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The effects of region, genotype, harvest year, and their interaction were evaluated in relation to volatile organic compounds (VOCs) in wheat kernels. The study provides a theoretical basis for wheat authentication. A total of 148 wheat samples with 10 genotypes (G) from three regions (R) across 2011/2012, 2012/2013, 2013/2014, 2014/2015, 2015/2016 (Y) were collected, and the VOCs were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS). The results showed that R, G, and Y all significantly affected the VOCs of wheat. Y significantly influenced 23 VOCs such as Dihydro-2(3h)-furanone (78.41%) and Pentan-1-ol (66.06%), and G significantly influenced Butanal (23.59%). Additionally, the linear discriminant analysis (LDA) analysis method results showed that the overall correct classification of the five harvest years was 98%, whereas the correct rate of distinguishing ten genotypes was only 27.00%. Under the interaction effects of multiple factors, the overall correct classification rate for identifying wheat from three regions was 54.10%. [Display omitted] • A total of 41 VOCs were identified in 148 wheat samples by GC-IMS. • R, G, Y, and their interactions significantly affected the VOCs of wheat kernels. • Dihydro-2(3h)-furanone (78.41%) and other 22 VOCs were mainly influenced by Y. • The discrimination rates for R, G, and Y were 54.1%, 27%, and 98%, respectively. [ABSTRACT FROM AUTHOR]

Food micro-structure plays a crucial role in determining the properties of its end products. Micro X-ray Computer Tomography (CT) provides an excellent tool to assess the internal components of food products non-destructively. This paper describes the 3D visualization and quantitative analysis of the internal features of Canadian Amber Durum Wheat kernel after damage caused by sprouting and insect infestation with Cryptolestes ferrugineus . Micro X-ray CT images were reconstructed and processed using image processing algorithms. Imaging techniques such as segmentation and thresholding were used to render visualization modules such as volume rendering, geometric reconstruction, skeletonization and absolute permeability experiment simulation. In addition to visualization, morphometric parameters such as porosity, anisotropy and absolute permeability were determined in a selected region of interest of the sprout damaged and insect infested wheat kernel. Our data revealed that sprouting damage is first evident in the middle section of the sprouted wheat kernel, and associated with greater porosity than the insect damaged kernel. Insect damage occurred on the bottom of the wheat kernel. Compared to sprout damaged kernels, insect damaged kernels had similar anisotropy. X-ray micro CT is a powerful tool for the non-invasive investigation of damage, and microstructure organization in biological materials such as single wheat kernels. [ABSTRACT FROM AUTHOR]

Rice-weevil (RW) has great harm to wheat kernels, when the RW-damaged wheat kernels are mixed into the sound wheat kernels, the quality of wheat products will be seriously reduced. In this work, a novel method of calibrating the model based on multi-angle near-infrared(NIR) hyperspectral data was proposed to identify sound wheat kernels and RW-damaged wheat kernels. Hyperspectral images on four sides of each wheat kernel were collected. Some commonly used spectral preprocessing methods and two feature extraction algorithms (successive projections algorithm (SPA), and random frog (RF)) were used to combine three machine learning models (partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM))) for modeling analysis. After multivariate data analysis, it was found that standard normal variate (SNV)-SPA-LDA was the best hybrid model. Finally, considering the actual situation, the reliability of the calibrated model was verified by external validation, and the classification results were visualized, in which the accuracy, sensitivity and specificity of the model were 97%, 98% and 96%, respectively. The results indicated that the model calibrated by hyperspectral data from four sides of wheat kernel was reliable. [Display omitted] • NIR-HSI technology was used to classify rice-weevil damaged wheat kernels. • The HSI data on four sides of wheat kernels were analyzed. • The external validation results showed that the calibrated model was reliable. • This work provided a reference for the development of multispectral online detection system. [ABSTRACT FROM AUTHOR]