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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis

文献类型： 外文期刊

作者： Zhang, Zishen 1 ; Cheng, Hong 2 ; Chen, Meiyu 2 ; Zhang, Lixin 2 ; Cheng, Yudou 2 ; Geng, Wenjuan 1 ; Guan, Junfeng 2 ;

作者机构： 1.Xinjiang Agr Univ, Coll Hort, Urumqi 830052, Peoples R China

2.Hebei Acad Agr & Forestry Sci, Inst Biotechnol & Food Sci, Shijiazhuang 050051, Peoples R China

3.Hebei Key Lab Plant Genet Engn, Shijiazhuang 050051, Peoples R China

4.Hebei Univ Engn, Coll Life Sci & Food Engn, Handan 056000, Peoples R China

关键词： hyperspectral imaging; pear; non-destructive detection; machine learning model; prediction model

期刊名称：FOODS （ 影响因子：5.1； 五年影响因子：5.6 ）

ISSN：

年卷期： 2024 年 13 卷 23 期

页码：

收录情况： SCI

摘要： The non-destructive detection of fruit quality is indispensable in the agricultural and food industries. This study aimed to explore the application of hyperspectral imaging (HSI) technology, combined with machine learning, for a quality assessment of pears, so as to provide an efficient technical method. Six varieties of pears were used for inspection, including 'Sucui No.1', 'Zaojinxiang', 'Huangguan', 'Akizuki', 'Yali', and 'Hongli No.1'. Spectral data within the 398 similar to 1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. The results indicated that the combination of Fast Detrend-Standard Normal Variate (FD-SNV) preprocessing and Competitive Adaptive Reweighted Sampling (CARS)-selected feature wavelengths yielded the best improvement in model predictive ability for forecasting key quality parameters such as firmness, soluble solids content (SSC), pH, color, and maturity degree. They could enhance the predictive capability and reduce computational complexity. Furthermore, in order to construct a quality prediction model, integrating hyperspectral data from six pear varieties resulted in an RPD (Ratio of Performance to Deviation) exceeding 2.0 for all the quality parameters, indicating that increasing the fruit sample size and variety number further strengthened the robustness of the model. The Backpropagation Neural Network (BPNN) model could accurately distinguish six distinct pear varieties, achieving prediction accuracies of above 99% for both the calibration and test sets. In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears.

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