Document details for 'Compositional method for nutritional labeling of industrial pastries and biscuits based on Vis/NIR spectroscopy'

Authors Cayuela-Sánchez, J.A., Palarea Albaladejo, J., Zira, T.P. and Moriana-Correro, E.
Publication details Journal of Food Composition and Analysis 92(2020), 103572. Elsevier.
Publisher details Elsevier
Keywords nutritional labeling; pastry; biscuits; compositional data; monounsaturated fat; polyunsaturated fat; saturated fat
Abstract Food nutritional labeling is compulsory in the European Union since 13 December 2016. The analysis of all batches of foodstuff using the official methods is expensive and the food industry seeks cost-efficient solutions for nutritional labeling. The information is commonly provided in the form of fractions or parts of a number of nutritional categories, typically expressed in percentage units. This corresponds with so-called compositional data for which specific statistical methods and models have been developed. Compositional data analysis focuses on log-ratios between the parts, so that their relative scale and inherent interplay are accounted for. This study set up and evaluates a new rapid technique to measure the nutritional makeup of industrial pastries and biscuits for labelling purposes. Compositional partial least squares (PLS) calibration models, formulated through adequate log-ratio coordinates, were fitted based on visible and near infrared spectroscopy (NIRS) and applied to estimate the composition of (1) moisture, total fat, protein, and carbohydrates and (2) saturated, polyunsaturated and monounsaturated fats from intact and ground samples of a representative collection of industrial pastry and biscuit products. This investigation proposes a useful and methodologically well-founded approach for nutritional labeling or its verification which may be set-up to other foodstuffs
Last updated 2020-06-26

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