Geographic classification of wines using Vis-NIR spectroscopy...Geographic classification of Spanish...
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The University of Adelaide
Geographic classification of wines using Vis-NIR spectroscopy
Master Thesis
by
Liang Liu
B. Eng. (Shenyang Pharmaceutical Universiy, China)
School of Chemical Engineering
Faculty of Engineering, Computer & Mathematical Sciences
November 2006
I
Declaration
This work contains no material which has been accepted for the award of any other
degree or diploma in any university or other tertiary institution and, to best of my
knowledge and belief, contains no material previously published or written by another
person, except where due reference has been made in the text.
I give consent to this copy of my thesis being made available in the University Library.
The author acknowledges that copyright of published works contained within this
thesis (as listed below) resides with the copyright holders of those works.
Liang Liu
November 2006
II
Summary
The determination of wine authenticity and the detection of adulteration are attracting
an increasing amount of attention for wine producers, researchers and consumers.
Wine authentication and classification based on geographical origin has been widely
studied. Most of these studies have achieved successful classification results.
However, these studies have involved complicated and expensive procedures. Visible
and near infrared spectroscopy (Vis-NIR) is recognized as a rapid and non-destructive
technique. In recent years, several studies have been conducted using Vis-NIR
spectroscopy to analyze wine for both quantitative and qualitative purposes. The aim
of this research was to investigate the geographical classification of wines using Vis-
NIR spectroscopy. The effect of temperature and measurement mode (transmission
and transflectance) on Vis-NIR spectra was investigated to identify optimal conditions
for wine sample analysis. It was found the optimal temperature is between 30 to 35 oC
and the shorter pathlength measurement condition has better prediction ability.
Classification by geographical origin using Vis-NIR spectroscopy was investigated for
sixty-three Tempranillo wines from Spain and Australia, and fifty Riesling wines from
Australia, New Zealand and Europe. Discriminant partial least square regression
(DPLS) and linear discriminant analysis (LDA) based on PCA scores were used to
perform classification. Over 90% of the Tempranillo wines were correctly classified
according to their geographical region using both DPLS and LDA. A classification
rate of 72% was achieved for the Riesling wines. Vis-NIR technique provides a
similar degree of reliability on wine classification comparable to those obtained using
chemical composition. The results of this study demonstrate potential for Vis-NIR
spectroscopy combined with multivariate analysis as a rapid method for classifying
wines by geographical origin.
III
Acknowledgements
I wish to express my sincere gratitude to my research supervisors, Dr. Chris Colby
and Dr. Daniel Cozzolino. I appreciate their expert guidance and never ending
patience. I also thank my co-supervisors, A/Prof. Brian O’Neill, Prof. Derek Abbott
and A/Prof. Graham Jones for their helpful guidance and encouragement.
The major part of the work reported in this thesis was performed at the Australia Wine
Research Institute, Adelaide, SA. I would like to thank the following staff from AWRI,
Dr. Wies Cynkar, Mr. Mark Gishen, Dr. Les Janik, Dr. Robert Dambergs, Mr.
Geoffrey Cowey, Mr Mathew Holdstock, Dr. Paul Smith, Ms. Megan Mecurio, and
their colleagues from the AWRI analytical lab. Their contributions made this work
possible.
Finally, I would like to thank my parents and my girlfriend for their continuing love
and support throughout my academic career.
IV
List of Publications
Liang Liu, Daniel Cozzolino, Wies Cynkar, Mark Gishen, Christopher Colby.
Geographic classification of Spanish and Australian Tempranillo red wines by visible
and near infrared spectroscopy combined with multivariate analysis. Journal of
Agriculture and Food Chemistry. (Published on web 12/08/2006)
Liang Liu, Daniel Cozzolino, Chris Colby, Bob Dambergs, Mark Gishen, Brian
O’Neill, Derek Abbott, (2006) Effect of temperature on visible and near infrared
spectra of wine. The 12th Australian Near Infrared Spectroscopy Conference,
Rockhampton, Queensland, Australia.
V
Table and Contents
SUMMARY............................................................................................................................................II
ACKNOWLEDGEMENTS ................................................................................................................ III
LIST OF PUBLICATIONS................................................................................................................. IV
LIST OF FIGURES ............................................................................................................................VII
LIST OF TABLES ............................................................................................................................... IX
CHAPTER 1 INTRODUCTION...........................................................................................................1
CHAPTER 2 LITERATURE REVIEW ...............................................................................................4
2.1 WINE QUALITY CATEGORY – GEOGRAPHICAL ORIGIN ......................................................................4 2.2 WINE CLASSIFICATION AND AUTHENTICATION.................................................................................5
2.2.1 Sensory evaluation .................................................................................................................5 2.2.2 Instrumental analysis .............................................................................................................6 2.2.3 Spectroscopic methods ...........................................................................................................9 2.2.4 Summary.................................................................................................................................9
2.3 NEAR INFRARED SPECTROSCOPY...................................................................................................10 2.3.1 Introduction..........................................................................................................................10 2.3.2. Effect of Sample presentation on Vis and NIR spectra ........................................................10 2.3.3. Use of NIR to classify food based on geographical origin..................................................12 2.3.4 NIR applications on wine analysis .......................................................................................13
SUMMARY AND RESEARCH GAPS.........................................................................................................15
CHAPTER 3 MATERIAL AND METHODS.....................................................................................16
3.1 WINE SAMPLES .............................................................................................................................16 3.2 WINE REFERENCE ANALYSIS..........................................................................................................17 3.3 SPECTROSCOPIC MEASUREMENTS .................................................................................................17 3.4 SPECTRA DATA ANALYSIS ..............................................................................................................19
3.4.1 Spectra pre-treatment ...........................................................................................................19 3.4.2 Multivariate analysis............................................................................................................20
CHAPTER 4 EFFECT OF SAMPLE PRESENTATION - SAMPLE TEMPERATURE EFFECT
ON THE ANALYSIS OF WINE..........................................................................................................24
4.1 INTRODUCTION .............................................................................................................................24 4.2 RESULTS AND DISCUSSION.............................................................................................................24
4.2.1 Chemical analysis ................................................................................................................24 4.2.2 Spectra interpretation and analysis......................................................................................26
VI
4.2.3 Influence of temperature on the Vis-NIR spectra of wine .....................................................29 Summary .......................................................................................................................................36 4.2.4 Principal component analysis ..............................................................................................36 4.2.5 Comparison of the prediction ability of different temperatures using PLS ..........................40
CHAPTER 5 EFFECT OF SAMPLE PRESENTATION – MEASUREMENT CONDITION
EFFECT ON THE ANALYSIS OF WINE .........................................................................................42
5.1 INTRODUCTION .............................................................................................................................42 5.2 RESULTS AND DISCUSSION.............................................................................................................42
5.2.1 Chemical analysis ................................................................................................................42 5.2.2 Spectra analysis ...................................................................................................................43 5.2.3 Principal component analysis ..............................................................................................45 5.2.4 Comparison using PLS.........................................................................................................47
CHAPTER 6 USE OF VISIBLE AND NIR TO CLASSIFY TEMPRANILLO WINES BASED
ON GEOGRAPHICAL ORIGINS. .....................................................................................................49
6.1 INTRODUCTION .............................................................................................................................49 6.2 RESULTS AND DISCUSSION.............................................................................................................50
6.2.1 Chemical analysis ................................................................................................................50 6.2.2 Spectra interpretation and analysis......................................................................................51 6.2.3 Principal component analysis ..............................................................................................52 6.2.4 Discrimination analysis........................................................................................................54
SUMMARY...........................................................................................................................................58
CHAPTER 7 USE OF VISIBLE AND NIR TO CLASSIFY RIESLING WINES BASED ON
GEOGRAPHICAL ORIGINS.............................................................................................................59
7.1 INTRODUCTION .............................................................................................................................59 7.2 RESULTS AND DISCUSSIONS...........................................................................................................59
7.2.1 Chemical analysis ................................................................................................................59 7.2.2 Spectra interpretation and analysis......................................................................................60 7.2.3 Principal component analysis ..............................................................................................62 7.2.4 Discrimination analysis........................................................................................................65
SUMMARY...........................................................................................................................................67
CONCLUSION .....................................................................................................................................69
REFERENCES .....................................................................................................................................71
VII
List of Figures
FIGURE 3. 1 FOSS NIRSYSTEM6500, SILVER SPRING, MD .....................................................................18 FIGURE 3. 2 SAMPLE PRESENTATION OF TRANSMITTANCE AND TRANSFLECTANCE ...................................19
FIGURE 4. 1 VIS-NIR SPECTRA OF RED WINE SAMPLES AT 5 DIFFERENT TEMPERATURES ..........................27 FIGURE 4. 2 VIS-NIR SPECTRA OF WHITE WINE SAMPLES AT 5 DIFFERENT TEMPERATURES ......................27 FIGURE 4. 3 SECOND DERIVATIVE SPECTRA OF RED AND WHITE WINE SAMPLES .......................................28 FIGURE 4. 4 WATER SPECTRA AT 6 DIFFERENT TEMPERATURES.................................................................28 FIGURE 4. 5 VIS-NIR SPECTRA OF ONE RED WINE SAMPLE (WW1) AT 6 DIFFERENT TEMPERATURES ........29 FIGURE 4. 6 SPECTRA AT 1450NM REGION ...............................................................................................30 FIGURE 4. 7 SPECTRA AT 2270NM TO 2300NM REGION.............................................................................30 FIGURE 4. 8 SECOND DERIVATIVE SPECTRA AT 1450NM REGION...............................................................31 FIGURE 4. 9 SECOND DERIVATIVE SPECTRA AT 2270NM TO 2300NM REGION ............................................31 FIGURE 4. 10 LINEAR RELATIONSHIP OF THE ABSORBANCE OF SECOND DERIVATIVE SPECTRA AT 962NM OF
RED WINES .....................................................................................................................................32 FIGURE 4. 11 LINEAR RELATIONSHIP OF THE ABSORBANCE OF SECOND DERIVATIVE SPECTRA AT 962NM OF
WHITE WINES .................................................................................................................................33 FIGURE 4. 12 LINEAR RELATIONSHIP OF THE ABSORBANCE OF SECOND DERIVATIVE SPECTRA AT 1412NM
OF RED WINES AVERAGE SPECTRA ..................................................................................................34 FIGURE 4. 13 LINEAR RELATIONSHIP OF THE ABSORBANCE OF SECOND DERIVATIVE SPECTRA AT 1462NM
OF RED WINES AVERAGE SPECTRA ..................................................................................................34 FIGURE 4. 14 LINEAR RELATIONSHIP OF THE ABSORBANCE OF RAW SPECTRA OF RED WINES AT 2268 NM.35 FIGURE 4. 15 LINEAR RELATIONSHIP OF THE ABSORBANCE OF RAW SPECTRA OF RED WINES AT 2306 NM.36 FIGURE 4. 16 SCORE PLOT OF PC1 AND PC2 OF THE WHITE WINE SAMPLES. NUMBERS REPRESENT THE
TEMPERATURES ..............................................................................................................................37 FIGURE 4. 17 EIGENVECTORS OF THE FIRST TWO PCS OF THE PCA FOR WHITE WINES .............................37 FIGURE 4.18 SCORE PLOT OF PC1 AND PC2 OF THE RED WINE SAMPLES. NUMBERS REPRESENT THE
TEMPERATURES ..............................................................................................................................38 FIGURE 4. 19 EIGENVECTORS OF THE FIRST TWO PCS OF THE PCA FOR THE RED WINES..........................38 FIGURE 4. 20 LINEAR RELATIONSHIP BETWEEN THE MEAN SCORE VALUES OF TEMPERATURE RELATED PC
OF WHITE RED SAMPLES AND TEMPERATURE VARIATION.................................................................39 FIGURE 4. 21 LINEAR RELATIONSHIP BETWEEN THE MEAN SCORE VALUES OF TEMPERATURE RELATED PC
OF WHITE WINE SAMPLES AND TEMPERATURE VARIATION...............................................................39
FIGURE 5. 1 VIS-NIR SPECTRA OF 6 RED AND 6 WHITE WINE SAMPLES AT 0.2 MM PATH LENGTH
TRANSFLECTANCE MODE................................................................................................................44 FIGURE 5. 2 VIS -NIR SPECTRA OF 6 RED AND 6 WHITE WINE SAMPLES AT 0.4 MM TRANSFLECTANCE MODE
......................................................................................................................................................44 FIGURE 5. 3 VIS-NIR SPECTRA OF THE SAME SAMPLE AT THREE DIFFERENT PATH LENGTHS.....................45
VIII
FIGURE 5. 4 PCA SCORE PLOT OF THE PC1 AGAINST PC2 ........................................................................46 FIGURE 5. 5 PCA SCORE PLOT OF THE PC1 AGAINST PC3 ........................................................................47
FIGURE 6. 1 SECOND DERIVATIVE OF THE VIS-NIR SPECTRA OF AUSTRALIAN AND SPANISH TEMPRANILLO
WINES ............................................................................................................................................52 FIGURE 6. 2 SCORE PLOT OF THE FIRST TWO PRINCIPAL COMPONENTS OF AUSTRALIAN (A) AND SPANISH
(S) TEMPRANILLO WINES USING VIS-NIR AFTER SNV AND SECOND DERIVATIVE PROCESSING ......53 FIGURE 6. 3 EIGENVECTORS OF THE THREE FIRST PRINCIPAL COMPONENTS OF AUSTRALIAN AND SPANISH
TEMPRANILLO WINES USING VIS-NIR AFTER SNV AND SECOND DERIVATIVE PROCESSING ............54 FIGURE 6. 4 PARTIAL LEAST SQUARES SCORE PLOT OF THE FIRST TWO PRINCIPAL COMPONENTS OF
AUSTRALIAN (A) AND SPANISH (S) TEMPRANILLO WINES USING VIS-NIR PRE-PROCESSED SPECTRA
FOR THE CALIBRATION SET .............................................................................................................57
FIGURE 7. 1 VIS -NIR RAW SPECTRA OF RIESLING WINES FROM AUSTRALIA, NEW ZEALAND AND EUROPE
......................................................................................................................................................61 FIGURE 7. 2 SNV AND 2ND DERIVATIVE PROCESSED SPECTRA OF RIESLING WINES FROM AUSTRALIA, NEW
ZEALAND AND EUROPE..................................................................................................................61 FIGURE 7. 3 SCORE PLOT OF THE FIRST 3 PRINCIPAL COMPONENTS OF AUSTRALIAN (AUS), NEW ZEALAND
(NZ) AND EUROPEAN (EUR) RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA...............62 FIGURE 7. 4 EIGENVECTORS OF THE THREE FIRST PRINCIPAL COMPONENTS OF AUSTRALIAN, NEW
ZEALAND AND EUROPEAN RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA..................63 FIGURE 7. 5 SCORE PLOT OF THE FIRST 3 PRINCIPAL COMPONENTS OF AUSTRALIAN (AUS), AND
EUROPEAN (EUR) RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA ...............................64 FIGURE 7. 6 SCORE PLOT OF THE FIRST 3 PRINCIPAL COMPONENTS OF AUSTRALIAN (AUS) AND NEW
ZEALAND (NZ) RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA...................................64 FIGURE 7. 7 SCORE PLOT OF THE FIRST 3 PRINCIPAL COMPONENTS OF NEW ZEALAND (NZ) AND
EUROPEAN (EUR) RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA ...............................65
IX
List of Tables
TABLE 2.1 APPELLATION SYSTEMS OF THE MOST EUROPE WINE PRODUCING COUNTRIES (KOLPAN ET AL.
1996; MACNEIL 2001) .....................................................................................................................5 TABLE 2. 2 GEOGRAPHICAL CLASSIFICATION OF WINES USING MULTI-ELEMENT AND STABLE ISOTOPE
RATIO. ..............................................................................................................................................7
TABLE 4. 1 WHITE WINE SAMPLES’ CODE, VARIETY, CHEMICAL COMPOSITIONS AND THE CORRESPONDING
STATISTICS OF SAMPLE ANALYZED..................................................................................................25 TABLE 4. 2 RED WINE SAMPLES’ CODE, VARIETY, CHEMICAL COMPOSITIONS AND THE CORRESPONDING
STATISTICS OF SAMPLE ANALYZED..................................................................................................26 TABLE 4. 3 STANDARD ERROR IN CROSS VALIDATION (SECV) OF PLS PREDICTION FOR CHEMICAL
ANALYSIS PARAMTERS....................................................................................................................41
TABLE 5. 1 SAMPLES’ CODE, CHEMICAL COMPOSITIONS AND THE CORRESPONDING STATISTICS OF SAMPLE
ANALYSED......................................................................................................................................43 TABLE 5. 2 THE STANDARD ERROR IN CROSS VALIDATION (SECV) OF THE PREDICTION MODELS FOR
EACH PARAMETER AT DIFFERENT PATH LENGTH..............................................................................48
TABLE 6. 1 VINTAGE AND ORIGIN OF COMMERCIAL TEMPRANILLO WINE SAMPLES ANALYSED ................49 TABLE 6. 2 RANGE OF CHEMICAL COMPOSITION FOR THE AUSTRALIAN AND SPANISH WINE ANALYSED...51 TABLE 6. 3 LDA CLASSIFICATION RESULTS OF AUSTRALIAN AND SPANISH TEMPRANILLO WINES USING
VIS-NIR RAW SPECTRA BASED ON THE FIRST 3 PCS (98% OF THE TOTAL VARIATION) ....................55 TABLE 6. 4 LDA CLASSIFICATION RESULTS OF AUSTRALIAN AND SPANISH TEMPRANILLO WINES USING
VIS-NIR PRE-PROCESSED SPECTRA BASED ON THE FIRST 3 PCS (77% OF THE TOTAL VARIATION) ..55 TABLE 6. 5 LDA CLASSIFICATION RESULTS OF AUSTRALIAN AND SPANISH TEMPRANILLO WINES USING
VIS-NIR PRE-PROCESSED SPECTRA BASED ON THE FIRST 9 PCS (95% OF THE TOTAL VARIATION) ..56 TABLE 6. 6 DISCRIMINANT PARTIAL LEAST SQUARES (DPLS) CLASSIFICATION RESULTS OF AUSTRALIAN
AND SPANISH TEMPRANILLO WINES USING VIS-NIR PRE-PROCESSED SPECTRA..............................58
TABLE 7. 1 VINTAGE AND ORIGIN OF RIESLING WINE SAMPLES ANALYZED ..............................................59 TABLE 7. 2 STATISTICS OF CHEMICAL COMPOSITION FOR RIESLING WINES FROM DIFFERENT
GEOGRAPHICAL REGION .................................................................................................................60 TABLE 7. 3 LDA CLASSIFICATION RESULTS OF AUSTRALIAN, NEW ZEALAND AND EUROPEAN RIESLING
WINES USING VIS-NIR PRE-PROCESSED SPECTRA...........................................................................66 TABLE 7. 4 LDA CLASSIFICATION RESULTS OF EACH TWO REGIONS OF AUSTRALIAN, NEW ZEALAND AND
EUROPEAN RIESLING WINES USING VIS-NIR PRE-PROCESSED SPECTRA .........................................66 TABLE 7. 5 DPLS CLASSIFICATION RESULTS OF AUSTRALIAN, NEW ZEALAND AND EUROPEAN RIESLING
WINES USING VIS-NIR PRE-PROCESSED SPECTRA ...........................................................................67
1
Chapter 1 Introduction
Authenticity is an important food quality criterion. Wine as one of the most important
beverages around the world requires meticulous and continuous control to maintain its
quality. Geographical origin of the wine is often used as an indicator of quality,
especially for fine wines where that higher quality production from particular regions
has long been recognized. Moreover, there is growing enthusiasm among consumers
for high quality food with a clear regional identity (Kelly et al. 2005). To classify and
authenticate wine samples based on their geographical origin is of obvious meaning
for both industry and consumers. Therefore, this problem has attracted great interest
from researchers.
Historically, sensory evaluation is the most direct and widely applied method to assess
and authenticate wine products. However, it is subjective and interferences may easily
lead to incorrect conclusions.
As a consequence, more objective methods, including routine chemical, instrumental
methods, based on the chemical composition of wines have been introduced as on
alternative. In these methods, multivariate analysis techniques (chemometrics) are
widely employed to enhance the classification ability. For example, instrumental
analyses in conjunction with pattern recognition techniques have been able to classify
wines from different geographical and varietals origin (Reid et al. 2006). These
studies employ advanced chromatographic (high performance liquid chromatography
(HPLC), gas chromatography (GC)) and/or spectroscopic (nuclear magnetic
resonance (NMR), mid infrared spectroscopy (MIR)) techniques (Arvanitoyannis et al.
1999; Reid et al. 2006). DNA-based and immunological techniques have also been
applied (Lockley and Bardsley 2000). Most studies have achieved
satisfactory/successful outcomes, with wines from different geographical origins were
correctly classified. However, most techniques require high cost instruments and
complicated procedures, and are not likely to gain wide application in the wine
industry unless the instrument and running costs are lowered.
2
The near infrared (NIR) spectroscopy technique has been recognized as a rapid and
non-destructive technique and has been widely applied in the agriculture and food
fields in recent decades. By recording the overtone and combination vibrations of the
molecular bonds, the resulting NIR spectrum produces a fingerprint of the sample.
However, the characteristics of broad, superimposed and weak absorption bands in the
NIR spectrum has limited its direct use. Consequently, the technique has been
neglected by spectroscopists for a long time. Fortunately, with the information
extraction tool – chemometrics and advances in computing power, NIR spectroscopy
now provides a practical alternative to classical chemistry methods.
A large number of studies have been conducted using NIR spectroscopy to perform
wine analysis for quantitative and for qualitative purposes. Different wine chemical
parameters have been quantified and different wine varieties have been classified
based on NIR spectra (Dambergs et al. 2002; Cozzolino et al. 2005; Urbano Cuadrado
et al. 2005). However, no workers have attempted to classify wines by NIR
spectroscopy based on their geographical origin. Several successes have been
achieved using NIR to geographically classify food products. With the success of
these previous studies with food products, classification of red (Tempranillo wines)
and white wines (Riesling wines) from different geographical origins using NIR
spectroscopy combined with chemometrics was investigated in this thesis.
To date, basic protocols for wine sample presentation employed in NIR studies were
mainly based on the experience with other food products. No systematic investigation
has been conducted of optimal experiment conditions for taking NIR measurements.
Therefore, in this study, the effects of sample temperature and measurement mode
(transmission and transflectance) were firstly examined before conducting the
classification study.
Therefore, the objectives of the research undertake was to:
• Examine the effect of experimental protocols for wine analysis using visible
and near infrared (Vis-NIR) spectroscopy, including sample temperature effect
and measurement mode (transmission and transflectance).
3
• Analyze and classify red (Tempranillo wine) and white (Riesling wine) wine
samples using Vis-NIR spectroscopy according to the geographical origin.
Chapter 2 of the thesis presents a literature review and identifies the knowledge gaps
that this work is filling. This is followed by the investigation of sample presentation
effects (temperature and scanning modes) for the Vis-NIR spectra for wine samples
(Chapters 4 and 5, respectively). Finally the study of wine classification based on the
geographical origin using Vis-NIR spectroscopy (Chapters 6 and 7) is presented.
4
Chapter 2 Literature review
2.1 Wine quality category – geographical origin
“Great wines do not come from just any where” (MacNeil 2001). Wine is one of the
agricultural products whose aroma and taste are influenced by where it grows (such as
tea, coffee, honey, and olive oil) (Pigott 2004). Geographical origin plays a role in
wine just like a mother does in the birth of her child. It is reflected in the aromas and
flavors of the vineyard from where the wine comes and the specific environment
where the vine grew. The harmonic convergence of every facet of nature produces the
finest wines. “Terroir” a word originally from France, presents the idea that the site
determines the quality of wine, and is now a buzz word all over the world (Kolpan et
al. 1996).
In the 1930’s, France was the first to develop a system of regulations based on the
geographical origin known as the ‘Appellation d’Origine Controlee’ (AOC) (Kolpan
et al. 1996; MacNeil 2001). Soon the system became a model for wine producing
countries around the world. Table 2.1 shows the category systems of several European
wine producing countries. All of these systems were designed to define and protect
wines from specific geographic areas, and also imply an indicator of wine quality
(Arvanitoyannis et al. 1999).
5
Table 2.1 Appellation systems of the most Europe wine producing countries (Kolpan et
al. 1996; MacNeil 2001)
France Appellation d’Origine Controlee(AOC)
Italy Denominazione di Origine Controllata (DOC)
Spain Denomination of origin (DO)
Portugal Denominations of controlled origin (DOC)
Germany Qualitätswein mit Prädikat (QmP)
Greece Appellation of Origin of Superior Quality and
Controlled Appellation of Origin (OPAP)
2.2 Wine classification and authentication
As a consequence, classification and authentication of wine based on their
geographical origin has attracted interest from researchers and the wine industry and
has been subject to extensive research using both sensory analysis and instrumental
methods (Kallithraks et al. 2001).
2.2.1 Sensory evaluation
Sensory evaluation by experienced tasters remains the widely used method to inspect
and authenticate wine by the industry and consumers. However, it does not always
lead to the correct conclusions. Frank and Kowalski (1984) have shown that sensory
data did not provide sufficient information to separate wines from different regions of
France and USA. Sensory descriptive scores also have been applied in conjunction
with pattern recognition analysis. Sivertsen et al. (1999) used 17 sensory attributes to
classify wines in conjunction with multivariate analysis methods from different
French wine regions. Only 63.3% of the samples were correctly classified, compared
with an 81.8% correct rate achieved by using 12 chemical parameters. Although
sensory experts are well trained and have an outstanding ability to “identify” coded
6
wine samples, incorrect conclusions commonly occur because of changes in
vinification, differences between vintage, and even the mental or physical fatigue of
the tasters. Therefore, objective methods based on wine chemical composition are
considered superior.
2.2.2 Instrumental analysis
Wine is a complicated mixture of chemical components, including various organic
and inorganic constituents. The chemical matrix reflects the character of the wine
sample. It has been used to explore wine classification based on geographical origins.
A diverse range of chemical parameters have been measured in wine to classify
samples according to their geographic origins.
2.2.2.1 Trace element and isotope analysis
Several authors have performed trace element and stable isotope analysis to identify
wine geographical origin. Isotope ratios are dependent on climate and variety.
Measurement of strontium (Sr) isotope ratios (87Sr/86Sr) by thermal ionization-mass
spectrometry (TIMS) was one of the earliest trials to discriminate wines growing in
different regions within a given country (e.g. France, Italy) (Horn et al. 1993). The
variation of δ18O value, associated with water in wine can indicate production region
(Breas et al. 1994). Deuterium content of water and ratios of the methyl group of
ethanol analyzed by a comprehensive NMR technique known as Site-specific Isotope
Fractionation measured by Nuclear Magnetic Resonance (SNIF-NMR) were also used
to identify geographical origin of wines (Day et al. 1995).
Metal elements are considered good indicators of wine origin since generally they are
not metabolized or modified during the vinification process (Kelly et al. 2005). The
most frequently quantified elements are Na, Fe, Zn, Rb, Ca, Mg, Mn, Cu, Cr, Co, Sb,
Cs, Br, Al, Ba, As, Li, Ag (Arvanitoyannis et al. 1999; Kelly et al. 2006). The
potential of multi-element analysis for determining wine geographical origin was
demonstrated by McCurdy et al. (1992). The separation of 112 Spanish and English
7
wines according to geographical origin was achieved by analysis of 48 elemental
concentrations using inductively coupled plasma mass spectrometry (ICP-MS)
(Baxter et al. 1997) Canonical discriminant analysis was applied to extract
geographical information from elemental composition. Table 2.2 provides examples
of the studies that have used elements or isotope ratios to authenticate wines based on
their provenance.
Table 2. 2 Geographical classification of wines using multi-element and stable isotope
ratio.
Chemical
content Instrument
Parameters
analysed
Geographical
origins
Multivariate
analysis Reference
Multi-
elements AAS
Ba, Ca, Mg,
Rb, Sr, Ba,
V
Slovakian and
European wines PCA, PCF
Korenovska
& Suhaj
2005
ICP-MS 40 elements Three areas of
South Africa DA
Coetzee et
al. 2005
AAS, AES 11 elements
Three areas of
Canary Islands
(Spain)
PCA, LDA,
SIMCA
Frias et al.
2003
ICP-MS 13 elements Four German
regions QDA
Gomez et
al. 2004
Isotope
analysis
IRMS &
SNIF-NMR
D/H, δC-13,
δO-18
Three regions of
Slovenia PCA, LDA
Orginc et al.
2001
ICP-MS B-11/B-10 South Africa,
France, and Italy
Coetzee et
al. 2005
2.2.2.2 Amino acids
Amino acids have been used to characterize wine by geographical origin. Forty-two
Greek white wines were analyzed for primary amino acids by HPLC and using
discriminant analysis, the amino acid profiles were demonstrated to be useful in
classification of wine provenance (Soufleros et al. 2003).
8
2.2.2.3 Phenolic compounds
Phenolic compounds, one of the most important constituents in wine, have also been
successfully applied to differentiate wines based on geographical origin. The phenolic
composition of red and white wines from four Spanish Appellations of Origin was
investigated using HPLC combined with statistical analysis methods (Pena-Neira et al.
2000). Several phenolic compounds were identified and quantified. The multivariate
analysis result indicated that the different geographical origins strongly influence the
phenolic composition of the final wine.
Thirty nine Galician certified brand of origin (CBO) red wines from Ribeira Sacra and
non-Ribeira Sacra area of Galicia were authenticated based on phenolic composition
(Rebolo et al. 2000). Nineteen major polyphenolic phytochemicals have been
determined by HPLC in forty experimental red wines. Multivariate chemometric
classification procedures were employed. The results indicated good performance in
terms of classification and differentiation of CBO Ribeira Sacra wines from wine
produced in other geographical areas.
2.2.2.4 Routine chemical analyses
Alcohol content, pH, colour parameters (color density, hue), etc. are routinely
analyzed chemical parameters for quality control. Normally, a single one of these
chemical parameters cannot explain the variation between wine provenance; however,
when combined with multivariate analysis techniques, wine patterns can be
recognized. Sivertsen et al. (1999) used a set of chemical parameters, including
alcohols, esters, pH and color, to identify twenty-two wines from four main wine
regions in France. An 81% correct classification rate was achieved. Although the
limitation in wine sample numbers decreased the reliability of this study, the result
demonstrated the potential of using general chemical data for geographical
classification.
9
2.2.3 Spectroscopic methods
In recent years, spectroscopic methods have been applied for wine authentication and
classification. Spectroscopic methods do not require complicated analytical
preparation procedures.
NMR spectroscopy has permitted the discrimination of red wines from three areas of
Italy’s Apulia region (Brescia et al. 2002).The results were comparable with those
obtained from chromatographic and ICP-AES analyses. However, the major
disadvantage of NMR is that it is one of the most expensive analytical techniques to
employ, both in initial capital outlay and running costs (Reid et al. 2006).
Mid infrared (MIR) spectroscopy reveals information about the fundamental
vibrations of molecular bonds. Palma and Barroso (2002) investigated MIR to
characterize and classify wines, brandies and other distilled drinks. Brandy samples
from four countries have been characterized according to their provenance.
2.2.4 Summary
Identification and quantification of trace elements, isotope ratios, phenolic
compounds, amino acids, and general chemical parameters are useful for the
authentication and classification of wines according to geographical origin. However,
to measure those chemical components, expensive instrument or complicated
analytical procedure were required, such as HPLC, NMR and ICP-MS. Routine
chemical analysis parameters, eg. alcohol, pH etc. cost less to achieve the
classification, but several parameters are needed simultaneously. Spectroscopic
methods (such as MIR, Ultra-violet (UV), NIR) provide more convenient procedures.
10
2.3 Near infrared spectroscopy
2.3.1 Introduction
In recent years, near infrared (NIR) spectroscopy has become an effective and
economical analytical technique for measuring food quality parameters. NIR
spectroscopy can analyse the entire sample in 30 seconds and can determine
multivariate parameters simultaneously. It is non-destructive and no sample pre-
treatment is required (Burns and Ciurczak 2001). Furthermore, spectroscopic
instruments are significantly cheaper than other instrumental methods (eg.
chromatography).
The wavelengths of the NIR region range between 750 and 2500 nm. This wavelength
region contains the overtones and combination vibration information of O-H, C-H,
and N-H bonds (Osborne et al. 1993), which are the principal structural components
of organic molecules. The NIR spectrum provides an overall fingerprint of the sample.
The spectra from NIR are very complicated. Hense, it is impossible to realize
meaningful information based on molecular structure (Williams and Norris 2001).
However, analytical information can be extracted from the NIR spectrum through
application of mathematical multivariate analysis techniques. This approach has been
demonstrated in a number of studies for NIR spectroscopy of food and wines. These
are presented and discussed in following sections.
2.3.2. Effect of Sample presentation on Vis and NIR spectra
2.3.2.1. Effect of sample temperature
When building a spectral database, the data is often sourced under varying conditions.
These variations may cause spectra variations. Sample temperature is an important
factor, especially for the liquid samples.
Previous study of the NIR spectrum of pure water has showed that an increase in the
11
temperature results in an intensity increase and peak shifting towards lower
wavelengths (Swierenga et al. 2000). While the hydroxyl group gives rise to two
bands for its stretching mode: a sharper band for the "free" OH groups and a broader
one for the stretch mode of hydrogen-bonded OH groups, as mentioned by Wulfert et
al. (1998), following a temperature increase, the absorption band for the free OH
groups increases in intensity.
Many researchers have studied the effect of temperature on spectra of water and
model wine solutions. Bianco et al. (2000) studied the influence of temperature on the
water spectra in the 1.2 µm region. The amplitudes and widths of the peaks varied
linearly with the temperature, and therefore, the authors believed that it was possible
to mathematically model the water spectrum with a high degree of precision. Spectra
variation has also been studied for ternary mixtures of ethanol, water and iso-propanol
(Swierenga et al. 2000). Absorption band variations occurred around 970 nm
wavelength and affected calibration models.
Multivariate calibration techniques have been used to deal with temperature
influences by including the temperature change in the calibration set (Wulfert et al.
2000; Hageman et al. 2005). Both linear regression and non-linear regression methods
were employed. For linear regression, principal component regression (PCR) and PLS
are the main techniques (Wulfert et al. 2000) and for non-linear regression, locally
weighted regression (LWR) and neural networks are frequently applied (Hageman et
al. 2005)
However, wine is a complex mixture of chemical components, not as simple as the
model solution containing only ethanol and water. Therefore, the temperature effect
for a real wine is expected to be more complicated than that for ethanol-water mixture
or model wine solutions.
2.3.2.2 Measurement mode effect
Measurement mode is another factor which may affect the application of NIR
spectroscopy. Reflectance (R) and transmission (T) are the two scanning modes that
12
have been applied for NIR spectroscopy. Transmission spectroscopy, where light
passes through a liquid sample and measured by the detector placed behind the
specimen, is well understood. Transflectance (TR) originally developed by Technicon
(now Bran+Luebbe, Germany) for the InfraAlyzer, was designed to study liquids in
an instrument using the reflectance measurement mode (Kawano 2002). In
transflectance, light passes through a sample, is reflected back from a diffuse mirror
placed behind the sample, then passes back through the sample and is measured by a
detector (Murray & Cowe 2004). Transflectance is not as popular as the transmission
mode. However, it can be successfully used for a liquid stream, frequently in
conjunction with optical bundle probes. It is suitable for in-line analysis and may be
more appropriate for industrial applications (Pasquini 2003).
Transmission mode has been adopted in most studies using NIR spectroscopy for
wine and other alcoholic beverage samples (Dambergs et al. 2002; Sauvage et al.
2002; Cozzolino et al. 2003). Some authors have used the tranflectance mode to
determine wine chemical parameters (Urbano Cuadrado et al. 2004). However, no
literature was found to compare the effect on the spectra and the analysis results for
different measurement modes for wine samples.
2.3.3. Use of NIR to classify food based on geographical origin
Several authors utilized NIR to classify food products based on their geographical
region. NIR was exploited to classify Japanese soy sauce based on their geographic
regions. Thirty-eight soy sauce samples were collected from three regions in Japan
(Iizuka and Aishima 1997). Approximately eighty percent of samples were correctly
classified using three pattern recognition methods, including linear discriminant
analysis (LDA), PLS and artificial neural network (ANN).
Bertran et al. (2000) has successfully applied NIR with pattern recognition methods to
authenticate virgin olive oils from very close geographical origins. Two chemometric
techniques, ANN and logistic regression (LR), were employed as classifying tools for
NIR spectra.
13
Samples of Emmental cheese sourced from six regions have been analyzed by NIR.
Using a combination of PCA and LDA, classification by region of origin of the
cheeses was achieved (Pillonel et al. 2002).
Honey samples produced in the European Community from different geographical
and botanical sources were characterized by NIR spectroscopy (Davies et al. 2002).
With chemometric methods, the botanical origins of the honey samples were
identified. However, separation based on the geographical origin could not be
achieved.
Arana et al. (2005) have authenticated the origins of white grapes from two different
wine production zones in Spain. The authors initially used the weight of the berries
and the soluble solids content, as important parameters to evaluate the grape. Only
59% of grapes were correctly classified. By contrast, classification using NIR spectra
achieved a 79.2% accuracy. The results from this study demonstrated the ability of
NIR spectra to identify the origin of white grapes and also indicated that NIR spectra
may achieve superior discrimination. Furthermore, if geographical differences can be
observed in grape spectra, similar information may also appear in the wine’s spectra.
2.3.4 NIR applications on wine analysis
The first application for NIR in wine analysis was the determination of some of the
main components of wine, such as ethanol, fructose, and tartaric acid, by Kaffka and
Norris. This preliminary study was performed on small number of test samples
prepared by standard addition of the components of interest to red or white wines.
Through their work, the critical wavelengths that could be used for multi-linear
regression analysis were identified (Dambergs et al. 2004).
Dambergs et al. (2002) accurately predicted the methanol concentration in wine-
fortifying spirit samples using NIR spectroscopy. Calibration models were developed
by combining sample NIR spectra and with the concentration measured by gas
chromatography (GC) tby several regression techniques, including partial least square
regression (PLS) and multiple linear regression (MLR). Comparisons of the standard
14
error of prediction demonstrated that the most useful NIR calibration model was built
by using continuous spectra, rather than a smaller number of fixed wavelengths.
Trace metals in twenty-four white wines were studied by atomic absorption
spectrometry (AAS) and NIR spectroscopy. Both MLR and PLS were applied to
construct calibration models by coupling NIR spectra with trace metal concentrations
analyzed by AAS. The regression correlation coefficients (R) and the standard error
of cross validation (SECV) for the calibration models indicated that the models were
acceptable for K, Na, Mg and Ca, but not for Cu and Fe (Sauvage et al. 2002).
Cozzolino et al. (2004) examined the potential of NIR spectroscopy to predict the
concentration of phenolic compounds of Australian red wine. The calibration
equations were built using PLS regression of the reference method (HPLC) and NIR
data. The calibration equations proved robust for the prediction of unknown samples.
This experiment demonstrated the ability of NIR for the quantitative analysis of wine
samples. The relationship between sensory analysis and NIR spectroscopy in
Australian Riesling and Chardonnay wines was also investigated (Cozzolino et al.
2005). The research suggested a correlation between sensory and NIR data but results
were considered unreliable due to the small sample size.
The feasibility of utilizing fifteen common wine parameters was studied using NIR
reflectance spectroscopy in conjunction with partial least square regression (Urbano
Cuadrado et al. 2005). Major components, such as alcohol, total acidity, pH, glycerol,
color and total polyphenol index were accurately determined. The SECV values
achieved from NIR spectra were close to those from reference methods. However, for
some organic acids including malic acid, tartaric acid and lactic acid, the accuracy of
prediction results were not as good as the above components.
The studies listed above demonstrated that NIR spectra contain the chemical
information for the analyzed samples and also can reveal characteristic information
about wine quality. This information can be extracted and applied for quality control
purposes. As well, this information is also predictive of geographical origin.
15
Summary and research gaps
As outlined in this review of the literature, wine classification based on geographical
origin may be successfully achieved using different instrumental analyses of the
chemical composition of wines. Unfortunately, most methods require expensive
instruments and/or complicated analysis procedures, and are problematic for industry
application. Visible and near infrared spectroscopy (Vis-NIR) is a relatively rapid and
low cost analytical technique. It has been employed to analyze wine samples and to
predict the value of several chemical parameters. However, previous work has not
focused on the geographical classification of wine using Vis-NIR spectroscopy.
Furthermore, no research has been performed to study the effect of sample
temperature and measurement mode for wine samples. Therefore, to fill these gaps in
knowledge, the aims of this research were to
1. to study the Vis-NIR spectra variation of wine samples and the corresponding
effect for the model calibration caused by sample temperature changes;
2. to examine the effect of measurement mode, including transmission and
transflectance, for wine Vis-NIR spectra and their influence on model
calibration;
3. to classify wine samples of same variety using Vis-NIR spectroscopy based on
their geographical origins, for red (Tempranillo) and white (Riesling) wines
respectively.
16
Chapter 3 Material and methods
The experimental investigation in this thesis involved measurement of Vis-NIR
spectra of wine samples and mathematical analysis of these spectra. This chapter
describes the wine samples analysed, the instruments, their method of use, and the
mathematical techniques which were employed.
3.1 Wine samples
Different wine samples were analyzed:
1. to study the effect of temperature on Vis-NIR spectra (Chapter 4). Ten red and
ten white wine samples (see Table 4.1) were collected randomly from the
Analytical Lab of the Australian Wine Research Institute (AWRI). All samples
were commercially available Australian wine. The red wines included three
wine varieties (Cabernet Sauvignon, Shiraz, and Pinot Noir) and one blend
(blend of Cabernet Sauvignon and Shiraz). There was one Rośe wine. The
white wines included six varieties (Chardonnay, Pinot Gris, Riesling,
Sauvignon Blanc, Semillon, Verdelho). Each sample was given a unique code,
initialled with R for red wine and W for white wine;
2. to study the measurement mode effect on Vis-NIR spectra (Chapter 5). Six red
and six white wines were randomly sourced from Analytical Lab of the
Australian Wine Research Institute (AWRI) (see Table 5.1). Each wine was
allocated a unique code;
3. to classify red wines based on geographical origin using Vis-NIR spectra
(Chapter 6). A total of sixty three bottles (8 labels x 4 replicates; 14 labels x 2
replicates and three bottles for three different labels) comprised of 25
Australian (n =15) and Spanish (n =10) Tempranillo wines were collected. All
samples were commercially available. The vintage of these wines ranged from
1999 to 2004 for the Spanish wines, and 2001 to 2004 for the Australian wines,
17
wines (Table 6.1).
4. to classify white wines based on geographical origin using Vis-NIR spectra
(Chapter 7). A total of fifty bottles (4 labels x 3 replicates and 19 labels x 2
replicates) comprised of 23 commercially available Australian (n =10), New
Zealand (n =5) and European (France and Germany) (n =8) Riesling wines
were collected. The vintage of these wines ranged from 2001 to 2005 (see
Table 7.1).
3.2 Wine reference analysis
Prior to spectra scanning, wine samples were analyzed to determine the key chemical
characteristics at the AWRI Analytical Service (http://www.awri.com.au/analytical_
service/analyses/). The chemical parameters included alcohol content, pH, titratable
acidity (TA), and glucose plus fructose (G+F) and (for geographical classification
samples only) total phenolics, color density and hue. The value of alcohol, pH, TA and
G+F were obtained from Wine Scan analysis (FOSS WineScan FT 120, Silver Spring,
MD, USA). The WineScan is a simple-to-use instrument for rapid analysis of wine. It
delivers results for the major quality parameters in a single analysis, including ethanol,
total acid, volatile acid, pH, glucose, fructose and reducing sugar. Total phenolics
were calculated by the absorbance at 280 nm measured using a UV/Vis
spectrophotometer (Cary 300, Varian, Inc., Palo Alto, CA, USA). For the Tempranillo
wines, wine color density was calculated by measuring the optical density (OD) of the
wine sample at two wavelengths at the actual wine pH (OD 520 nm plus OD420 nm).
Wine color hue was calculated as the ratio OD420/OD520 (Jackson 2000).
3.3 Spectroscopic measurements
Wine samples from freshly opened bottles were scanned in transmission or
transflectance mode using a FOSS NIRSystems6500 spectrophotometer (see Figure
3.1) (FOSS NIRSystems, Silver Spring, MD, USA). A 1mm path length cuvette was
used to contain the wine sample for transmission measurement mode. 0.2 mm and 0.4
18
mm depth transflectance sample cells were used for transflectance mode. Figure 3.2
depicts the operation of these two different measurement modes. Samples were pre-
equilibrated at the measurement temperature for 3 minutes before scanning. In the
investigation of temperature effect on Vis-NIR spectra, six temperatures were applied,
including ambient, 30 ºC, 35 ºC, 40 ºC, 45 ºC and 50 ºC. For other analyses, samples
were pre-equilibrated at 33 ºC before measuring spectra.
Figure 3. 1 FOSS NIRSystem6500 spectrophotometer, Silver Spring, MD
19
Figure 3. 2 Sample presentation of transmittance and transflectance
Spectral data were recorded using Vision software (version 1.0, FOSS NIRSystems,
Silver Spring, USA). The full wavelength range (400- 2500 nm) including the visible
region was analyzed. Spectral data were stored as the logarithm of the reciprocal of
transmittance [log (1/T)] or reflectance [log (1/R)] at 2 nm intervals. Instrument
performance was checked following the diagnostic protocols provided by the
manufacturer.
3.4 Spectra data analysis
Spectra were exported from the Vision software in NSAS format into The
Unscrambler software (version 9.2, CAMO ASA, Oslo, Norway) for chemometric
analysis.
3.4.1 Spectra pre-treatment
Standard Normal Variate (SNV) and second derivative transformation were used to
pre-process the spectra. SNV was performed for scatter correction. SNV was invented
to reduce spectral noise and eliminate background effects of NIR data (Barnes et al.
1989). It is a row-oriented transformation which centres and scales individual spectra
such that:
20
SNVi =(Yi -Ymean) / Stdev (Y) [3.1]
Where: SNV = Standard normal variate for the value of log (1/T) at the ith wavelength
Yi = value of log (1/T) at the ith wavelength
Stdev = standard deviation of the log (1/T) at all wavelengths
Second derivative transformation was performed using Savitzky-Golay derivative and
smoothing (left 5 and right 5 points and 2nd order filtering operation) to reduce
baseline variation and enhance the spectral features (Hruschka 1992). In NIR spectra,
peak overlapping is commonly observed (Williams and Norris 2001). Second
derivative transformation is a simple and frequently used approach to improve
spectral resolution, by which peak width is decreased and more peaks appear (Naes et
al. 2002). As a result, the spectral features are enhanced. However, a disadvantage of
derivatives is that they can amplify noise. This problem was overcome by using
Savitsky-Golay smoothing (Brereton 2003).
3.4.2 Multivariate analysis
3.4.2.1 Principal component analysis
Principal component analysis (PCA) was used in this investigation to reduce the
dimensionality of the data to a smaller number of components, to examine any
possible grouping of samples, and to visualize the presence of outliers.
PCA is a mathematical procedure widely used to transform sets of possibly correlated
data into a new set of orthogonal components, which are called principal components
(PCs) (Naes et al. 2002). The PCs reduce the data in a way that maximises between-
sample spectral variation. A set of n spectra can be expressed as a n x p data matrix X
containing n values of transmission absorbance at each of the p wavelength. The
general equation for PC calculation (Otto 1999) is:
X = TPT + E [3.2]
21
Where T is the score matrix,
P is the transposed eigenvector matrix,
E is the residual matrix.
The scores are the new values of spectra in the coordinate system defined by PCs and
the eigenvectors are the link between the wavelengths of the X matrix and the
principal component space (Otto 1999).
Fifteen PCs were derived from the spectral data in the studies of this thesis. PCA
models were developed using both raw and pre-processed data.
3.4.2.2 Partial least square regression
Calibration models between chemical composition and NIR spectra were developed
using partial least square regression (PLS). PLS is a multivariate data analysis
technique which can be used to relate several response (Y) variables to several
explanatory (X) variables (Otto 1999). The method aims to create new explanatory
variables by linear combination of the original X variables that maximize the
covariance between the response variables and the explanatory variables (Massart et
al. 1988; Otto 1999; Naes et al. 2002). This process is similar to PCA in the fact that
PLS also creates scores and loadings. However, the difference is that PCA creates its
scores by finding the linear combination of the explanatory variable that has
maximum variance among those X variables, but without considering the response
variables.
Calibration statistics, including the coefficient of determination in calibration (R2) and
the standard errors of cross validation (SECV), were calculated to evaluate the
accuracy of PLS calibration models.
To compare the calibration models achieved at different experiment conditions, the
SECVs were compared using an F test (Naes et al. 2002):
F = SECV22 / SECV1
2, where SECV1 < SECV2 [3.3]
22
The calculated F value was compared with the confidence limit F limit (1-α, n1-1, n2-2),
obtained from the distribution F table, where α is the test significance level (α=0.05 in
this experiment), n1 is the sample number measured at the first condition and n2 is the
sample number measured at the second condition. The differences between the SECV
are significant when F > F limit.
3.4.2.3 Discriminant analysis
Discrimination models were developed using linear discriminant analysis based on
PCA scores and the discriminant PLS techniques, respectively (Massart et al. 1988;
Naes et al. 2002).
Linear discriminant analysis (LDA) is a supervised classification technique where the
number of categories and the samples that belong to each category are previously
defined (Otto 1999; Naes et al. 2002). The criterion of LDA for selection of latent
variables is maximum differentiation between the categories that minimizes the
variance within categories (Naes et al. 2002). The method produces a number of
orthogonal linear discriminant functions, equal to the number of categories minus one,
that allow the samples to be classified in one or another category (Naes et al. 2002).
LDA was carried out on the PCA sample scores using JMP software (version 5.01,
SAS Institute Inc., Cary, NC, USA). This procedure has previously been used to
authenticate instant coffee and differentiate apple juice samples and meats (Charlton
et al. 2002; Reid et al. 2005; Cozzolino et al. 2005). The first several components
which yield the highest level of separation and explain most variation of the spectra
matrix in the PCA models, were input to the LDA analysis.
Discriminant partial least square regression (DPLS) is a variant of partial least square
regression (PLS). In this technique, each sample in the calibration set is assigned a
dummy variable as a reference value (eg. for Tempranillo wines, set to 1 = Australian
wines and 2 = Spanish wines) (Naes et al. 2002; Brereton 2003). The classification of
the wine samples accordingly to geographic origins was based on a 0.5 cut-off value.
The coefficient of determination in calibration (R2) and SECV were calculated to
evaluate the DPLS calibration models. The sample numbers correctly classified in
23
prediction were counted to calculate the classification rate.
The PCA, PLS, LDA and DPLS models were developed using full cross validation
(CV) (leave one out method). Cross-validation estimates the prediction error by
splitting the calibration samples into groups, where in the case of full cross validation,
each sample can be seen as one group (Otto 1999; Naes et al. 2002; Brereton 2003).
24
Chapter 4 Effect of sample presentation - sample
temperature effect on the analysis of wine
In order to study the classification ability of Vis-NIR spectroscopy, the initial step in
this research was to determine the optimal experimental conditions for obtaining
“fingerprints” of wine samples. The dominant parameters influencing the Vis-NIR
spectrum include the scanning temperature and measurement mode.
4.1 Introduction
NIR records the overtones and combination vibrational information of the molecular
bonds. Temperature changes can affect the vibration intensity of molecular bonds,
hence, the vibrational spectrum will change according to the temperature variation.
Consequently different temperatures may affect the result of a classification or
calibration model. There has been no study of the impact of temperature on the Vis-
NIR spectra of real wines. This chapter will focus on this question.
4.2 Results and discussion
4.2.1 Chemical analysis
Tables 4.1 and 4.2 summarized the profiles of the red and white wine samples
analysed.
25
Table 4. 1 White wine sample codes, variety, chemical composition and the
corresponding statistics of samples analyzed
Sample
code Variety Alcohol
(% v/v)pH TA
(g L-1)
G+F
(g L-1) Ww1 Chardonnay 13.75 3.36 6.55 1.80 Ww2 Chardonnay 13.37 3.41 6.73 4.10
Ww3 Chardonnay 13.43 3.26 6.54 0.70
Ww4 Pinot Gris 13.76 3.13 7.48 1.60
Ww5 Riesling 13.21 3.18 6.52 8.0
Ww6 Sauvignon Blanc 12.99 3.27 6.26 2.10
Ww7 Semillon 11.70 3.27 6.3 0.70
Ww8 Semillon 11.09 3.18 7.05 2.60
Ww9 Unwooded Chardonnay 13.55 3.36 6.32 9.30
Ww10 Verdelho 12.90 3.41 6.91 4.10
Mean 12.98 3.28 6.67 3.50 S.D. 0.89 0.10 0.39 2.97
Min 11.09 3.13 6.26 0.70
Max 13.76 3.41 7.48 9.30 a TA, titratable acidity; G+F, glucose + fructose; S.D., standard deviation; Min,
minimum value; Max, maximum value.
26
Table 4. 2 Red wine sample codes, variety, chemical composition and the corresponding
statistics of samples analyzed
Sample
code Variety
Alcohol
(% v/v) pH
TA
(g L-1)
G+F
(g L-1)
Rw1 Cabernet Sauvignon 13.61 3.53 6.63 0.30 Rw2 Cabernet Sauvignon 13.08 3.57 6.04 1.80
Rw3 Cabernet Sauvignon 12.49 3.43 7.35 0.40
Rw4 Cabernet Sauvignon 13.21 3.49 7.78 3.90
Rw5 Cabernet Sauvignon 12.92 3.36 7.22 0.20
Rw6 Pinot Noir 13.51 3.62 7.31 0.50
Rw7 Rośe 13.08 3.36 6.06 4.60
Rw8 Shiraz 13.65 3.54 6.44 0.20
Rw9 Shiraz 14.08 3.63 6.73 0.60
Rw10 Blend of Cabernet Sauvignon
and Shiraz 14.15 3.43 6.58 0.30
Mean 13.29 3.50 6.84 1.34
S.D. 0.52 0.10 0.58 1.64
Min 12.49 3.43 6.04 0.2
Max 14.15 3.63 7.78 4.6
4.2.2 Spectra interpretation and analysis
Figures 4.1 and 4.2 present the Vis-NIR spectra for the red and white wines at six
temperatures. Both varieties have high absorption at 1450 nm and 1950 nm.
Absorption at 1450 nm is the first overtone of O-H stretching vibration and absorption
at 1950 nm is a combination band of OH stretch and deformation (Osborne et al.
1993). Minor peaks appear around 976 nm, 1690 nm, 2268 nm, and 2306 nm. The
976 nm area is associated with the O-H stretch second overtone of water and ROH
(Osborne et al. 1993). The absorption at 1690 nm is related to C-H stretch first
overtones (Osborne et al. 1993). The absorption band at 2268 nm is related to C-H
combination and O-H stretch overtones and absorption at 2306 nm is related to C-H
combinations (Burns and Ciurczak 2001). Red wines have a peak in the visible region
around 540 nm which is related to the pigments (Somers 1998).
27
500 1000 1500 2000 25000
1
2
3
Red wines
2268 & 2306 nm
1950 nm
1450 nm
540 nm
log
1/T
wavelength (nm)
1690 nm
Figure 4. 1 Vis-NIR spectra of red wine samples at six different temperatures
500 1000 1500 2000 25000.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
log
1/T
Wavelength (nm)
1450 nm
1690 nm
1950 nm
2268 & 2306 nm
White wines
Figure 4. 2 Vis-NIR spectra of white wine samples at six different temperatures
Figure 4.3 shows the second derivative of spectra of red and white wine samples. The
second derivative transformation inverts the spectra, so the peaks of the original
spectra become troughs. The peaks become sharper, and some of the overlapping
peaks are separated (Hruschka 1992). From Figure 4.3, it can be noticed that peaks at
1450 nm and 1950 nm were separated into two peaks, and an additional peak has
appeared at 2306 nm.
28
500 1000 1500 2000 2500-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
Seco
nd d
eriv
ativ
e
Wavelength (nm)
Figure 4. 3 Second derivative spectra of red and white wine samples
Figure 4.4 presents the spectra of water. It is similar to that for wine, also exhibiting
strong absorptions at 1450 nm and 1950 nm. However, peaks do not occur at 1690 nm
and 2200 to 2300 nm, and the absorption of the 1450 nm peak is higher.
500 1000 1500 2000 25000.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Wavelenth (nm)
Log
1/T
ambient 30oC 35oC 40oC 45oC 50oC
water
Figure 4. 4 Water spectra at six different temperatures
29
4.2.3 Influence of temperature on the Vis-NIR spectra of wine
Figure 4.5 shows the spectra of one red sample Rw1 at six different temperatures.
These spectra are typical of all wine samples. Figures 4.6 and 4.7 present
enlargements of the spectra at 1450 nm and 2270 to 2300 nm. Peak shifting and
increased peak height were observed with temperature. However, peak shifting at
1450 nm is different to that occurring at 2270 to 2300 nm. These displacements can
also be observed in the second derivative of the spectra. Figures 4.8 and 4.9 show the
second derivative spectra at 1450 nm and 2270 to 2300 nm. To establish the
relationship between the temperature and spectral variation, four wavelengths regions:
960~1000 nm, 1410~1470 nm, 1660~1706 nm and 2250~2360 nm were analysed.
500 1000 1500 2000 25000.0
0.5
1.0
1.5
2.0
2.5
3.0 ambient 30oC 35oC 40oC 45oC 50oC
Log
1/T
Wavelength (nm)
2268 & 2306nm
1450nm
540nm
1950nm
Figure 4. 5 Vis-NIR spectra for a red wine sample (Ww1) at six different temperatures
30
1410 1420 1430 1440 1450 1460 1470 1480 14900.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
Wavelenth (nm)
Log
1/T
ambient 30oC 35oC 40oC 45oC 50oC
Figure 4. 6 Wine spectra at 1450 nm region
2250 2260 2270 2280 2290 2300 2310 2320 23300.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
Wavelength (nm)
Log
1/T
ambient 30oC 35oC 40oC 45oC 50oC
Figure 4. 7 Wine spectra at 2270 nm to 2300 nm region
31
1410 1420 1430 1440 1450 1460 1470 1480
-3.0x10-3
-2.8x10-3
-2.6x10-3
-2.4x10-3
-2.2x10-3
-2.0x10-3
-1.8x10-3
-1.6x10-3
-1.4x10-3
-1.2x10-3
-1.0x10-3
-8.0x10-4
-6.0x10-4
-4.0x10-4
-2.0x10-4
ambient 30oC 35oC 40oC 45oC 50oC
Seco
nd d
eriv
ativ
e
Wavelength (nm)
Figure 4. 8 Second derivative wine spectra at 1450 nm region
2260 2280 2300 2320 2340-0.003
-0.002
-0.001
0.000
0.001
0.002
ambient 30oC 35oC 40oC 45oC 50oC
Sec
ond
Der
ivat
ive
Wavelength (nm)
Figure 4. 9 Second derivative wine spectra at 2270 nm to 2300 nm region
a. 960~1000 nm
The peak in this region occurs at approximately 976 nm, and is related to the O-H
second overtone (Osborne et al. 1993). Wine samples produced a peak at 976 nm at
30 ºC, which shifts to 972 nm at 50 ºC. The peak for water shifts from 974 nm at 30
32
ºC to 970 nm at 50 ºC. Plotting the peak height against temperature change displayed
no apparent linear or other kind of relationship (data not shown).
In the second derivative spectra, a peak in all wine samples and water at six
temperatures occurs at 962 nm. Figures 4.10 and 4.11 present the plots of the second
derivative peak heights at 962 nm for the average spectra of red and white wines
versus temperature. A linear relationship was observed.
30 35 40 45 50
-1.30x10-4
-1.25x10-4
-1.20x10-4
-1.15x10-4
-1.10x10-4
-1.05x10-4
-1.00x10-4
Sec
ond
deriv
ativ
e
Temperature oC
y = -1.394E-06x - 5.835E-05
R2 = 9.990E-01P < 0.0001
Figure 4. 10 Linear relationship of the absorbance of second derivative spectra at 962nm
of red wines
33
30 35 40 45 50
-1.35x10-4
-1.30x10-4
-1.25x10-4
-1.20x10-4
-1.15x10-4
-1.10x10-4
-1.05x10-4
-1.00x10-4
Sec
ond
Der
ivat
ive
Temperature oC
y = -1.358E-06x - 6.223E-05
R2 = 9.975E-01P < 0.0001
Figure 4. 11 Linear relationship of the absorbance of second derivative spectra at 962nm
of white wines
b. 1410~1470 nm
The peaks in this region experienced a similar shifting trend as that observed for the
960~1000 nm region. However, the extent of shifting was larger. The peak points
moved from 1454 nm at 30 ºC to 1444 nm at 50 ºC. No obvious linear relationship
existed between absorbance and temperature of the peaks in the raw spectra (data not
shown).
After transforming the spectra to second derivative, the major peak located at
approximately 1450 nm separated into two peaks, around 1414 nm and 1462 nm.
Both peaks displayed a linear relationship between height and temperature. However,
the peak height at 1414 nm decreased with temperature increase, whilst the peak
height at 1460 nm increased with temperature. Figures 4.12 and 4.13 show the linear
relationships of second derivative peaks against temperature of red wines. White
wines have similar relationships (data not presented). This behavior might be
explained by the increase in free hydroxyl bonds as temperature increases (Wulfert et
al. 1998). The 1414 nm peak may be attributed to the free hydroxyl bond, whilst the
1462 nm peak might be associated with stretch mode of hydrogen-bonded OH groups.
34
30 35 40 45 50-0.0036
-0.0034
-0.0032
-0.0030
-0.0028
-0.0026
-0.0024
-0.0022
-0.0020
Sec
ond
deriv
ativ
e
Temperature oC
y = -6.380E-05x - 2.680E-04R2 = 9.992E-01P < 0.0001
Figure 4. 12 Linear relationship of the absorbance of second derivative spectra at 1412
nm of red wines average spectra
30 35 40 45 50-2.06x10-3
-2.04x10-3
-2.02x10-3
-2.00x10-3
-1.98x10-3
-1.96x10-3
-1.94x10-3
-1.92x10-3
-1.90x10-3
-1.88x10-3
-1.86x10-3
-1.84x10-3
Sec
ond
deriv
ativ
e
Temperature oC
y = 9.000E-06x - 2.308E-03
R2 = 9.985E-01P < 0.0001
Figure 4. 13 Linear relationship of the absorbance of second derivative spectra at 1462
nm of red wines average spectra
35
c. 1660~1710 nm
There was no wavelength shifting of raw and second derivative spectra in the
1660~1710 nm region. The peak of the raw spectra appeared at 1694 nm and at 1688
nm in the second derivative. In both cases, the relationship between peak height and
temperature appears non-linear (data not presented).
d. 2250~2360nm
In this region, spectral absorptions increased with temperature. No noticeable peak
shifts were observed in this area. All peaks occurred at identical wavelengths for the
raw and second derivative spectra: 2268 nm and 2306 nm.
A linear relationship was found in the raw spectra between absorbance and
temperature. Figures 4.14 and 4.15 show the linear relationships of raw spectra peaks
against temperature of red wines. Similar relationships were observed for white wines
(data not presented). However, no linear relation existed in the second derivative
spectra (data not presented).
30 35 40 45 501.095
1.100
1.105
1.110
1.115
1.120
1.125
1.130
1.135
Log
1/T
Temperature oC
y = 1.653E-03x + 1.051
R2 = 9.802E-01P= 0.0012
Figure 4. 14 Linear relationship of the absorbance of raw spectra of red wines at 2268
nm
36
30 35 40 45 50
1.23
1.24
1.25
1.26
1.27
1.28
Log
1/T
Temperature oC
y = 1.838E-03x + 1.179
R2 = 9.669E-01P = 0.0025
Figure 4. 15 Linear relationship of the absorbance of raw spectra of red wines at 2306
nm
Summary
From the analysis of these four wavelength regions, it was noticed that the second
derivative of the spectra minimizes the peak shifting effect caused by temperature
variation. The peaks related to O-H overtones exhibit linear relationships between
peak absorbance and temperature variation in the second derivative spectra. For the
peaks related to C-H bonds (2250 – 2360 nm), a linear relationship was observed in
the raw spectra between peak absorbance and temperature increase.
4.2.4 Principal component analysis
PCA was performed to analyze the wine raw spectra. Figures 4.16 and 4.18 illustrate
the score plots of PC1 and PC2 for the white wines and red wines, respectively.
Samples scanned at the same temperature were clustered. The groups were dispersed
from left to right as the temperature increased. However, the behaviour of red and
white wines was different. Comparing the first two PCA eigenvectors of red and white
wine samples (see Figures 4.17 and 4.19), it was found that the second PC of the red
37
wine is similar to the first PC of the white samples. Eigenvectors of PC1 of red wines
are primarily associated with absorbance at 540 nm which is related to the pigments
of the wine. PC2 of the red wines and the PC1 of the white samples were associated
with changing temperature.
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6 ambient 30oC 35oC 40oC 45oC 50oC
PC
2
PC1
Figure 4. 16 Score plot of PC1 and PC2 of the white wine samples.
500 1000 1500 2000 2500-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
PC1 (76%) PC2 (15%)
PC
A E
igen
vect
ors
Wavelength (nm)
Figure 4. 17 Eigenvectors of the first two PCs of the PCA for white wines
38
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
ambient 30oC 35oC 40oC 45oC 50oC
PC2
PC1
Figure 4.18 Score plot of PC1 and PC2 of the red wine samples.
500 1000 1500 2000 2500
-0.12
-0.10
-0.08
-0.06
-0.04
-0.020.00
0.02
0.04
0.06
0.08
0.100.12
0.14
0.16
0.18
PC
A E
igen
vect
ors
Wavelength
PC1 (55%) PC2 (32%)
Figure 4. 19 Eigenvectors of the first two PCS of the PCA for the red wines
A linear relationship was observed between the mean score value of the wine samples
at the same temperature and temperature variation in the first PC for white and second
PC for red wines. Figures 4.20 and 4.21 show these linear relationships. The linear
relationships of red and white wine samples possessed similar slopes and intercepts.
39
30 35 40 45 50
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
PC
A m
ean
scor
e va
lue
Temperature oC
y = 0.0681x - 2.6259R2 = 0.9929P = 0.0002
Figure 4. 20 Linear relationship between the mean score values of temperature related
PC of white red samples and temperature variation
30 35 40 45 50
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
30 35 40 45 50
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
PC
A m
ean
scor
e va
lue
Temperature oC
y = 0.0676x - 2.5913R2 = 0.9981
PC
A m
ean
scor
e va
lue
Temperature oC
y = 0.0676x - 2.5913R2 = 0.9981P < 0.0001
Figure 4. 21 Linear relationship between the mean score values of temperature related
PC of white wine samples and temperature variation
40
4.2.5 Comparison of calibration performance at different temperatures using
PLS
To compare the influence of temperature on wine spectra, calibration models of wine
chemical data and Vis-NIR spectra were performed using PLS. The SECV obtained
from different temperatures were compared. A smaller SECV presents a better
prediction result. Table 4.3 lists the standard error in cross validation (SECV) of the
prediction models for each parameter at different temperatures. Temperature affects
the SECV of the red and white wines differently. For red wines, no significant
difference was observed between 30 ºC and 35 ºC in the four chemical parameters.
However, the SECVs of one or several parameters were significantly different at
ambient temperature, 40 ºC, 45 ºC and 50 ºC. For white wines, the SECVs of the four
parameters did not differ significantly with temperature (from ambient to 45 ºC).
However, at 50 ºC, the SECVs for alcohol and G+F were significantly different from
the valures at other temperatures. Moreover, for both red and white wines, the SECV
at 50 oC were the maximal. Clearly, the model at 50 oC has the worst prediction
capability. Generally, for both red and white wines, the SECV of the chemical
parameter at 30 ºC and 35 ºC, were smaller than those ones obtained at other
temperatures. This implies that the optimal temperature for wine analysis using Vis-
NIR spectroscopy lies between 30 ºC to 35 ºC.
41
Table 4. 3 Standard error in cross validation (SECV) of PLS prediction using Vis-NIR
raw spectra for chemical analysis paramters
SECV
Red wine Alcohol pH TA GF
ambient 0.084 ab 0.038 b 0.18 b 0.54 b
30 ºC 0.059 a 0.013 a 0.12 a 0.18 a
35 ºC 0.062 a 0.017 a 0.071 a 0.27 a
40 ºC 0.14 b 0.029 b 0.18 b 0.51 b
45 ºC 0.30 c 0.059 c 0.11 a 0.43 b
50 ºC 0.097 b 0.027 b 0.17 b 0.59 b
White wine
ambient 0.077 a 0.056 a 0.19 a 0.64 a
30 ºC 0.070 a 0.058 a 0.17 a 0.66 a
35 ºC 0.074 a 0.059 a 0.22 a 0.80 a
40 ºC 0.12 a 0.065 a 0.23 a 1.04 a
45 ºC 0.069 a 0.040 a 0.17 a 0.58 a
50 ºC 0.23 b 0.08 a 0.24 a 2.58 b
a,b: Levels in column not connected by the same letter are significantly different, α <
0.05
42
Chapter 5 Effect of sample presentation –
measurement condition effect on the analysis of wine
5.1 Introduction
Following recent advances in NIR spectroscopy, new NIR instruments have been
developed. Some of these instruments offer convenience and easier scanning
procedures. Different scanning modes are available. For wine or grape juice samples,
some instruments can analyse the sample in a sample cup in transflectance mode and
others use transmittance. However, no studies have compared differences between the
transmittance and transflectance scanning mode for wine analysis. Does the mode
affect the analysis result when using NIR, and which one produces better prediction
results? To answer these questions, different scanning modes and different path
lengths for transflectance mode were applied to wine samples and the prediction
errors were compared.
5.2 Results and discussion
5.2.1 Chemical analysis
Table 5.1 lists the chemical profiles of the red and white wine samples analysed.
43
Table 5. 1 Sample codes, chemical composition and the corresponding statistics of
samples analysed
Sample code Alcohol
(% v/v) pH TA (g/L) G+F (g/L)
Red wines
3012 14.25 3.46 6.81 0.7
0443 13.68 3.48 6.79 0.7
2138 13.41 3.61 7.29 0.3
2139 12.81 3.82 6.84 0.4
R590 13.08 3.57 6.04 1.8
R598 13.13 3.48 7.71 4
Mean 13.39 3.57 6.91 1.32
S.D. 0.51 0.14 0.56 1.42
Min 12.81 3.46 6.04 0.3
Max 14.25 3.82 7.71 4
White wines
1930 11.05 3.26 7.38 77.7
0107 13.27 3.48 6.77 9.7
1162 13.21 3.4 6.68 1.8
1724 11.95 3.37 6.58 0.5
2705 12.47 3.27 6.83 1.8
2166 13.03 3.22 6.61 2.2
Mean 12.50 3.33 6.81 15.62
S.D. 0.87 0.10 0.30 30.59
Min 11.05 3.22 6.58 0.5
Max 13.27 3.48 7.38 77.7
5.2.2 Spectra analysis
Figures 5.1 and 5.2 show the spectra of the wine samples scanned in transflectance
44
mode for 0.2 mm and 0.4 mm depth. Figure 5.3 presents a comparison of the spectra
of the identical sample scanned under different measurement conditions. The 0.2 mm
transflectance spectrum exhibits the lowest absorbance among the spectra. The 0.4
mm spectrum has higher peaks at 540 nm, 1450 nm and 2300 nm, and has a flat peak
around 1950 nm, lower than that for the 1 mm transmission spectrum. Although the
spectra absorptions differ for the spectra obtained from different scanning conditions,
the absorbance peaks occurred at the identical wavelengths.
400 600 800 1000 1200 1400 1600 1800 2000 2200 24000.0
0.5
1.0
1.5
2.0
2.5
log
1/T
Wavelength (nm)
0.2 mm Transflectance
Figure 5. 1 Vis-NIR spectra of six red and six white wine samples at 0.2 mm
transflectance mode
400 600 800 1000 1200 1400 1600 1800 2000 2200 24000.0
0.5
1.0
1.5
2.0
2.5
log
1/T
Wavelength (nm)
0.4mm Transflectance
Figure 5. 2 Vis -NIR spectra of six red and six white wine samples at 0.4 mm
transflectance mode
45
400 600 800 1000 1200 1400 1600 1800 2000 2200 24000.0
0.5
1.0
1.5
2.0
2.5
3.0
log
1/T
Wavelength (nm)
0.2mm (TR)pathlength 0.4mm (TR) 1mm (T)
Figure 5. 3 Vis-NIR spectra of the same sample at three different path lengths
5.2.3 Principal component analysis
The spectra data were analyzed by PCA. The first three PCs together explained more
than 99% of the variation of the spectral data set: PC1 73%, PC2 26% and PC3 1%.
The first two PCs account for most of the variation. Figure 5.4 is the score plot of the
first two PCs. It can be observed that spectra of the same path length were clustered
together. The spectra obtained using transflectance mode were negative on the PC2
axis, while spectra obtained at transmittance mode were positive. This suggests that
PC2 is associated with the measurement mode.
46
-10 -8 -6 -4 -2 0 2 4 6-4
-3
-2
-1
0
1
2
3
4
5
0.2mm TR 0.4mm TR 1mm T
PC
2
PC1
Figure 5. 4 PCA score plot of the PC1 against PC2 using Vis-NIR raw spectra
It was found in the score plot of PC1 and PC3 (Figure 5.5) that the samples were
placed in a same sequence along the PC3, no matter which scanning condition was
used. This suggests that PC3 explains wine information. It also demonstrates that
although the spectra look visibly differently, the information they contain was similar.
The sample presentation does not affect or change the sample information recorded by
Vis-NIR.
47
-10 -8 -6 -4 -2 0 2 4 6 8 10-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
R598
3012
0443
R590
2139
213821382139
R590
0.2mm TR 0.4mm TR 1mm T
PC3
PC1
R598
3012
0443
R5902139
2138
R598
3012
0443
Figure 5. 5 PCA score plot of the PC1 against PC3 using Vis-NIR raw spectra
5.2.4 Comparison using PLS
Calibration models were constructed using PLS regression between the spectra and
the wine chemical parameters, including alcohol content, pH, titratable acidity and
glucose plus fructose. Table 5.1 describes the chemical parameters of the red and
white wine samples. The predicted values of each chemical parameter of the wine
samples were compared between scanning arrangements by ANOVA (Table 5.2). The
differences between the predicted value and the reference value of each sample using
the scanning conditions were also compared. No significant difference was observed
between these mean values, which mean the spectra acquired from different scanning
conditions produced a similar prediction result for the same sample.
However, Table 5.2 indicates that the standard error using cross validation (SECV)
increased with the path length whilst the coefficient of correlation decreased, which
indicates that the calibration accuracy was reduced. The SECV of the measurement
conditions were compared by F test. No significant difference was observed between
0.4 mm transflectance mode and 1 mm transmittance mode of all the chemical
48
parameters for both red and white wines. However, the SECV of all the chemical
parameters for red wine and alcohol and pH for white wine were significant between
0.2 mm transflectance mode and 0.4 mm transflectance or 1mm transmittance mode.
While the effective pathlength of 0.4 mm transflectance mode is approximately 0.8
mm, the light pathlength is comparable with the 1 mm transmittance mode. Since no
significant difference was observed between these two modes, it can be concluded
that under an analogous effective pathlength, the different measurement modes can
produce similar prediction results. Both the SECV and the coefficient of correlation
indicated that the shorter path length measurement mode provides a more precise
prediction ability. However, the sample loading procedure for transflectance
measurement mode was more complicated than the transmission mode. Therefore it
was decided to use transmission mode for further study.
Table 5. 2 The Standard Error in Cross Validation (SECV) of the prediction models for
each parameter at different scanning modes
Alcohol pH TA G+F
R SECV R SECV R SECV R SECV
1 mm T 0.992 0.0615a 0.982 0.024a 0.983 0.0948a 0.988 0.2a
0.4 mm TF 0.995 0.048a 0.994 0.0132a 0.987 0.0865a 0.996 0.108a Red
wines 0.2 mm TF 0.999 0.0107b 0.999 0.0047b 0.999 0.0243b 0.998 0.0784b
Alcohol pH TA G+F
R SECV R SECV R SECV R SECV
1 mm T 0.996 0.0696a 0.97 0.024a 0.989 0.0413a 0.998 1.88a
0.4 mm TF 0.995 0.078a 0.989 0.0137ab 0.997 0.0199a 0.996 2.45a White
wines 0.2 mm TF 0.999 0.0154b 0.997 0.0065b 0.993 0.0334a 0.99 3.86a
T: transmission mode; TF: transflectance mode;
R: Correlation; a,b: Levels not connected by the same letter in column are
significantly different, α < 0.05
49
Chapter 6 Use of Visible and NIR to classify
Tempranillo wines based on geographical origins.
6.1 Introduction
Tempranillo is the most abundant indigenous grape variety in Spain, especially in
Rioja region (MacNeil 2001). “Tempranillo” means early, which is why the grape was
given that name, because it ripens earlier than most red varietals. Its wine was
characterized by fruity mouth feel and subtle aromas (Kolpan et al. 1996). In recent
years, it has been planted in many Australia vineyards and the wines have become
popular with consumers. Since the different regions may bring different wine
characters, the objective of this experiment was to explore the use of visible and near
infrared spectroscopy to analyze Tempranillo wines from Australia and Spain and
classify them accordingly to their geographical origin (refer Table 6.1).
Table 6. 1 Vintage and origin of commercial Tempranillo wine samples analysed
1999 2000 2001 2002 2003 2004 Total
Australia 2 6 10 18 36
South Australia 2 2 13
New South Wales 2
Victoria 4 4 5
Western Australia 4
Spain (D.O.) 2 2 6 4 13 27
Rioja 4 2 5
La Mancha 2
Ribera del Duero 2 2 2 4
Toro 4
D.O. = denomination of origin
50
6.2 Results and discussion
6.2.1 Chemical analysis
Table 6.2 shows the alcohol content, pH, titratable acidity (TA), glucose plus fructose
(G+F), total phenolics, wine colour density and wine colour (Hue) of the wine
samples analysed. It was noticed that the range in chemical composition for the
Australian wines varied the most, compared with the Spanish wines. No statistically
significant differences were observed for alcohol content, G+F, total phenolics, colour
density, and hue values in the set of wine analysed. Statistically significant differences
were observed in pH and TA, suggesting that the Australian wines contain more
acidity than the Spanish wines. It was noticed that some Australian wines
corresponding to 2004 vintage have high alcohol content (higher than 15% Alc). This
tendency of high ethanol content was also observed for Australian Cabernet
Sauvignon, Shiraz and Merlot wines and reported elsewhere (Godden and Gishen
2005).
51
Table 6. 2 Range of chemical composition for the Australian and Spanish wine analysed
Alcohol
(%) pH
TA
pH8.2
(g L-1)
G+F
(g L-1)
Total
Phenolic
(A.U.)
Colour
Density
(A.U.)
Hue
Mean 13.8 3.6 6.2 1.2 53.2 7.6 8.8
S.D. 0.8 0.1 0.5 1.6 7.2 1.1 0.6
Min 12.6 3.4 5.4 0.0 40.5 5.8 7.9 Australia (n=36)
Max 15.2 3.8 7.1 5.7 63.2 9.7 10.3
Mean 14.0 3.7 5.2 0.3 57.2 7.1 8.6
S.D. 0.2 0.1 0.6 0.2 4.7 1.1 0.5
Min 13.6 3.5 4.4 0.2 50.1 5.2 7.9 Spain (n=27)
Max 14.2 3.9 5.9 0.9 62.8 9.2 9.7
Significance of
difference NS * * NS NS NS NS
TA, titratable acidity; G+F, glucose + fructose; S.D., standard deviation; Min, the minimum
value; Max, the maximum value; A.U., absorbance unit; NS, not significant; *, significant
difference between the mean value (p < 0.05).
6.2.2 Spectra interpretation and analysis
Figure 6.1 show the Vis and NIR spectra of wines after SNV and second derivative
transformation. The second derivative inverts the spectra, so the peaks of the original
spectra become troughs (Hruschka 1992). Two wavelength regions were not used for
calibration, namely between 1000-1100 nm (changes of detector in the instrument);
and between 1880-2000 nm (off-scale, absorbance higher than 2.6 absorbance units).
Absorptions at 1450 nm, 1950 nm (not included for chemometric analysis) are related
to first overtone of O-H stretching vibration and combination band of OH stretch and
deformation (Osborne et al. 1993). Additionally, absorptions were observed around
976 nm related to O-H stretch third overtone associated with water and ethanol, at
1690 nm related to C-H stretch first overtones, and at 2268 nm, and 2306 nm, due to
52
C-H combination and overtones (Burns and Ciurczak 2001). Absorption in the visible
region occurred at around 540 nm related to wine pigments (anthocyanins and
pigmented tannins) (Somers 1998). No obvious differences between wine samples
from different geographical origins were observed; therefore the spectra were
processed by means of multivariate analysis.
500 1000 1500 2000 2500-0.03
-0.02
-0.01
0.00
0.01
0.02
Seco
nd d
eriv
ativ
e an
d SN
V
Wavelength (nm)
Tempranillo wines
Figure 6. 1 Second derivative of the Vis-NIR spectra of Australian and Spanish
Tempranillo wines
6.2.3 Principal component analysis
PCA was applied to both the raw and the pre-processed spectra (SNV and 2nd
derivative). It was noticed that the separation in the score plot of the first two
principal components (PCs) using raw spectra was less clear than that showed using
SNV and second derivative pre-processed spectra (data not presented). Figure 6.2
shows the score plot of the first two PCs from the Vis and NIR pre-processed spectra.
The first three PCs explained 68% of the total variance of the spectra in the set of
wines analysed. A separation between wines according to the geographical origin was
observed. However, it was noticed that some Australian wines overlapped with some
Spanish wine samples.
53
-0.003-0.002
-0.0010.000
0.0010.002
0.003
PC1
-0.002
-0.001
0.000
0.001
0.002
PC
2
AAAA
AAAA
AAAA
AAAA
SSS
S
SS
SS
SSSS
SSSS
SSSS
SS
AAA
A
AA
AA
AA
AA
AA
AA
SS
SS
Figure 6. 2 Score plot of the first two principal components of Australian (A) and
Spanish (S) Tempranillo wines using Vis-NIR after SNV and second derivative
processing
Figure 6.3 shows the eigenvectors corresponding to PC1 (51%), PC2 (17%) and PC3
(9%). The highest eigenvectors in PC1 were observed in the NIR region around 2180
to 2300 nm wavelengths. This wavelength region is related to C-H and O-H
combinations, which indicated that the difference is caused by organic components in
the wine such as alcohol content, sugars, phenolic compounds, organic acids that
contribute to variations among the wines produced in different geographical origins.
The highset eigenvectors in PC2 were observed in the Vis region around 400 to 700
nm, related to wine pigments (colour). The highest eigenvectors in PC3 were
observed around Vis region (450 - 700 nm) and 2200 to 2300 nm, are related to wine
pigments and ethanol and phenolic compounds, respectively. These four absorption
regions explained most of the variance between the spectra of the samples, which may
also relate to differences arising from different geographical regions.
54
500 1000 1500 2000 2500
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
Eige
nvec
tors
Wavelenth (nm)
PC1 (51%) PC2 (17%) PC3 (9%)
Figure 6. 3 Eigenvectors of the three first principal components of Australian and
Spanish Tempranillo wines using Vis-NIR after SNV and second derivative processing
6.2.4 Discrimination analysis
6.2.4.1 Linear discrimination based on PCA scores
LDA was carried out using the PCA sample scores on PCs. Table 6.3 shows the LDA
classification according to geographical origin based on the first three PC scores of
PCA using raw spectra, which account for more than 98% of the variance of the
spectra data. A total of 45 (71%) samples were correctly classified. Table 6.4 lists the
classification result using the first three PCs of pre-processed spectra. A 76% of
correct classification was achieved. In this case, Australian wines were 70% correctly
classified, while 85% of the Spanish wines were correctly classified.
55
Table 6. 3 LDA classification results of Australian and Spanish Tempranillo wines using
Vis-NIR raw spectra based on the first 3 PCs (98% of the total variation)
Prediction
Australia Spain
Overall correct
classification
Australia (n=36) 26 (72%) 10
Spain (n=27) 8 19 (70%) 45 (71%)
Table 6. 4 LDA classification results of Australian and Spanish Tempranillo wines using
Vis-NIR pre-processed spectra based on the first 3 PCs (77% of the total variation)
Prediction
Australia Spain
Overall correct
classification
Australia (n=27) 25 (70%) 11
Spain (n=27) 4 23 (85%) 48 (76%)
The classification obtained from the pre-processed spectra was slightly better than
obtained from the raw spectra. However, when using the first three PCs of the pre-
processed spectra, only 77% of the total variation was explained. This implies that
20% less information was involved in the classification. Consequently, the first nine
PCs of the pre-processed data were included for LDA, which explained 95% variation
of the spectra (Table 6.5). These nine PCs improved the overall correct classification
rate to 90%, where 89% of Australian wine and 93% of Spanish wines were correctly
classified.
56
Table 6. 5 LDA classification results of Australian and Spanish Tempranillo wines using
Vis-NIR pre-processed spectra based on the first 9 PCs (95% of the total variation)
Prediction
Australia Spain Overall
Australia (n=27) 32 (89%) 4
Spain (n=27) 2 25 (93%) 57 (90%)
It was concluded that the pre-processed spectra achieved a better classification result
compared to that derived from the raw spectra. In another words, these spectra contain
more information than the raw spectra for geographical classification. The reasons for
this improvement may include: second derivative spectra resolve more peaks than the
raw spectra; and SNV and second derivative remove influence of baseline shifts and
improve the signal/noise ratio of the spectra.
6.2.4.2 DPLS classification
The DPLS model was developed using the Vis and NIR pre-processed spectra. Wine
samples were split randomly into calibration (n = 32) and validation sets (n= 31). The
validation set was used to evaluate the accuracy of the models to classify samples
according to the geographical origin. Figure 6.4 presents the score plot of the first two
PCs of the DPLS model using the calibration set. It is similar to the PCA score plot;
however the separation of wines according to the geographical origin was more
obvious than in the PCA. This is probably be explained by the fact that the DPLS
algorithm may maximise the variance between-groups rather than in the group
(Kemsley, 1996). The DPLS loadings for the calibration models were similar to those
observed in the PCA analysis (eigenvectors) (data not presented).
57
-0.002 -0.001 0.000 0.001 0.002-0.002
-0.001
0.000
0.001
AAAA
AAAAAAAA
AAAA
SSS S
SS
SS
S SSS
SSSS
SS
SS
SS
AA A A
AA
A A
AA
AA
AA
AA
AA
SSSS
AA S
PC
2
PC1
Figure 6. 4 Partial least squares score plot of the first two principal components of
Australian (A) and Spanish (S) Tempranillo wines using Vis-NIR pre-processed spectra
for the calibration set
The R2 and RMSECV in calibration were 0.95 and 0.16 (6 PLS latent variables),
respectively. The calibration statistics indicated that the model developed will be
acceptable to classify new samples. Table 6.6 shows the DPLS classification rate
(percent of classification) for the validation set according to geographical origin. The
DPLS yield an overall rate of correct classification of 93.5%. Wine samples
belonging to Australia were 100% correctly classified, while Spanish wines were
85.7% correctly classified.
58
Table 6. 6 Discriminant partial least squares (DPLS) classification results of Australian
and Spanish Tempranillo wines using Vis-NIR pre-processed spectra
Prediction
Australia Spain
Overall correct
classification
Australia 18 (100%) 0
Spain 2 (15.3%) 11 (84.7%) 29 (93.5%)
Summary
Both methods, DPLS and LDA, achieved overall correct classification rates exceeding
90%. The DPLS models achieved the highest rate of classification. These
discrimination results verified that differences existed between the wines from
different geographical origins and confirmed that the Vis-NIR spectra contain
information sufficient to discriminate between samples using these mathematical
techniques.
59
Chapter 7 Use of Visible and NIR to classify Riesling
wines based on geographical origins
7.1 Introduction
Riesling is the leading white grape variety of Germany’s noble wine (MacNeil 2001).
Wines made from Riesling are usually characterised of a light to medium body, floral
and fruity, and an implicit sweetness (Kolpan et al. 1996). Excellent dry Riesling
wines are also made in Alsace of France, Australia, and New Zealand. The objective
of this experiment was to explore the use of visible and near infrared spectroscopy to
analyze Riesling wines from Australia, New Zealand and Europe and to classify them
accordingly to their geographical origin (refer Table 7.1).
Table 7. 1 Vintage and origin of Riesling wine samples analyzed
2001 2002 2003 2004 2005 Total
Australia 4 9 8 21
New Zealand 2 10 12
Europe 2 3 8 4 17
7.2 Results and discussions
7.2.1 Chemical analysis
Table 7.2 shows the statistics of the chemical compositions of the Riesling wine
samples from different regions. No statistically significant differences were observed
for the mean values of alcohol content, total phenolics, and volatile acid in the set of
wine analysed grouped by geographical areas. Statistically significant differences
were observed in pH, TA and glucose plus fructose contents.
60
Table 7. 2 Statistics of chemical composition for Riesling wines from different
geographical region
Alcohol
(% v/v) pH
VA
(g/L)
TA
(g/L)
G+F
(g/L)
Total
Phenolics
(A.U.)
Mean 12.02A 3.01A 0.44A 7.32B 1.74B 8.51A
Min 11.06 2.84 0.38 6.14 0.00 6.83
Max 12.97 3.20 0.59 8.64 5.38 11.64
Australia
(n=21)
S.D. 0.54 0.13 0.06 0.65 1.94 1.18
Mean 11.52A 2.95A 0.49A 8.35A 17.10A 8.12A
Min 8.87 2.57 0.40 7.14 4.45 6.58
Max 13.19 3.25 0.67 10.81 56.49 12.18
New
Zealand
(n=12)
S.D. 1.40 0.21 0.11 1.43 20.00 1.96
Mean 11.86A 3.26B 0.46A 6.97B 12.40A 8.90A
Min 10.07 3.07 0.34 5.87 2.15 7.68
Max 13.19 3.76 0.81 7.96 41.44 11.91
Europe
(n=17)
S.D. 1.02 0.17 0.13 0.73 12.70 1.11
* A, B, Levels in columns not connected by the same letter are significantly different,
p<0.05
7.2.2 Spectra interpretation and analysis
Figures 7.1 and 7.2 present the raw and SNV and second derivative processed spectra
of the Riesling wines. No obvious differences were detected from visual observation
of the spectra between the wine samples from different geographical origin. All wines
possessed absorption bands at 1450 nm, related to O-H first overtone of both water
and ethanol (Osborne et al. 1993). Absorption regions were observed at 1690 nm
61
related to C-H stretch first overtones, and at 2268 nm, and 2306 nm, associated with
C-H combination and overtones (Burns and Ciurczak 2001). The absorption bands at
1790 and 2268 nm are believed associated with sucrose, fructose, and glucose in fruit
juices (Dambergs et al. 2002; Cozzolino et al. 2003).
500 1000 1500 2000 25000.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1790 nm
2306 nm
2268 nm
1950 nm
1690 nm
1450 nm
Riesling wine
log
(1/T
)
Wavelength (nm)
Figure 7. 1 Vis -NIR raw spectra of Riesling wines from Australia, New Zealand and
Europe
500 1000 1500 2000 2500-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
Sec
ond
deriv
ativ
e
Wavelength (nm)
Figure 7. 2 SNV and 2nd Derivative processed spectra of Riesling wines from Australia,
New Zealand and Europe
62
7.2.3 Principal component analysis
The pre-processed spectra of all wine samples were firstly analysed by PCA. Figure
7.3 is the score plot of the first three PCs. These PCs account for 97% of the variation
in the spectra. A general grouping was observed among the samples from different
regions; however, the separation was not very clear and an intense overlap occurred
around the centre of the 3D space. The overlapped samples were predominantly for
wines from New Zealand and Europe. Eigenvectors for the first three PCs were
investigated (Figure 7.4). PC1 explains 93% of the total variance in the samples’
spectra, with the highest eigenvectors occurring around 2250-2350 nm which is
related to C-H combinations and O-H stretch overtones. Eigenvectors also occurred
around 1400-1460 nm and around 1660-1760 nm region related to O-H first overtones
and C-H first overtones, respectively. The highest eigenvectors in PC2 (3%) appeared
around 1400-1460 nm and 2250-2350 nm. The highest eigenvectors in PC3 (2%) and
some eigenvectors in PC2 occurred in the visible region, around 410 – 540nm.
NZ
NZ
NZ
NZ
Aus
Aus
Aus
Aus
Eur
Eur
AusAus
Eur
Eur
EurEur
AusAus
AusAus
AusAus
AusAus
AusAus
AusAus
NZNZ
NZNZ
NZNZ
EurEur
AusAus
EurEur
Aus
NZ
NZ
EurEurEur
Eur
Eur
Eur
Eur
PC1
PC2
PC3
Figure 7. 3 Score plot of the first 3 principal components of Australian (Aus), New
Zealand (NZ) and European (Eur) Riesling wines using Vis-NIR pre-processed spectra
63
When analyzing all the samples from three regions together, overlapping was
observed. This may indicate the similarity among some samples, and also may be
caused by the sample matrix, with insufficient samples to present the pattern of their
region. To enhance the comparison, samples from geographical regions were analyzed
as pairs by PCA (e.g. samples from Australia versus Europe, Australia versus New
Zealand and New Zealand versus Europe).
Figures 7.5, 7.6 and 7.7 show the score plots of the first three PCs of the PCA of
samples for each pair. Clearer separations were observed between the wines from
Australia and Europe or New Zealand. However, poor separation was observed
between the samples from Europe and New Zealand.
500 1000 1500 2000 2500
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Eig
enve
ctor
s
Wavelength(nm)
PC1 (92%) PC2 (3%) PC3 (2%)
Figure 7. 4 Eigenvectors of the three first principal components of Australian, New
Zealand and European Riesling wines using Vis-NIR pre-processed spectra
64
AusAus
AusAus
Aus
Aus
EurEur
EurEur
AusAus
Aus
Aus
AusAus
AusAus
AusAus
AusAus
EurEur
Aus
Aus
EurEur
Aus
Eur
Eur
EurEur
Eur
EurEur
PC1
PC2
PC3
Figure 7. 5 Score plot of the first three principal components of Australian (Aus), and
European (Eur) Riesling wines using Vis-NIR pre-processed spectra
NZ
NZ
Aus
Aus
Aus
Aus
Aus
Aus
AusAus
Aus
Aus
AusAus
AusAus
AusAus
AusAus
NZ
NZ
NZNZNZ
NZ
Aus
Aus
Aus
NZ
NZ
PC1
PC2
PC3
Figure 7. 6 Score plot of the first three principal components of Australian (Aus) and
New Zealand (NZ) Riesling wines using Vis-NIR pre-processed spectra
65
NZ
NZ
NZ
NZEur
Eur
Eur
Eur
EurEur
NZNZ
NZNZ
NZ
NZ
EurEurEur
Eur
NZ NZ
Eur
EurEur
Eur
EurEur
Eur
PC1
PC2
PC3
Figure 7. 7 Score plot of the first three principal components of New Zealand (NZ) and
European (Eur) Riesling wines using Vis-NIR pre-processed spectra
7.2.4 Discrimination analysis
7.2.4.1 Linear discriminant analysis
LDA was performed on the first several PCs from the PCA result of the total samples.
The best classification rate was achieved by using the first three PCs, which account
for 97% variation of the spectral data. Table 7.3 shows the classification result
according to the wine provenance. Up to 72% of the total samples were correctly
classified. It was noticed that a similar classification rate, around 75%, was achieved
for the samples from Australia and New Zealand; however, a lower classification rate
(65%) was obtained for the samples from Europe, where several samples were
misclassified.
66
Table 7. 3 LDA classification results of Australian, New Zealand and European Riesling
wines using Vis-NIR pre-processed spectra
3 PCs Australia Europe New Zealand Overall correct
classification
Australia (n=21 ) 16 (76%) 2 (10%) 3 (14%)
Europe (n=17 ) 2 (11%) 11 (65%) 4 (24%)
New Zealand (n=12 ) 1 (8%) 2 (17%) 9 (75%)
36 (72%)
As displayed in the PCA score plots of wine samples from side-by-side comparisons
of geographical regions, Figures 7.5, 7.6 and 7.7, clearer groupings were observed.
LDA was performed based on PC scores of PCA. The number of PCs involved were
selected based on the best classification result obtained. The classification results
were summarized in Table 7.4. Most of the wines from Australia were correctly
classified as distinct from wines from Europe or New Zealand, 95% (Australia versus
Europe) and 86% (Australia versus New Zealand) respectively. It was more difficult
to discriminate between Riesling samples when comparing with wines from Europe
and New Zealand. An overall correct classification rate of 72% was achieved between
these samples. This comparatively low classification rate might indicate style
similarity of Riesling wines from New Zealand and Europe whilst wines from
Australia were different.
Table 7. 4 LDA classification results of each two regions of Australian, New Zealand and
European Riesling wines using vis-NIR pre-processed spectra
Number
PCs
Variance
Explained Sample geographical origins
Overall correct
classification
Australia Europe 3 94%
20 (95%) 13(76%) 33 (87%)
Australia New Zealand 3 96%
18 (86%) 9 (75%) 27 (82%)
Europe New Zealand 4 98%
11(65%) 10(83%) 21 (72%)
67
7.2.4.2 DPLS analysis
DPLS was employed to build a calibration models for classification of the samples
However, a disappointing result was obtained. Half of the samples were misclassified
and classification by DPLS was unsuccessful.
DPLS was applied to discriminate the samples based on side-by-side comparisons
between geographical regions. Table 7.5 lists classification rate of Rieslings from
these comparisons. Similar classification rates were obtained to those achieved from
LDA based on the PCA scores. Most of the wines from Australia were correctly
classified. However, wines from Europe and New Zealand remained difficult to
discriminate.
Table 7. 5 DPLS classification results of Australian, New Zealand and European Riesling
wines using vis-NIR pre-processed spectra
Sample geographical origins Overall correct
classification
Australia Europe
20 (95%) 12(71%) 32 (84%)
Australia New Zealand
21 (100%) 9 (75%) 30 (91%)
Europe New Zealand
11(65%) 7(58%) 18 (62%)
Summary
White wine classification was achieved between the samples from three geographical
origins. Wines from Australian were most easily classified. Greater than 86% of
samples from Australia were correctly classified using different multivariate analysis
methods when compared with samples from New Zealand or Europe. However, lower
classification rates were achieved between samples from New Zealand and Europe.
68
The style similarity of wines from New Zealand and Europe might explain the poor
classification result. Furthermore, the small sample number may affect the data matrix,
which may shape the result. To further test the ability of Vis-NIR to classify white
wine samples, a larger sample set is required.
69
Conclusion
Vis-NIR spectra of wine samples are affected by different factors of sample
presentation, such as sample temperature, optical path length and measurement mode.
For temperature, changes in the spectra were observed, with peak shifting and
absorbance increasing with temperature. The use of second derivative transformation
minimizes the effect of peak shifting in the NIR spectra due to temperature variation.
When PCA was performed on the wine spectra, temperature related changes on the
scores and eigenvectors were observed for both red and white wines.
In relation to the effect of measurement mode, transmittance versus transflectance,
variations in the spectra were observed. However, the absorption peaks of the spectra
appeared at the same wavelength regardless of scanning mode. When PCA was
performed on the wine spectra, measurement related changes on the scores and
eigenvectors were observed. The prediction of chemical composition using PLS
calibration models showed that the spectra acquired using transmission and
transflectance modes with similar pathlength produced equivalent prediction results.
However, longer pathlengths appeared to increase the standard error of cross
validation (SECV) and the coefficient of correlation, implying a lower prediction
accuracy.
It has been demonstrated that Vis-NIR spectroscopy combined with multivariate
analysis can be used as a classification tool to differentiate geographical origin of both
red and white wine samples.
Tempranillo wines from Australia and Spain, were classified using discriminant
partial least squares and linear discriminant analysis based on the PCA scores. The
models developed achieved an overall correct classification rates over 90%. Riesling
wine samples from three geographical origins were also correctly classified with
acceptable rates, over 75%.
The discrimination results demonstrated the differences between the wines from
70
different geographical origins and suggested that the Vis and NIR spectra (fingerprint)
store information able to discriminate among the wine samples.
Vis-NIR spectroscopy is a secondary method relying strongly on reference methods
during the modeling step. Therefore the calibration sample matrix should present as
much as possible the variability of the aimed feature. To employ this technique for
industry application with the objective of geographical classification of wines, further
research is recommended. The future work should expand the set of wine samples
analysed to build a “fingerprint data bank” which includes as many as representative
wines from specific or different regions to collect as much information as possible.
This will maximise the predictive reliability for geographical classification of the
method.
71
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