A data fusion approach on confocal Raman microspectroscopy and electronic nose for quantitative evaluation of pesticide residue in tea

Pesticide residue in tea is always known as a major concern in the process of tea quality assessment, as it poses potential risks to health even at very low levels. To explore the best approach for quantitative detection of pesticide residue in tea, electronic nose (e-nose) and confocal Raman microspectroscopy (CRM) were applied to prove their capability in the determination of chlorpyrifos concentration. Partial least square (PLS) regression was developed based on the e-nose and spectral variables separately and the fusion of the variables into a single array, to analyze the performance of the variable selection algorithms and use the complementary data acquired by e-nose and CRM sensing technologies. Support vector machine (SVM) and artificial neural network (ANN) models were then developed to the individual datasets and the fused dataset, to correlate the signals obtained by both technologies, with the concentration of pesticide residues measured in samples by reference analytical method. The detection model developed based on the data fusion outperformed those based on e-nose and CRM separately, and the result showed that both technologies had a major role in predicting the contamination of pesticide residue. The best prediction performance was derived with 32 effective variables selected from the fusion dataset by the ANN model, with the optimum value of RMSEP (0.0135) and R(2)p value of 0.973. This research demonstrated the high potential to combine e-nose and CRM data as an alternative approach for determining pesticide residues in tea processing, especially coupled with efficient chemometric strategies. (C) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study focused on the quantitative detection of pesticide residue in tea using electronic nose (e-nose) and confocal Raman microspectroscopy (CRM), and found that combining the data from both technologies provided better prediction performance for pesticide residues in tea. The best prediction performance was achieved with 32 effective variables selected from the fusion dataset by the artificial neural network (ANN) model.