College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China.
College of Food Science and Engineering, Shanxi Agricultural University, Jinzhong, China.
J Food Sci. 2023 Oct;88(10):4327-4342. doi: 10.1111/1750-3841.16728. Epub 2023 Aug 17.
In this study, two prediction models were developed using visible/near-infrared (Vis/NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) for the detection of pesticide residues of avermectin, dichlorvos, and chlorothalonil at different concentration levels on the surface of cauliflowers. Five samples of each of the three different types of pesticide were prepared at different concentrations and sprayed in groups on the surface of the corresponding cauliflower samples. Utilizing the spectral data collected in the Vis/NIR as input values, comparison and analysis of preprocessed spectral data, and regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) were used in turn to downscale the data to select the main feature wavelengths, and PLS-DA and LS-SVM models were built for comparison. The results showed that the RC-LS-SVM was the best discriminant model for detecting avermectin residues concentration on the surface of cauliflowers, with a prediction set discriminant accuracy of 98.33%. For detecting different concentrations of dichlorvos, the SPA-LS-SVM had the best predictive accuracy of 95%. The accuracy of the model based on CARS-PLS-DA to identify chlorothalonil at different concentration levels on cauliflower surfaces reached 93.33%. The results demonstrated that the Vis/NIR spectroscopy combined with chemometrics could quickly and effectively identify pesticide residues on cauliflower surfaces, affording a certain reference for the rapid recognition of different pesticide residue concentrations on cauliflower surfaces. PRACTICAL APPLICATION: Vis/NIR spectroscopy can detect the concentration levels of pesticide residues on the surface of cauliflowers and help food regulators quickly and non-destructively detect traces of pesticides in food, providing a guarantee for food safety. The technique also provides a basis for determining pesticide residue concentrations on the surface of other vegetables.
在这项研究中，我们开发了两种预测模型，分别使用可见/近红外（Vis/NIR）光谱结合偏最小二乘判别分析（PLS-DA）和最小二乘支持向量机（LS-SVM），用于检测花椰菜表面不同浓度水平的阿维菌素、敌敌畏和百菌清农药残留。三种不同类型的农药各制备了五个样本，分别在不同浓度下混合，并喷洒在相应的花椰菜样本表面。利用 Vis/NIR 采集的光谱数据作为输入值，对预处理后的光谱数据进行比较和分析，依次采用回归系数（RC）、连续投影算法（SPA）和竞争自适应重加权采样（CARS）对数据进行降维，选择主要特征波长，并建立 PLS-DA 和 LS-SVM 模型进行比较。结果表明，RC-LS-SVM 是检测花椰菜表面阿维菌素残留浓度的最佳判别模型，预测集判别准确率为 98.33%。对于检测不同浓度的敌敌畏，SPA-LS-SVM 具有最佳的预测精度，为 95%。基于 CARS-PLS-DA 的模型识别花椰菜表面不同浓度百菌清的准确率达到 93.33%。结果表明，Vis/NIR 光谱结合化学计量学可以快速有效地识别花椰菜表面的农药残留，为快速识别花椰菜表面不同浓度的农药残留提供了一定的参考。实际应用：Vis/NIR 光谱可以检测花椰菜表面的农药残留浓度，帮助食品监管机构快速、无损地检测食品中的农药残留痕迹，为食品安全提供保障。该技术还为确定其他蔬菜表面的农药残留浓度提供了依据。
