Shedding Lighton the Living World

Prof. ZOUEU T. JérémieINP-HB Yamoussoukro

AFSIN Coordinator (Cameroon, Ghana, Kenya, Mali, Burkina Faso, Togo, Côte d’Ivoire, Senegal, Lund University)

https://twas.org/article/shedding-light-living-world

19/05/2021 AFSIN-ISP, May 11, 2021 1

Acknowledgements

These activities have been financially supported by:

o ISP (International Swedish Program) of Uppsala university

oTWAS (Third World Academy of Science)

o INP-HB, Yamoussoukro

Carried out with scientific partnership of:

oUniversity of Lund – Sweden

oCEDRES – Abidjan

oCSRS, Abidjan

o Institut Pasteur, Abidjan

19/05/2021 AFSIN-ISP, May 11, 2021 2

Outline

3

A Journey Towards AFSIN

Light: An Ambassador from nanoscopic scale to remote objects

From a medical perspective

Improving Agricultural Production

To a Better Environment

So where Are We Today?

And What Next?

A Journey Towards AFSIN

• 2009: A workshop to build a multispectral and multimodal optical microscope with an application to malaria diagnostic

• Six copies of the microscope were distributed to the participating countries

• Participants were trained in the area of statistical multivariate methods applied multispectral imaging

• The project had attracted broad interest

• 2010: During a LAM conference in Dakar, a meeting was organized to create AFSIN

19/05/2021 AFSIN-ISP, May 11, 2021 4

A Journey Towards AFSIN

• The overall goal of our network is: to contribute, to the benefit of the laboratories in members’ countries, to the development of international standard research activities in the fields of applied spectroscopy and spectral imaging.

• The strategy to achieve our overall goals is to organize annual workshops for the training of students and young scientists in the areas of optics, photonics and imaging techniques applied to medicine, agriculture and the environment.

• We therefore build instruments for realistic diagnostic tools related to tropical diseases such as malaria, pollution monitoring or natural disasters affecting agriculture and environment, and to use optical diagnostics for quality control and improved crop yield.

• Significant effort is currently devoted to training program

19/05/2021 AFSIN-ISP, May 11, 2021 5

Light: An Ambassador from nanoscopic scale to

remote objects

Light properties:IntensityPositionPropagationPhaseTemporal modulationPolarization Spectral content

Light source Detection

Subject tomeasurement

New light properties:IntensityPositionPropagationPhaseTemporal modulationPolarization Spectral content

Comparison

Possible events:Specular reflection

AbsorptionTransmissionFluorescence

Scatteringetc...

Conclusion

Inve

nti

on

s an

d s

olu

tio

ns

in e

ngi

nee

rin

g ar

t

PH

OTO

NIC

S

Light: An Ambassador from nanoscopic scale to remote objects

Mic

roM

acro

Tele

Photon migration

Laser remote sensing

Medicine EcologyOrganic

products

BIO

Problems by clinicians, experts and researchersWhite

biologyGreen

biology

µm

cm

km

Experimental methods

oBlood Smear Spectral Staining

oAutomatic Parasitemia Measurement

o Species Diagnosis

o Life Stages Diagnosis

AFSIN-ISP, May 11, 2021 8

From a Medical Perspective

Multispectral and Multimodal LED Microscope

19/05/2021

Aboma J. Merdasa, Mikkel Brydegaard, SuneSvanberg, Jeremie T. Zoueu, "Staining-free malaria diagnostics by multispectral and multimodality light-emitting-diode microscopy," J. Biomed. Opt. 18(3) 036002 (1 March 2013)

Spectral Imaging Microscope

19/05/2021 AFSIN-ISP, May 11, 2021 9

Transmittance Scattering Reflectance

The results of the spectral staining of the blood smear in three modalities

Malaria Diagnosis

- HCA is applied

- Spectral fingerprints are extracted

- Cell content is analyzed

- State of the cell is known

AFSIN-ISP, May 11, 2021 19/05/2021 10

Malaria Diagnosis

- Each blood cell will be analyzed according to its spectral fingerprint

- The cells content will be classified according to their spectral similarities

- Decision will then be made to declare whether they are healthy of infected

- Healthy cells are symmetric and infected ones are dissymmetric because of the presence of the parasite or byproduct (Hemozoin)

- This method has been used for drug-target studies

19/05/2021 AFSIN-ISP, May 11, 2021 11

Zoueu, J. T., & Zan, S. G. T. (2012). Trophozoite stage infected erythrocyte contents analysis by use of spectral imaging LED microscope. Journal of microscopy, 245(1), 90-99.

Optical Tweezers

• Cell Manipulation

• PicoNewton force Application

• Mechanical force Measurements

• Etc.

19/05/2021 AFSIN-ISP, May 11, 2021 12

P. Yale, JM Konin, M Kouacou, J. Zoueu, New

Detector Sensitivity Calibration and the

Calculation of the Interaction Force between

Particles Using an Optical Tweezer,

Micromachines 9 (9), 425

Buruli Ulcer Diagnosis

• Diffuse reflectance

• Cell content classification

• Earlier diagnosis

19/05/2021 AFSIN-ISP, May 11, 2021 13

D. K. Yable, L. Canale, P. Dupuis, T. C. Haba, J. T. Zoueuand G. Zissis, "Characterization and Optical Early Diagnosisby Diffuse Reflectance Spectroscopy," 2020 IEEE International Conference on Environment and ElectricalEngineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, 2020, pp. 1-5, doi: 10.1109/EEEIC/ICPSEurope49358.2020.9160668.

Long pass

filter GG420

Excitation

source

600μm collection

fiber

SMA

UG1, UG11

excitation filter

Reflectance

source

Epoxy glue

Sample

Field Of View

Cobber piece

Plastic

casing

Light Sheet Imagery

o Surpassing Beer-Lambert Law

o Balistic photons detection

o Spectroscopy in Dense media

o Diagnostic in Dense

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Guy-Oscar Regnima, Thomas Koffi, Olivier Bagui, Abaka Kouacou, Elias Kristensson, Jeremie Zoueu, and Edouard Berrocal, "Quantitative measurements of turbid liquidsvia structured laser illumination planarimaging where absorption spectrophotometryfails," Appl. Opt. 56, 3929-3938 (2017)

Improving Agricultural Production

• Degni, Banah Florent, Haba, Cissé Théodore, Dibi, Wilfried Gauthier, Soro, Doudjo and Zoueu, Jérémie Thouakesseh. "Effect of light spectrum on growth, development, and mineral contents of okra (Abelmoschus esculentus L.)" Open Agriculture, vol. 6, no. 1, 2021, pp. 276-285. https://doi.org/10.1515/opag-2021-0218

•WG Dibi, J Bosson, IC Zobi, BT Tié, J. T. Zoueu, Use of Fluorescence and Reflectance Spectra for Predicting Okra,

(Abelmoschus esculentus) Yield and Macronutrient Contents of Leaves, Open Journal of Applied Sciences 7 (10), 537

• M Sangare, C Tekete, OK Bagui, A Ba, J. T. Zoueu, Identification of Bacterial Diseases in Rice Plants Leaves by the Use of Spectroscopic Imaging. Applied Physics Research 7 (6), 61

• M Sangare, TA Agneroh, OK Bagui, I Traore, A Ba, J. T. Zoueu, Classification of African Mosaic Virus Infected Cassava Leaves by the Use of Multi-Spectral Imaging Optics and Photonics Journal 5, 261-272

AFSIN-ISP, May 11, 2021 15

Laboratoire de Physique des Matériaux et des Composants à Semi-conducteurs (LPMCS)

Research Group: Optique, Photonique, Lasers & Apllications (OPLA)

Universitof Lomé, Lomé - TogoAFSIN Node

Milohum Mikesokpo DZAGLI, (Assoc, Prof,)

Research activities on applied spectroscopy1. Determination of the quality and authentication of foods and products on the Togolese market

using non-destructive spectroscopic techniques (UV-Vis, Flurescence and Raman spectroscopy

2. Multivariate spectral analysis – PCA, etc

3. Environmental Monitoring using the Satellite Observation and spectroscopic techniques (gasespollutants, heavy metals) in mining, industries and agglomerations,

4. Predictive theoretical methods and calculations such as modeling, density functional theory, etc

Main Spectroscopy techniques available

Laser 785nm spectrometer

optical fiber

SILVER-Nova Spectrometer

Measurements in the 190-1100nm rangeResolution 1nm with 25um slit

Raman Spectrometer✓RIFRaman-785, Probe RI785 ~ 450 mW, 200-3700cm-1

✓S/N- 1000:1, Detect, 3648 pixels, f - 150

Some Results

The encircled peaks correspond to the

peaks that differentiate the oils. The first

is at 1265 cm-1 (=C - H) and the second

at 1655 cm-1(C = C) .

Distribution of different oils

using the principal component

analysis.

To a Better Environment

• Benoit K. Kouakou, Samuel Jansson, Mikkel Brydegaard, and Jeremie T. Zoueu, "Entomological Scheimpflug lidar for estimating unique insect classes in-situ field test from Ivory Coast," OSA Continuum 3, 2362-2371 (2020)

• M. Brydegaard, B. Kouakou, S. Jansson, J. Rydell and J. Zoueu, "High DynamicRange in Entomological Scheimpflug Lidars," in IEEE Journal of SelectedTopics in Quantum Electronics, vol. 27, no. 4, pp. 1-11, July-Aug. 2021, Art no. 6900711, doi: 10.1109/JSTQE.2021.3062088.

• M. Brydegaard, B. Kouakou, S. Jansson, J. Rydell and J. Zoueu, "Lidar profilingbiological targets : - detection limits and dynamic range.," 2020 International Conference Laser Optics (ICLO), 2020, pp. 1-1, doi: 10.1109/ICLO48556.2020.9285848.

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Entomological Lidar to detect flying mosquitoes

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Drosophila

Entomological Scheimpflug Lidar For Estimating Unique Insect Classes In Situ-Field Test From Ivory Coast

Benoît Kouakou, samuel jansson, MikkelBrydegaard, and Jeremie Zoueu, OSA Continuum, 2020

Remote modulation spectroscopy with kHz entomological lidar

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Some results from our node in GhanaAbstract • Cassava mosaic disease (CMD) is a major constraint to cassava production in

cassava growing regions.

• Severity of CMD symptoms on cassava leaves is usually assessed visually using an arbitrary scale, which is semi-qualitative, and does not represent the actual surface area of diseased leaf.

• The objective of this study was to develop a quantitative method of assessing the severity of CMD.

• A combination of polarimeteric digital colour images, L*a*b* colour model and K-means clustering algorithm were used to determine the areas of CMD symptoms and healthy areas on leaves.

• The severity of CMD on a leaf is determined by computing the percentage of the CMD symptomatic area to the total leaf area.

• The analysis provides relatively fast and accurate classification of Cassava mosaic diseased leaves.

• The proposed method will enable plant scientists to obtain accurate and reliable data, forming the basis for better decision making.

Optical Imaging Method for Determining Symptoms Severity of Cassava Mosaic Disease • Applied Physics Research; Vol. 7, No. 6; 2015

(a) Unprocessed polarimeteric digital colours

images of (i) healthy leaf and (ii-v) cassava mosaic

infected leaves in red green and blue (RGB) colour

space and (b) the corresponding images in CIE

L*a*b* colour space when L*a*b* colour model

is applied

• Illustration of Graphical User Interface (GUI) developed using MATLAB codes to analyse any polarimeteric digital colour image of a cassava leaf and estimated the percentage CMD severity. K=3 –means clustering is illustrated under the processed images and the knobs needed to load and process the image is found on the right-hand side of the GUI

www.ccsenet.org/apr Applied Physics Research Vol. 7, No. 6; 2015

39

the successive iterations of the algorithms. The chromaticity colour layers using the pixel labels then separated the

objects in the image.

Figure 5 shows the comparison of some of the polarimetric digital colour images of (i) healthy and (ii-v) the green

and yellow portions of cassava mosaic diseased leaves after using the chromaticity layers a* and b* and the k-

means clustering. Apart from the yellow curve in (b)(i) , which shows the boundary between the background and

the leaf, the yellow and green portions of the images in (b) and (c) conform well with their original images in

(i).This is taken care of with normalization when the percentage severity is evaluated.

Figure 5. Comparison of polarimeteric digital colour images of (i) healthy leaf and (ii-v) CMD infected leaves

with the corresponding analysed images. (a) shows the unprocessed polarimeteric digital colour images and (b)

the extracted yellow patches, and (c) green patches from the leaves

Figure 6. Illustration of Graphical User Interface (GUI) developed using MATLAB codes to analyse any

polarimeteric digital colour image of a cassava leaf and estimate the percentage CMD severity. K=3 – means

clustering is illustrated under the processed images and the knobs needed to load and process the image is found

on the right-hand side of the GUI

• Some polarimeteric digital colour images of the Cassava mosaic disease infected leaves and their corresponding percentage symptoms severity

www.ccsenet.org/apr Applied Physics Research Vol. 7, No. 6; 2015

40

The significance of this segmentation technique is that the separated regions do not intersect each other in any way

and each region meets the reliability conditions in specific regions (Gonzalez & Woods, 2010; Patil & Bodhe,

2010). We show in Figure 6 an illustration of a Graphical User Interface (GUI) developed, using MATLAB codes,

to automate the whole analysis process. Processing time for using this GUI on a digital leaf image of 300 x 200 x

3 pixel counts was 10 seconds on average.

Table 1 shows some polarimeteric digital colour images of the cassava mosaic disease infected leaves and their

corresponding percentage symptoms severity obtained from the analysis. On the average, percentage error for

calculating the percentage symptoms severity of cassava leaves for all the samples considered are in the orders of

10-5.

Table 1. Some polarimeteric digital colour images of the Cassava mosaic disease infected leaves and their

corresponding percentage symptoms severity

Images Symptoms Severity (%)

46.7262

55.8935

63.6498

70.2953

73.2614

4. Conclusions

The task of determining the severity of cassava mosaic disease (CMD) affecting cassava leaves using image

analysis has been demonstrated. The method is based on polarimeteric digital colour image, L*a*b* colour space

and K-means clustering algorithms. In this approach, a commercial digital camera was used to acquire the image.

The method quantifies the diseased area as a percentage of the total leaf area in order to make objective conclusions

about the severity of CMD. This method is new, simple, and objective for classifying cassava leaves in terms CMD

severity and does not need a specialist, because it is easy to use and do not require any special training. This method

will enable plant pathologists to make better-informed decisions as the data is more accurate and precise.

Acknowledgements

The authors are grateful to the Associate Scheme and the Office of External Activities (OEA) of Abdus Salam

ICTP Trieste, Italy for their financial support during their stay at the centre and the use of the library and computing

Conclusions

• The task of determining the severity of cassava mosaic disease (CMD) affecting cassava leaves using image analysis has been demonstrated.

• The method was based on polarimeteric digital colour image, L*a*b* colour space and K-means clustering algorithms.

• In this approach, a commercial digital camera was used to acquire the image.

• The method quantifies the diseased area as a percentage of the total leaf area in order to make objective conclusions about the severity of CMD.

• This method is new, simple, and objective for classifying cassava leaves in terms CMD severity and does not need a specialist, because it is easy to use and do not require any special training.

• This method will enable plant pathologists to make better-informed decisions as the data is more accurate and precise.

Multispectral Imaging in Combination with Multivariate Analysis Discriminates Selenite Induced Cataractous Lenses from Healthy Lenses of Sprague-Dawley Rats

Abstract

• Cataracts are the leading cause of blindness worldwide.

• Current methods for discriminating cataractous lenses from healthy lenses of Sprague-Dawley rats during preclinical studies are based on either histopathological or clinical assessments which are weakened by subjectivity.

• In this work, both cataractous and healthy lens tissues of Sprague-Dawley rats were studied using multispectral imaging technique in combination with multivariate analysis.

• Multispectral images were captured in transmission, reflection and scattering modes. In all, five spectral bands were found to be markers for discriminating cataractous lenses from healthy lenses; 470 nm and 625 nm discriminated in reflection mode whereas 435 nm, 590 nm and 700 nm discriminated in transmission mode.

• With Fisher’s Linear discriminant analysis, the midpoints for classifying cataractous from healthy lenses were found to be 14.718 × 10−14 and 3.2374 × 10−14 for the two spectra bands in the reflection mode and the three spectral bands in the transmission mode respectively. Images in scattering mode did not show significant discrimination.

• These spectral bands in reflection and transmission modes may offer potential diagnostic markers for discriminating cataractous lenses from healthy lenses thereby promising multispectral imaging applications for characterizing cataractous and healthy lenses.

• Grayscale images of a Group (A) lens (left image) and a Group (B) lens (right image) captured in transmission mode using the MSLEDIM system at 590 nm spectral band.

P. O.-W. Adueming et al.

149

( )Group A Group B c xP R x PC PC S PC−− −−= = − (3)

where R− is the discriminant vector, Group APC and Group BPC are the average

values of PC1, PC2 and PC3 from the two groups, Sc is the common covariance

matrix of the two groups and PCx is the average PC values of the new lens to be

classified.

A new observation PCxo is allocated to Group A, if

( )Group A Group B0 c XoP PC PC S PC m−− −= − − (4)

where

( ) ( )Group A Group B Group A Group B

1

2cm PC PC S PC PC−− −= − + (5)

is the midpoint between two group averages, else PCxo is allocated to Group B if

0P m< (6)

3. Results and Discussion

The images shown in Figure 2 are grayscale images of a Group A (left image)

and Group B (right image) lens tissues captured at 590 nm spectra band in the

transmission mode. The images have the same dimensions but different grays-

cales. Group A images show no evidence of disruption of the fibre cells that aid

in more transmission and less reflection of the light and this contributes to the

lens’ transparency. The spots found in the both images are artifacts.

Since the discrimination between the two (2) groups, in transmission, reflec-

tion and scattering modes, cannot be easily assessed by observation of the grays-

cale images, their averaged pixel intensities in each mode were extracted and

plotted are shown in Figure 3. The averaged reflected pixel intensity values from

Group A and Group B lenses are shown in Figure 3(a). The figure shows that

Group A lenses are much higher at three (3) specific spectral bands 470 nm, 525

nm and 625 nm than that of Group B lenses. Also, these three (3) spectra bands

are higher (>150 a.u) in both the Group A and Group B lenses compared to the

other spectral bands. This is an indication that the lenses do reflects light inten-

sities but much higher from 470 nm, 525 nm and 625 nm. This can be attributed

to the smooth, clear and glassy nature of the Group A compared to the rough

Figure 2. Grayscale images of a Group (A) lens (left image) and a Group (B) lens (right

image) captured in transmission mode using the MSLEDIM system at 590 nm spectral

band.

• Scatter plot of the first three principal components of Group A and Group B lenses for (a) three transmission spectral bands (470 nm and 625 nm) (b) two reflection spectral bands (470 nm and 625 nm).

P. O.-W. Adueming et al.

152

Figure 4. Scatter plot of the first three principal components of Group A and Group B lenses for (a) three transmission spectral

bands (470 nm and 625 nm) (b) two reflection spectral bands (470 nm and 625 nm).

values describing 99.6 % of the variability dataset. The first eigenvalue, the one

describing the largest amount of variability on the dataset, 77.91%, describes the

overall offset of the transmitted intensity data from the lenses. The second,

9.54% of the dataset’s variability was described by the second eigenvalue whiles

the third, 7.08% of the dataset’s variability was describe by the third eigenvalue.

In the case of reflection, the PCs were obtained from 470 nm and 625 nm

spectral bands with eigenvalues describing 99.5% of the variability of the dataset.

As in the previous case, the first eigenvalue represented the overall reflection in-

tensities from the lenses and described 85.07% of the dataset’s variability. The

second, 5.56% of the dataset’s variability was described by the second eigenvalue

whiles the third, 3.0% of the dataset’s variability was describe by the third eigen-

value. Transmission and reflection for the three and two spectral bands showed

discrete classification between Group A and Group B lenses as shown in Figure

4(a) and Figure 4(b). Scatter plots (not shown) of the first three PCs in the

scattering mode could not discriminate between Group A and Group B lenses.

This is attributed to the low pixel intensity values obtained from both Group A

and Group B lenses.

Using trained transmission data from 435 nm, 590 nm and 700 nm, the allo-

cation rule obtained from the Fisher’s linear discriminant function with equal

cost and equal priors for the sample data of the Group A and Group B lenses and

for maximum separation of the two stained sectioned lenses is given as

1 1 2 2 3 3oP K r K r K r= + + (7)

with a midpoints m = 3.2374 × 10−14, where r1, r2 and r3 representing PC1, PC2

and PC3 respectively with K1, K2 and K3 being the coefficient. In this case K1, K2

and K3 were found to be 0.99, −0.08 and −0.10 respectively. Thus, if Po ≥ m, then

the lens is Group A (healthy), else it is Group B (cataractous). This can be seen

in Figure 5(a) that shows lens data in the coordinates of the first two Fisher’s

discriminants in the transmission mode. The blue and red data represents the

P. O.-W. Adueming et al.

152

Figure 4. Scatter plot of the first three principal components of Group A and Group B lenses for (a) three transmission spectral

bands (470 nm and 625 nm) (b) two reflection spectral bands (470 nm and 625 nm).

values describing 99.6 % of the variability dataset. The first eigenvalue, the one

describing the largest amount of variability on the dataset, 77.91%, describes the

overall offset of the transmitted intensity data from the lenses. The second,

9.54% of the dataset’s variability was described by the second eigenvalue whiles

the third, 7.08% of the dataset’s variability was describe by the third eigenvalue.

In the case of reflection, the PCs were obtained from 470 nm and 625 nm

spectral bands with eigenvalues describing 99.5% of the variability of the dataset.

As in the previous case, the first eigenvalue represented the overall reflection in-

tensities from the lenses and described 85.07% of the dataset’s variability. The

second, 5.56% of the dataset’s variability was described by the second eigenvalue

whiles the third, 3.0% of the dataset’s variability was describe by the third eigen-

value. Transmission and reflection for the three and two spectral bands showed

discrete classification between Group A and Group B lenses as shown in Figure

4(a) and Figure 4(b). Scatter plots (not shown) of the first three PCs in the

scattering mode could not discriminate between Group A and Group B lenses.

This is attributed to the low pixel intensity values obtained from both Group A

and Group B lenses.

Using trained transmission data from 435 nm, 590 nm and 700 nm, the allo-

cation rule obtained from the Fisher’s linear discriminant function with equal

cost and equal priors for the sample data of the Group A and Group B lenses and

for maximum separation of the two stained sectioned lenses is given as

1 1 2 2 3 3oP K r K r K r= + + (7)

with a midpoints m = 3.2374 × 10−14, where r1, r2 and r3 representing PC1, PC2

and PC3 respectively with K1, K2 and K3 being the coefficient. In this case K1, K2

and K3 were found to be 0.99, −0.08 and −0.10 respectively. Thus, if Po ≥ m, then

the lens is Group A (healthy), else it is Group B (cataractous). This can be seen

in Figure 5(a) that shows lens data in the coordinates of the first two Fisher’s

discriminants in the transmission mode. The blue and red data represents the

• Group A and Group B lens data plotted in the coordinates of the first two Fisher’s discriminants (a) three transmission spectral bands (435 nm 590 nm and 700 nm) (b) two reflection spectral bands (470 nm and 625 nm). The black star represents the classification mid-point (m).

P. O.-W. Adueming et al.

153

Figure 5. Group A and Group B lens data plotted in the coordinates of the first two Fisher’s discriminants (a) three transmission

spectral bands (435 nm 590 nm and 700 nm) (b) two reflection spectral bands (470 nm and 625 nm). The black star represents the

classification mid-point (m).

Table 1. Mid points values for discriminating Group A from Group B lenses in transmis-

sion and reflection mode.

Mode Spectral Bands (nm) Mid-point (m)

Reflection 470; 625 14.718 × 10−14

Transmission 435; 590; 700 3.2374 × 10−14

Group A and B lenses respectively. The black star in the middle is the classifica-

tion midpoint between the Group A and Group B lenses. Evaluation of the Fish-

ers’ linear discriminant function with ten (10) transmitted data showed 90%

success of the discrimination function using the PCs of the Group A and the

Group B lenses.

The lens data from the reflection mode in the coordinates of the first two

Fisher’s discriminants is shown in Figure 5(b). The allocation rule obtained us-

ing the reflectance intensities from 470 nm and 625 nm and for maximum sepa-

ration of the two lenses is given by

1 1 2 2 3 3oP L r L r L r= + + (8)

The values for L1, L2 and L3, which are the coefficients were found to be 0.97,

−0.12 and −0.19 respectively. The midpoints values for discriminating Group A

from Group B lenses in both transmission and reflection mode is shown in Ta-

ble 1. Evaluation of the Fishers’ linear discriminant function with eight (8) ref-

lection data showed 87% success of the discrimination function using the PCs of

the Group A lenses and the Group B ones.

4. Conclusion

Using extracted average pixel intensities from grayscale multispectral images of

healthy lenses (Group A) and cataractous lenses (Group B) from rat, five (5)

P. O.-W. Adueming et al.

153

Figure 5. Group A and Group B lens data plotted in the coordinates of the first two Fisher’s discriminants (a) three transmission

spectral bands (435 nm 590 nm and 700 nm) (b) two reflection spectral bands (470 nm and 625 nm). The black star represents the

classification mid-point (m).

Table 1. Mid points values for discriminating Group A from Group B lenses in transmis-

sion and reflection mode.

Mode Spectral Bands (nm) Mid-point (m)

Reflection 470; 625 14.718 × 10−14

Transmission 435; 590; 700 3.2374 × 10−14

Group A and B lenses respectively. The black star in the middle is the classifica-

tion midpoint between the Group A and Group B lenses. Evaluation of the Fish-

ers’ linear discriminant function with ten (10) transmitted data showed 90%

success of the discrimination function using the PCs of the Group A and the

Group B lenses.

The lens data from the reflection mode in the coordinates of the first two

Fisher’s discriminants is shown in Figure 5(b). The allocation rule obtained us-

ing the reflectance intensities from 470 nm and 625 nm and for maximum sepa-

ration of the two lenses is given by

1 1 2 2 3 3oP L r L r L r= + + (8)

The values for L1, L2 and L3, which are the coefficients were found to be 0.97,

−0.12 and −0.19 respectively. The midpoints values for discriminating Group A

from Group B lenses in both transmission and reflection mode is shown in Ta-

ble 1. Evaluation of the Fishers’ linear discriminant function with eight (8) ref-

lection data showed 87% success of the discrimination function using the PCs of

the Group A lenses and the Group B ones.

4. Conclusion

Using extracted average pixel intensities from grayscale multispectral images of

healthy lenses (Group A) and cataractous lenses (Group B) from rat, five (5)

Laser-induced fluorescence combined with multivariate techniques identifies the geographical origin of antimalarial herbal plants Vol. 37, No. 11 / November 2020 / Journal of the Optical Society of America A

Abstract

• Laser-induced fluorescence (LIF) combined with multivariate techniques has been used in identifying antimalarial herbal plants (AMHPs) based on their geographical origin.

• The AMHP samples were collected from four geographical origins (Abrafo, Jukwa, Nfuom, and Akotokyere) in the Cape Coast Metropolis, Ghana.

• LIF spectra data were recorded from the AMHP samples.

• Utilizing multivariate techniques, a training set for the first two principal components of the AMHP spectra data was modeled through the use of K-nearest neighbor (KNN), support vector nachine (SVM), and linear discriminant analysis (LDA) methods.

• The SVM and KNN methods performed best with 100% success for the prediction data, while the LDA had a 99% success rate.

• The KNN and SVM methods are recommended for the identification of AMHPs based on their geographical origins.

• Deconvoluted peaks from the LIF spectra of all the AMHP samples revealed compounds such as quercetin and berberine as being present in all the AMHP samples

• Normalized LIF spectra of pulverized Vernonia amygdalina (VA) from four geographical origins (A, Ak, J, and N) with superimposed fitted spectra as well as deconvoluted peaks.

• Normalized LIF spectra of pulverized Acanthospermumhispidum (AH) from four geographical origins (A, Ak, J, and N) with superimposed fitted spectra as well as deconvoluted peaks.

Score plot for the 10 AMHP samples (color markers) from the four geographical origins (A, Ak, J, and N). The inset shows the expanded clustered scores from the selected rectangular section of the main plot.

Confusion matrix for the LDA model for the prediction set of the 10 AMHP samples for the four geographical origins (A, Ak, J, and N)

• In this work, the LIF method in combination with multivariate techniques have been utilized for the identification of AMHPs from four geographical origins (Abrafo, Jukwa, Nfuom, and Akotokyere) in the Cape Coast Metropolis, Ghana

• SVM achieved 100% identification for both the training and prediction sets, while LDA produced 99% identification for the same.

• LIF combined with multivariate techniques (SVM and KNN) is recommended for the identification of AMHPs according to their geographical origin.

• We also report that the deconvoluted peaks for the LIF spectra of the AMHPs can be attributed to compounds such as quercetin and berberine being present in all the AMHP samples.

• The applied methods may also serve as tools for quality control systems for AMHPs.

Dissolved organic matter in hand‐dug well water as groundwater quality indicator: assessment using laser‐induced fluorescence spectroscopy and multivariate statistical techniques

Abstract

• In groundwater, dissolved organic matter (DOM), a complex material, is a contaminant of concern owing to its ability to influence water quality and stimulate microbial metabolism.

• Using a 445-nm diode laser-induced fluorescence (LIF) spectroscopy, DOM contamination levels have been investigated of well water samples fetched from ten privately owned hand-dug wells during dry and wet seasons of 2016, 2017 and 2018, in Ghana.

• The results showed spatio-temporal het- erogeneities in the LIF spectra, and the fluorescence intensity peaks were generally higher and broader during the wet season than the dry season.

• In this study, DOM fluorescence spectra at an emission wavelength band of 460–650 nm showed two distinct broad peak shoulders within 480–500 nm and 550–570 nm, engulfing the water Raman peak at 527 ± 2 nm for all the water samples studied.

• Furthermore, principal component analysis and cluster analysis were used to differentiate the 2016 water samples based on theirDOM contamination levels.

• In each case, three groups or clusters were identified based on their similarities and dissimilarities.

• The study revealed humic DOM substances as the most typical well water fluorophores.

• Applying the K-nearest neighbour algorithm as a classifier method for the classification of 30 water samples studied in 2016, 16.7% (5/30) were classified as very good drinking water, 46.7% (14/30) as good, 26.7% (8/30) as fairly good, and 10% (3/30) as baddrinking water samples.

• In general, levels of dissolved organic matter contamination increased over the study period during the rainy seasons for wells situated in close proximity to septic tanks, refuse dumps, public toilets and in wetlands.

• Thus, in the study the fluorescence intensity depends on the sampling site and the season, and indicates the DOM contamination level.

The LIF spectra of sample fetched for February, March and April of 2016, 2017 and 2018, respectively

• Scatter plot of the first two principal components discrimi-nating the water samples

• Euclidean distances from one data point (singly distilled water) to all other well water samples in two dimensions for K-nearest neighbour test for water purity classification

So where Are We Today?

AFSIN

-ISP, M

ay 11

, 20

21

• Biological Specimen Label-Free Detection

• Light Technologies applied to fight against hunger

• Light Technologies applied to water quality

• Drug-Target Studies

• Entomological studies

• Biological samples investigation with SLIPI

• Optical Tweezers for biological specimen mechanical studies

• 5 workshops organized

• 5 instruments constructed

• More than 50 MSc and PhD graduated

19/05/2021

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And What Next?

Mic

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acro

Tele

Photon migration

Laser remote sensing

Medicine EcologyOrganic

products

BIO

Problems by clinicians, experts and researchersWhite

biologyGreen

biology

µm

cm

km

Experimental methods

Thank you for your attention

46AFSIN-ISP, May 11, 2021 19/05/2021