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用于 GC⁃IMS 的呼气分析数据处理方法.

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Title: 用于 GC⁃IMS 的呼气分析数据处理方法. (Chinese)
Alternate Title: Data Processing Method for GC⁃IMS Exhaled Breath Analysis. (English)
Authors: 马 睿, 林建华, 慕世龙, 徐 陈, 贾 建, 何秀丽, 高晓光
Source: Journal of Test & Measurement Technology; Jun2026, Vol. 40 Issue 3, p327-334, 8p
Subject Terms: Breath tests, Electronic data processing, Machine learning, Ensemble learning, Classification, Signal processing, Ion mobility spectroscopy
Abstract (English): A novel data processing method of gas chromatography-ion mobility spectrometry (GC-IMS) is proposed to address the challenges of peak overlapping in GC-IMS spectra for exhaled breath analysis and the limited and continuously updated samples in applications. Breath samples simulating different physiological states were collected from healthy volunteers before and after drinking coffee, and were analyzed by GC-IMS directly. Overlapping peaks in the GC-IMS two-dimensional spectra were resolved using a Gaussian second derivative peak sharpening algorithm, and a Mondrian forest (MF) incremental learning model was constructed for classification of the two states. The results indicate that the method successfully resolves overlapping peaks in the original GC-IMS spectra, increasing the peak signal to noise ratio (PSNR) to 50 dB. Features such as the positions and intensities of the resolved peaks in the GC-IMS spectra are extracted to build the MF incremental learning model, which maintain a high average classification accuracy of 93. 75% as samples increase, significantly outperforming comparative models like random forest and Hoeffding trees. This exhaled breath analysis data processing method enhances classification accuracy, showing promising practical application prospects. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 针对呼气分析中气相色谱-离子迁移谱 (Gas Chromatography-Ion Mobility Spectrometry, GC-IMS) 谱峰重叠和实际应用场景下样本有限、需持续更新等问题, 提出一种呼气分析数据处理方法。以健康志愿者 饮用咖啡前后模拟不同生理状态, 利用 GC-IMS 直接分析呼出气体, 通过高斯二阶导数谱峰锐化算法解析 GC-IMS 呼气指纹图中的重叠峰, 构建蒙德里安森林 (Mondrian forest, MF) 增量学习模型实现分类。结果表 明, 该方法成功解析了 GC-IMS 原始指纹图中的重叠峰, 将峰值信噪比提高至 50 dB; 提取 GC-IMS 指纹图 中解析出的谱峰位置及强度作为特征, 构建的 MF 增量分类模型随着样本增加, 平均分类准确率达到 93. 75%, 显著优于对比的随机森林和 Hoeffding 树模型。这种呼气分析数据处理方法提高了分类的准确性, 具有良好的实际应用前景。 [ABSTRACT FROM AUTHOR]
Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Complementary Index
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PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: 用于 GC⁃IMS 的呼气分析数据处理方法. (Chinese)
– Name: TitleAlt
  Label: Alternate Title
  Group: TiAlt
  Data: Data Processing Method for GC⁃IMS Exhaled Breath Analysis. (English)
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22马+睿%22">马 睿</searchLink><br /><searchLink fieldCode="AR" term="%22林建华%22">林建华</searchLink><br /><searchLink fieldCode="AR" term="%22慕世龙%22">慕世龙</searchLink><br /><searchLink fieldCode="AR" term="%22徐+陈%22">徐 陈</searchLink><br /><searchLink fieldCode="AR" term="%22贾+建%22">贾 建</searchLink><br /><searchLink fieldCode="AR" term="%22何秀丽%22">何秀丽</searchLink><br /><searchLink fieldCode="AR" term="%22高晓光%22">高晓光</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Journal of Test & Measurement Technology; Jun2026, Vol. 40 Issue 3, p327-334, 8p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Breath+tests%22">Breath tests</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Ion+mobility+spectroscopy%22">Ion mobility spectroscopy</searchLink>
– Name: AbstractNonEng
  Label: Abstract (English)
  Group: Ab
  Data: A novel data processing method of gas chromatography-ion mobility spectrometry (GC-IMS) is proposed to address the challenges of peak overlapping in GC-IMS spectra for exhaled breath analysis and the limited and continuously updated samples in applications. Breath samples simulating different physiological states were collected from healthy volunteers before and after drinking coffee, and were analyzed by GC-IMS directly. Overlapping peaks in the GC-IMS two-dimensional spectra were resolved using a Gaussian second derivative peak sharpening algorithm, and a Mondrian forest (MF) incremental learning model was constructed for classification of the two states. The results indicate that the method successfully resolves overlapping peaks in the original GC-IMS spectra, increasing the peak signal to noise ratio (PSNR) to 50 dB. Features such as the positions and intensities of the resolved peaks in the GC-IMS spectra are extracted to build the MF incremental learning model, which maintain a high average classification accuracy of 93. 75% as samples increase, significantly outperforming comparative models like random forest and Hoeffding trees. This exhaled breath analysis data processing method enhances classification accuracy, showing promising practical application prospects. [ABSTRACT FROM AUTHOR]
– Name: AbstractNonEng
  Label: Abstract (Chinese)
  Group: Ab
  Data: 针对呼气分析中气相色谱-离子迁移谱 (Gas Chromatography-Ion Mobility Spectrometry, GC-IMS) 谱峰重叠和实际应用场景下样本有限、需持续更新等问题, 提出一种呼气分析数据处理方法。以健康志愿者 饮用咖啡前后模拟不同生理状态, 利用 GC-IMS 直接分析呼出气体, 通过高斯二阶导数谱峰锐化算法解析 GC-IMS 呼气指纹图中的重叠峰, 构建蒙德里安森林 (Mondrian forest, MF) 增量学习模型实现分类。结果表 明, 该方法成功解析了 GC-IMS 原始指纹图中的重叠峰, 将峰值信噪比提高至 50 dB; 提取 GC-IMS 指纹图 中解析出的谱峰位置及强度作为特征, 构建的 MF 增量分类模型随着样本增加, 平均分类准确率达到 93. 75%, 显著优于对比的随机森林和 Hoeffding 树模型。这种呼气分析数据处理方法提高了分类的准确性, 具有良好的实际应用前景。 [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.62756/csjs.1671-7449.2026049
    Languages:
      – Code: chi
        Text: Chinese
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 327
    Subjects:
      – SubjectFull: Breath tests
        Type: general
      – SubjectFull: Electronic data processing
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: Classification
        Type: general
      – SubjectFull: Signal processing
        Type: general
      – SubjectFull: Ion mobility spectroscopy
        Type: general
    Titles:
      – TitleFull: 用于 GC⁃IMS 的呼气分析数据处理方法.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: 马 睿
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            NameFull: 林建华
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            NameFull: 慕世龙
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            NameFull: 徐 陈
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            NameFull: 贾 建
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            NameFull: 何秀丽
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            NameFull: 高晓光
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 40
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            – TitleFull: Journal of Test & Measurement Technology
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