Exploiting multispectral imaging for non-invasive contamination assessment and mapping of meat samples

Recently, imaging and machine vision are gaining attention to food stakeholders since these are considered to be the emerging tools for food safety and quality assessment throughout the whole food chain. Herein, multispectral imaging, a surface chemistry sensor type, has been evaluated in terms of monitoring aerobically packaged beef filet spoilage at different storage temperatures (2, 8, and 15°C) and storage time. Spectral data acquired from the surface of meat samples (with/without background flora; +BF/-BF respectively) along with microbiological analysis. Qualitative analysis was employed for the discrimination of meat samples in two microbiological quality classes based on the values of total viable counts (TVC<2log₁₀CFU/g and TVC>2log₁₀CFU/g). Furthermore, a Support Vector Regression model was developed to provide quantitative estimations of microbial counts during storage. Results exhibit good performance with overall correct classification rate for the two quality classes ranging from 89.2% to 80.8% for model validation. The calculated regression results to an R-square of 0.98.