Quantitative prediction of AFB in various types of edible oil based on absorption, scattering and fluorescence signals at dual wavelengths.

This study aims to address the challenge of matrix interference of various types of edible oils on intrinsic fluorescence of aflatoxin B (AFB) by developing a novel solution. Considering the fluorescence internal filtering effect, the absorption (μ) and reduced scattering (μ') coefficients at dual wavelengths (excitation: 375 nm, emission: 450 nm) were obtained by using integrating sphere technique, and were used to improve the quantitative prediction results for AFB contents in six different kinds of edible oils. A research process of "Monte Carlo (MC) simulation - phantom verification - actual sample validation" was conducted. The MC simulation was used to determine interference rule and correction parameters for fluorescence, the results indicated that the escaped fluorescence flux nonlinearly decreased with the μ, μ' at emission wavelength (μ, μ') and μ at excitation wavelength (μ), however increased with the μ' at excitation wavelength (μ'). And the required optical parameters to eliminate the interference of matrix on fluorescence intensity are: effective attenuation coefficients at excitation and emission wavelengths (μ, μ) and μ'. Phantom verification was conducted to explore the feasibility of fluorescence correction based on the identified parameters by MC simulation, and determine the optimal machine learning method. The modelling results showed that least squares support vector regression (LSSVR) model could reach the best performance. Three kinds of edible oil (peanut, rapeseed, corn), each with two brands were used to prepare oil samples with different AFB contamination. The LSSVR model for AFB based on μ, μ, μ' and fluorescence intensity at 450 nm was calibrated, both correlation coefficients for calibration (R) and the validation (R) sets could reach 1.000, root mean square errors for calibration (RMSEC) and the validation (RMSEV) sets were as low as 0.038 and 0.099 respectively. This study proposed a novel method which is based solely on the absorption, scattering, and fluorescence characteristics at excitation and emission wavelengths to achieve accurate prediction of AFB content in different types of vegetable oils.