Detection of chicken and fat adulteration in minced lamb meat by VIS/NIR spectroscopy and chemometrics methods

Abstract Meat fraud has been changed to an important challenge to both industry and governments because of the public health issue. The main purpose of this research was to inspect the possibility of using VIS/NIR spectroscopy, combined with chemometric techniques to detect the adulteration of chicken meat and fat in minced lamb meat. 180 samples of pure lamb, chicken and fat and adulterated samples at different levels: 5, 10, 15 and 20% (w/w) were prepared and analyzed after pre-processing techniques. In order to remove additive and multiplicative effects in spectral data, derivatives and scatter-correction preprocessing methods were applied. Principle Component Analysis (PCA) as unsupervised method was applied to compress data. Moreover, Support Vector Machine (SVM) and Soft Independent Modeling Class Analogies (SIMCA) as supervised methods was applied to estimate the discrimination power of these models for nine and three class datasets. The best classification results were 56.15 and 80.70% for classification of nine class and three class datasets respectively with SVM model. This study shows the applicability of VIS/NIR combined with chemometrics to detect the type of fraud in minced lamb meat.