Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches

By Ye Sun, Gu Xinzhe, Ke Sun, Haijiang Hu, Miao Xu, Zhengjie Wang, K. Tu, L. Pan. 2017

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Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, methodology): “Sun et al. (2017) extracted mean spectral features from the VINIR hyperspectral reflectance imaging (400–1000 nm) for classifying peaches effected by chilling injury.”
Identification of Bruise and Fungi Contamination in Strawberries Using Hyperspectral Imaging Technology and Multivariate Analysis (2018, methodology): “…were split into as calibration and prediction data set at the ratio of 2:1 (number of calibration: number of prediction = 54:27) (Zhang et al. 2016; Sun et al. 2016; Pan et al. 2016); hence, two-thirds of the samples (162 = 54 strawberries × 3 groups) of all groups were used for calibration…”