数字农科院2.0

A low-cost discrete Vis-NIR optical sensing method for the determination of pear internal blackheart

文献类型： 外文期刊

作者： Li, Long;Zhang, Yifan;Bai, Yajuan;Sun, Yufeng;Tong, Litao;Fan, Bei;Yang, Huihui;Li, Minmin;Wang, Yutang;Wang, Fengzhong

作者机构：

关键词： Pear blackheart disease;Micro-optical sensor;Savitzky-Golay convolution smoothing (SG);k nearest neighbor (KNN)

期刊名称： SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

ISSN： 1386-1425

年卷期： 2024 年 304 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： In this study, a moldy crown pear core detection system based on a micro-optical sensor was developed. The micro-optical sensor has seven specific wavelengths, 425, 455, 515, 615, 660, 700, and 850 nm, and a costeffective advantage. For the discrete spectrum, 7 kinds of preprocessing methods were compared. Traditional preprocessing methods, such as the standard normal transform (SNV) and multiple scattering correction (MSC) methods, cannot improve the efficiency of the spectrum. It was verified that the Savitzky Golay (SG) convolution smoothing preprocessing method could be applied to preprocess discrete spectral data. The correlation of the spectrum after SG preprocessing in the partial least squares regression (PLSR) prediction model was 0.86, and the root mean square error (RMSE) was 0.19. Furthermore, the difference between the nonlinear modeling method without preprocessing and the PLS prediction model after preprocessing was compared. The results showed that the accuracy of the nonlinear modeling method for the discrete spectrum was much higher than that of the PLS linear modeling. The average model accuracy was above 0.9, and the k nearest neighbor (KNN) algorithm had the best effect, reaching an accuracy of 0.96. Finally, a prediction model accuracy of 0.98 was obtained by combining SG + KNN. In summary, the micro-optical sensor system had the advantages of low-cost performance , high precision, which are convenient for popularization and application in practice.

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