Application of Infrared Spectroscopy with Multivariate Analysis and Soft Indepen dent Modelling of Class Analogies (Simca) for the Detection of Meat Species
DOI:
https://doi.org/10.48165/jms.2026.21.01.2Keywords:
FTIR spectroscopy, chemometric analysis, SIMCA, meat adulterationAbstract
Meat speciation is currently monitored using various chemical and biological methods that rely on proteins, DNA, and RNA. However, these approaches are slow, costly, and difficult for automation. Infrared spectroscopy presents itself as a convenient analytical tool for monitoring food quality. This study aimed to evaluate the potential of FTIR spectroscopy and multivariate analyses in identifying meat species. Dried powders were prepared from defatted goat meat, chicken and pork. Initially, powders from single meat species were subjected to spectral scanning in the range of 400-400 cm-1. Subsequently, mixtures of chicken and goat, pork and goat, and chicken and pork in a 50:50 ratio were prepared and analysed using FTIR spectroscopy. Chemometric analyses, employing multivariate analysis, were conducted for each sample. Spectral signatures of 16-18 samples for each type of mixed powder were acquired. Results of Principal component analysis showed clear clustering of samples with sum of PC1 and PC2 described 98% variance. Further, SIMCA correctly classified the chicken, goat and pig powder into their respective classes with an accuracy of 93.75%, 88.88% and 93.75% respectively. In conclusion, NIR spectral analysis proves to be a novel, rapid, and cost-effective technique for identifying meat species in various meat and meat products. Experiments were carried out in ICAR-CIPHET, Ludhiana.
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References
Bro, R., & Smilde, A. K. (2014). Principal component analysis. Analytical Methods, 6, 2812–2831.
(2010). Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef. Meat Science, 86, 511–519.
Che Man, Y. B., Rohman, A., & Mansor, T. S. T. (2011). Differentiation of lard from other edible fats and oils by means of Fourier transform infrared spectroscopy and chemometrics. Journal of the American Oil Chemists’ Society, 88, 187–192.
Dashti, A., Weesepoel, Y., Müller-Maatsch, J., Parastar, H., Kobarfard, F., Daraei, B., & Yazdanpanah, H. (2022). Assessment of meat authenticity using portable Fourier transform infrared spectroscopy combined with multivariate classification techniques. Microchemical Journal, 181, 107735.
Fengou, L. C., Lianou, A., Tsakanikas, P., Mohareb, F., & Nychas, G. J. E. (2021). Detection of meat adulteration using spectroscopy-based sensors. Foods, 10, 861.
Frank, L. E., & Friedman, J. H. (1993). A statistical view of some chemometrics regression tools. Technometrics, 35, 109–135.
Luykx, D. M., & Van Ruth, S. M. (2008). An overview of analytical methods for determining the geographical origin of food products. Food Chemistry, 107, 897–911.
Manley, M. (2014). Near-infrared spectroscopy and hyperspectral imaging: Non-destructive analysis of biological materials. Chemical Society Reviews, 43, 8200–8214.
Meza-Márquez, O. G., Gallardo-Velázquez, T., & Osorio-Revilla, G. (Reference incomplete in provided text).
Montowska, M., & Pospiech, E. (2010). Authenticity determination of meat and meat products on the protein and DNA basis. Food Reviews International, 27, 84–100.
Naveena, B. M., Jagadeesh, D. S., Babu, A. J., Rao, T. M., Kamuni, V., Vaithiyanathan, S., & Rapole, S. (2017). OFFGEL electrophoresis and tandem mass spectrometry approach compared with DNA-based PCR method for authentication of meat species from raw and cooked ground meat mixtures containing cattle meat, water buffalo meat and sheep meat. Food Chemistry, 233, 311–320.
Rahman, M. A., Rahman, M. M., Reichman, S. M., Lim, R. P., & Naidu, R. (2014). Heavy metals in Australian grown and imported rice and vegetables on sale in Australia: Health hazard. Ecotoxicology and Environmental Safety, 100, 53–60.
Song, Z., Storesletten, K., & Zilibotti, F. (2011). Growing like China. American Economic Review, 101, 196–233.
Varmuza, K., & Filzmoser, P. (2016). Introduction to multivariate statistical analysis in chemometrics. CRC Press, Boca Raton.
Vishnuraj, M. R., Renuka, J., Kumar, N. A., Ajay, G., Chauhan, H. A., Vaithiyanathan, S., & Barbuddhe, S. B. (2023). Differential detection of sheep and goat meat using duplex real-time PCR and high-resolution melt analysis. Food Chemistry Advances, 2, 100309.
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Copyright (c) 2026 Suresh k Devatkal, Praneeta Jaiswal, Rahul Anurag, Kalyani Jatoth, Chandana Yadagiri
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