Abstract
This study assesses the feasibility of Near Infrared Spectroscopy in combination with various spectral treatments and chemometric approaches, i.e. the Partial Least Squares-Discriminant Analysis and the Soft Independent Modelling of Class Analogies, for the onsite classification of lamb hamburgers according to the presence of cherry (presence/absence) as well as the percentage of cherry (2%, 6% and 10%) in cherry lamb hamburgers. The models developed using the Partial Least Squares-Discriminant Analysis offered high classificatory suitability for classification of lamb hamburgers according to the presence and the percentage of cherry, yielding an accuracy higher than 95% and 84%, respectively. Furthermore, the models proved their reliability after external validation, as the accuracy values remained above 81% and 90% respectively. Furthermore, the models proved their reliability after external validation. Such results might help support onsite quality control and simplify the management of stock, traceability and logistics operations applied to processed meat products.
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