Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics

Abstract

This work aims to investigate a feasible and practical technique for the authentication of edible animal blood food (EABF) using Fourier transform near-infrared (FT-NIR) coupled with fast chemometrics. A total of 540 samples were used, including raw duck blood tofu (DBT), cow blood-based gel (CBG), pig blood-based gel (PBG), and DBT binary and ternary adulterated with CBG and PBG. The protein, fat, total sugar, and 16 kinds of amino acids were measured to validate the difference in basic organic matters among EABFs according to species. Fisher linear discriminate analysis (Fisher LDA) and extreme learning machine (ELM) were implemented comparatively to identify the adulterated EABF. To predict adulteration levels, four extreme learning machine regression (ELMR) models were constructed and optimized. Results showed that, by analyzing 27 crucial spectral variables, the ELM model provides higher accuracy of 93.89% than Fisher LDA for the independent samples. All the correlation coefficients of the optimized ELMR models’ training and prediction sets were better than 0.94, the root mean square errors were all less than 3.5%, and the residual prediction deviation and the range error ratios were all higher than 4.0 and 12.0, respectively. In conclusion, the FT-NIR paired with ELM have great potential in authenticating the EABF. This work presents amino acids content in EABFs for the first time and built tracing models for rapid authentication of DBT, which can be used to manage the EABF market, thereby preventing illegal adulteration and unfair competition.

Review of visual analytics methods for food safety risks

“…These findings can help risk managers identify the principal factors that influence food fraud, and thus, enhance their ability to risk management and food fraud risk mitigation. ELM and newly introduced CNNs are currently commonly used supervised learning algorithms in food fraud classification, such as the extreme learning machine regression (ELMR) model for identifying adulterated edible animal blood food (EABF) 67 , the CNN for classifying natural and artificially ripened bananas 68 and the improved CNN for classification of turmeric powder images to detect fraud 69 . These studies suggest that DL has emerged as an effective method for assessing food quality and identifying fraud.…”

Tracing models for checking beef adulterated with pig blood by Fourier transform near-infrared paired with linear and nonlinear chemometrics

“…Due to the optical fiber used, it is possible to separate the spectrometer from the sample over several meters. Thus, industrial installations with a high degree of flexibility and complete automation are possible (Han et al, 2022c).…”