Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis

Abstract

Hyperspectral images in the spectral range of 874–1734 nm were collected for 14,015, 14,300 and 15,042 grape seeds of three varieties, respectively. Pixel-wise spectra were preprocessed by wavelet transform, and then, spectra of each single grape seed were extracted. Principal component analysis (PCA) was conducted on the hyperspectral images. Scores for images of the first six principal components (PCs) were used to qualitatively recognize the patterns among different varieties. Loadings of the first six PCs were used to identify the effective wavelengths (EWs). Support vector machine (SVM) was used to build the discriminant model using the spectra based on the EWs. The results indicated that the variety of each single grape seed was accurately identified with a calibration accuracy of 94.3% and a prediction accuracy of 88.7%. An external validation image of each variety was used to evaluate the proposed model and to form the classification maps where each single grape seed was explicitly identified as belonging to a distinct variety. The overall results indicated that a hyperspectral imaging (HSI) technique combined with multivariate analysis could be used as an effective tool for non-destructive and rapid variety discrimination and visualization of grape seeds. The proposed method showed great potential for developing a multi-spectral imaging system for practical application in the future.

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

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Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods

“…Due to their high content of bioactive substances, grape seeds have been increasingly used as a natural source for producing food, nutraceutical, cosmetic, and pharmaceutical derivatives [4]. The composition of grape seeds has an impact on their suitability for industrial exploitation, and compositional variation in grape seeds is associated with environmental and viticultural conditions and the cultivar [5,6]. Full utilization of grape seeds of a specific cultivar with desirable properties helps to lower the cost of product making, which indicates the great importance of cultivar discrimination of grape seeds.…”

Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

“…succeeded in using the HSI (874-1734 nm, 975.01-1645.82 nm) with a radial basis function neural network algorithm for maize seed variety classification at an accuracy of 91.0% [13]. This HSI system was also used to discriminate the varieties of grape seed, where the highest accuracy of 88.7% was achieved in the prediction set [14]. Xie et al, (2018) carried out research to recognize the varieties of mung beans using the HSI (380-1023 nm) system, where the extreme learning machine algorithm performed with accuracies ranging from 99.17% to 100% [10].…”