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相关概念视频

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

712
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
712

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从复杂的食品矩阵中优化高附加值化合物的回收,使用多变量方法.

Yixuan Liu1, Basharat N Dar2, Hilal A Makroo2

  • 1Research Group in Innovative Technologies for Sustainable Food (ALISOST), Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés, s/n, 46100 Burjassot, Spain.

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概括

通过响应表面方法 (RSM) 和人工神经网络 (ANN) 优化食品加工可以提高复合物回收和可持续性. 这些方法减少了浪费,并提高了产品质量,为更绿色的食品工业.

关键词:
一个年龄,一个年龄.在 RSM RSM 中.食品行业 食品行业 食品行业具有高附加值的化合物.优化的优化优化优化.

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科学领域:

  • 食品科学与技术 食品科学与技术
  • 过程优化 过程优化
  • 可持续的制造业 可持续的制造业

背景情况:

  • 优化高价值化合物的回收对于提高食品工业的质量和产量至关重要.
  • 多变量统计方法是实现这些优化的关键工具.
  • 食品加工的可持续性需要高效的资源利用和减少浪费.

研究的目的:

  • 为了比较响应表面方法 (RSM) 和人工神经网络 (ANN) 的技术优势.
  • 检查RSM和ANN在食品加工中的可持续性影响.
  • 突出这些方法在推动创新和绿色实践方面的潜力.

主要方法:

  • 对结构化过程建模的响应表面方法 (RSM) 的审查和比较.
  • 对处理非线性数据和大型数据集的人工神经网络 (ANN) 的审查和比较.
  • 对结合RSM和ANN进行增强预测建模的协同方法的分析.

主要成果:

  • RSM提供了一个结构化的方法来建模复杂的食品加工系统.
  • 在管理非线性关系和广泛的数据集方面,ANN表现出熟练.
  • 结合的RSM和ANN为预测准确性,营养保存和保质期延长提供了协同效应的好处.

结论:

  • RSM和ANN是优化资源使用和减少食品行业浪费的强大工具.
  • 整合RSM和ANNs支持可持续的食品加工,并提高产品质量.
  • 需要进一步的研究来探索这些方法的可扩展性和与新兴技术的整合,以便在更广泛的行业应用中.