Zhang Zishen, Cheng Hong, Chen Meiyu, Zhang Lixin, Cheng Yudou, Geng Wenjuan, Guan Junfeng
College of Horticulture, Xinjiang Agricultural University, Urumqi 830052, China.
Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, China.
Foods. 2024 Dec 8;13(23):3956. doi: 10.3390/foods13233956.
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 398~1004 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 (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.
水果品质的无损检测在农业和食品工业中不可或缺。本研究旨在探索将高光谱成像（HSI）技术与机器学习相结合用于梨的品质评估，以提供一种高效的技术方法。选用了六个梨品种进行检测，包括“酥梨1号”、“早金香”、“皇冠”、“秋月”、“鸭梨”和“红梨1号”。分析了398~1004 nm波长范围内的光谱数据，使用不同的预处理方法和选定的特征波长，比较最小二乘支持向量机（LS-SVM）模型对各种品质参数的预测性能。结果表明，快速去趋势-标准正态变量变换（FD-SNV）预处理与竞争性自适应重加权采样（CARS）选定的特征波长相结合，在预测硬度、可溶性固形物含量（SSC）、pH值、颜色和成熟度等关键品质参数的模型预测能力方面有最佳提升。它们可以提高预测能力并降低计算复杂度。此外，为构建品质预测模型，整合六个梨品种的高光谱数据后，所有品质参数的性能偏差比（Performance to Deviation，PRESS）均超过2.0，表明增加水果样本量和品种数量进一步增强了模型的稳健性。反向传播神经网络（BPNN）模型能够准确区分六个不同的梨品种，校准集和测试集的预测准确率均达到99%以上。总之，HSI与机器学习模型相结合能够实现对梨品质的高效、快速且无损检测，为梨的品质控制和商业加工提供了实用价值。
