Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
Department of Agricultural Engineering and Technology, Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
Sensors (Basel). 2021 Apr 26;21(9):3032. doi: 10.3390/s21093032.
In this study, the possibility of non-destructive detection of tomato pesticide residues was investigated using Vis/NIRS and prediction models such as PLSR and ANN. First, Vis/NIR spectral data from 180 samples of non-pesticide tomatoes (used as a control treatment) and samples impregnated with pesticide with a concentration of 2 L per 1000 L between 350-1100 nm were recorded by a spectroradiometer. Then, they were divided into two parts: Calibration data (70%) and prediction data (30%). Next, the prediction performance of PLSR and ANN models after processing was compared with 10 spectral preprocessing methods. Spectral data obtained from spectroscopy were used as input and pesticide values obtained by gas chromatography method were used as output data. Data dimension reduction methods (principal component analysis (PCA), Random frog (RF), and Successive prediction algorithm (SPA)) were used to select the number of main variables. According to the values obtained for root-mean-square error (RMSE) and correlation coefficient (R) of the calibration and prediction data, it was found that the combined model SPA-ANN has the best performance (RC = 0.988, RP = 0.982, RMSEC = 0.141, RMSEP = 0.166). The investigational consequences obtained can be a reference for the development of internal content of agricultural products, based on NIR spectroscopy.
本研究旨在利用可见/近红外光谱（Vis/NIRS）和偏最小二乘法回归（PLSR）与人工神经网络（ANN）等预测模型，探索非破坏性检测番茄农药残留的可能性。首先，通过分光辐射计记录了 180 个未施农药番茄样本（用作对照处理）和在 350-1100nm 之间浓度为 2L/1000L 的农药浸渍样本的 Vis/NIR 光谱数据。然后，将其分为两部分：校准数据（70%）和预测数据（30%）。接下来，比较了经过 10 种光谱预处理方法处理后的 PLSR 和 ANN 模型的预测性能。将光谱获得的光谱数据作为输入，气相色谱法获得的农药值作为输出数据。使用数据降维方法（主成分分析（PCA）、随机森林（RF）和连续预测算法（SPA））选择主要变量的数量。根据校准和预测数据的均方根误差（RMSE）和相关系数（R）的值，发现 SPA-ANN 组合模型的性能最佳（RC=0.988，RP=0.982，RMSEC=0.141，RMSEP=0.166）。本研究结果可为基于近红外光谱的农产品内部含量检测提供参考。
