这项研究得到了普罗吉托·阿格尔−方达齐奥尼的支持。项目首字母缩略词小提琴 - 通过创新的分析工具（https://olivoeolio.progettoager.it/index.php/i-progetti-olio-e-olivo/violin-valorization-of-italian-olive-products-through-innovative-analytical-tools/violin-il-progetto）对意大利橄榄产品进行评估。GC 图像软件可免费试用，供希望演示和测试协议的读者使用。

1. 导入原始数据
注意:这创建了用于可视化和处理的二维流氓阵列。
<ol><li>启动图像软件。</li>
	<li>选择 文件|进口:导航到并选择GC×GC-TOF MS 系统获得的原始数据文件，名为"VIOLIN 101.lsc"（补充文件 1）:然后，单击 "打开"。色谱图在此软件中打开。 注:原始数据文件格式取决于仪器制造商。该软件导入用户指南中列出的各种文件格式。</li>
	<li>在导入对话中，将调制期（PM）设置为3.5 s:然后，单击"确定"。 注:某些收购软件可能无法记录调制期。</li>
	<li>选择 文件|保存图像为:导航到所需的文件夹;输入名称"石油1 RAW.gci"（补充文件2）:然后，单击 "保存"。</li>
</ol>2. 改变调制阶段
注意:这使每个调制周期中的所有峰值都进入相同的图像列，包括环绕调制期末端的峰值，进入下一个调制周期20的空隙时间。
<ol><li>选择处理|换档阶段。</li>
	<li>在 移位阶段 对话中，将 移位量 设置为-0.8:然后，单击 "确定"。</li>
</ol>3. 基线修正21
<ol><li>选择 图形|绘制矩形。</li>
	<li>单击并拖动以在未检测到峰值的图像中绘制矩形。</li>
	<li>选择 工具|可视化数据:注意探测器信号的平均值和标准偏差，此处为 21.850 ± 1.455 SD 无单位数字号码 （DN）:然后，关闭工具。</li>
	<li>选择 处理|正确的基线。</li>
</ol>4. 使用值图和彩色地图着色色图20
<ol><li>选择 视图|着色。</li>
	<li>在着色对话框中，选择进出口选项卡:选择作为补充材料提供的#AAAA（补充文件3）自定义彩色地图:然后，单击"导入"。</li>
	<li>在 值映射 控件上，将值范围设置为最低值和最大值:然后，单击 "确定"。</li>
</ol>5. 分析18的2D 峰值（即斑点）检测
<ol><li>选择 处理|检测 具有默认设置的Blobs;然后，观察一些峰值被分割，并有虚假的检测。</li>
	<li>选择 配置|设置|斑点检测:然后将第一个维度的 平滑度 设置为 0.1，第二个维度设置为 2.0，并将 最小音量 （即汇总值阈值）设置为 1.00 E6:然后，单击 "确定"。</li>
	<li>选择 处理|用新设置检测斑点 ;然后，观察改进。</li>
</ol>6. 2D 峰值过滤
注意:这样做是为了自动删除由于沿 1D 的列出血和沿 2D 的罢工或尾矿而导致的毫无意义的检测。
<ol><li>选择 处理|交互式斑点检测。</li>
	<li>注意斑点检测设置;然后，单击 "检测"。</li>
	<li>在 高级筛选器 生成器中，单击 "添加":然后， 在新的约束 对话中，选择 保留 II:然后，单击 "确定"。</li>
	<li>在 约束 滑块中，为过滤器设置最小和最长 2D 保留时间，以减少错误峰值的数量，而不会丢失真正的峰值。</li>
	<li>单击 "应用":然后，单击 "是" 以使用新筛选器保存到检测设置。 注:可能需要更先进的工具来处理特定的检测问题，如离子峰值检测或共排19的分解。</li>
</ol>7. 线性保留指数校准
注意:执行此步骤22 （IT），针对保留指标 （RI） 标准（通常 为 n-alkanes）的特定保留时间。
<ol><li>选择配置|RI 表|保留索引（第一号上校）。</li>
	<li>在RI 表配置对话框上，单击"导入":然后，选择名为"LRI表.csv"的RI校准文件（以CSV格式命名、保留时间和保留索引）- （补充文件4）。</li>
	<li>选择 文件|保存图像 A.导航到所需的文件夹;输入名称"石油 1 LRI 校准.gci" （补充文件 5）:然后，单击 "保存"。</li>
</ol>8. 在 NIST17 MS 库23中搜索峰值光谱
<ol><li>选择 配置|设置|搜索库。</li>
	<li>在 搜索库 对话中，将 频谱类型 设置为峰值 MS， 强度阈值 设置为 100，NIST 搜索类型 设置为简单（相似性 ），NIST RI 列类型 设置为标准极性 ，NIST RI 阈值 设置为 10:然后，单击 "确定"。NIST MS 搜索提供许多其他设置，这些设置设置为此处的默认值。</li>
	<li>选择 处理|搜索库中的所有斑点。</li>
</ol>9. 审查和更正分析身份
<ol><li>在工具调色板上，将光标模式设置为 Blob |选择斑点。</li>
	<li>在 图像 视图中，右键单击所需的峰值。</li>
	<li>在 Blob属性 对话中，检查斑点属性;然后，单击 "命中列表"。</li>
	<li>检查命中列表;然后，如果标识不正确，请选择正确标识旁边的复选标记。</li>
	<li>在 Blob 属性 对话中，输入 组名 以指定化学类和任何其他所需的元数据:然后，单击 "确定"。</li>
	<li>选择 文件|保存图像为:导航到所需的文件夹;输入名称"用于模板构造的油 1 颜色化。gci" （补充文件 6）:然后，单击 "保存"。 注:此文件包含在补充存档中，可为步骤 10 打开。</li>
</ol>10. 创建具有目标峰值15的模板
<ol><li>在"图像"视图中（仍处于步骤 9.1 中的"选择 Blobs" 模式），单击第一个峰值并 单击 附加峰值，选择所需的峰值。</li>
	<li>在工具调色板上，单击 "添加到模板 "按钮。</li>
	<li>模板完成后，选择 文件|保存模板:指定文件夹和文件名称;然后，单击 "保存"。</li>
	<li>选择 文件|关闭图像。 注意:此时，这些说明继续使用创建的模板，以包括所需的目标峰值，可作为"目标 tamplate.bt"（补充文件 7）。</li>
</ol>11. 匹配并应用模板
注意:匹配识别检测到的峰值中的模板模式，新色谱图。在模板中的新色度图中应用匹配的组标识和其他元数据。
<ol><li>选择 文件|打开图像:导航到并选择"油 2 颜色.gci" （补充文件 8） 色谱文件 （这是预先处理）:然后，单击 "打开"。</li>
	<li>在工具调色板上，将光标模式设置为 模板|选择对象。</li>
	<li>选择 模板|加载模板。</li>
	<li>在加载模板对话中，单击"浏览":导航到并选择目标峰值模板"目标 template.bt"（补充文件 7）:然后，单击"打开"。</li>
	<li>在负载模板对话中，单击"加载"，然后"取消"。</li>
	<li>在图像视图中，右键单击模板峰值:然后，检查其对象属性，包括qCLIC和参考MS。</li>
	<li>选择 模板|交互式匹配和转换模板。</li>
	<li>在 交互式匹配 界面中，单击 "匹配所有":然后，查看表中和图像中的匹配结果，其中每个模板峰值都标有未填充的圆圈，如果匹配，则检测到的峰值有一个填充圆的链接。</li>
	<li>根据需要编辑匹配项;满意时，单击 "应用" 将元数据从模板传输到色谱图。 注:匹配约束（如 qCLIC）有助于匹配新色谱图中检测到的峰值之间的正确模式。约束参数包括用作模板参考的 MS 签名类型（峰值 MS或blob MS）以及光谱相似性阈值（直接匹配因子 （DMF） 和反向匹配因子 （RMF）。在这里，参数设置基于以前的研究13，14，以限制虚假的负匹配:峰值MS和DMF和RMF相似性阈值700。</li>
</ol>12. 将模板转换为完全不同的色谱
注意:除非色谱条件发生重大变化，导致模板与新色谱失调，例如在长期研究中或安装新列后的情况，否则此步骤是没有必要的。在这种情况下，模板可以在色谱保留时间平面上几何形状，以更好地适应新的色谱图12，13。在此示例中，模板和色谱的峰值模式相似，但在保留时间几何形状上有所不同，例如在不同的色谱条件下可以看到。
<ol><li>重复步骤 11.2–11.5，但导航到、选择和加载目标模板 2.bt（补充文件 9）除外。</li>
	<li>选择 模板|交互式匹配模板:然后，单击 "编辑转换"。</li>
	<li>在 变换模板 界面中，更改 1D 和 2D 比例、翻译和剪切，以更好地使模板与检测到的峰值对齐:然后，单击 "转换模板"。</li>
	<li>使用已转换的模板，单击 "编辑匹配":然后，重复步骤11.8–11.9。</li>
</ol>13. 在一组色谱图上执行非目标和有针对性的综合分析
注:一个组合的非目标和目标 （UT） 模板，也称为功能模板24，25，当匹配到每组色谱图时，建立非目标分析与目标分析之间的对应关系，然后提取一致的交叉样本功能以进行模式识别。
<ol><li>对集中的所有色谱图（即 2 D 色度油图）执行预处理（步骤 1–6）和 UT模板匹配（步骤 11.1–11.9）。或者，用项目软件或类似软件自动启动此步骤，这里没有描述。</li>
	<li>启动调查器软件。</li>
	<li>选择文件|开放式分析:然后，选择并打开"功能乔夫苏70 eV.gca"（补充文件10）。</li>
	<li>单击 "确定 "以打开并检查结果。</li>
	<li>单击 化合物 选项卡，查看特定分析的公制值和统计数据（即具有相关化学名称的定向分析）或与 （#） 标识符对齐的所有色谱的非目标分析，然后执行以下步骤。<ol>
<li>单击 属性 选项卡，查看色谱图中特定指标的值和统计数据。</li>
		<li>单击 摘要 选项卡以查看化合物和特征的汇总统计。如果色谱图来自不同的类别，如本例中由在意大利两个不同地区收获的橄榄产生的油，则 摘要 选项卡列出了 Fisher 比率统计数据 （F 和 FDR），这些统计数据提供了对区分类特征的见解。</li>
		<li>查看所有选项卡上的各种图表，如果需要，在 属性 选项卡上执行主要组件分析 （PCA）。</li>
	</ol>
</li>
</ol>14. 修改UT模板以进行平行MS分析
注:分析同时使用70 eV和12 eV（即高低）电子电电化能量26、27进行。
<ol><li>打开 12 个 eV 色谱图之一，例如，"油 1 12 eV RAW.gci" （补充文件 11），执行预处理（步骤 1–6），并加载 UT 模板"UT 模板 70 relaxed.bt" （补充文件 12），如步骤 11.1–11.6 所述。文件作为补充材料提供。</li>
	<li>如有必要，调整模板以适应步骤 12 中描述的检测到的 12 个 eV 峰值。在这里，没有明显的不对齐，因为串联信号是多路复用的。但是，应当指出，由于不同的电离设置产生不同的碎片，因此有必要放松对 DMF 和 RMF 光谱相似性的 qCLIC 限制的限制（此处未显示）。</li>
	<li>选择 文件|保存模板:指定文件夹和文件名称，例如"UT 模板 12.bt"（补充文件 13）:然后，单击 "保存"。</li>
</ol>15. 在 12 个 eV 色谱图上执行非目标和定向综合分析
<ol><li>选择 文件|开放式分析:然后选择并打开"功能乔夫苏12 eV.gca"- 提供补充文件14 文件。</li>
	<li>单击 "确定 "以打开并检查结果。</li>
	<li>单击 化合物 选项卡以查看公制值，参考特定分析的 12 个 eV 响应和统计数据（即具有相关化学名称的靶向分析）或与 （#） 标识符对齐所有色谱的非目标分析，然后执行以下步骤。<ol>
<li>单击 属性 选项卡，查看色谱图中特定指标的值和统计数据。</li>
		<li>单击 摘要 选项卡，查看 12 eV 化合物和功能的汇总统计。如果色谱图来自不同的类别，如本例中由在意大利两个不同地区收获的橄榄产生的油，则摘要选项卡列出了 Fisher 比率统计数据 （F 和 FDR），这些统计数据提供了对区分类特征的见解。</li>
		<li>查看所有选项卡上可用的各种图表，并在必要时在属性选项卡上执行主要组件分析 （PCA）。</li>
	</ol>
</li>
</ol>

<ol><li>Tranchida, P. Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Potential+of+comprehensive+chromatography+in+food+analysis%5BTitle%5D)+AND+%22Trends+in+Analytical+Chemistry%22%5BJournal%5D)">Potential of comprehensive chromatography in food analysis.</a> Trends in Analytical Chemistry. 52, 186-205 (2013).</li>
<li>Cordero, C., Kiefl, J., Reichenbach, S. E., Bicchi, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Characterization+of+odorant+patterns+by+comprehensive+two-dimensional+gas+chromatography%3A+A+challenge+in+omic+studies%5BTitle%5D)+AND+%22Trends+in+Analytical+Chemistry%22%5BJournal%5D)">Characterization of odorant patterns by comprehensive two-dimensional gas chromatography: A challenge in omic studies.</a> Trends in Analytical Chemistry. 113, 364-378 (2019).</li>
<li>Cordero, C., Kiefl, J., Schieberle, P., Reichenbach, S. E., Bicchi, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comprehensive+two-dimensional+gas+chromatography+and+food+sensory+properties%3A+potential+and+challenges%5BTitle%5D)+AND+%22Analytical+and+Bioanalytical+Chemistry%22%5BJournal%5D)">Comprehensive two-dimensional gas chromatography and food sensory properties: potential and challenges.</a> Analytical and Bioanalytical Chemistry. 407, 169-191 (2015).</li>
<li>Adahchour, M., Beens, J., Vreuls, R. J. J., Brinkman, U. A. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Recent+developments+in+comprehensive+two-dimensional+gas+chromatography+%28GC+%C3%97+GC%29+-+Introduction+and+instrumental+set-up%5BTitle%5D)+AND+%22Trends+in+Analytical+Chemistry%22%5BJournal%5D)">Recent developments in comprehensive two-dimensional gas chromatography (GC × GC) - Introduction and instrumental set-up.</a> Trends in Analytical Chemistry. 25 (5), 438-454 (2006).</li>
<li>Prebihalo, S. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multidimensional+gas+chromatography%3A+Advances+in+instrumentation%2C+chemometrics%2C+and+applications%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">Multidimensional gas chromatography: Advances in instrumentation, chemometrics, and applications.</a> Analytical Chemistry. 90 (1), 505-532 (2018).</li>
<li>Cordero, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Profiling+food+volatiles+by+comprehensive+two-dimensional+gas+chromatography+coupled+with+mass+spectrometry%3A+advanced+fingerprinting+approaches+for+comparative+analysis+of+the+volatile+fraction+of+roasted+hazelnuts+%28Corylus+Avellana+L.%29+from+different+origins%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Profiling food volatiles by comprehensive two-dimensional gas chromatography coupled with mass spectrometry: advanced fingerprinting approaches for comparative analysis of the volatile fraction of roasted hazelnuts (Corylus Avellana L.) from different origins.</a> Journal of Chromatography A. 1217, 5848-5858 (2010).</li>
<li>Cordero, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Targeted+and+non-targeted+approaches+for+complex+natural+sample+profiling+by+GC%C3%97GC-QMS%5BTitle%5D)+AND+%22Journal+of+Chromatography+Sciences%22%5BJournal%5D)">Targeted and non-targeted approaches for complex natural sample profiling by GC×GC-QMS.</a> Journal of Chromatography Sciences. 48 (4), 251-261 (2010).</li>
<li>Maio, D., Maltoni, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Direct+gray-scale+minutiae+detection+in+fingerprints%5BTitle%5D)+AND+%22IEEE+Transactions+on+Pattern+Analysis+and+Machine+Intelligence%22%5BJournal%5D)">Direct gray-scale minutiae detection in fingerprints.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 19 (1), 27-40 (1997).</li>
<li>Jain, A. K., Hong, L., Pankanti, S., Bolle, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+identity-authentication+system+using+fingerprints%5BTitle%5D)+AND+%22Proceedings+of+IEEE%22%5BJournal%5D)">An identity-authentication system using fingerprints.</a> Proceedings of IEEE. 85 (9), 1365-1388 (1997).</li>
<li>Parsons, B. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Tile-based+fisher+ratio+analysis+of+comprehensive+two-dimensional+gas+chromatography+time-of-flight+mass+spectrometry+%28GC+%C3%97+GC-TOFMS%29+data+using+a+null+distribution+approach%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">Tile-based fisher ratio analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) data using a null distribution approach.</a> Analytical Chemistry. 87 (7), 3812-3819 (2015).</li>
<li>Pierce, K. M., Kehimkar, B., Marney, L. C., Hoggard, J. C., Synovec, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Review+of+chemometric+analysis+techniques+for+comprehensive+two+dimensional+separations+data%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Review of chemometric analysis techniques for comprehensive two dimensional separations data.</a> Journal of Chromatography A. 1255, 3-11 (2012).</li>
<li>Reichenbach, S. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Alignment+for+comprehensive+two-dimensional+gas+chromatography+with+dual+secondary+columns+and+detectors%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">Alignment for comprehensive two-dimensional gas chromatography with dual secondary columns and detectors.</a> Analytical Chemistry. 87 (19), 10056-10063 (2015).</li>
<li>Rempe, D. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Effectiveness+of+global%2C+low-degree+polynomial+transformations+for+GCxGC+data+alignment%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">Effectiveness of global, low-degree polynomial transformations for GCxGC data alignment.</a> Analytical Chemistry. 88 (20), 10028-10035 (2016).</li>
<li>Stilo, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Untargeted+and+targeted+fingerprinting+of+extra+virgin+olive+oil+volatiles+by+comprehensive+two-dimensional+gas+chromatography+with+mass+spectrometry%3A+challenges+in+long-term+studies%5BTitle%5D)+AND+%22Journal+of+Agricultural+and+Food+Chemistry%22%5BJournal%5D)">Untargeted and targeted fingerprinting of extra virgin olive oil volatiles by comprehensive two-dimensional gas chromatography with mass spectrometry: challenges in long-term studies.</a> Journal of Agricultural and Food Chemistry. 67 (18), 5289-5302 (2019).</li>
<li>Reichenbach, S. E., Carr, P. W., Stoll, D. R., Tao, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Smart+templates+for+peak+pattern+matching+with+comprehensive+two-dimensional+liquid+chromatography%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Smart templates for peak pattern matching with comprehensive two-dimensional liquid chromatography.</a> Journal of Chromatography A. 1216 (16), 3458-3466 (2009).</li>
<li>Reichenbach, S. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Informatics+for+cross-sample+analysis+with+comprehensive+two-dimensional+gas+chromatography+and+high-resolution+mass+spectrometry+%28GCxGC-HRMS%29%5BTitle%5D)+AND+%22Talanta%22%5BJournal%5D)">Informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography and high-resolution mass spectrometry (GCxGC-HRMS).</a> Talanta. 83 (4), 1279-1288 (2011).</li>
<li>Reichenbach, S. E., Tian, X., Cordero, C., Tao, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Features+for+non-targeted+cross-sample+analysis+with+comprehensive+two-dimensional+chromatography%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Features for non-targeted cross-sample analysis with comprehensive two-dimensional chromatography.</a> Journal of Chromatography A. 1226, 140-148 (2012).</li>
<li>Latha, I., Reichenbach, S. E., Tao, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comparative+analysis+of+peak-detection+techniques+for+comprehensive+two-dimensional+chromatography%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Comparative analysis of peak-detection techniques for comprehensive two-dimensional chromatography.</a> Journal of Chromatography A. 1218 (38), 6792-6798 (2011).</li>
<li>Reichenbach, S. E., Tao, Q., Cordero, C., Bicchi, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+data-challenge+case+study+of+analyte+detection+and+identification+with+comprehensive+two-dimensional+gas+chromatography+with+mass+spectrometry+%28GC%C3%97GC-MS%29%5BTitle%5D)+AND+%22Separations%22%5BJournal%5D)">A data-challenge case study of analyte detection and identification with comprehensive two-dimensional gas chromatography with mass spectrometry (GC×GC-MS).</a> Separations. 6 (3), 38(2019).</li>
<li>Reichenbach, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Chapter+4+Data+Acquisition%2C+Visualization%2C+and+Analysis%5BTitle%5D)+AND+%22Comprehensive+Analytical+Chemistry%22%5BJournal%5D)">Chapter 4 Data Acquisition, Visualization, and Analysis.</a> Comprehensive Analytical Chemistry. , 77-106 (2009).</li>
<li>Reichenbach, S. E., Ni, M., Zhang, D., Ledford, E. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Image+background+removal+in+comprehensive+two-dimensional+gas+chromatography%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Image background removal in comprehensive two-dimensional gas chromatography.</a> Journal of Chromatography A. 985 (1-2), 47-56 (2003).</li>
<li>Kratz, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Generalization+of+the+retention+index+system+including+linear+temperature+programmed+gas-liquid+partition+chromatography%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">A Generalization of the retention index system including linear temperature programmed gas-liquid partition chromatography.</a> Journal of Chromatography A. 11, 463-471 (1963).</li>
<li>NIST Mass Spectrometry Data Center. NIST Standard Reference Database 1A: NIST/EPA/NIH Mass Spectral Library (NIST 08) and NIST Mass Spectral Search Program (Version 2.0f). <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22National+Institute+of+Standards+and+Technology+%28NIST%29%22">National Institute of Standards and Technology (NIST)</a>. , Gaithersburg MD. (2005).</li>
<li>Magagna, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Combined+untargeted+and+targeted+fingerprinting+with+comprehensive+two-dimensional+chromatography+for+volatiles+and+ripening+indicators+in+olive+oil%5BTitle%5D)+AND+%22Analytica+Chimica+Acta%22%5BJournal%5D)">Combined untargeted and targeted fingerprinting with comprehensive two-dimensional chromatography for volatiles and ripening indicators in olive oil.</a> Analytica Chimica Acta. 936, 245-258 (2016).</li>
<li>Reichenbach, S. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Benchmarking+machine+learning+methods+for+comprehensive+chemical+fingerprinting+and+pattern+recognition%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Benchmarking machine learning methods for comprehensive chemical fingerprinting and pattern recognition.</a> Journal of Chromatography A. 1595, 158-167 (2019).</li>
<li>Cialiè Rosso, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Adding+extra-dimensions+to+hazelnuts+primary+metabolome+fingerprinting+by+comprehensive+two-dimensional+gas+chromatography+combined+with+time-of-flight+mass+spectrometry+featuring+tandem+ionization%3A+insights+on+the+aroma+potential%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Adding extra-dimensions to hazelnuts primary metabolome fingerprinting by comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry featuring tandem ionization: insights on the aroma potential.</a> Journal of Chromatography A. 1614 (460739), 1-11 (2020).</li>
<li>Cordero, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comprehensive+two-dimensional+gas+chromatography+coupled+with+time+of+flight+mass+spectrometry+featuring+tandem+ionization%3A+challenges+and+opportunities+for+accurate+fingerprinting+studies%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Comprehensive two-dimensional gas chromatography coupled with time of flight mass spectrometry featuring tandem ionization: challenges and opportunities for accurate fingerprinting studies.</a> Journal of Chromatography A. 1597, 132-141 (2019).</li>
<li>Ni, M., Reichenbach, S. E., Visvanathan, A., TerMaat, J., Ledford, E. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Peak+pattern+variations+related+to+comprehensive+two-dimensional+gas+chromatography+acquisition%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Peak pattern variations related to comprehensive two-dimensional gas chromatography acquisition.</a> Journal of Chromatography A. 1086, 165-170 (2005).</li>
<li>Reichenbach, S. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Reliable+peak+selection+for+multisample+analysis+with+comprehensive+two-dimensional+chromatography%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">Reliable peak selection for multisample analysis with comprehensive two-dimensional chromatography.</a> Analytical Chemistry. 85 (10), 4974-4981 (2013).</li>
<li>Markes International. Select-EV: The next Generation of Ion Source Technology. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Technical+Note%22">Technical Note</a>. , (2016).</li>
<li>Tao, Q., Reichenbach, S. E., Heble, C., Wu, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((New+investigator+tools+for+finding+unique+and+common+components+in+multiple+samples+with+comprehensive+two-dimensional+chromatography%5BTitle%5D)+AND+%22Chromatography+Today%22%5BJournal%5D)">New investigator tools for finding unique and common components in multiple samples with comprehensive two-dimensional chromatography.</a> Chromatography Today. , February-March (2018).</li>
<li>Seeley, J. V., Seeley, S. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multidimensional+gas+chromatography%3A+fundamental+advances+and+new+applications%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">Multidimensional gas chromatography: fundamental advances and new applications.</a> Analytical Chemistry. 85 (2), 557-578 (2013).</li>
<li>Tranchida, P. Q., Aloisi, I., Giocastro, B., Mondello, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Current+state+of+comprehensive+two-dimensional+gas+chromatography-mass+spectrometry+with+focus+on+processes+of+ionization%5BTitle%5D)+AND+%22Trends+in+Analytical+Chemistry%22%5BJournal%5D)">Current state of comprehensive two-dimensional gas chromatography-mass spectrometry with focus on processes of ionization.</a> Trends in Analytical Chemistry. 105, 360-366 (2018).</li>
</ol>

斯蒂芬·雷琴巴赫教授和陶庆平博士在GC图像有限责任公司有经济利益。丹妮拉·佩罗尼博士是SRA仪器公司的员工，SRA仪器是GC图像在意大利和法国的分销商。费德里科·斯蒂洛博士、基亚拉·科德罗教授和卡洛·比奇教授宣布没有利益冲突。

GC×GC-TOF MS 数据的可视化是适当理解全面二维分离所取得成果的基本步骤。具有自定义着色的图像图使分析人员能够欣赏探测器响应差异，从而欣赏样本组件的差异分布。这种视觉方法完全改变了分析师对色谱图的解释和阐述的看法。第一步，一旦被色谱学家理解并自信地使用，为进一步加工开辟了新的视角。
数据处理的另一个基本方面是所有样本点的完整数据矩阵（即 MS 光谱数据和响应）的可访问性，每个样本点都与单个探测器事件相对应。在这方面，2D峰值集成，使探测器事件的集合对应于单一分析特代表关键步骤。在当前协议中，2D 峰值检测基于分水岭算法18，包括一些适应，以提高检测灵敏度，以防部分共发化合物。为了使这一进程更加具体，必须进行权力下放，并采用更复杂的程序。这可以通过对MS数据执行离子峰值检测:该算法处理数据阵列，并根据光谱配置文件19，31分离单一分析器的反应。
协议的一个重要但关键的步骤，以及任何GC×GC-MS数据解释过程，都与分析数据识别有关。这一程序在第8和第9阶段提出，如果没有具有真实标准的确认性分析，必须由分析员认真进行。任何商业软件中都有自动操作;它们包括针对收集的参考光谱（即光谱库）的MS光谱签名相似性评价，以及对限定/定量电量离子中特征比率的评价。然而，需要附加的确认标准来消除异位素的识别。该协议建议采用线性保留索引，以优先确定候选人名单的优先级:此处的限制与保留数据的可用性及其一致性有关。
使这种方法独特的主要特点是模板匹配12，13，15，29。模板匹配以非常有效、具体和直观的方式实现2D 模式识别。可以通过应用自定义阈值和/或约束功能来设置，而分析员可以通过与转换函数参数积极交互来监督程序。这一过程的特殊性取决于在统一批次的样品之间以及在具有相同名义条件下获得的样品之间交叉对齐目标和非目标峰值信息的可能性，尽管存在中到严重的不对齐。此操作的优点涉及保留所有有针对性的分析数据识别的可能性，这对分析员来说是一项耗时的任务，并且所有元数据都保存在以前阐述会话中的目标和非目标峰值。
模板匹配在计算时间方面也非常有效:低分辨率 MS 数据文件由大约 1=2 Gb 的外包装数据组成，而高分辨率 MS 分析可能达到每单次分析运行 10=15 Gb。模板匹配并不是每次都处理完整的数据矩阵，但首先，使用模板峰值执行色谱图之间的保留时间对齐，然后在搜索窗口内处理候选峰值，以获得与模板中引用的相似性匹配。在发生严重错位、最具挑战性的情况时，全球二阶多声变换比本地方法表现更好，同时缩短了计算时间13。
GC×GC技术要广泛传播到学术界和研究实验室之外，数据处理工具必须便于可视化和色谱检查的基本操作:分析人员的识别应提供采用标准化算法和程序（例如，NIST搜索算法和 IT 校准）的可能性:交叉比较分析应直观、有效且由交互式工具支持。建议的方法解决了这些需求，同时提供了处理复杂情况的高级选项和工具，如分析联发性、多分析校准、组型分析和并行检测对齐。
参考文献很好地涵盖了GC×GC以及更普遍的全面二维色谱学的许多可能情况，提供了独特的解决方案和可靠的结果，而1D色谱学在单次运行分析中无法实现这些解决方案和可靠结果。5、32、33 虽然 GC ×GC 是提高分离能力和灵敏度的最强大工具，但分离功率、灵敏度和其他系统容量始终存在局限性。随着这些系统性限制的接近，数据分析变得越来越困难。因此，研究和开发必须继续改进我们掌握的分析工具。

综合二维气相色谱学与飞行时间质谱检测（GC×GC-TOF MS）相结合，是目前对复杂样品1、2、3、4、5的化学特征最翔实的分析方法。在 GC 中×GC 中，列由调制器（例如热或基于阀门的对焦接口）串联和接口，该调节器在将组件从第一维度（1D） 列捕获，然后再重新注入第二维度（2D） 列。此操作在固定调制时间段（PM）内完成，一般在 0.5–8 s 之间。通过热调节，该过程包括低温捕获和对焦的流光带，对整体分离功率有一定的好处。
虽然 GC ×GC 是一种二维分离技术，但该过程会产生顺序数据值。探测器模拟数字转换器（A/D）以一定频率获得色谱信号输出。然后，数据以特定的专有格式存储，不仅包含数字化数据，还包含相关元数据（有关数据的信息）。GC×GC 系统中采用的 A/D 转换器有助于将色谱信号的强度映射到数字数字 （DN） 中，作为两个分析维度中的时间函数。单通道探测器（如火焰电阻探测器 （FID）、电子捕获探测器 （ECD）、硫化学发光探测器 （SCD） 等）每次采样时间产生单个值，而多通道探测器（例如质谱探测器 （MS））在分析运行中每个采样时间产生多个值（通常超过光谱范围）。
要可视化 2D 数据，详细阐述首先将单个调制周期（或周期）数据值的 raster 化为像素列（与探测器事件相对应的图片元素）。沿着定向（Y轴，从下到上）可视化 2D分离时间。像素列按顺序处理，以便腹肌（X轴，从左到右）报告 1D分离时间。此命令在右手笛卡尔坐标系中显示 2D 数据 ，1D 保留通常作为进入阵列的第一个索引。
2D 色谱图的数据处理使访问的信息水平高于原始数据，从而实现2D 峰值检测、峰值识别、提取响应数据进行定量分析和交叉比较分析。
2D 峰值模式可被视为样品的独特指纹，检测到的化合物可作为细枝末节特征，以便进行有效的交叉比较分析。这种方法，被称为基于模板的指纹6，7，灵感来自生物识别指纹6。事实上，自动生物识别指纹验证系统依赖于独特的指尖特征:脊裂和末端，本地化，并从墨迹或详细图像中提取。这些特征，称为细枝末节功能，然后与可用的存储模板8，9交叉匹配。
如上所述，每个GC×GC分离模式由 2D峰值组成，合理分布在二维平面上。每个峰值对应于单个分析，具有其信息潜力，可视为比较模式分析的单一特征。
在这里，我们提出了一个有效的化学指纹方法，由GC×GC-TOFMS具有串联电电。目标是从一组色谱图中全面和定量地对特征进行编目。
与现有的商业软件或内部程序10，11采用峰值功能方法相比，基于模板的指纹识别的特点是高特异性、高效率和有限的计算时间。此外，它有一个内在的灵活性，使小数功能（即2D峰值）之间的交叉对齐严重不对齐的色谱图作为那些由不同的仪器或长期框架研究12，13，14获得。
简要介绍了建议方法的基本操作，引导读者对 二维模式的复杂性和信息能力有一个良好的理解。然后，通过探索仪器输出数据矩阵，在二维空间上进行化学识别并识别目标分析。然后构建目标峰值的模板并应用于在同一分析批次中获得的一系列色谱图。与保留时间、光谱签名和响应（绝对和相对）相关的元数据从目标峰值的重新对齐模式中提取，并用于揭示样本集的组成差异。
作为该过程的一个额外、独特的步骤，还将在预先瞄准的色谱图上进行非目标和有针对性的 （UT） 指纹组合，以将指纹潜力扩展到已知和未知的分析。该过程生成一个UT模板，用于真正全面的比较分析，基本上可以自动化。
作为最后一步，该方法在两个平行探测器信号中执行特征的交叉对齐，这两个信号产生于高低电子电离能量（70 和 12 eV）。
该协议在支持单色谱或一组色谱分析以及可变色谱和/或多个探测器方面相当灵活。在这里，协议通过市售的 GC ×GC 软件套件（参见材料表）与 MS 库和搜索软件（参见材料表）相结合进行演示。其他软件中也有一些必要的工具，类似的工具可以独立于Reichenbach和同事15、16、17、18、19的文献描述来实施。演示的原始数据来自在作者的实验室14进行的特级初榨橄榄（EVO）油的研究。特别是，意大利EVO油的挥发性部分（即挥发性）通过头部空间固体相微缩（HS-SPME）进行采样，由GC×GC-TOF MS进行分析，以捕获诊断指纹，获得样品的质量和感官资格。材料表中提供了样品、取样条件和分析设置的详细信息。
步骤 1–6 描述色谱图的预处理。步骤 7–9 描述单个色谱图的处理和分析。步骤 10–12 描述模板创建和匹配，这是交叉样本分析的基础。步骤 13–16 描述在一组色谱图中应用协议，步骤 14–16 用于 UT 分析。

<table><tbody><tr><td>&lt;sup&gt;1&lt;/sup&gt;D SolGel-Wax column (100% polyethylene glycol; 30 m &amp;times; 0.25 mm dc &amp;times; 0.25 &amp;mu;m df). Carrier gas helium at a constant nominal flow of 1.3 mL/min.</td><td>Trajan SGE Analytical Science, Ringwood, Australia</td><td>PN 054796</td><td>Carrier gas helium at a constant nominal flow of 1.3 mL/min. Oven temperature programming set as follows: 40&amp;deg;C (2 min) to 240&amp;deg;C (10 min) at 3.5&amp;deg;C/min.</td></tr><tr><td>&lt;sup&gt;2&lt;/sup&gt;D OV1701 column (86% polydimethylsiloxane, 7% phenyl, 7% cyanopropyl; 1 m &amp;times; 0.1 mm dc &amp;times; 0.10 &amp;mu;m df) from .</td><td>Mega, Legnano, Milan, Italy</td><td>PN MEGA-1701</td><td></td></tr><tr><td>Automated system for sample preparation: SPR Autosampler for GC</td><td>SepSolve-Analytical, Llantrisant, UK</td><td></td><td></td></tr><tr><td>Extra Virgin Olive oils: Sicily and Tuscany, Italy</td><td>Project VIOLIN (Ager - Fondazioni in rete per la ricerca agroalimentare)</td><td></td><td>Samples (n=10) were collected during the production year 2018 within the &quot;Violin&quot; project sampling campaign. Oils were submitted to HS-SPME to sample volatiles according to a reference protocol validated in a previous study of Stilo et al.14</td></tr><tr><td>Gas chromatograph: Model 7890B GC</td><td>Agilent Technologies Wilmington DE, USA</td><td></td><td></td></tr><tr><td>GC Image GC&amp;times;GC edition V 2.9</td><td>GC Image LLC, Lincoln, Nebraska</td><td></td><td>https://www.gcimage.com/gcxgc/trial.html</td></tr><tr><td>Image processing software</td><td>GC Image LLC, Lincoln, Nebraska</td><td></td><td>https://www.gcimage.com/gcxgc/trial.html</td></tr><tr><td>Mass spectrometer: BenchTOF-Select</td><td>Markes International Llantrisant, UK</td><td></td><td></td></tr><tr><td>Methyl-2-octynoate (CAS 111-12-6)</td><td>Merck-Millipore/Supelco</td><td>PN: 68982</td><td></td></tr><tr><td>Modulator controller: Optimode v2.0</td><td>SRA Intruments, Cernusco sul Naviglio, Milan, Italy</td><td></td><td></td></tr><tr><td>Modulator: KT 2004 loop type</td><td>Zoex Corporation Houston, TX, USA</td><td></td><td></td></tr><tr><td>MS library and search software: NIST Library V 2017, Software V 2.3</td><td>National Institute of Standards and Technology (NIST), Gaithersburg MD</td><td></td><td>https://www.nist.gov/srd/nist-standard-reference-database-1a-v17</td></tr><tr><td>&lt;em&gt;n&lt;/em&gt;-alkanes C8-C40 for retention indexing</td><td>Merck-Millipore/Supelco</td><td>PN: 40147-U</td><td></td></tr><tr><td>&lt;em&gt;n&lt;/em&gt;-hexane (CAS 110-54-3) gas chromatography MS SupraSolv</td><td>Merck-Millipore/Supelco</td><td>PN: 100795</td><td></td></tr><tr><td>Solid Phase Microextraction fiber</td><td>Merck-Millipore/Supelco</td><td>PN 57914-U</td><td></td></tr><tr><td>&amp;alpha;- /&amp;beta;-thujone (CAS 546-80-5)</td><td>Merck-Millipore/Sigma Aldrich</td><td>PN: 04314</td><td></td></tr></tbody></table>

chromatographic fingerprinting by template matching for data collected by comprehensive two-dimensional gas chromatography

数据处理和评价是综合二维气相色谱 （GCxGC） 的关键步骤，尤其是在与质谱学相结合时。数据中加密的丰富信息可能非常有价值，但难以有效访问。数据密度和复杂性可能导致长时间的阐述时间，并且需要费力的、依赖分析师的程序。因此，有效但可访问的数据处理工具是使这种先进的多维技术在实验室中推广和接受供日常使用的关键。本作品中提供的数据分析协议使用色谱指纹和模板匹配，以实现将复杂的二维色谱高度自动解构为单个化学特征的目标，从而高级识别单个色谱图和跨色谱图中的信息模式。该协议提供高一致性和可靠性，很少干预。同时，分析员的监督可以在各种设置和约束功能中进行，这些设置和约束功能可以定制，以提供灵活性和能力来适应不同的需求和目标。模板匹配在这里显示是一个强大的方法来探索特级初榨橄榄油挥发性。山峰的交叉对齐不仅针对已知目标，也针对非目标化合物，这显著提高了各种应用的表征功率。举例证明在类似条件下分析的样本集对色谱模式进行分类和比较的性能。

GC×GC-TOF MS 优质特级初榨橄榄油伏洛美模式显示约 500 2D 峰值高于 100 的信号与噪声比 （SNR） 阈值。先前对食物挥发性14、27的调查将这一阈值定义为超过阈值的最低相对信号，以获得可靠的光谱进行交叉比较分析。组件根据两个色谱维度的相对保留分布在色谱空间中，并特别根据其在 1D 中的波动性/极性和2D 中的波动性进行分布。在这里，列组合是极性×半极（即卡博瓦克斯20米×OV1701）。
2D 模式显示高度的顺序。同源系列和类的相对保留模式显示在图1A中，注释（组图形和峰值气泡图）用于线性饱和碳氢化合物（黑色）、不饱和碳氢化合物（黄色）， 线性饱和醛（蓝色）、单不饱和醛（红色）、多不饱和醛（鲑鱼）、原醇（绿色）和短链脂肪酸（氰酸）。
然后，通过比较从整个2D 峰值（blob频谱） 或最大频谱 （顶点频谱） 中提取的平均 MS 频谱，可以识别检测到的2D 峰值。图 2说明了顶点频谱搜索 blob 5 的输出，并为（E）-2-hexenal 返回了高相似性匹配（前 10 次命中）。所探索的数据库是分析员在方法的第 8 步中预先选择的数据库。
通过主动保留索引验证标识。实验性 IT值是针对2D 峰值计算的，因此在现阶段，库搜索会优先考虑具有表化IT的连贯值的结果。可根据分析员经验、根据静止阶段对参考数据库值的可靠性以及应用的分析条件定制公差窗口。雷琴巴赫等人最近开发并讨论了无需与n-alkanes进行实验校准的线性保留指数的智能校准新工具。
可采用已识别的 2D 峰值（即目标峰值）的集合来构建目标峰值模板，以便迅速在所有样本色谱图上建立同一化合物之间的可靠对应关系。目标模板峰值的集合可视化为 图 1B。红圈对应于 196 种目标化合物，包括两个与连接线模板峰值相连的内部标准 （IS）。IS 用于响应规范化，连接线有助于可视化所包含的 IS 中的哪一个，以使每个 2D 峰值/blob 响应正常化。
在图 1B中，填充圆表示模板峰值与实际模式之间的正匹配，而空圆表示未验证通信的模板峰值。假负匹配可以通过适当选择阈值参数、参考光谱和约束功能 13、14、18、19 来加以限制。对于具有多个共发的复杂模式，基于光谱去卷积的离子峰值检测功能是可取的，并且可能是一个有效的选项19。模板峰值元数据显示在图 1B的放大面板中，用于（E）-2-六位体。
模板匹配的特殊性依赖于应用约束功能的可能性，这些约束功能限制了与属于算法搜索窗口、MS 光谱相似性超过一定阈值的考生峰值的正对应函。在这种情况下，在第11步，相似性阈值23被设置为700根据以前的实验旨在定义最佳参数限制假负匹配14。图 1B中模板峰值属性的突出区域显示有关参考 MS 频谱字符串和 qCLIC 约束功能（即匹配（"匹配"（"<ms>"） >= 700.0）和（RMatch（"<ms>"）>= 700.0）的信息。
通过将模板应用于一组的所有色谱图，人们可能会遇到具有挑战性的情况，例如模式部分不对齐的情况。这可能是由于烤箱温度不一致，载体气体流动/压力不稳定，或由于手动干预系统的情况下，列替代或调制器环毛细线更换14，28。图3显示了目标模板与实际色谱图之间部分不对齐的情况。对于最小的不对齐，交互式模板转换（图 3，控制面板）可以重新定位模板峰值，以便更好地配合。重新定位后，模板可以匹配以建立通信。在示例中，模板（图 3，步骤 12）峰值与实际的 2D 模式正确匹配。如果出现严重错位，这里没有讨论，重复匹配转换更新操作可以反复调整模板峰值位置，以实际峰值模式12，13，14。
在这里，目标峰（即已知分析）提供约40%的色谱结果（平均约500个可探测峰的196个目标峰）。其他60%的化合物，连同它们带来的信息，在有针对性的分析中没有考虑到。为使调查真正全面，还应建立非目标 2D峰值的一致交叉对齐。模板匹配扩展到所有可检测的分析人员处理烤咖啡7的复杂伏拉托姆的第一个应用程序。此过程使用软件（例如，调查员）实现自动化，此处以步骤 14-15 显示。
在此过程中，通过交叉匹配所有图像模式29来定义可靠的峰值，用于定义所研究样本（20 个样本）的预先定位的图像。随后，构建了复合色谱图，从中可以识别 UT 可靠的峰值和峰值区域（即 2D 峰值足迹）在所谓的功能模板17中。
对于在 70 eV 获得的分析，该过程确定了 144 个可靠峰值，可靠性宽松29个，其中 76 个属于目标峰值列表。基于这 144 个可靠的峰值，该过程将所有色谱图与可靠峰值的平均保留时间一致，然后将其组合在一起创建复合色谱图。 图4 显示了根据石油（左）生产区域标记的所有样品的列表，以及每个样品（右）中可靠的峰值/blob体积列表。
非目标功能模板由合成色度图中检测到的分析中的2个 D 峰值组成，这些峰值与可靠的峰值模板（n = 168 - 目标峰值的红色圆圈和非目标峰值的绿色圆圈）相匹配。复合峰的质谱及其保留时间记录在扩大区域中（Z） -3-六酚醋酸的特征模板中。峰值区域以红色图形显示在图 5B中:相反，它们是由复合色谱图（n = 3578）中检测到的所有2D 峰值的轮廓来定义的。
当主要成分分析的无监督模式识别应用于 20 个分析样本中的目标峰值分布时，西西里岛和托斯卡纳油团分别表明，pedo-气候条件和风土会影响挥发物的相对流行。结果显示在图 6A 中，PCA 结果来自可靠的峰值分布，如图 6B所示。这两种方法交叉验证了来自不同地理区域的油具有不同的，而相干的化学特征，无论是靶向化合物还是非目标化合物，或者两者兼有。
最后，该软件能够在并行检测通道上快速有效地重新调整模式。在此应用程序中，建议对串联电电化信号进行重新对齐。MS 的离子源在两个电离能量（即 70 和 12 eV）之间发生多路复用，每通道 30 的采集频率为50Hz 。由此产生的两种色谱模式紧密对齐，而光谱数据（即光谱特征和响应）则带来具有不同动态响应范围的补充信息26、27。对齐的模式允许提取具有单声道 ID 的特征（2D 峰和峰值区域）（即目标山峰的化学名称和未定向峰和峰值区域的唯一编号 # ）。
模板匹配允许有效的交叉对齐。在这种情况下，没有太多的不对齐，但MS限制必须放宽，以允许匹配UT峰值。另一方面，没有 MS 限制的特色 UT 峰值区域会及时匹配，而不会出现任何虚假的负面匹配。 图 5C 显示了 12 eV 色谱图的放大区域，其中匹配了从 70 个 eV 数据构建的功能模板。由于 qCLIC 限制降低（例如 DMF 阈值为 600），可靠的 UT 峰值与之正匹配。需要注意的是，在 12 eV 时，由于低电离能引起的有限碎片，检测到的峰值较少。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61529/61529fig01.jpg"> 图1:投标的轮廓图和目标模板。（A） 托斯卡纳特级初榨橄榄油挥发部分的轮廓图。同源系列和类的有序模式以不同的颜色和线突出显示:线性饱和碳氢化合物（黑线和 2D轮廓）不饱和碳氢化合物（黄色）、线性饱和醛（蓝色）单不饱和醛（红色）、多不饱和醛（鲑鱼）、原醇（绿色）和短链脂肪酸（氰酸）。（B） 将已知分析（红色圆圈）的目标模板与连接内部标准 （ISs） 的连接线过度放大。面板显示 2D 峰值/blob 属性元数据 （十进制） 或模板峰值属性。 <a href="https://www.jove.com/files/ftp_upload/61529/61529fig01large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61529/61529fig02.jpg"> 图2:顶点MS搜索。输出的顶点MS搜索斑点5。具有最高相似性匹配的数据库条目列表和库提供的相关元数据。 <a href="https://www.jove.com/files/ftp_upload/61529/61529fig02large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61529/61529fig03.jpg"> 图3:模板调整。工作流说明允许通过转换重新调整模板的步骤。 <a href="https://www.jove.com/files/ftp_upload/61529/61529fig03large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61529/61529fig04.jpg"> 图4:GC调查员界面。调查人员小组根据石油生产区域（左）和每个样品（右）中可靠的峰值/blob体积列表标记所有选定的图像。 <a href="https://www.jove.com/files/ftp_upload/61529/61529fig04large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61529/61529fig05.jpg"> 图5:目标和UT模板。（A） 第11步自动处理产生的可靠峰值:红色圆圈对应于已知的分析，而绿色圆圈则不为人知。在叠加的面板中，模板对象属性显示为 （Z）-3-六角形。（B） 显示 UT 模板的 UT 峰值（红色和绿色圆圈）和峰值区域（红色图形）的扩大区域，与以 70 eV 电传能量获得的样品油相匹配。（C） UT 模板与以 12 eV 电电化能量获得的样品油相匹配。 <a href="https://www.jove.com/files/ftp_upload/61529/61529fig05large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/61529/61529fig06.jpg"> 图6:PCA加载图。它们通过（A）定向峰分布或（B）UT 峰值分布显示样本（来自托斯卡纳和西西里岛的油）的自然构象。 <a href="https://www.jove.com/files/ftp_upload/61529/61529fig06large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
补充文件。<a href="https://www.jove.com/files/ftp_upload/61529/Supplemental_Files.zip" target="_blank">请单击此处下载这些文件。</a>

Watch this Scientific Journal Video about Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography at JoVE.com

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

该协议提出了指纹的方法，并探索通过综合二维气相色谱图与质谱学相结合收集的多维数据。应用专用模式识别算法（模板匹配）来探索特级初榨橄榄油挥发性分数（即挥发性）中加密的化学信息。

通过模板匹配对综合二维气相色谱收集的数据进行色谱指纹

Data processing and evaluation are critical steps of comprehensive two-dimensional gas chromatography (GCxGC), particularly when coupled to mass ...

chromatographic-fingerprinting-template-matching-for-data-collected

Research

JoVE Journal

Chemistry

4.9K 次观看.  Università degli Studi di Torino. 该协议提出了指纹的方法，并探索通过综合二维气相色谱图与质谱学相结合收集的多维数据。应用专用模式识别算法（模板匹配）来探索特级初榨橄榄油挥发性分数（即挥发性）中加密的化学信息。

视频: Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

The direct liquid deposition of solution standards onto sorbent-filled thermal desorption tubes is used for the quantitative analysis of trace explosive vapor samples. The direct liquid deposition method yields a higher fidelity between the analysis of vapor samples and the analysis of solution standards than using separate injection methods for vapors and solutions, i.e., samples collected on vapor collection tubes and standards prepared in solution vials. Additionally, the method can account for instrumentation losses, which makes it ideal for minimizing variability and quantitative trace chemical detection. Gas chromatography with an electron capture detector is an instrumentation configuration sensitive to nitro-energetics, such as TNT and RDX, due to their relatively high electron affinity. However, vapor quantitation of these compounds is difficult without viable vapor standards. Thus, we eliminate the requirement for vapor standards by combining the sensitivity of the instrumentation with a direct liquid deposition protocol to analyze trace explosive vapor samples.

Trace explosive vapors of TNT and RDX collected on sorbent-filled thermal desorption tubes were analyzed using a programmed temperature desorption system coupled to GC with an electron capture detector. The instrumental analysis is combined with direct liquid deposition method to reduce sample variability and account for instrumentation drift and losses.

定量检测痕量爆炸物蒸气通过程序升温脱附 - 气相色谱 - 电子捕获检测器

The direct liquid deposition of solution standards onto sorbent-filled thermal desorption tubes is used for the quantitative analysis of trace explosive ...

科研

化学

On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes

The shift to heavy crude oils and the use of alternative fossil resources such as shale oil are a challenge for the petrochemical industry. The composition of heavy crude oils and shale oils varies substantially depending on the origin of the mixture. In particular they contain an increased amount of nitrogen containing compounds compared to the conventionally used sweet crude oils. As nitrogen compounds have an influence on the operation of thermal processes occurring in coker units and steam crackers, and as some species are considered as environmentally hazardous, a detailed analysis of the reactions involving nitrogen containing compounds under pyrolysis conditions provides valuable information. Therefore a novel method has been developed and validated with a feedstock containing a high nitrogen content, i.e., a shale oil. First, the feed was characterized offline by comprehensive two-dimensional gas chromatography (GC &#215; GC) coupled with a nitrogen chemiluminescence detector (NCD). In a second step the on-line analysis method was developed and tested on a steam cracking pilot plant by feeding pyridine dissolved in heptane. The former being a representative compound for one of the most abundant classes of compounds present in shale oil. The composition of the reactor effluent was determined via an in-house developed automated sampling system followed by immediate injection of the sample on a GC &#215; GC coupled with a time-of-flight mass spectrometer (TOF-MS), flame ionization detector (FID) and NCD. A novel method for quantitative analysis of nitrogen containing compounds using NCD and 2-chloropyridine as an internal standard has been developed and demonstrated.

A method combining comprehensive two-dimensional gas chromatography with nitrogen chemiluminescence detection has been developed and applied to on-line analysis of nitrogen containing compounds in a complex hydrocarbon matrix.

在线含硫化合物在复杂油气矩阵氮分析

The shift to heavy crude oils and the use of alternative fossil resources such as shale oil are a challenge for the petrochemical industry. The ...

A Two-Step Pyrolysis-Gas Chromatography Method with Mass Spectrometric Detection for Identification of Tattoo Ink Ingredients and Counterfeit Products

Tattoo inks are complex mixtures of ingredients. Each of them possesses different chemical properties which have to be addressed upon chemical analysis. In this method for two-step pyrolysis online coupled to gas chromatography mass spectrometry (py-GC-MS) volatile compounds are analyzed during a first desorption run. In the second run, the same dried sample is pyrolyzed for analysis of non-volatile compounds such as pigments and polymers. These can be identified by their specific decomposition patterns. Additionally, this method can be used to differentiate original from counterfeit inks. Easy screening methods for data evaluation using the average mass spectra and self-made pyrolysis libraries are applied to speed up substance identification. Using specialized evaluation software for pyrolysis GS-MS data, a fast and reliable comparison of the full chromatogram can be achieved. Since GC-MS is used as separation technique, the method is limited to volatile substances upon desorption and after pyrolysis of the sample. The method can be applied for quick substance screening in market control surveys since it requires no sample preparation steps.

This method for two-step pyrolysis online coupled to gas chromatography with mass spectrometric detection and data evaluation protocol can be used for multi-component analysis of tattoo inks and discrimination of counterfeit products.

一种用于检测纹身油墨成分和假冒产品的两步热解-气相色谱法

Tattoo inks are complex mixtures of ingredients. Each of them possesses different chemical properties which have to be addressed upon chemical analysis. ...

Qualitative Characterization of the Aqueous Fraction from Hydrothermal Liquefaction of Algae Using 2D Gas Chromatography with Time-of-flight Mass Spectrometry

Two-dimensional gas chromatography coupled with time-of-flight mass spectrometry is a powerful tool for identifying and quantifying chemical components in complex mixtures. It is often used to analyze gasoline, jet fuel, diesel, bio-diesel and the organic fraction of bio-crude/bio-oil. In most of those analyses, the first dimension of separation is non-polar, followed by a polar separation. The aqueous fractions of bio-crude and other aqueous samples from biofuels production have been examined with similar column combinations. However, sample preparation techniques such as derivatization, solvent extraction, and solid-phase extraction were necessary prior to analysis. In this study, aqueous fractions obtained from the hydrothermal liquefaction of algae were characterized by two-dimensional gas chromatography coupled with time-of-flight mass spectrometry without prior sample preparation techniques using a polar separation in the first dimension followed by a non-polar separation in the second. Two-dimensional plots from this analysis were compared with those obtained from the more traditional column configuration. Results from qualitative characterization of the aqueous fractions of algal bio-crude are discussed in detail. The advantages of using a polar separation followed by a non-polar separation for characterization of organics in aqueous samples by two-dimensional gas chromatography coupled with time-of-flight mass spectrometry are highlighted.

A two-dimensional gas chromatography-time-of-flight mass spectrometry method is described for characterization of the aqueous fraction of bio-crude produced from hydrothermal liquefaction of algae. This protocol can also be employed to analyze the aqueous fraction of liquid products from fast pyrolysis, catalytic fast pyrolysis, catalytic deoxygenation and hydro-treating.

从藻类水热液化使用二维气相色谱时间飞行质谱法水提取部分的定性鉴定

Two-dimensional gas chromatography coupled with time-of-flight mass spectrometry is a powerful tool for identifying and quantifying chemical components in ...

生物工程

Nitrogen Compound Characterization in Fuels by Multidimensional Gas Chromatography

Certain nitrogen-containing compounds can contribute to fuel instability during storage. Hence, detection and characterization of these compounds is crucial. There are significant challenges to overcome when measuring trace compounds in a complex matrix such as fuels. Background interferences and matrix effects can create limitations to routine analytical instrumentation, such as GC-MS. In order to facilitate specific and quantitative measurements of trace nitrogen compounds in fuels, a nitrogen-specific detector is ideal. In this method, a nitrogen chemiluminescence detector (NCD) is used to detect nitrogen compounds in fuels. NCD utilizes a nitrogen-specific reaction that does not involve the hydrocarbon background. Two-dimensional (GCxGC) gas chromatography is a powerful characterization technique as it provides superior separation capabilities to one-dimensional gas chromatography methods. When GCxGC is paired with a NCD, the problematic nitrogen compounds found in fuels can be extensively characterized without background interference. The method presented in this manuscript details the process for measuring different nitrogen-containing compound classes in fuels with little sample preparation. Overall, this GCxGC-NCD method has been shown to be a valuable tool to enhance the understanding of the chemical composition of nitrogen-containing compounds in fuels and their impact on fuel stability. The % RSD for this method is &#60;5% for intraday and &#60;10% for interday analyses; the LOD is 1.7 ppm and the LOQ is 5.5 ppm.

Here, we present a method utilizing two-dimensional gas chromatography and nitrogen chemiluminescence detection (GCxGC-NCD) to extensively characterize the different classes of nitrogen-containing compounds in diesel and jet fuels.

多维气相色谱在燃料中的氮化合物表征

Certain nitrogen-containing compounds can contribute to fuel instability during storage. Hence, detection and characterization of these compounds is ...

Gas Chromatography-Mass Spectrometry Paired with Total Vaporization Solid-Phase Microextraction as a Forensic Tool

Gas Chromatography &#8211; Mass Spectrometry (GC-MS) is a frequently used technique for the analysis of numerous analytes of forensic interest, including controlled substances, ignitable liquids, and explosives. GC-MS can be coupled with Solid-Phase Microextraction (SPME), in which a fiber with a sorptive coating is placed into the headspace above a sample or immersed in a liquid sample. Analytes are sorbed onto the fiber which is then placed inside the heated GC inlet for desorption. Total Vaporization Solid-Phase Microextraction (TV-SPME) utilizes the same technique as immersion SPME but immerses the fiber into a completely vaporized sample extract. This complete vaporization results in a partition between only the vapor phase and the SPME fiber without interference from a liquid phase or any insoluble materials. Depending upon the boiling point of the solvent used, TV-SPME allows for large sample volumes (e.g., up to hundreds of microliters). On-fiber derivatization may also be performed using TV-SPME. TV-SPME has been used to analyze drugs and their metabolites in hair, urine, and saliva. This simple technique has also been applied to street drugs, lipids, fuel samples, post-blast explosive residues, and pollutants in water. This paper highlights the use of TV-SPME to identify illegal adulterants in very small samples (microliter quantities) of alcoholic beverages. Both gamma-hydroxybutyrate (GHB) and gamma-butyrolactone (GBL) were identified at levels that would be found in spiked drinks. Derivatization by a trimethylsilyl agent allowed for conversion of the aqueous matrix and GHB into their TMS derivatives. Overall, TV-SPME is quick, easy, and requires no sample preparation aside from placing the sample into a headspace vial.

Total Vaporization Solid Phase Microextraction (TV-SPME) completely vaporizes a liquid sample whilst analytes are sorbed onto a SPME fiber. This allows for partitioning of the analyte between only the solvent vapor and the SPME fiber coating.

气相色谱-质量光谱学与总蒸发固体相微切成一对，作为取证工具

Gas Chromatography &#8211; Mass Spectrometry (GC-MS) is a frequently used technique for the analysis of numerous analytes of forensic interest, including ...

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations.
We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples&#8211;facilitated by a robot-assisted system for online monitoring of fermentations&#8211;and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.

This protocol describes an experimental procedure to quantitatively and comprehensively investigate the metabolism of multiple nutrient sources. This workflow, based on a combination of isotopic tracer experiments and an analytical procedure, allows the fate of consumed nutrients and the metabolic origin of molecules synthetized by microorganisms to be determined.

基于同位素示踪实验结合的工作流研究多营养源的微生物代谢

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods ...

Gas Chromatography (GC) with Flame-Ionization Detection

Source: Laboratory of Dr. B. Jill Venton - University of Virginia
Gas chromatography (GC) is used to separate and detect small molecular weight compounds in the gas phase. The sample is either a gas or a liquid that is vaporized in the injection port. Typically, the compounds analyzed are less than 1,000 Da, because it is difficult to vaporize larger compounds. GC is popular for environmental monitoring and industrial applications because it is very reliable and can be run nearly continuously. GC is typically used in applications where small, volatile molecules are detected and with non-aqueous solutions. Liquid chromatography is more popular for measurements in aqueous samples and can be used to study larger molecules, because the molecules do not need to vaporize. GC is favored for nonpolar molecules while LC is more common for separating polar analytes.
The mobile phase for gas chromatography is a carrier gas, typically helium because of its&#160;low molecular weight and being chemically inert. Pressure is applied and the mobile phase moves the analyte through the column. The separation is accomplished using a column coated with a stationary phase. Open tubular capillary columns are the most popular columns and have the stationary phase coated on the walls of the capillary. Stationary phases are often derivatives of polydimethylsiloxane, with 5&#8211;10% of the groups functionalized to tune the separation. Typical functional groups are phenyl, cyanopropyl, or trifluoropropyl groups. Capillary columns are usually 5&#8211;50 m&#160;long. Narrower columns have higher resolution but require higher pressures. Packed columns can also be used where the stationary phase is coated onto beads packed in the column. Packed columns are shorter, 1&#8211;5 m. Open tubular capillaries are generally preferred because they allow higher efficiencies, faster analyses, and have higher capacities.
Flame-ionization detection (FID) is a good general detector for organic compounds in GC that detects the amount of carbon in a sample. After the column, samples are burned in a hot, hydrogen-air flame. Carbon ions are produced by the combustion. While the overall efficiency of the process is low (only 1 in 105 carbon ions produce an ion in the flame) the total amount of ions is directly proportional to the amount of carbon in the sample. Electrodes are used to measure the current from the ions. FID is a destructive detector, as the entire sample is pyrolyzed. FID is unaffected by noncombustible gases and water.

Gas Chromatography GC with Flame-Ionization Detection

气相色谱 (GC) 与火焰电离检测

Source: Laboratory of Dr. B. Jill Venton - University of Virginia
Gas chromatography (GC) is used to separate and detect small molecular weight compounds ...

教育

JoVE Science Education

分析化学

Gas Chromatography: Introduction

Gas chromatography (GC) is a technique for separating and analyzing volatile compounds in a sample. Its primary purpose is to identify and quantify components in complex mixtures, making it essential in fields such as environmental analysis, pharmaceuticals, and petrochemicals. GC is also called vapor-phase chromatography (VPC) or gas-liquid partition chromatography (GLPC).
In GC,&#160; a sample is vaporized and mixed with an inert carrier gas (the mobile phase), which transports it through a column. The column is packed or coated with a stationary phase, either a liquid or solid material. Compounds in the sample are separated based on their boiling points, and compounds with lower boiling points elute first. The elution order- the sequence in which the compound exits the column- is visible on a chromatogram, where each peak corresponds to a specific compound.
There are two main types of gas chromatography:
1. Gas-liquid chromatography (GLC): In GLC, the stationary phase is a nonvolatile, thermally stable, chemically inert liquid, while the mobile phase is a carrier gas, typically helium. This technique is commonly used to separate compounds based on their boiling points.
&#160; 2. Gas-solid chromatography (GSC): GSC uses solid particulates like diatomaceous earth as the stationary phase and a carrier gas like helium as the mobile phase. GSC is primarily used for permanent gases and small polar molecules not retained by gas-liquid columns due to severe tailing of elution peaks.

气相色谱法：简介

Gas chromatography (GC) is a technique for separating and analyzing volatile compounds in a sample. Its primary purpose is to identify and quantify ...

通过模板匹配对综合二维气相色谱收集的数据进行色谱指纹

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