Classification of oat and groat kernels using NIR hyperspectral imaging

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• 作者：Silvia Serranti^a ; ^{silvia.serranti@uniroma1.it}Author Vitae ; Daniela Cesare^a ; Federico Marini^b ; Giuseppe Bonifazi^a
• 关键词：Near infrared (NIR) hyperspectral imaging ; Oats ; Principal component analysis (PCA) ; Partial least squares-discriminant analysis (PLS-DA) ; Sorting ; Quality control
• 刊名：Talanta
• 出版年：2013
• 出版时间：15 January, 2013
• 年：2013
• 卷：103
• 期：Complete
• 页码：276-284
• 全文大小：1307 K

文摘

An innovative procedure to classify oat and groat kernels based on coupling hyperspectral imaging (HSI) in the near infrared (NIR) range (1006-1650 nm) and chemometrics was designed, developed and validated. According to market requirements, the amount of groat, that is the hull-less oat kernels, is one of the most important quality characteristics of oats. Hyperspectral images of oat and groat samples have been acquired by using a NIR spectral camera (Specim, Finland) and the resulting data hypercubes were analyzed applying Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares-Discriminant Analysis (PLS-DA) to build the classification models to discriminate the two kernel typologies. Results showed that it is possible to accurately recognize oat and groat single kernels by HSI (prediction accuracy was almost 100 % ). The study demonstrated also that good classification results could be obtained using only three wavelengths (1132, 1195 and 1608 nm), selected by means of a bootstrap-VIP procedure, allowing to speed up the classification processing for industrial applications. The developed objective and non-destructive method based on HSI can be utilized for quality control purposes and/or for the definition of innovative sorting logics of oat grains.