The objective of the present study was to develop multivariate process monitoring models for weight loss of high-moisture mozzarella cheese during shelf-life. Eighty-two production batches from an industrial cheese factory were sampled in four non-consecutive days. Chemical composition of curd and mozzarella was analyzed using near infrared spectroscopy. Weight loss of mozzarella balls at 10 and 21 days after production were evaluated as measure of process efficiency. Principal component regression and linear regression coupled with variable selection were tested for their accuracy in predicting weight loss. Analysis of variance highlighted that shelf-life and sampling day had the strongest effect on mozzarella cheese chemical composition. Weight loss was predicted from curd and mozzarella composition with coefficient of determination ranging from 0.49 to 0.54. Batches with unsatisfactory performances were determined with accuracy ranging between 0.81 and 0.84. Possible applications of proposed models were evaluated based on their performances as well on their usability in dairies. Overall, findings suggest that the most suitable method for routine process control is linear regression algorithm on selected variables.
Investigation of weight loss in mozzarella cheese using NIR predicted chemical composition and multivariate analysis
Marco Franzoi
;Matteo Ghetti;Massimo De Marchi
2021
Abstract
The objective of the present study was to develop multivariate process monitoring models for weight loss of high-moisture mozzarella cheese during shelf-life. Eighty-two production batches from an industrial cheese factory were sampled in four non-consecutive days. Chemical composition of curd and mozzarella was analyzed using near infrared spectroscopy. Weight loss of mozzarella balls at 10 and 21 days after production were evaluated as measure of process efficiency. Principal component regression and linear regression coupled with variable selection were tested for their accuracy in predicting weight loss. Analysis of variance highlighted that shelf-life and sampling day had the strongest effect on mozzarella cheese chemical composition. Weight loss was predicted from curd and mozzarella composition with coefficient of determination ranging from 0.49 to 0.54. Batches with unsatisfactory performances were determined with accuracy ranging between 0.81 and 0.84. Possible applications of proposed models were evaluated based on their performances as well on their usability in dairies. Overall, findings suggest that the most suitable method for routine process control is linear regression algorithm on selected variables.
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