Detection of Adulteration of Alpaca (Vicugna Pacos) Meat Using a Portable Nir Spectrometer and Nir-Hyperspectral Imaging Coupled Chemometrics

Alpaca meat has high protein content, good tenderness and low intramuscular fat content, being more expensive than traditional meats (e.g., beef). In this study, a 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 meat samples using NIR-HSI, primarily associated with fatty acid composition. Principal Component Analysis (PCA) grouped samples into pure and non-pure alpaca meat classes 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. Both NIR-based and NIR-HSI-based DD-SIMCA models achieved 100% sensitivity and 99.7 – 100% specificity in testing. Subsequently, Partial Least Squares Regression (PLSR) models were trained and tested to predict the concentration of pork, chicken, and beef meat in alpaca meat, using the full and selected wavelength range. Here, NIR-HSI outperformed the portable NIR spectrometer in predicting adulterant concentrations, yielding RPD values of 3.39 – 10.19, and RMSEP values of 1.53 – 3.93%, indicating excellent predictive ability to detect adulterants. In conclusion, both devices supported by chemometric can be implemented as screening methods to detect fraud in alpaca meat.