一种从机器学习中获得的图形特征图的开发方法,以及其在洛卡特果汁分类中的应用
Qingyue Zhang1, Yixiao Wang2, Jing Hu3
1School of Chemistry, University of Nottingham, NG7 2RD, United Kingdom.
Food chemistry
|June 12, 2025
概括
本研究介绍了一种使用加权人工神经网络 (w-ANNs) 创建图形特征图的新方法,以分类汁品种. 该方法使用化学化合物分析有效区分loquat_baisha和loquat_hongsha.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 果汁品种的准确分类对于质量控制和真实性验证至关重要.
- 传统的方法往往缺乏精确度,以根据复杂的化学配置来区分密切相关的品种.
- 开发先进的分析技术对于细微的食品分析至关重要.
研究的目的:
- 通过加权人工神经网络 (w-ANNs) 创建图形特征图的新方法.
- 使用卷积神经网络 (CNN) 应用此方法来对洛卡特果汁品种 (loquat_baisha和loquat_hongsha) 的分类.
- 为了识别关键的化学化合物和分子特征描述符,以区分loquat品种.
主要方法:
- 权重人工神经网络 (w-ANNs) 用于图形特征地图生成.
- 卷积神经网络 (CNN) 与TensorFlow (TF) 进行分类.
- 头空间气体色谱-离子质谱 (HS-GC-IMS) 用于化学化合物识别.
- 沙普利添加剂解释 (SHAP) 用于识别有影响力的分子特征描述器 (MFD).
- 使用PubChem数据开发一个loquat化学库.
主要成果:
- HS-GC-IMS为loquat_baisha和loquat_hongsha确定了七种关键化合物的独特组合.
- SHAP分析强调了Kappa2,Gasteiger收费和LogP作为loquat_baisha的重要MFD.
- 卡帕2,卡帕3和Fraction_SP3被确定为loquat_hongsha的关键MFD.
- 图形特征地图已成功构建,以帮助分类.
- 建立了一个全面的loquat化学图书馆.
结论:
- 开发的方法提供了一种新的方法,用于根据化学配置文件对洛卡特果汁品种进行分类.
- 已识别的关键化合物和MFD为loquat_baisha和loquat_hongsha之间的化学区别提供了见解.
- 该方法的有效性取决于上下文,需要对不同的应用进行评估.
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