RESUMEN
Beef is easily spoiled, resulting in foodborne illness and high societal costs. This study proposed a novel olfactory visualization system based on colorimetric sensor array and chemometric methods to detect beef freshness. First, twelve color-sensitive materials were immobilized on a hydrophobic platform to acquire scent information of beef samples according to solvatochromic effects. Second, machine vision algorithms were used to extract the scent fingerprints, and principal component analysis (PCA) was employed to compress the feature dimensions of the fingerprints. Finally, four qualitative models, k-nearest neighbor, extreme learning machine, support vector machine (SVM), and random forest, were constructed to evaluate the beef freshness according to the value of total volatile basic nitrogen (TVB-N) and total viable counts (TVC). Results demonstrated that SVM had a preferable prediction ability, with 95.83% and 95.00% precision in the training and prediction sets, respectively. The results revealed that the simple constructed olfactory visualization sensor system could rapidly, robustly, and accurately assess beef freshness.
