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• W3047368295 abstract "Abstract Tea storage period is difficult to be assessed by traditional sensory and chemical analysis methods. In this research, hyperspectral imaging (HSI) covering the spectral range of 400–1000 nm was combined with chemometric methods to predict the storage period of green tea samples. Tea samples were incubated under two storage temperatures of 4 °C (Sample set 1) and 25 °C (Sample set 2) for 360 days. The reflectance spectra were acquired every 90 days. Based on the preprocessed full spectra, partial least squares-discriminant analysis (PLS-DA)- and support vector machine (SVM)-based models had acceptable outputs for the two sample sets with an accuracy of over 90%. In addition, regression coefficients (RCs) of PLS-based models, second derivative (2-Der), and successive projections algorithm (SPA) were applied to select the optimal wavelengths. Satisfactory results were achieved by SVM-based models on the basis of optimal wavelengths. The SPA-SVM- and 2-Der-SVM-based models had the best performance to discriminate the storage period of the two sample sets with the prediction accuracies of 98% and 98%, respectively. The results demonstrated that HSI could be used to evaluate the storage period of green tea, providing an alternative for a real-time monitoring of tea quality." @default.
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• W3047368295 date "2020-11-01" @default.
• W3047368295 modified "2023-10-18" @default.
• W3047368295 title "Intelligent evaluation of storage period of green tea based on VNIR hyperspectral imaging combined with chemometric analysis" @default.
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• W3047368295 doi "https://doi.org/10.1016/j.infrared.2020.103450" @default.
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