Near infrared hyperspectral imaging as detection method for pre-germination in whole wheat, barley and sorghum grains

Engelbrecht, Paulina (2011-03)

Thesis (MSc Food Sc)--University of Stellenbosch, 2011.

Thesis

ENGLISH ABSTRACT: The use of near infrared (NIR) hyperspectral imaging for distinguishing between pre-germinated and non pre-germinated barley, wheat and sorghum kernels and, the effect of kernel shape on hyperspectral images, have been investigated. Two sample sets were imaged. The first sample set was divided into six subsets; these subsets were treated with water and left to pre-germinate for different times (0, 6, 9, 12, 18 and 24 hrs). Subset viability was determined with the tetrazolium test. The second sample set was divided into seven subsets, treated with water and left to pre-germinate for 0, 3, 6, 9, 12, 18, 24 or 30 hrs. Individual kernel viability was determined with the tetrazolium test. NIR hyperspectral images were acquired using two different SisuCHEMA hyperspectral imaging systems. The first system acquired images with a 150 9m spatial resolution (first sample set) and the second system acquired images with a 30 9m spatial resolution (second sample set). Principal component analysis (PCA) was performed and a distinction between pre-germinated and non pre-germinated kernels was illustrated in PCA score images. Loading line plots showed that the main compounds contributing to spectral variation were starch, water and protein. These compounds were related to starch and protein hydrolysis. The distinction between pre-germinated and non pre-germinated kernels observed in the 30 9m spatial resolution images indicated NIR hyperspectral imaging was perhaps sensing incomplete endosperm degradation. Some kernels determined as pre-germinated by the tetrazolium test had the same chemical composition according to the score image as non pre-germinated kernels in the 30 9m spatial resolution images. A partial least squares discriminant analysis (PLS-DA) model with two classes (pre- germinated and non pre-germinated) was developed for each of the cultivars of the first sample set. The two classes were assigned in principal component (PC) 1 vs. PC 5 score plots. The model created for the barley cultivars resulted in excessive false positives and false negatives. The prediction results of wheat cultivars revealed that the model had a classification rate of 81% for the non pre-germinated class and 93% for the pre-germinated class. The sorghum prediction results revealed that the model correctly predicted 97% of the non pre-germinated class and 93% of the pre-germinated class. Two different PLS-DA models were developed for one image of each cultivar of the 30 9m spatial resolution images. The first model was developed by assigning each kernel in the score image and the second model was developed by assigning pixels in the score plot to either the pre- germinated or non pre-germinated class. Model 1 resulted in excessive false negatives. Model 2 resulted in excessive false positives. The differences between pre-germinated and non pre-germinated kernels were only observed in higher (PC 5 and 6) order PCs of the 150 9m spatial resolution images. The lower (PCs 1 to 4) order PCs (of each commodity) were subsequently examined with the aid of classification gradients. Kernel shape effects were observed in these PCs. The use of NIR hyperspectral imaging for distinguishing between pre-germinated and non pre-germinated grain kernels shows promise.

AFRIKAANSE OPSOMMING: Die gebruik van naby infrarooi (NIR) hiperspektrale beeld-analise is geëvalueer om onderskeid te tref tussen voor-ontkiemde en nie-voor-ontkiemde gars, koring en sorghum korrels. Die effek van korrelvorm op hiperspektrale beelde is ook geëvalueer. Die eerste stel graan-monsters is gebruik vir 150 9m ruimtelike resolusie beelde en die tweede stel is gebruik vir 30 9m ruimtelike resolusie beelde. Die eerste kultivar stel is verdeel in ses sub-stelle en met gedistilleerde water behandel vir 0, 6, 9, 12, 18 en 24 hr. Sub-stel lewensvatbaarheid is met die tetrazolium toets vasgestel. Elke kultivar in die tweede stel is in sewe sub-stelle verdeel en is vir 0, 3, 6, 9, 12, 18, 24 of 30 hr geïnkubeer. Individuele korrel lewensvatbaarheid is met die tetrazolium toets vasgestel. NIR hiperspektrale beelde is verkry deur gebruik te maak van twee verskillende SisuCHEMA kameras. Die verskillende kameras is gebruik om verskillende resolusie (30 en 150 9m ruimtelike resolusie) beelde te verkry. Hoofkomponent analise (HKA) is uitgevoer en ’n verskil tussen voor- ontkiemde en nie-voor-ontkiemde korrels is waargeneem in die 150 9m ruimtelike resolusie beelde. HK ladings stippe het water, stysel en proteïene uitgesonder as die verbindings wat bydrae het tot spektrale variasie. ’n Verskil tussen die voor-ontkiemde korrels en nie-voor-ontkiemde korrels is ook gesien vir die 30 9m ruimtelike resolusie beelde. Dit is egter ook waargeneem dat sommige korrels as voor-ontkiem bepaal is deur die tetrazolium toets, maar dié korrels het dieselfde chemiese samestelling volgens die punte beeld as nie-voor-ontkiemde korrels. Onvolledige endosperm hidrolise is ’n moontlike verduideliking vir die verskynsel. Die verbindings wat bygedra het tot die variasie is water, stysel en proteïene. ’n Parsiële kleinste kwadrate diskriminant analise (PKW-DA) model met twee klasse is ontwikkel vir elke kultivar van die 150 9m ruimtelike resolusie beelde. Die klasse is aangewys in the punte stip. Die model met die hoogste variasie in Y is gekies om die ander kultivars van dieselfde kommoditeit te voorspel. The PKW-DA resultate vir die gars kultivars het getoon dat die model vals positiewes en vals negatiewes opgelewer het. Die koring PKW-DA model het ’n klassifikasie koers van 81% vir die nie-voor-ontkiemde klasse en 93% vir die voor-ontkiemde klasse opgelewer. The PKW-DA resultate vir sorghum het getoon dat die model ’n klassifikasie koers van 97% vir die nie-voor-ontkiemde klasse en 93% vir die voor-ontkiemde klasse opgelewer. Twee verskillende PKW-DA modelle is ontwikkel vir elke beeld van elke kultivar van die 30 9m ruimtelike resolusie beelde. Die eerste model is ontwikkel deur elke korrel in die punte beeld aan te wys tot een van twee klasse en die tweede model is ontwikkel deur die beeldelemente in die punte stip tot een van twee klasse toe te skryf. Model 1 het vals negatiewes opgelewer en model 2 vals positiewes. Die verskille tussen die nie-voor-ontkiemde en voor-ontkiemde korrels is eers verduidelik in hoër orde HK van die 150 9m ruimtelike resolusie beelde. Die laer orde HK is dus ondersoek vir hul bydrae tot spektrale variasie met die hulp van klassifikasie gradiënte. Korrel vorm effekte is waargeneem. Die gebruik van NIR hiperspektrale beelding om onderskeid te tref tussen voor-ontkiemde en nie-voor-ontkiemde graan korrels, lyk belowend.

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