模拟和优化草本强化新鲜康布查奶酪:一种人工神经网络方法来提高质量特征
Biljana Lončar1, Lato Pezo2, Mirela Iličić1
1Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia.
Foods (Basel, Switzerland)
|February 24, 2024
概括
人工神经网络 (ANN) 通过分析化学和微生物特征来优化新鲜奶酪的生产. 一个ANN模型确定了提取物是最优的,改善了乳制品行业的质量和产品开发.
科学领域:
- 食品科学与技术 食品科学与技术
- 食品加工中的人工智能
- 乳制品开发 乳制品开发
背景情况:
- 传统的新鲜奶酪生产在持续实现所需的质量参数方面面临着挑战.
- 用天然提取物,如野生红枣和提供了增强营养和功能性质的潜力.
- 优化成分,加工和储存之间的复杂相互作用对于产品质量至关重要.
研究的目的:
- 应用一个人工神经网络 (ANN) 模型来优化新鲜奶酪的生产.
- 评估野生红枣和提取物对新鲜奶酪的化学成分,抗氧化能力和微生物特征的影响.
- 用ANN模型确定最佳的过程参数,以提高新鲜奶酪的质量.
主要方法:
- 开发和应用人工神经网络 (ANN) 模型,特别是多层感知器 (MLP 6-10-16).
- 康布查新鲜奶酪的强化与野粉,野粉的超临界流体提取物,地面,和粉的超临界流体提取物.
- 综合分析化学成分 (干物质,脂肪,灰,蛋白质),水活性,pH,抗氧化能力 (TP,DPPH,ABTS,FRAP) 和微生物特征.
- 使用高R平方值的培训,测试和验证周期验证ANN模型.
主要成果:
- 该ANN模型展示了强大的概括能力,准确地预测了新鲜奶酪的质量参数.
- 高R平方值 (0.993用于培训,0.992用于测试,0.992用于验证) 证实了该模型的预测准确性.
- 最佳的新鲜奶酪样品被确定为一种用的超临界液体提取物强化,在储存的第20天进行了优化.
- 特定的有利的过程参数包括干物质,脂肪,抗氧化剂潜力和微生物特征被确定为最佳样本.
结论:
- 人工神经网络是优化复杂食品生产流程的有效工具,例如新鲜奶酪制造.
- 用的超临界液体提取物进行强化可以显著提高新鲜奶酪的质量属性.
- 该研究为乳制品行业的高效新鲜奶酪生产和产品开发提供了宝贵的见解,通过数据驱动的优化.
关键词:
在ANN建模中,使用ANN建模.萨尔维亚官员植物 (Salvia officinalis) 是一种植物.甲状腺蛇形 L. 甲状腺蛇形对抗微生物的潜在作用.抗氧化剂活性 抗氧化剂活性提取物提取物 提取物新鲜的奶酪和新鲜的奶酪.科姆布查 (Kombucha) 是一个葡萄酒.最好的配方最优的配方更多相关视频
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