Mohammad Golzarijalal, Lydia Ong, Uwe Aickelin, Dalton J. E. Harvie, Sally L. Gras
{"title":"冷冻和解冻对马苏里拉奶酪的影响:来自工业规模实验和数学和数字分析的见解","authors":"Mohammad Golzarijalal, Lydia Ong, Uwe Aickelin, Dalton J. E. Harvie, Sally L. Gras","doi":"10.1007/s11947-025-03787-9","DOIUrl":null,"url":null,"abstract":"<div><p>Freezing can assist the distribution of low-moisture Mozzarella cheese, but the impact of freezing under industrial conditions in a pallet is not well understood. Heat transfer during the freezing and thawing of 96 blocks of 10 kg cheese was slower than observed for smaller masses of cheese (0.70–0.87 °C day<sup>−1</sup> for freezing and 0.80–6.00 °C day<sup>−1</sup> for thawing). The rate of heat transfer also differed between inner and outer blocks, particularly during thawing. Block temperature was predicted with a maximum root mean square error of 3.60 °C, using heat and mass transfer simulations. While several changes in physicochemical properties were observed, the impact on cheese functionality appeared small. Large reversible salt migration was observed by simulation, causing local concentrations of up to 33% salt in free moisture in outer blocks at the end of freezing. Intact casein was 3–4% lower after thawing compared to in refrigerated control cheese but the microstructural, textural, and functional properties were similar, except for the appearance of a greater number of calcium crystal complexes in inner blocks. The microstructural, textural, and functional properties of inner and outer blocks were also similar, despite differing rates of heat transfer. Linear regression could predict the concentration of soluble nitrogen in thawed samples using data for refrigerated samples. Machine learning methods were also applied to predict non-linear behavior while minimizing the need for experimental data. A linear multi-fidelity Gaussian process model best predicted soluble nitrogen by combining historical data from refrigerated samples with limited experimental data from thawed samples. This study increases our understanding of freezing and thawing of cheese in an industrial setting and offers tools for optimizing these processes to minimize proteolysis in order to reduce the impact on product quality.</p></div>","PeriodicalId":562,"journal":{"name":"Food and Bioprocess Technology","volume":"18 6","pages":"5634 - 5653"},"PeriodicalIF":5.3000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11947-025-03787-9.pdf","citationCount":"0","resultStr":"{\"title\":\"The Effect of Freezing and Thawing on Mozzarella Cheese: Insights from Industrial-Scale Experiments and Mathematical and Digital Analysis\",\"authors\":\"Mohammad Golzarijalal, Lydia Ong, Uwe Aickelin, Dalton J. E. Harvie, Sally L. Gras\",\"doi\":\"10.1007/s11947-025-03787-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Freezing can assist the distribution of low-moisture Mozzarella cheese, but the impact of freezing under industrial conditions in a pallet is not well understood. Heat transfer during the freezing and thawing of 96 blocks of 10 kg cheese was slower than observed for smaller masses of cheese (0.70–0.87 °C day<sup>−1</sup> for freezing and 0.80–6.00 °C day<sup>−1</sup> for thawing). The rate of heat transfer also differed between inner and outer blocks, particularly during thawing. Block temperature was predicted with a maximum root mean square error of 3.60 °C, using heat and mass transfer simulations. While several changes in physicochemical properties were observed, the impact on cheese functionality appeared small. Large reversible salt migration was observed by simulation, causing local concentrations of up to 33% salt in free moisture in outer blocks at the end of freezing. Intact casein was 3–4% lower after thawing compared to in refrigerated control cheese but the microstructural, textural, and functional properties were similar, except for the appearance of a greater number of calcium crystal complexes in inner blocks. The microstructural, textural, and functional properties of inner and outer blocks were also similar, despite differing rates of heat transfer. Linear regression could predict the concentration of soluble nitrogen in thawed samples using data for refrigerated samples. Machine learning methods were also applied to predict non-linear behavior while minimizing the need for experimental data. A linear multi-fidelity Gaussian process model best predicted soluble nitrogen by combining historical data from refrigerated samples with limited experimental data from thawed samples. This study increases our understanding of freezing and thawing of cheese in an industrial setting and offers tools for optimizing these processes to minimize proteolysis in order to reduce the impact on product quality.</p></div>\",\"PeriodicalId\":562,\"journal\":{\"name\":\"Food and Bioprocess Technology\",\"volume\":\"18 6\",\"pages\":\"5634 - 5653\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s11947-025-03787-9.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food and Bioprocess Technology\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11947-025-03787-9\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food and Bioprocess Technology","FirstCategoryId":"97","ListUrlMain":"https://link.springer.com/article/10.1007/s11947-025-03787-9","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
The Effect of Freezing and Thawing on Mozzarella Cheese: Insights from Industrial-Scale Experiments and Mathematical and Digital Analysis
Freezing can assist the distribution of low-moisture Mozzarella cheese, but the impact of freezing under industrial conditions in a pallet is not well understood. Heat transfer during the freezing and thawing of 96 blocks of 10 kg cheese was slower than observed for smaller masses of cheese (0.70–0.87 °C day−1 for freezing and 0.80–6.00 °C day−1 for thawing). The rate of heat transfer also differed between inner and outer blocks, particularly during thawing. Block temperature was predicted with a maximum root mean square error of 3.60 °C, using heat and mass transfer simulations. While several changes in physicochemical properties were observed, the impact on cheese functionality appeared small. Large reversible salt migration was observed by simulation, causing local concentrations of up to 33% salt in free moisture in outer blocks at the end of freezing. Intact casein was 3–4% lower after thawing compared to in refrigerated control cheese but the microstructural, textural, and functional properties were similar, except for the appearance of a greater number of calcium crystal complexes in inner blocks. The microstructural, textural, and functional properties of inner and outer blocks were also similar, despite differing rates of heat transfer. Linear regression could predict the concentration of soluble nitrogen in thawed samples using data for refrigerated samples. Machine learning methods were also applied to predict non-linear behavior while minimizing the need for experimental data. A linear multi-fidelity Gaussian process model best predicted soluble nitrogen by combining historical data from refrigerated samples with limited experimental data from thawed samples. This study increases our understanding of freezing and thawing of cheese in an industrial setting and offers tools for optimizing these processes to minimize proteolysis in order to reduce the impact on product quality.
期刊介绍:
Food and Bioprocess Technology provides an effective and timely platform for cutting-edge high quality original papers in the engineering and science of all types of food processing technologies, from the original food supply source to the consumer’s dinner table. It aims to be a leading international journal for the multidisciplinary agri-food research community.
The journal focuses especially on experimental or theoretical research findings that have the potential for helping the agri-food industry to improve process efficiency, enhance product quality and, extend shelf-life of fresh and processed agri-food products. The editors present critical reviews on new perspectives to established processes, innovative and emerging technologies, and trends and future research in food and bioproducts processing. The journal also publishes short communications for rapidly disseminating preliminary results, letters to the Editor on recent developments and controversy, and book reviews.