In this paper (open access) the authors built a classification model to discriminate premium from non-premium grades of vacuum packed sliced Iberian ham. They used a Near Infrared Scanner reading directly through the packaging.
The model was constructed from a database of 312 purchased from retail on a weekly basis over a two-year period (2023–2024). The samples were obtained as vacuum-packed slices from a range of commercial brands and industrial producers, in a manner analogous to typical consumer purchasing behaviour in supermarkets, encompassing the four official commercial categories: black seal, red seal, green seal, and white seal. These samples were preliminarily grouped into premium (201 samples) and non-premium (111 samples) categories based on their commercial labelling. The researchers further verified the label categorisation by free fatty acid analysis.
The classification was based on the quality and sensory differences that appear in products derived from animals fed with natural resources (acorn and grass) in extensive systems (premium category), as opposed to those from animals fed with compound feeds (non-premium category}
The authors report 100 % sensitivity, specificity, and non-error rate (NER) for both of two different NIR sensors tested during external validation. They report that a lower cost miniaturised model performed less well, with 100 % sensitivity but 85.71 % specificity and 94.74 % NER, limiting its applicability for samples near the classification threshold.
They conclude that their results confirm the suitability of NIRS technology for rapid and non-destructive in situ classification of high-value foods, including pre-sliced Iberian ham.
