Introduction



Tomato is one of the most widely consumed and nutritious crops, and its rich antioxidant content plays an important role in the prevention and treatment of various diseases and cancers. On the other hand, pesticide residues in agricultural products pose a serious threat to human health and may cause significant short- and long-term effects; therefore, accurate monitoring and control of these residues are highly important. In recent years, spectroscopic methods, especially NIRS and VIS/NIR, have emerged as fast, low-cost, non-destructive, and environmentally friendly techniques for detecting pesticide residues. These methods allow qualitative and quantitative analysis without complex sample preparation, and their accuracy can be improved using statistical and machine-learning models (such as artificial neural networks, deep learning, multiple regression, SVM, and optimization algorithms). Previous studies have shown promising results for these technologies in detecting pesticide residues in agricultural products like apples, oranges, pumpkin, and okra. Specifically, in the case of tomatoes, which are commonly found in daily meals, detecting pesticide residues is particularly important. Therefore, this research aims to investigate the capability of the VIS/NIR method for the non-destructive detection of indoxacarb pesticide residue in tomatoes



Materials and Methods



Experimental Design: Twenty-five tomato seedlings (variety 8320) were grown under uniform conditions. After fruiting, plants were sprayed with Indoxacarb 15% SC at volumetric concentrations of 0.625%, 1.25%, 2.5%, and 5%, plus an untreated control group. A total of 75 samples were harvested. Spectral Measurements: A NIRS spectrometer (wavelength range 400-2500 nm) was used on days 1, 3, 5, 7, and 8 after spraying, with three replicates. Data Preprocessing: Five methods were applied: Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Vector Normalization (VN), First Derivative (FD), and Second Derivative (SD). Synthetic Minority Over-sampling Technique (SMOTE) was used for data balancing, and Principal Component Analysis (PCA) for dimensionality reduction. Classification & Analysis: Support Vector Machines (SVM) were employed to classify different concentrations (70% training, 30% testing). Model performance was evaluated using confusion matrix, accuracy, sensitivity, detectability, and F1-score. Validation: HPLC (AZURA model, Germany) validated pesticide residue presence and established a linear equation correlating chromatogram peak area with concentration.



Results and Discussion In this section, the spectral signature of tomato samples treated with indoxacarb pesticide was compared with that of the control samples. The results showed that on the first day after spraying, the absorption pattern of the contaminated samples was generally similar to that of the control, although their absorption amplitude was reduced. These features were mainly associated with internal fruit constituents such as sugars, acids, and pigments including chlorophyll, beta-carotene, and lycopene. Moreover, the key spectral regions in the visible and near-infrared ranges were linked to chemical bonds in the indoxacarb structure, particularly C-H, N-H, C=O, and aromatic groups. Over time, especially by the eighth day after spraying, the spectral differences between treated and control samples decreased markedly, particularly at lower pesticide concentrations. This indicates that the residual effect of the pesticide gradually diminished and the fruit characteristics became closer to those of untreated tomatoes. The greatest spectral differences were observed in the 900–2500 nm range, especially within 900–1350 nm and 1550–1900 nm. Finally, support vector machine classification was used to distinguish contaminated samples from the control group, and raw data were compared with preprocessed data to identify the best classification performance. The classification results showed that among the preprocessing methods, vector normalization (VN) achieved the best performance, followed by SNV. This superiority is likely due to VN’s ability to minimize the effects of illumination differences in hyperspectral data, thereby removing intensity-related variation while preserving the spectral shape. It was also found that the 400–900 nm and 900–1900 nm spectral ranges were effective for distinguishing samples, especially on the eighth day after spraying. Although pre-processing improved accuracy on the first day, methods such as SNV and VN may have reduced performance on the eighth day by removing some useful signals. Nevertheless, the results showed that even at lower pesticide concentrations, classification accuracy remained acceptable, exceeding 80%.This suggests that indoxacarb can also influence the visible range, making it possible to detect pesticide residues using simpler and lower-cost instruments without the need to analyze higher spectral bands. In the residue estimation section, partial least squares regression (PLSR) was used. Five principal components explained more than 97% of the output variation, and the model was compared with HPLC measurements. The results indicated that pesticide residue decreased over time, especially on days seven and eight. Overall, visible/near-infrared spectroscopy was found to be an effective non-destructive, fast, and low-cost method for monitoring pesticide residues in tomatoes. Previous studies show that spectroscopy in different wavelength ranges can be used to detect pesticide residues. It also cites a study on eggplant, where indoxacarb residues were measured using HPLC. The initial residue levels on eggplant fruits decreased over time and became undetectable after about 15 to 20 days. Overall, the results of this study suggest that the developed regression model for estimating indoxacarb residues can be extended in the future to detect other pesticides as well, offering a fast, accurate, and non-destructive approach for monitoring pesticide residues in agricultural products.



Conclusion The results showed that visible/near-infrared spectroscopy in the 400–2500 nm range, especially when applied to pre-processed data, was able to distinguish sprayed samples from unsprayed ones. The method achieved higher accuracy on days close to spraying and still maintained about 77% accuracy even eight days after application. In addition, the partial least squares regression model in the 400–900 nm range was able to estimate pesticide residue effectively, with a coefficient of determination of 90%.