Objective: The objective of this research was to assess the suitability of FTIR spectroscopy coupled with multivariate analysis of partial least square regression (PLSR) along with pattern recognition technique of principal component analysis (PCA) for rapid quantitative and qualitative (identification) analysis of dog meat in beef meatball formulation.
Materials and Methods: The lipid fraction of meatball was obtained by employing two different extraction techniques, namely Bligh-Dyer and Folch method. FTIR spectral bands correlated with beef fat, pork fat, chicken fat and rat fat were measured, interpreted, and qualitatively analyzed. The small variations among spectra were exploited as a basis tools to differentiate between dog fat and other animal fats.
Results: PCA at combined wavenumber regions of 1700-700 cm-1 was capable of identifying dog meat in meatball. These wavenumbers were also used for quantitative analysis of dog meat in meatball using PLSR model. Based on statistical parameters used, namely R2, RMSEC and RMSEP, Folch extraction method offered higher R2 and lower RMSEC and RMSEP than Bligh-Dyer. PCA is succesfully applied for classification between meatball containing dog meat and other meats.
Conclusion: FTIR spectroscopy coupled with multivariate analyses of PLSR and PCA was effective means for rapid screening of dog meat in meatball products.
Key words: Bligh-Dryer; Chemometrics; Dog meat; Folch; FTIR spectroscopy