Identification of traditional East Asian handmade papers through multivariate data analysis of pyrolysis-GC/MS data

Abstract : 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 Py-GC/MS analysis of the 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 pretreatment (raw responses vs normalized values) including different weighting of the variables (weighting as 1 vs weight 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 the 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, 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 data matrix for statistical analysis avoiding the interference from 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 characteristic EICs was for the first time proposed in the identification of traditional East Asian handmade papers. This procedure could be very beneficial in cultural heritage applications.
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https://hal.archives-ouvertes.fr/hal-01938318
Contributor : Michel Sablier <>
Submitted on : Wednesday, November 28, 2018 - 3:38:09 PM
Last modification on : Friday, August 30, 2019 - 4:52:02 PM

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Bin Han, Jérôme Vial, Shouji Sakamoto, Michel Sablier. Identification of traditional East Asian handmade papers through multivariate data analysis of pyrolysis-GC/MS data. Analyst, Royal Society of Chemistry, In press, ⟨10.1039/C8AN01898G⟩. ⟨hal-01938318⟩

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