Liang Min, Wang Zhiqiang, Lin Yu, Li Caixia, Zhang Liang, Liu Yaxi
Triticeae Research Institute, Sichuan Agricultural University, Chengdu, Sichuan, China.
College of Information Engineering, Sichuan Agricultural University, Ya 'an, China.
Front Plant Sci. 2024 Sep 30;15:1459886. doi: 10.3389/fpls.2024.1459886. eCollection 2024.
Tobacco is a critical economic crop, yet its cultivation heavily relies on chemical pesticides, posing health risks to consumers, therefore, monitoring pesticide residues in tobacco is conducive to ensuring food safety. However, most current research on pesticide residue detection in tobacco relies on traditional chemical methods, which cannot meet the requirements for real-time and rapid detection.
This study introduces an advanced method that combines hyperspectral imaging (HSI) technology with machine learning algorithms. Firstly, a hyperspectral imager was used to obtain spectral data of tobacco samples, and a variety of spectral pre-processing technologies such as mean centralization (MC), trend correction (TC), and wavelet transform (WT), as well as feature extraction methods such as competitive adaptive reweighted sampling (CARS) and least angle regression (LAR) were used to process the spectral data, and then, grid search algorithm (GSA) is used to optimize the support sector machine (SVM).
The optimized MC-LAR-SVM model achieved a pesticide classification accuracy of 84.1%, which was 9.5% higher than the original data model. The accuracy of the WT-TC-CARS-GSA-SVM model in the fenvalerate concentration classification experiment was as high as 91.8 %, and it also had excellent performance in other metrics. Compared with the model based on the original data, the accuracy, precision, recall, and F1-score are improved by 8.3 %, 8.2 %, 7.5 %, and 0.08, respectively.
The results show that combining spectral preprocessing and feature extraction algorithms with machine learning models can significantly enhance the performance of pesticide residue detection models and provide robust, efficient, and accurate solutions for food safety monitoring. This study provides a new technical means for the detection of pesticide residues in tobacco, which is of great significance for improving the efficiency and accuracy of food safety detection.
烟草是一种重要的经济作物，但其种植严重依赖化学农药，对消费者健康构成风险，因此，监测烟草中的农药残留有助于确保食品安全。然而，目前大多数关于烟草中农药残留检测的研究依赖于传统化学方法，无法满足实时快速检测的要求。
本研究引入了一种将高光谱成像（HSI）技术与机器学习算法相结合的先进方法。首先，使用高光谱成像仪获取烟草样品的光谱数据，并采用均值中心化（MC）、趋势校正（TC）和小波变换（WT）等多种光谱预处理技术，以及竞争性自适应重加权采样（CARS）和最小角回归（LAR）等特征提取方法对光谱数据进行处理，然后，使用网格搜索算法（GSA）对支持向量机（SVM）进行优化。
优化后的MC-LAR-SVM模型农药分类准确率达到84.1%，比原始数据模型高9.5%。WT-TC-CARS-GSA-SVM模型在氰戊菊酯浓度分类实验中的准确率高达91.8%，在其他指标上也具有优异的性能。与基于原始数据的模型相比，准确率、精确率、召回率和F1分数分别提高了8.3%、8.2%、7.5%和0.08。
结果表明，将光谱预处理和特征提取算法与机器学习模型相结合可以显著提高农药残留检测模型的性能，为食品安全监测提供可靠、高效、准确的解决方案。本研究为烟草中农药残留检测提供了一种新的技术手段，对提高食品安全检测的效率和准确性具有重要意义。
