Quantification and identification of adulteration in the fat content of chicken hamburgers using digital images and chemometric tools

Abstract

In this work, we developed an eco-friendly methodology for quantification and identification of adulteration in the fat content of chicken hamburgers by combining color histograms (in RGB, HSI, and Grayscale channels) obtained from digital images and chemometric tools. For this, 74 samples of chicken hamburgers with a fat content of 14.27–47.55% (w w−1) were studied, taking into account adulterations with a fat content higher than 20% (w w−1), as limited by Argentinean legislation. In both quantitative and qualitative approaches, chemometric models containing HSI histograms achieved the best results, because this is very suitable in situations where there is a need to separate the chromaticity from the intensity. In other words, the opacity of the sample surfaces increases with increasing fat content. PLS/HSI achieved the best quantification result with a R2 of 0.95, RMSEP of 2.01% w w−1, REP of 7.26% w w−1 and RPD of 4.47 in the prediction set, while SPA-LDA/Grayscale + HSI reached the most satisfactory in the test set with only one misclassified sample. Therefore, the proposed methodologies represent excellent alternatives to conventional Soxhlet extraction method, since they follow the primary principles of Green Analytical Chemistry, avoiding waste generation, besides not using either chemical reagents or solvents.