Non-contact fluorescent detection of pesticide residues based on segment prediction using PLS and a curve fitting algorithm.

Abstract

Fluorescence spectral analysis is an important method to detect the pesticide residues, which is vital for food safety issues. It has been demonstrated that the traditional curve fitting (CF) method can predict the concentration of pesticide with a high accuracy. However, low absorption of the samples at low concentration of pesticide is required; moreover, the pre-process of fruit juice is time-consuming and destructive to the samples. To overcome these disadvantages while maintaining the high accuracy in the high concentration range, the segment detection method is proposed in this paper. Two models were employed to predict the concentration according to the fluorescence intensity. The partial least squares (PLS) model was used to predict the concentration of the samples when the fluorescence intensity at 356 nm was smaller than 1, while the CF method was used to predict the concentration of samples when the fluorescence intensity at 356 nm was larger than 1 in our system. In total, 101 samples with concentration ranging from 0 to 0.0714 mg/mL were used to validate this method. The results indicated that the PLS method exhibited a high sensitivity in the low concentration range, while the CF method exhibited high accuracy in the high concentration range.