Evaluation of Different Models for Non-Destructive Detection of Tomato Pesticide Residues Based on Near-Infrared Spectroscopy.

Araz Soltani Nazarloo,Vali Rasooli Sharabiani,Mariusz Szymanek,Yousef Abbaspour Gilandeh,Ebrahim Taghinezhad

doi:10.3390/s21093032

Araz Soltani Nazarloo, Vali Rasooli Sharabiani + Show 3 more

Open Access

https://doi.org/10.3390/s21093032

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Abstract

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.

Highlights

Tomato (Solanum lycopersicum) is one of the most widely used crops in the world, which is rich in antioxidants such as carotenoids, total phenols, vitamin E, and vitaminC [1]
Spectral data obtained with a spectroradiometer and reference data obtained by a gas chromatography equipment were used as input and output of the models used in this study, respectively
All spectral data without dimension reduction and spectral data obtained from variable selection algorithms were used to predict the amount of pesticide in tomatoes

Summary

Introduction

Tomato (Solanum lycopersicum) is one of the most widely used crops in the world, which is rich in antioxidants such as carotenoids, total phenols, vitamin E, and vitaminC [1]. Tomato (Solanum lycopersicum) is one of the most widely used crops in the world, which is rich in antioxidants such as carotenoids, total phenols, vitamin E, and vitamin. Related empirical studies have shown that vitamin C affects the human immune system and prevents diseases such as Alzheimer’s [2]. The prevention of illnesses by fruits and vegetables depends on antioxidants [3]. Tomatoes need intensive pest management due to their low resistance to pests and diseases. The need to use pesticides can leave harmful residues in the product. Organophosphorus pesticides can be stable for a considerable time even after washing and cooking in the product if used without observing its pre-harvest interval [4,5,6]

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