The fluorescence spectrum combined with a broad learning system to characterize the content of difenoconazole in...

Pesticide residue detection plays an important role in vegetable quality and food safety. In this work, we propose a method for detecting difenoconazole pesticide residues based on fluorescence spectroscopy technology and machine learning algorithms. First, through the application of three-dimensional fluorescence spectroscopy technology, we determined that the optimal excitation wavelength for difenoconazole is 420 nm. Next, we constructed qualification determination models using the K-nearest neighbors (KNN) algorithm and decision tree algorithm. We then selected the uninformative variable elimination (UVE) method and successive projections algorithm (SPA) as wavelength selection methods. The selected wavelengths were introduced into the broad learning system (BLS) for modeling the prediction of difenoconazole content and compared with traditional partial least squares regression (PLSR) and echo state network (ESN) models. The results indicate that the decision tree algorithm performed exceptionally well in the qualification determination model, achieving an accuracy of 97% in the prediction set. In the content prediction model, the UVE combined with BLS model exhibited excellent performance in predicting difenoconazole content, with a prediction set coefficient of determination (R_p²) of 0.959 and a root mean square error of prediction (RMSEP) of 1.358. This study has successfully demonstrated the feasibility of combining fluorescence spectroscopy technology with the broad learning system, providing a reference for the online monitoring system of pesticide residue content.