Abstract
Chinese walnuts have extraordinary nutritional and organoleptic qualities, and counterfeit Chinese walnut products are pervasive in the market. The aim of this study was to investigate the feasibility of hyperspectral imaging (HSI) technique to accurately identify and visualize Chinese walnut varieties. Hyperspectral images of 400 Chinese walnuts including 200 samples of Ningguo variety and 200 samples of Lin’an variety were acquired in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and principal component analysis (PCA) of spectra showed that the characteristic second principal component (PC2) was potentially effective in variety identification. The PC transformation was also conducted to hyperspectral images to make an exploratory visualization according to pixel-wise PC scores. Three different modeling methods including partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (KNN), and support vector machine (SVM) were individually employed to develop classification models. Results indicated that raw full spectra constructed PLS-DA model performed best with correct classification rates (CCRs) of 97.33%, 95.33%, and 92.00% in calibration, cross-validation, and prediction sets, respectively. Successful projects algorithm (SPA), competitive adaptive reweighted sampling (CARS), and PC loadings were individually used for effective wavelengths selection. Subsequently, simplified PLS-DA model based on wavelengths selected by CARS yielded the best 96.33%, 95.67% and 91.00% CCRs in the three sets. This optimal CARS-PLS-DA model acquired a sensitivity of 93.62%, a specificity of 88.68%, the area under the receiver operating characteristic curve (AUC) value of 0.91, and Kappa coefficient of 0.82 in prediction set. Classification maps were finally generated by classifying the varieties of each pixel in multispectral images at CARS-selected wavelengths, and the general variety was then readily discernible. These results demonstrated that features extracted from HSI had outstanding ability, and could be applied as a reliable tool for the further development of an on-line identification system for Chinese walnut variety.
Highlights
Walnuts are nutrient-dense foods, which are extremely rich in unsaturated fatty acids and phytochemicals like proteins and antioxidants [1]
The general similar spectral curves should be derived from their similar tissue composition, structure, and color presentation in the sample surface
The competitive adaptive reweighted sampling (CARS)-partial least squares-discriminant analysis (PLS-DA) model yielded a sensitivity of 93.62%, a specificity of 88.68%, area under the ROC curve (AUC) of 0.91, and Kappa coefficient of 0.82 in prediction set. These results suggested that CARS-PLS-DA model had great potential to identify Chinese walnut varieties without any chemical or physical information
Summary
Walnuts are nutrient-dense foods, which are extremely rich in unsaturated fatty acids and phytochemicals like proteins and antioxidants [1]. Walnuts are widely consumed or used to produce liquor or oil. They are often used as food additives in a variety of foods such as baked items, ice cream, pastries, etc. The health benefits and extensive consumption of walnuts have led to the establishment of an important walnut market. Chinese walnuts (Carya cathayensis Sarg.) are more and more popular among consumers due to their special organoleptic characteristics and benefits to human health. Quality characteristics are strongly influenced by the Chinese walnut varieties. The identification of Chinese walnut varieties is becoming a very important task to provide consumers exact information about the Chinese walnut products they purchase
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