An overview of implementing Multispectral Imaging coupled with machine learning for the assessment of microbiological quality and authenticity in foods

Multispectral Imaging is an increasingly applied technique for the estimation of several quality parameters across the food chain. The microbiological quality and safety as well as the detection of food fraud are among the most significant aspects in food quality and safety assessment. MSI analysis was performed using a VideometerLab instrument (Videometer A/S, Videometer, Herlev, Denmark), while more than 9000 food samples were examined in total, for the assessment of microbiological quality and the detection of food fraud. For estimating microbial populations, total aerobic counts (TAC) were determined. Several regression and classification algorithms were employed, including partial least squares regression (PLS-R), support vector machines (SVM), partial least squares discriminant analysis (PLS-DA), tree-based algorithms etc. The slope of the regression line, root mean squared error (RMSE), coefficient of determination (R-squared) and accuracy score were used as metrics for the evaluation of models’ performance. In adulteration case, the prediction of different levels of pork in chicken meat and vice versa yielded high accuracy scores i.e., over 90% , while, using the SVM algorithm, the presence of bovine offal in beef was successfully detected. Additionally, Random Forest algorithm was efficient (accuracy>93% ) in discriminating seabass and seabream fish fillets. Concerning microbiological quality, as indicated by the performance indices, the developed models exhibited satisfactory performance in predicting microbial load in different foods (RMSE<1.00, R-squared>0.80). Indicatively, MSI spectral data combined with PLS-R could satisfactorily predict TAC and Pseudomonas spp. counts on the surface of chicken fillets regardless of storage temperature and batch variation based on the performance metrics (R-squared: 0.89, RMSE: 0.88) while, this algorithm presented also satisfactory performance in estimation microbial populations in brown edible seaweed (R-squared: 0.80, RMSE: 0.90). However, in this case, selecting the appropriate analytical approaches and machine learning algorithms is still challenging.