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

IF 5.3 2区 农林科学 Q1 FOOD SCIENCE & TECHNOLOGY
Mohammad Golzarijalal, Lydia Ong, Uwe Aickelin, Dalton J. E. Harvie, Sally L. Gras
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引用次数: 0

摘要

冷冻可以帮助低水分马苏里拉奶酪的分布,但在工业条件下在托盘中冷冻的影响还不清楚。在冷冻和解冻过程中,96块10公斤奶酪的热量传递比较小质量的奶酪慢(冷冻第一天0.70-0.87℃,解冻第一天0.80-6.00℃)。内部和外部块之间的传热速率也不同,特别是在解冻期间。利用传热传质模拟,预测块体温度,最大均方根误差为3.60°C。虽然观察到一些物理化学性质的变化,但对奶酪功能的影响似乎很小。通过模拟观察到大量的可逆盐迁移,在冻结结束时,导致外部块中游离水分的局部浓度高达33%。与冷藏对照奶酪相比,解冻后的完整酪蛋白含量降低了3-4%,但微观结构、质地和功能特性相似,只是在内部块中出现了更多的钙晶体复合物。内部和外部块的微观结构、纹理和功能特性也相似,尽管传热速率不同。利用冷冻样品的数据,线性回归可以预测解冻样品中可溶性氮的浓度。机器学习方法也被应用于预测非线性行为,同时最大限度地减少对实验数据的需求。线性多保真高斯过程模型通过结合冷冻样品的历史数据和解冻样品的有限实验数据来预测可溶氮。这项研究增加了我们对工业环境下奶酪冷冻和解冻的理解,并为优化这些过程提供了工具,以最大限度地减少蛋白质水解,从而减少对产品质量的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Food and Bioprocess Technology
Food and Bioprocess Technology 农林科学-食品科技
CiteScore
9.50
自引率
19.60%
发文量
200
审稿时长
2.8 months
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.
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