Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning

Abstract

Eating repeatedly used hotpot oil will cause serious harm to human health. In order to realize rapid non-destructive testing of hotpot oil quality, a modeling experiment method of fluorescence hyperspectral technology combined with machine learning algorithm was proposed. Five preprocessing algorithms were used to preprocess the original spectral data, which realized data denoising and reduces the influence of baseline drift and tilt. The feature bands extracted from the spectral data showed that the best feature bands for the two-classification model and the six-classification model were concentrated between 469 and 962 nm and 534-809 nm, respectively. Using the PCA algorithm to visualize the spectral data, the results showed the distribution of the six types of samples intuitively, and indicated that the data could be classified. Based on the modeling analysis of the feature bands, the results showed that the best two-classification models and the best six-classification models were MF-RF-RF and MF-XGBoost-LGB models, respectively, and the classification accuracy reached 100 %. Compared with the traditional model, the error was greatly reduced, and the calculation time was also saved. This study confirmed that fluorescence hyperspectral technology combined with machine learning algorithm could effectively realize the detection of reused hotpot oil.

Keywords: Feature selection; Fluorescence spectroscopy; Hotpot oil; Nondestructive testing; Visual display.