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There is a growing market for packaged slices of dry-cured ham. The heterogeneity of the composition of slices between packages is an important drawback when aiming to offer consumers a product with a known and constant composition which fits individual consumer expectations. The aim of this work was to test the feasibility of NIR interactance imaging for on-line analysis of water, fat and salt and their spatial distribution in dry-cured ham slices. PLSR models for predicting water, fat and salt contents with NIR spectra were developed with a calibration set of samples (n=82). The models were validated with an external validation set (n=42). The predictive models were accurate enough for screening purposes. The errors of prediction were 1.34%, 1.36% and 0.71% for water, fat and salt, respectively. The spatial distribution of these components within the slice was also obtained.

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An accurate knowledge and optimization of dry-cured ham elaboration processes could help to reduce operating costs and maximize product quality. The development of nondestructive tools to characterize chemical parameters such as salt and water contents and a(w) during processing is of special interest. In this paper, predictive models for salt content (R(2) = 0.960 and RMSECV = 0.393), water content (R(2) = 0.912 and RMSECV = 1.751), and a(w) (R(2) = 0.906 and RMSECV = 0.008), which comprise the whole elaboration process, were developed. These predictive models were used to develop analytical tools such as distribution diagrams, line profiles, and regions of interest (ROIs) from the acquired computed tomography (CT) scans. These CT analytical tools provided quantitative information on salt, water, and a(w) in terms of content but also distribution throughout the process. The information obtained was applied to two industrial case studies. The main drawback of the predictive models and CT analytical tools is the disturbance that fat produces in water content and a(w) predictions.

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