首页> 外文期刊>The Analyst: The Analytical Journal of the Royal Society of Chemistry: A Monthly International Publication Dealing with All Branches of Analytical Chemistry >Identification of traditional East Asian handmade papers through the multivariate data analysis of pyrolysis-GC/MS data
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Identification of traditional East Asian handmade papers through the multivariate data analysis of pyrolysis-GC/MS data

机译:通过热解 - GC / MS数据的多元数据分析识别传统东亚手工论文

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摘要

An analytical approach based on the multivariate analysis of on-line pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) data is proposed for the identification of traditional East Asian handmade papers from different fiber material origins. This approach utilized several biomarkers detected during the Py-GC/MS analysis of paper samples. At first, the total ion chromatogram (TIC) was taken as the response and then the extracted ion chromatograms (EICs) were considered to improve the discrimination of papers. The influence of different data pretreatments (raw responses vs. normalized values) including different weightings of the variables (weighting as 1 vs. weighting as 1/STD, where STD stands for standard deviation) for principal component analysis was also investigated. The results showed that compared to the commonly used microscopy techniques, the Py-GC/MS technique proved to be able to discriminate against handmade paper materials that have similar microscopic morphologies such as Morus species vs. Broussonetia species. The data pretreatment influenced PCA modeling: the analysis based on normalized values showed more interpretable PCA group features for Moraceae species. PCA without weighting resulted unsurprisingly in discrimination through the presence of high intensity response biomarkers, while when applying weight as 1/STD, a PCA loading plot was shown to provide a group of compounds, most of them being present at low levels, to be discriminating. Additionally, the characteristic EICs can provide a data matrix for statistical analysis avoiding the interference from a co-eluting compound and background compared to the data matrix obtained from the TIC. As a result, a quick Py-GC/MS based handmade paper identification procedure using PCA modeling of the characteristic EICs was proposed for the first time in the identification of traditional East Asian handmade papers. This procedure could be very beneficial for cultural heritage applications.
机译:提出了一种基于在线热解 - 气相色谱/质谱/质谱(PY-GC / MS)数据的多变量分析的分析方法,用于识别来自不同纤维材料的传统东亚手工纸。该方法利用在纸样品的Py-GC / MS分析期间检测到几种生物标志物。首先,将总离子色谱图(TIC)作为响应,然后考虑提取的离子色谱图(EIC)以改善纸张的鉴别。还研究了不同数据预处理的影响(原始响应与标准化值)的不同加权(加权为1 / STD,其中STD为标准偏差的1 / STD)。结果表明,与常用的显微镜​​技术相比,PY-GC / MS技术证明能够鉴别具有类似的微观形态的手工纸材料,例如Morus物种与溴葡萄球菌等物种。数据预处理影响了PCA建模:基于标准化值的分析显示了Moraceae物种的更多可解释的PCA组特征。通过在没有加权的情况下,通过存在高强度响应生物标志物的鉴别导致PCA导致鉴别,而当施加重量为1 / std时,显示PCA负载图以提供一组化合物,其中大多数存在于低水平下,以辨别。另外,特性EIC可以提供用于统计分析的数据矩阵,避免与从TIC获得的数据矩阵相比来自共用化合物和背景的干扰。结果,首次在传统东亚手工论文的识别中首次提出了一种使用PCA建模的快速PY-GC / MS的手工纸识别程序。这一程序对于文化遗产应用来说可能非常有益。

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