Study on Dissipation Law of Pesticides in Cauliflower Based on Hyperspectral Image Technique

Abstract

In order to rapidly and non-destructively detect the residual rate of emamectin benzoate+indoxacarb pesticides on cauliflower, a study was conducted using hyperspectral technology to investigate the dissipation law of this pesticide over time. Hyperspectral imaging was employed to capture spectral data from cauliflower samples with and without the pesticide, focusing on the region of interest. The spectral data, consisting of 216 bands (ranging from 950 nm to 1666 nm), were preprocessed using techniques such as Savitzky–Golay convolution smoothing (S-G), multivariate scattering correction (MSC), and standard normal variate (SNV). Next, characteristic spectra for each pesticide were extracted using the competitive adaptive reweighted sampling algorithm (CARS). This study utilized the partial least squares (PLS) algorithm to construct a discriminative model aimed at identifying pesticide residues on cauliflower. The accuracy of the hyperspectral imaging technique was validated by comparing the results with those obtained through chromatography. The PLS model, optimized using the SNV method, exhibited the highest discriminant accuracy, achieving a recognition rate of 100%. The residual rate of indoxacarb detected through hyperspectral technology closely corresponded to the results obtained through chromatography. It was found that the discrepancy in the half-life of pesticides as detected by hyperspectral and chromatographic methods is a mere 0.14 days. These findings highlight the potential of hyperspectral imaging technology for studying pesticide dissipation on cauliflower and detecting pesticide residues.