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Detecting meat adulteration for quality control and accurate labeling is important and needs convenient analytical methods. This study aimed to investigate and compare the application of the transmission and ATR approaches of FTIR followed by principal component analysis (PCA) to not only discriminate between chicken and beef meat but also quantizing chicken portion of mixtures. Two different approaches are presented; spectra preprocessing with focus on wavenumber region of 1700-1071 cm -1 , and no preprocessed where PCA was applied on the whole spectra range of mid-FTIR. The results suggest that applying PCA on specified preprocessed spectra could detect hidden relationships between variables in chicken and beef in both approaches. PCA successfully clustered these kinds of meats when applied on transmission mode spectra without any preprocessing treatment, while applying it on ATR mode's raw spectra failed to cluster them. Additionally, the preprocessed ATR-FTIR spectrum was used to prepare regression models by Partial Least Square Regression (PLS-R) and artificial neural networks (ANN) for predicting presence and percentage of chicken meat in the beef meat mixture. The results demonstrated the superiority of ANN over PLS-R in this assessment with an R2 of 0.999.

journal of food science and technology/journal of food science and technology

Comparison of transmission FTIR and ATR spectra for discrimination between beef and chicken meat and quantification of chicken in beef meat mixture using ATR-FTIR combined with chemometrics