An improved method for predicting soluble solids content in apples by heterogeneous transfer learning and near-infrared spectroscopy

文献类型： 外文期刊

第一作者： Liu, Sanqing

作者： Liu, Sanqing;Fan, Shuxiang;Huang, Wenqian;Fan, Shuxiang;Liu, Sanqing;Lin, Lin;Huang, Wenqian

作者机构：

关键词： Soluble solids content; Biological variability; Long-term prediction; Robustness; Asymmetric transform

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 203 卷

页码：

收录情况： SCI

摘要： Predicting fruit soluble solids content (SSC) is a hot topic in non-destructive detection. Biological variability of fruit decreases the accuracy of the prediction model. Therefore, modeling methods that can reduce the negative effect of biological variability are necessary. In this paper, an improved modeling method based on the partial least squares (PLS) regression was proposed, using convolutional autoencoder and heterogeneous transfer learning for feature extraction. The dataset used for calibration and prediction contains spectra and corre-sponding SSC values of apples collected from 2012 to 2018. In comparison with the traditional PLS method, the proposed method performed better for long-term SSC prediction of apples with biological variability. The cor-relation coefficients calculated on validation set were 0.934, 0.940, 0.915, 0.899, 0.901, respectively. The root mean square errors calculated on validation set were 0.736 degrees Brix, 0.694 degrees Brix, 0.674 degrees Brix, 0.571 degrees Brix, 0.620 degrees Brix, respectively. Besides, the proposed method could still achieve relatively satisfactory results when the quantity of calibration samples was less.

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