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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
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Applications of IR Spectroscopy: Overview01:11

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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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先进的数据驱动可解释分析用于预测米中的耐药粉含量,使用NIR光谱学.

Qian Zhu1, Yuanliang Gao1, Bang Yang1

  • 1Zhejiang University of Science and Technology, Hangzhou, China.

Food chemistry
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概括

这项研究提出了一种快速,具有成本效益的方法,用于使用近红外 (NIR) 光谱和人工智能预测耐性粉 (RS). 该方法提供了高精度,并识别了关键波长,简化了食品质量分析.

关键词:
卷积神经网络 (CNN) 是一种神经网络.模型的解释性 模型的解释性近红外 (NIR) 光谱学近红外 (NIR) 光谱学耐药粉是一种耐药性粉.沙普利的添加式扩展 (SHAP)

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科学领域:

  • 食品科学与技术 食品科学与技术
  • 分析化学 分析化学
  • 生物技术是生物技术.

背景情况:

  • 耐性粉 (RS) 提供了显著的健康益处,但传统的量化方法对于大规模使用是无效的.
  • 现有的RS分析方法通常是劳动密集型,昂贵,不适合实时工业应用.
  • 需要快速,具有成本效益和可扩展的分析解决方案来确定食品中的RS.

研究的目的:

  • 开发和验证一个数据驱动的框架,用于准确和高效的耐药粉预测.
  • 整合近红外 (NIR) 光谱与先进的机器学习模型进行定量分析.
  • 提高深度学习模型在食品质量评估的光谱分析中的可解释性.

主要方法:

  • 利用近红外 (NIR) 光谱仪来获取光谱数据.
  • 开发了一个卷积神经网络 (CNN) 模型,将数据增强用于RS预测.
  • 采用了SHapley添加式解释 (SHAP) 来解释CNN模型并确定关键的光谱区域.

主要成果:

  • 该CNN模型实现了卓越的预测准确性 (Rp2 = 0.992),超过了PLSR和SVMR等传统方法.
  • SHAP分析确定了特定的关键波长 (2000-2500纳米),对RS预测作出了重大贡献.
  • 优化的光谱范围减少了数据采集时间和分析成本.

结论:

  • 集成的NIR-CNN-SHAP框架为抗性粉量化提供了一个快速,经济有效和可解释的解决方案.
  • 这种方法提高了数据采集效率,并简化了食品质量控制的操作复杂性.
  • 该研究建立了一种实用且可扩展的方法,用于在工业食品生产环境中部署NIR光谱.