Paul Williams Department of Food Science Stellenbosch University - - PowerPoint PPT Presentation

Paul Williams Department of Food Science Stellenbosch University

• Dr. M. Manley, Prof P. Geladi & Dr. G. Fox

• Objective
• What is NIR hyperspectral imaging?
• How does it work?
• Maize Hardness
• Fumonisins
• Concluding remarks

• The global objective of my project is to find localised chemical

differences in maize using NIR hyperspectral imaging, in so doing studying the quality and food safety aspects of natural products.

• Combination of single-point NIR spectroscopy and digital imaging
• Able to collect both spectral and spatial information simultaneously in
• ne rapid measurement
• In contrast to digital imaging that captures a complete set of information

regarding an object in only three layers, red, green and blue (RGB), NIR hyperspectral imaging acquires data pertaining the object in many wavelengths, depending on the instrument.

• It is not only capable of identifying the chemical species and

determining the concentration present in a sample, but it is also able to indicate location.

(Burger, 2006)

(Burger, 2006)

(Gowen, 2007)

Digital image of maize samples Spectral Dimensions Matrix NIR instrument

Raw NIR hyperspectral image (960 nm) Digital image

Classification image PCA Score plot (81 920 pixels)

92.11% 7.28% PCA Score plot (23 798 remaining pixels) PCA Score image

7.28% 0.58%

PC 2 loading line plot

960 1020 1080 1140 1200 1260 1320 1380 1440 1500 1560 1620 Wavelength (nm)

• 0.14
• 0.12
• 0.10
• 0.08
• 0.06
• 0.04
• 0.02

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14

Arbitrary units

hard endosperm soft endosperm Starch Starch Protein Starch, H2O Starch, H2O

PC 3 loading line plot

960 1020 1080 1140 1200 1260 1320 1380 1440 1500 1560 1620 Wavelength (nm)

• 0.25
• 0.20
• 0.15
• 0.10
• 0.05

0.00 0.05 0.10 0.15 0.20 0.25

Arbitrary units

hard endosperm soft endosperm Starch Starch H2O

(a) Explained variation (R2) after 6 PLS components 1 2 3 4 5 6

• Four PLS components were adequate to model

85% of the variation within the data (Fig. 3a).

• After 2 PLS components most of the floury

endosperm was explained in the model (Fig. 3b).

• Four PLS components were necessary to model the

glassy endosperm (Fig. 3c). Modelled Not modelled

Loading line plot for PC2

990 1050 1110 1170 1230 1290 1350 1410 1470 1530 1590 Wavelength (nm)

• 0.16
• 0.12
• 0.08
• 0.04

0.00 0.04 0.08 0.12 Arbitrary Value

Protein Starch H2O Starch p[2] non-infected p[2] infected

990 1050 1110 1170 1230 1290 1350 1410 1470 1530 1590 Wav elength (nm)

• 0.20
• 0.15
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0.00 0.05 0.10 0.15 0.20 0.25 Arbitrary Value

Protein Protein Starch Oil H2O p[4] non-infected p[4] infected

Loading line plot for PC4

• It is possible to distinguish between hard and soft maize using NIR

hyperspectral imaging.

• NIR hyperspectral imaging can be used to indirectly determine the

possible presence of fumonisins on maize kernels.

• Images on a longer wavelength instrument has been acquired and

evaluated.

• The software package limits us to applying the calibration equation to

new sample sets.