Sensor Array and SMOTE-Based Algorithms for Volatile-Fingerprint Classification of Pesticide-Treated Soil with a...

Soil pesticide-residue screening is important for ecological protection, food safety, and public health. However, conventional chromatographic and spectroscopic methods often require complex sample pretreatment, expensive instruments, trained operators, and long analysis times, which limits their use for rapid and large-scale screening. In this study, an electronic-nose system was developed for volatile-fingerprint classification of pesticide-treated loess soil. Six commercial pesticide formulations from three chemical categories were evaluated: deltamethrin and cyfluthrin as pyrethroids, glyphosate and chlorpyrifos as organophosphorus pesticides, and zineb and mancozeb as organosulfur pesticides. These compounds were selected to represent commonly used pesticides with different chemical structures and volatile profiles. A mirror-symmetric gas-sensing chamber was designed for a 26-sensor metal oxide semiconductor (MOS) array to improve gas-flow uniformity and response repeatability. A total of 960 pesticide-treated electronic-nose response curves were collected from four soil depths. Eight feature extraction methods and four classifiers were compared. The Synthetic Minority Over-sampling Technique (SMOTE) and Geometric SMOTE (G-SMOTE) were then evaluated using training-fold-only oversampling to reduce data-leakage risk in imbalanced classification. The results showed that k-nearest neighbors (KNN) combined with direct or transform-based features provided strong classification performance under controlled laboratory conditions. For minority-class recognition, SMOTE showed more consistent improvement than G-SMOTE in the tested pesticide-depth-feature combinations, although the effect depended on feature representation and pesticide class. These findings indicate that the proposed chamber/sensor-array/SMOTE framework is feasible for rapid volatile-fingerprint classification of pesticide-treated soil, but larger independent field datasets and quantitative chemical validation are still required before general deployment.