Abstract
Minced pork was stored aerobically and in MAP conditions at five different temperatures (0, 5, 10, 15, and 20°C) and microbiological analysis in terms of total viable counts (TVC) was performed in parallel with e-nose measurements and sensory analysis until spoilage was evident in the samples. The volatile patterns collected from e-nose were initially subjected to Principal Component Analysis (PCA) for dimensionality reduction and subsequently to Support Vector Machines (SVM) analysis, using different kernels (linear, polynomial, and radial basis function), in order to classify meat in three distinct quality classes namely, fresh, semi-fresh, and spoiled. Results showed that SVM with radial basis function kernel provided good discrimination of minced pork samples regarding spoilage status. The overall correct classification in the three sensory classes was 81%, whereas correct classification for fresh, semi-fresh and spoiled samples amounted to 76, 87, and 78%, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
