Detection of adulteration of Alpaca (Vicugna pacos) meat using a portable NIR spectrometer and NIR-hyperspectral imaging

Abstract

Alpaca meat has high protein content; good tenderness and low intramuscular fat content, being more expensive than meat from other animals (e.g., beef). Hence, alpaca meat may face adulteration, which demands chemical analytical methods to be identified. In this study, chemical-free methods, e.g., portable NIR spectrometer and NIR-HSI were employed to detect adulteration of alpaca meat with pork, chicken, and beef (0 – 50% w/w). Spectral analysis revealed significant differences in the spectra of pure alpaca meat samples using NIR-HSI. Principal Component Analysis (PCA) grouped samples into pure and non-pure alpaca meat using both devices as sources of spectra. Next, we developed and validated one-class Data Driven Soft Independent Class Analogy (DD-SIMCA) models to authenticate pure alpaca meat. DD-SIMCA models using spectra acquired by both devices achieved 100% sensitivity and 100% specificity for external set of samples. Besides, Partial Least Squares Regression (PLSR) based on NIR-HSI outperformed the portable NIR spectrometer to predict the concentration of adulterant in alpaca meat. In conclusion, both devices supported by chemometric approaches can be implemented as screening methods to detect adulteration in alpaca meat.