.894688
Variance 0.002029 0.004114 0.004564 0.009512 0.005426
141
Count 8 8 8 8 32
Sum 6.74 7.11 7.19 7.23 28.27
Average 0.8425 0.88875 0.89875 0.90375 0.883438
Variance 0.001879 0.002612 0.003384 0.00257 0.002965
Total
Count 80 80 80 80
Sum 67.8 71.87 73.21 73.37
74
Average 0.8475 0.898375 0.915125 0.917125
Variance 0.003508 0.004773 0.005438 0.018059
ANOVA
Source of Variation SS df MS F P-value F crit
Sample 0.222583 9 0.024731 3.148083 0.001237 1.913399
Columns 0.252903 3 0.084301 10.73076 1.07E-06 2.636845
Interaction 0.088156 27 0.003265 0.415609 0.996016 1.525695
Within 2.199688 280 0.007856
Total 2.76333 319
Anova conducted to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Magnesium (Mg) between Groups: Fresh, 3 Days, 6 Days, 9 Days
Null Hypothesis: µ1 = µ2 = µ3 = µ4
With Alternate Hypothesis: µ1 ≠ µ2 ≠ µ3 ≠ µ4
Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis, stating that the effect of haemolysis on the trace mineral Magnesium (Mg) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Magnesium (Mg) is different between groups.
5.3.3.6 Haemolysis and Iron
Table 5.20: Anova Table: Iron
Iron (Fe) Anova: Two-Factor With Replication
SUMMARY Fresh 3Days 6 Days 9 Days Total
103
Count 8 8 8 8 32
Sum 315.64 371.63 435.82 514.35 1637.44
Average 39.455 46.45375 54.4775 64.29375 51.17
Variance 11.83446 25.77963 125.5091 307.742 194.7519
104
Count 8 8 8 8 32
Sum 320.46 388.16 379.58 464.47 1552.67
Average 40.0575 48.52 47.4475 58.05875 48.52094
Variance 65.47414 53.8294 20.52985 244.3262 129.0043
114
Count 8 8 8 8 32
Sum 359.51 400.14 458.57 522.9 1741.12
Average 44.93875 50.0175 57.32125 65.3625 54.41
Variance 53.78201 89.47199 183.7488 288.2475 200.2001
117
75
Count 8 8 8 8 32
Sum 328.35 368.39 417.46 516 1630.2
Average 41.04375 46.04875 52.1825 64.5 50.94375
Variance 188.5319 173.4337 70.14959 457.4839 280.1746
119
Count 8 8 8 8 32
Sum 384.9 423.95 437.05 523.42 1769.32
Average 48.1125 52.99375 54.63125 65.4275 55.29125
Variance 161.3263 200.6669 152.0538 309.3921 227.2262
120
Count 8 8 8 8 32
Sum 299.47 350.75 438.87 475.05 1564.14
Average 37.43375 43.84375 54.85875 59.38125 48.87938
Variance 3.15337 12.3668 229.4049 103.355 156.6832
123
Count 8 8 8 8 32
Sum 329.99 378.78 467.9 496.44 1673.11
Average 41.24875 47.3475 58.4875 62.055 52.28469
Variance 77.67024 85.31002 348.1657 157.1175 223.1825
124
Count 8 8 8 8 32
Sum 343.57 384.24 460.07 516.76 1704.64
Average 42.94625 48.03 57.50875 64.595 53.27
Variance 67.96503 86.39751 266.5112 212.2411 215.2866
129
Count 8 8 8 8 32
Sum 327.58 355.01 406.17 459.06 1547.82
Average 40.9475 44.37625 50.77125 57.3825 48.36938
Variance 166.3284 131.8225 160.9571 189.144 187.1626
141
Count 8 8 8 8 32
Sum 273.73 315.6 365.89 411.07 1366.29
Average 34.21625 39.45 45.73625 51.38375 42.69656
Variance 8.490884 15.015 15.95763 89.67888 72.29997
Total
Count 80 80 80 80
Sum 3283.2 3736.65 4267.38 4899.52
Average 41.04 46.70813 53.34225 61.244
Variance 84.65213 89.87889 156.1351 228.4447
ANOVA
Source of Variation SS df MS F P-value F crit
Sample 3892.942 9 432.5491 3.083928 0.001514 1.913399
Columns 18188.31 3 6062.77 43.22549 5.5E-23 2.636845
Interaction 1004.254 27 37.19461 0.265185 0.999935 1.525695
Within 39272.56 280 140.2592
76
Total 62358.07 319
Conduct Anova to determine if there was a significant difference in the effect of haemolysis on the trace mineral level Iron (Fe) between Groups: Fresh, 3 Days, 6 Days, 9 Days
Null Hypothesis: µ1 = µ2 = µ3 = µ4
With Alternate Hypothesis: µ1 ≠ µ2 ≠ µ3 ≠ µ4
Result: Since the calculated F test statistic exceeds the F critical value obtained, it can therefore be said that there is significant evidence to conclude that the Null hypothesis, stating that the effect of haemolysis on the trace mineral Iron (Fe) in all the groups is equal, is to be rejected. The alternate hypothesis will therefore be accepted, stating that the effect of haemolysis on the level of trace mineral Iron (Fe) is different between groups
77
5.3.4 Demonstration of normal ranges
5.3.4.1. Effect of Haemolysis on CopperFigure 5.16: Effect of Haemolysis on Copper: Fresh
Figure 5.18: Effect of Haemolysis on Copper: 6 Days
Figure 5.17: Effect of Haemolysis on Copper: 3 Days
Figure 5.19: Effect of Haemolysis on Copper: 9 Days
78
5.3.4.2. Effect of Haemolysis on Zinc
Figure 5.20: Effect of Haemolysis on Zinc: Fresh
Figure 5.22: Effect of Haemolysis on Zinc: 6 Days
Figure 5.21: Effect of Haemolysis on Zinc: 3 Days
Figure 5.23: Effect of Haemolysis on Zinc: 9 Days
79
5.3.4.3. Effect of Haemolysis on Calcium
Figure 5.24: Effect of Haemolysis on Calcium: Fresh
Figure 5.26: Effect of Haemolysis on Calcium: 6 Days
Figure 5.25: Effect of Haemolysis on Calcium: 3 Days
Figure 5.27: Effect of Haemolysis on Calcium: 9 Days
80
5.3.4.4. Effect on Haemolysis on Phosphorous
Figure 5.28: Effect of Haemolysis on Phosphorous: Fresh
Figure 5.30: Effect of Haemolysis on Phosphorous: 6 Days
Figure 5.29: Effect of Haemolysis on Phosphorous: 3 Days
Figure 5.31: Effect of Haemolysis on Phosphorous: 9 Days
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5.3.4.5. Effect of Haemolysis on Magnesium
Figure 5.32: Effect of Haemolysis on Magnesium: Fresh
Figure 5.34: Effect of Haemolysis on Magnesium: 6 Days
Figure 5.33: Effect of Haemolysis on Magnesium: 3 Days
Figure 5.35: Effect of Haemolysis on Magnesium: 9 Days
82
5.3.4.6. Effect of Haemolysis on Iron
Figure 5.36: Effect of Haemolysis on Iron: Fresh
Figure 5.38: Effect of Haemolysis on Iron: 6 Days
Figure 5.37: Effect of Haemolysis on Iron: 3 Days
Figure 5.39: Effect of Haemolysis on Iron: 9 Days
83
5.4.5 Observation Analysis
With reference to the appearance of the initial samples collected after centrifugation took
place it was observed that there was indeed on average, a difference in the intensity between
the different group from Fresh being almost colourless to the Frozen samples being
exceedingly dark. Upon measurement of these samples the spectrophotometer reading
corroborated this observation.
The means and frequency of responses to a brief survey, given to a panel of laboratory staff
considered to be competent in the biochemistry section, were determined to be important in
order to ascertain focus area for improvement. Given a lickets scale of six potential answers,
Statistical analysis conducted on feedback revealed the following:
Figure 5.40: Graphic Representation of Results of Observation Data collected:
Shortfalls: Range of responses in the form of answers obtained from the panel of laboratory staff competent in Biochemistry
Question 1 Question 2
Question 3 Question 4
Question 5
Pie chart for QUES1
5: 33.33 %
6: 66.67 %
Pie chart for QUES2
SD: 33.33 %
MA: 66.67 %
Pie chart for QUES3
CD: 33.33 %
MD: 66.67 %
Pie chart for QUES4
CD: 33.33 %
MD: 66.67 %
Pie chart for QUES5
CD: 33.33 %
MD: 66.67 %
84
Figure 5.41: Graphic Representation of Results of Observation Data collected:
Improvement: Range of responses in the form of answers obtained from the panel of laboratory staff competent in Biochemistry
Question 1 Question 2
Question 3 Question 4
Question 5
The staff was also provided with pictures of samples in the form of a blind test (samples not
identified), and requested to associate + values to them based on colour intensity. The means
and frequencies of these responses were also statistically analysed revealing the following:
Pie chart for QUES1
MA: 33.33 %
CD: 66.67 %
Pie chart for QUES2
CD: 33.33 %
MD: 33.33 %
CD: 33.33 %
Pie chart for QUES3
MA: 100 %
Pie chart for QUES4
SA: 33.33 %
MA: 33.33 %
CD: 33.33 %
Pie chart for QUES5
MA: 66.67 %
CD: 33.33 %
85
Figure 5.42: Graphic Representation of Results of Observation Data collected:
Haemolysis Grading: Range of responses in the form of answers obtained from the panel of laboratory staff competent in
Biochemistry
Tube 1: Fresh Sample Tube 2: Three Day old Samples
Tube 3: Six Day old Sample Tube 4: Nine Day old Sample
Tube 5: Frozen Sample
5.3.5 Protocol Analysis
The spider chart below illustrates the focal areas of the protocol analysis as determined by the
affinity diagram. (Affinity diagram identified major categories, spider charts demonstrate the
depth/importance of the investigation required in all). Thus graphical representation was
developed based on the answers from the checklist drawn up to test the systems.
Pie chart for TUBE1
0: 100 %
Pie chart for TUBE2
+: 100 %
Pie chart for TUBE3
++: 40 %
+++: 60 %
Pie chart for TUBE4
++: 20 %
+++: 60 %
++++: 20 %
Pie chart for TUBE5
++++: 20 %
+++++: 80 %
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Figure 5.43: Spiderchart: Results of Protocol Analysis
5.4 INTERPRETATION OF STATISTICAL ANALYSIS
5.4.1 Haemolysis in relation to time
Based on investigation of regression and correlation analysis it was determined that a linear
positive relationship exists between the independent variable of time with haemolysis being a
dependent variable of this. Furthermore the charts provide illustrating the trend of an
increase of level of haemolysis in samples with the progression of time. Further tests are
deemed necessary to indicate the impact on quality
5.4.2 Haemolysis effect in groups
Statistical examination of the effect of haemolysis effect on individual trace elements
revealed the following
5.4.2.1 Haemolysis vs. Copper
An increase in copper levels in serum samples was detected with the increase in haemolysis
levels. Upon further investigation involving regression and correlation, it was determined
September 22, 2010
Page 1
Chart Type
Survey Sample - 8 Categories
Operational Systems in Biochemistry Environment
100
90
80
70
60
50
40
30
20
10
LegendPre-analytical
FactorsEquipment
Factors
Process Factors External Factors
Support Requirements Defined: Documentation and Records
Process Structure: Logical, sequential,
comprehensive
Process Requirements Defined: Documentation and Records
Corrective Action/
Preventative Measure in
system
Control Measures in system
Traceabilty and adequate Identification of samples in system
Calibration in system
Effectivity
Notes:
This chart illustrates the results of protocol analysis on the operational environment of biochemistry laboratory in relation to the various systems which comprise of it namely:
Pre-analytical Systems Core Process Systems Equipment Factor Systems External Systems input
A graphic representation is given, enabling the user to identify the strengths as well as the weakesses in each of the pre-defined systems
87
that a positive linear relationship existed between the independent variable of haemolysis in
relation to dependent variable of copper.
5.4.2.2 Haemolysis vs. Zinc
The association between the independent variable haemolysis and dependent variable does
not appear to be as firm as the relationship between haemolysis and copper mean values,
based on examination of the chart, despite evidence of a leaning towards the same trend as
the prior. However upon further investigation involving regression and correlation it was
established that a linear positive relationship exists between haemolysis and zinc.
5.4.2.3 Haemolysis vs. Calcium
By observing charted mean values, it was detected with the increase in haemolysis levels led
to the subsequent decrease in calcium levels in serum samples. Upon further investigation
involving correlation analysis, it was determined that a negative linear dependence
relationship existed between the independent variable of haemolysis in relation to dependent
variable of calcium.
5.4.3.4 Haemolysis vs. Phosphorous
An increase in phosphorous levels in serum samples was observed with the increase in
haemolysis levels in the samples. Upon further investigation involving regression and
correlation, it was determined that a positive linear relationship existed between the
independent variable of haemolysis in relation to dependent variable of phosphorous.
5.4.3.5 Haemolysis vs. Magnesium
It was observed that with the increase of haemolysis levels, magnesium levels were also
found to increase, based on the charted mean values. Upon further investigation involving
regression and correlation, it was determined that a positive linear relationship existed
between the independent variable of haemolysis in relation to dependent variable of
magnesium.
5.4.3.6 Haemolysis vs. Iron
An increase in iron levels in serum samples was detected with the increase in haemolysis
levels. Upon further investigation involving regression and correlation, it was determined
88
that a positive linear relationship existed between the independent variable of haemolysis in
relation to dependent variable of iron.
5.4.3 Haemolysis effect between groups
Interpretation of Anova Hypothesis testing, based on
Null hypothesis:µF = µ3 Days = µ6 Days = µ9 Days
With Alternate Hypothesis: µF ≠ µ3 Days ≠ µ6 Days ≠ µ9 Days,
For all groups, with the exception of Zinc, it was found that it could be statistically proven that there was a difference between the effect of haemolysis on trace mineral levels in the different groups.
5.4.4 Demonstration of Normal Ranges
Interpretation of these charts reveal where mean values for trace minerals in the different time
groups and trace mineral groups, can be found in relation to recommended normal values
used by the laboratory. It was found that:
Mean copper mineral levels were significantly out of recommended normal range, as
expected due to known environmental factor of soil copper levels in the Stellenbosch
area being traditionally low. Copper levels were elevated however with the upward
progression of haemolysis in samples, however this elevation was detrimental to
result quality output even though it was out of recommended normal range.
Mean zinc mineral levels stayed within recommended normal range, however a trend
was observed of the mean zinc levels being elevated with the upward progression of
haemolysis levels. Thus it can be said that uncontrolled haemolysis in samples would
have a detrimental effect to result quality output.
Mean calcium trace mineral levels were initially found to be within the recommended
normal range, however with the upward progression of haemolysis, the mean calcium
levels were found to significantly decrease, thus uncontrolled haemolysis in samples
would have a detrimental effect to result quality output. Group 9 mean calcium levels
were out of recommended normal range.
The mean phosphorous trace mineral level of the fresh group was found to be within
recommended normal range, however with the upward progression of haemolysis the
mean trace mineral value steadily moved upwards and significantly out of
recommended normal range.
89
Mean magnesium trace mineral levels stayed within recommended normal range,
however an upward trend was detected in relation to the upward progression of
haemolysis.
The mean iron trace mineral level of the fresh group was only slightly out of
recommended normal range. It was determined that this could also be as a result of
environmental factor impacting on research, however the significant observation to be
made during interpretation, is considered to be the steady and marked upward trend of
mean iron levels with the upward progression of haemolysis occurrence in samples.
Mean iron levels of groups Day 3, 6 and 9 are considered to be significantly out of
recommended normal range.
5.4.5 Observation Analysis
Interpretation of the first phase of observation analysis ensues the relationship between the
colour intensity and spectrophotometer readings obtained of those samples. Data collected is
found to substantiate and corroborate this positive linear relationship.
Interpretation of the second phase of observation analysis is found to be the significant
finding that the opinions, based on the responses obtained from survey group were all very
similar. In the 3 instances where opinions were sought, the mean results is demonstrated to
all fall with the 15.8th percentile or within 1 standard deviation of each other. The
importance of this is the guidance and direction it provides with regard to a quality
improvement to be made to the system
5.4.6 Protocol Analysis
The results illustrated by application of the protocol analysis data collected into a spider
chart, indicates the areas in the operational system in biochemistry which need to be address
in order to make a quality improvement in the section. As can be seen from the chart,
Process factors for the Pre-analytical system, Core process system and Equipment factors
were logical and comprehensive. Furthermore QMS documentation, support systems,
traceability, maintenance and adequate control measures were found to be effectively
operating in these systems. The results of the spider chart indicate that the most important
system to focus on, in order to attain quality improvement in the section, would be the system
comprised of external factors impacting on sample processing within the biochemistry
section.
90
5.5 PROBLEMS ENCOUNTERED DURING RESEARCH
Frozen blood could not be read spectrophotometrically, as the spectrophotometer could not
read such high values. As further testing on the samples (in terms of the effect of
haemolysis) would be futile since the level of haemolysis could not be detected, the
batch/group of samples was discarded by project plan.
It was found that normal ranges of the animal control group used for this project were out of
range of the recommended normal control ranges. This development was not completely
unexpected, as it is a typically known fact that environmental and handling factors has
influence normal ranges and it is also a known fact that WCPVL is situated in a copper
deficient area thus sheep are routinely dosed with Multimins copper supplement due to low
Cu in soil. The animal control group received supplementation on the 5th February 2010 and
approximately every 6-8weeks thereafter.
5.6 KEY RESEARCH FINDINGS
Data Analysis reveals the following key research findings:
The Quality Management System in operation in the Biochemistry Section is predominantly reliable and the basis for good quality processes occurring within the section. It appears as if the aspect of corrective action could require consideration however it is accepted that with nature of an environment such as biochemistry in addition to the good quality procedures already implemented in the section, in the event of an unforeseeable non-conformance occurring, the only appropriate corrective action would take the form of a Re-do. The event of samples being redone, results in the cost of analysis increasing twofold
The most detrimental effect on the quality of the system in operation in Biochemistry takes the form of external factors which influence the system.
Haemolysis levels have an influence on the range of trace mineral levels occurring in samples and can be demonstrated when compared to recommended normal reference values
A linear relationship is found to exist between haemolysis levels and trace mineral levels
The extent of the relationship is found to be different for each of the different trace elements, thus the actual effect of the extent of haemolytic impact differed from element to element
91
In line with the individual effect of the haemolytic impact on a particular element, this effect was found to be progressive over time, in all cases.
A linear positive relationship is found to exist between time before centrifugation takes place and haemolysis level.
92
CHAPTER 6: CONCLUSION AND RECOMMENDATIONS
6.1 BACKGROUND
The absence of the event of screening blood samples sent to the Western Cape Provincial
Veterinary Laboratory in order to determine their suitability for analytical purposes, has been
a quality concern for management of the laboratory in recent times. Thus it was undertaken
to investigate the impact of factors suspected of possibly impacting the quality of results
delivered by the Biochemistry section, as a result of unscreened blood being analysed by the
section.
6.2 THE RESEARCH PROBLEM RE-VISITED
Trace Mineral Results from analysis carried out in the Biochemistry laboratory of WCPVL
are possibly invalid or of poor quality due to levels of sample haemolysis going unscreened
in the section.
The exercise of processing samples in the Biochemistry section would prove have a futile
purpose, if minimum requirements for sample suitability to provide adequate results cannot
be determined. A known factor believed to influence trace mineral values is the presence of
haemolysis in samples. Haemolysis, resulting from the lyses of red blood corpuscles in blood
serum, delivers a visual effect, which can be optically observed in samples. Optical
observation, as a stand-alone method is not however, scientifically acceptable in order to
validate and accept results in terms of reliability neither quality. It is necessary thus to
determine a method to substantiate optical observation, such as the use of a light
spectrophotometer and furthermore determine at critical level or value above which samples
can no longer be accepted for trace mineral analysis.
6.3 PRIMARY RESEARCH QUESTION RE-VISITED
6.3.1 Primary Question
93
What is the maximum haemolysis level acceptable, as measured in terms of optical density
using a spectrophotometer at 540nm wavelength, in order to accept samples for Trace
Mineral Analysis?
6.4 INVESTIGATIVE RESEARCH QUESTIONS RE-VISITED
6.4.2 Investigative Questions
Is there a difference in the haemolysis level of samples read on Day 0, Day 3, Day 6, Day
9 and Frozen.
Are unacceptable samples being accepted as suitable in the current system?
What are the shortfalls of the current system?
What are the practical considerations or recommendations that can be made to manage
acceptance procedures of samples?
6.5 RESEARCH OBJECTIVES RE-VISITED
To determine exact values of acceptable levels of haemolysis when accepting blood samples
for trace mineral analysis in terms of the concentrations of trace minerals present in serum,
measured in nanometre (nm) units when read on a spectrophotometer.
Secondary objectives evolving from the primary objective:
To determine practical measurable methods available to identify unacceptable samples.
To determine the effects of haemolysis levels of blood samples at WCPVL in terms of the
quality system of the laboratory and implications thereof.
To identify preventative measures required to be implemented within biochemistry laboratory
to prevent future acceptance of unsuitable samples.
94
6.6 RECOMMENDATIONS
Based on literature reviewed in addition to data analysis and
interpretation, the following recommendations are made in order to effect a quality
improvement in the Biochemistry section:
It can be said that the presence of haemolysis in samples has a direct effect and impact of
the quality standards in the laboratory where the tests are being conducted, resulting in
erroneous results being produced by analysis carried out on inadequate samples. (Thomas,
2010: online) Erroneous results have detrimental implications to clinical laboratories in
terms of quality. Lippi,, (2009: online) states that a major worldwide concern for all clinical
laboratories is in vitro haemolysis as through affecting test results it seriously impacts on
patient care and the laboratory’s reputation. (Lippi, 2009: online)
Spencer, Rogers (1995: online) suggests that between quality improvement and haemolysis a
direct link exists. Although it is physically possible to produce results at a remarkable speed
and accurately within decimal point ad infinitum, it becomes redundant if the specimen is
unsuitable. Walters, Williams, Hazer, and Kameneva, (2007: online) argue that the baseline
degree of hemolysis present in blood is determined by the level of free hemoglobin in
plasma/serum. (Walters et al, 2007: online)
It is thus recommended that samples received by the section for trace element analysis must
be screened for suitability for analysis.
Trying to eliminate unsuitable specimens such as haemolysed specimens can thus be seen to
be part of a Quality Improvement Process (QIP) and Spencer and Rodgers (1995: online)
proposed a 4 step system in order to do so. The implementation of countermeasures as Step
3, is further described by Spencer et al., (1995: online), with the goal of providing uniformity
in operation through creating an easy-to-use visual aid for grading hemolysis. By the
collation of information in addition to this information being formatted, a user-friendly chart
of acceptable levels of hemolysis for specific tests is developed for use in specific testing
procedures. (Spencer et al., 1995: online)
95
The recommendation is thus also made that the use of a colour chart be employed as a
practical measure in order to screen samples received for trace mineral analysis at WCPVL.
Spectrophotometers are standard research tools, used in chemistry laboratories, utilizing the
relationship absorption of light and colour as principle for the way it works. (Hoydt, n.d.:
online) As Henry, Cannon, and Winkelman assert that Spectrophotomic methods can be to
read hemoglobin levels. (Henry et al., 1974 (6)) in addition to Raphael (1983: (17)) affirming
that for analytical purposes a spectrophotometer is used to identify and quantify a substance
by determine the extent of the absorption of light energy, the recommendation is thus made
that if any uncertainty surrounding the acceptability of a particular sample avails, a
spectrophotometric test may be done to establish that the sample does not exceed the
maximum acceptance level of 0.377nm
A flowchart, also known as a process map, helps organisations improve the efficiency of their
systems asserts Snow (2005: (1)). According to Snow, process maps can be used in a number
of ways to analyse performance, including the evaluation of the current situation, the
identification of break-downs in the current system such as duplication of effort, gaps,
bottlenecks etc. Thus “a process map can be utilised to identify strengths and weaknesses of
a system, in carrying out it’s purpose” leading to the satisfaction of customers and
stakeholders and ultimately quality improvement. (Snow, 2005: (2))
A recommendation is thus made, that the following revision be made to the workflow
procedure in Biochemistry: See Annexure C: Modified Workflow Flowchart
The post analysis detection erroneous results involves Re-do. Although cost considerations
are not ranked as highly by the laboratory management as the transience implications due to
the Laboratory being the Department of Agriculture’s essential diagnostic service provider, in
any business it, cost remains important. Logically and unarguably, re-sampling, and the re-
processing of samples are associated to cost implications for the laboratory. Literature
reviewed reveals that Ong, Chan, Lim (2009: online) found a cost saving occurred with a
reduction in sample hemolysis from 19.8% (before) to 4.9% (after) (P <.001). This further
translated into a cost savings of SGD$834.40 (USD$556.30) per day in a study conducted by
them, and SGD$304,556 (USD$203,037) per year. (Ong et al., 2009: online). In terms of
WCPVL, the cost of a trace element analysis test conducted by the laboratory is R39.85 and
96
the cost of Cu or Zn analysis is also R39.85. Based on average amount of 211 samples per
month, it is thus determined that a potential total savings of R8408.35 on average per month,
dependent on the amount of unsuitable samples submitted, will be achieved by the laboratory.
Thus it is considered, as purported by Ong, Chan, Lim (2009: online), haemolysis poses a
problem in terms improvement of quality relating to monetary benefit, and thus
recommendation is made to implement the above-mentioned
recommended quality improvement measures in order to attain cost
benefit for the laboratory.
6.7 CONCLUSION
In conclusion, it can be said that the ultimately objective set out to be achieved, of
establishing an acceptance level in terms of haemolysis level, in order to assure quality of
results issued by the Biochemistry section has been successfully accomplished through the
research conducted and can be identified as a value not exceeding 0.377nm
It is believed that this critical value, associated to an indicator presented on an acceptance
colour chart, will fulfil a very beneficial role in assuring quality results thereby ensuring
quality practices are followed in the laboratory and ultimately customer satisfaction. By the
implementation of recommendations made by this research, it will enable the diagnostic
institute known as the Western Cape Provincial Veterinary Laboratory to build on their
existing reputation of being frontrunner in terms of the diagnostic quality of results they
deliver. This improvement action undertaken serves as confirmation of importance WCPVL
places on the accuracy, precision and reliability of their diagnostic problem, thereby truly
fulfilling their role of delivering an outstanding first-rate and high quality service to the
department of agriculture and agricultural community at large, in the Western Cape Province.
97
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2007. Modification of suction induced hemolysis during cell salvage. Anaesthesia and
Analgesia IRAS 104(3) pg 684-687
http://www.anesthesia-analgesia.org/content/104/3/684.full [10 April 2010]
42. Watkins, Dr. J. A, 2008. Theses/ Dissertations/ Research Reports: A practical guide
for students to the preparation of written presentations of academic research, Privately
published by author
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September 2010]
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http://www.clinchem.org/cgi/reprint/38/4/575 [10 February 2010]
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ANNEXURE A: SURVEY TO LABSTAFF
September 2010
Biographical Details (for administrative purposes only)Employer:Race – Tick alongside box:
African Asian Coloured White
Gender Male FemaleStaff Categories Veterinary
ManagementTechnical Laboratory Staff Support Staff Admin
Highest Academic Qualification
Doctorate Post Graduate Degree
Undergraduate Degree
Grade 12 with Diploma/certificate
Grade 12
Lower than Grade 12
Time in years employed at the institution
This survey is anonymous. Please do not write your name on this survey. Responses cannot be traced to any individual. The free and frank expression of you opinion will be most helpful
There are no right or wrong answers to any items in this questionnaire. It is your opinion on each of the statements that matters.
This survey contains a 10 statements related to the Quality of Sample Processes (Operations) in the Biochemistry Section of the Western Cape Provincial Veterinary Laboratory. You are requested to respond to each of the statements by placing a TICK MARK in the space, which most accurately fits the extent to which you agree that the statement is describing.
If you completely agree with this statement, you would tick on the number 7. If on the other hand, you slightly agree with the statement you would tick on the number 3, etc.
Completely Agree
Mostly Agree Slightly Agree Undecided Slightly Disagree
Mostly Disagree
Completely Disagree
7 6 5 4 3 2 1
YOUR OPINION ON SHORTFALLS IN OPERATIONAL SYSTEM IN BIOCHEMISTRY
1There are shortfalls in the operational system with regard to sample inputs that affect the quality of analysis conducted in the system.
7 6 5 4 3 2 1
2There are shortfalls in the operational system with regard to sample inputs, which affect the quality of results issued by the system.
7 6 5 4 3 2 1
3There are shortfalls in the operational system with regard to process inputs that affect the quality of procedures/analysis conducted in the system.
7 6 5 4 3 2 1
4There are shortfalls in the process of validation of sample processing, affecting quality in the operational system. 7 6 5 4 3 2 1
5There are shortfalls in the operational system with regard to process inputs that affect the quality of results issued by the system.
7 6 5 4 3 2 1
YOUR OPINION ON RECOMMENDATIONS TO IMPROVE THE OPERATIONAL SYSTEM IN BIOCHEMISTRY
6By identifying and addressing any factor suspected to be negatively influencing sample condition will result in a Quality Improvement in the operational system.
7 6 5 4 3 2 1
7If QMS focus is placed in terms of improving analysis process in the system, it will result in a Quality Improvement in the section.
7 6 5 4 3 2 1
8If QMS focus is placed in terms of generating policy, education and training for pre-analytical input channels, it will result in a Quality Improvement in the section
7 6 5 4 3 2 1
9The rejection of samples, deemed unsuitable analysis purposes according to credible methods, on the grounds that results on these cannot be validated, will result in a Quality Improvement in the section
7 6 5 4 3 2 1
Use of a colour indicator chart will result in a Quality
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10 Improvement in the Operational System of the Biochemistry Laboratory
7 6 5 4 3 2 1
Ratings:Tube 1 Tube 2 Tube 3 Tube 4 Tube 5
Scale:None No haemolysis apparent+ Slight haemolysis present+ + Mildly Haemolysed+ + + Moderate Haemolysis+ + + + Definite Haemolysis+ + + + + Unacceptable Haemolysis
Dear Participant
Your participation in a brief survey, undertaken for the purposes of Quality Improvement at the WCPVL Stellenbosch, as well as for submission as part of requirements for a B. Tech qualification at Quality Faculty of CPUT, will be greatly valued and appreciated. Your input is requested in order to associate “+” values to blood serum in an attempt determine correlate sample appearance in a colour index to actual spectrophotometric readings taken of them.
Could you kindly please take the time to indicate with number of “+” what you would consider each tube to be rated.
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ANNEXURE B PROTOCOL ANALYSIS
Factors impacting on samples and impacting on sample behaviour in the Biochemistry Research environment
1. Has a process-based system been developed and implemented for the work conducted on samples in Biochemistry?
Y/ N/ N/A
2. Are the adequate Work Instructions in the form of SOP’s (Standard Operating Procedures) that have been developed for all core sample processes (sample registration, preparation and analysis)
Y/ N/ N/A
3. Does this extend to support processes involving samples in the Biochemistry section, such as laboratory cleaning operations?
Y/ N/ N/A
4. Does this extend to support processes involving samples in the Biochemistry section readily available and easily accessible?
Y/ N/ N/A
5. Have the necessary worksheets and forms needed for core sample processing, been developed and are readily available for use?
Y/ N/ N/A
6. Are Work Instructions in the form of SOP’s (Standard Operating Procedures) for all core sample processes (sample registration, preparation and analysis)
Y/ N/ N/A
7. Are adequate records being maintained regarding the work carried out on samples
Y/ N/ N/A
8. Are adequate records being kept regard the support processes involving sample (e.g. temperature readings of refrigerators samples are stored in before processing)
Y/ N/ N/A
9. Sample identification and traceability in the system Y/ N/ N/A
10. Does the system incorporate the use of a back-up system to ensure traceability of samples in the system, such as LIMS (Laboratory Information Management System)
Y/ N/ N/A
11. Are reference standards prepared according to standard operating procedure for each different analysis and used with every batch of samples being analysed
Y/ N/ N/A
12. Are control samples included in every batch of samples as specified by the standard operating procedure?
Y/ N/ N/A
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13. Have calibration and maintenance of equipment being used to process samples, been addressed in SOP’s
Y/ N/ N/A
14. Is calibration and maintenance being performed regularly and is there records/evidence of this.
Y/ N/ N/A
15. Have methods been determined and implemented to verify results of analysis performed on samples, such as interlaboratory testing
Y/ N/ N/A
16. Are samples processes timeously and results send out within preset time constraints?
Y/ N/ N/A
17. If unforeseen circumstances present themselves if there a corrective action procedure in place?
Y/ N/ N/A
18. Has this procedure been documented and are records kept surrounding this
Y/ N/ N/A
Comments
ANNEXURE C: MODIFIED FLOWCHART
Reception: Samples arrive at Lab
Sample information captured
Samples delivered to Biochemistry Section
Sample Reception at BiochemistrySamples information recorded.Biochem lab number assigned.
Test Allocation.Samples stored under ideal
conditions until testing
Veterinarian reviewsCompiles with results from other labs
Issues report
Technologist reviews result
Issues it for release from Biochem
Validation: Checks performed to see if SOP
Followed. Controls and Standards in spec
Sample Analysis:Verifiable analytic method
according to SOP.Controlled conditions
Use of Controls and Standards
Quality Management SystemAll systems in Biochemistry
Critical Suppliers
Record keeping
Satisfied Service Customer
Quality Management Documents and Records
Additional Step:Screening Test
NO: Corrective action involves
redo
YES
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