A series of authentic and offal-adulterated beefburger samples was produced. Authentic product (36 samples) comprised either only lean meat and fat (higher quality beefburgers) or lean meat, fat, rusk and water (lower quality product). Beef offal adulterants comprised heart, liver, kidney and lung. Adulterated formulations (46 samples) were produced using a D-optimal experimental design. Fresh and frozen-then-thawed samples were modelled, separately and in combination, by a classification (partial least squares discriminant analysis) and class-modelling (soft independent modelling of class analogy) approach. With the former, 100% correct classification accuracies were obtained separately for fresh and frozen-then-thawed material. Separate class-models for fresh and frozen-then-thawed samples exhibited high sensitivities (0.94 to 1.0) but lower specificities (0.33-0.80 for fresh samples and 0.41-0.87 for frozen-then-thawed samples). When fresh and frozen-then-thawed samples were modelled together, sensitivity remained 1.0 but specificity ranged from 0.29 to 0.91. Results indicate a role for this technique in monitoring beefburger compliance to label.

Microwave vacuum drying of banana slices was investigated in this study. Near-infrared hyperspectral imaging (NIR-HSI, 950–1650 nm with 7 nm increments) and chemometrics were used to predict moisture content, hardness, and fracturability of banana slices during the entire drying process. A computer vision system was also used to evaluate browning index (BI) of slices at different drying stages; dark brown spots observed in slices were subjected to over 21-min total microwave heating. In addition, thermal imaging results showed that higher temperature occurred in the central part of banana slices. Calibration models were developed based on partial least square regression and support vector machine regression (SVM-R) using mean spectra with standard normal variate pretreatment. The results showed that SVM-R models performed better in the prediction of the three quality attributes with of 0.996, 0.927, and 0.961, respectively. The moisture distribution maps were generated using NIR-HSI; uniformly distributed moisture patterns were observed in each drying batch with small variations observed within individual slices. This study demonstrated the potential of spectral and imaging techniques for nondestructive quality evaluation of food products during drying processes.