研究目的
To ascertain the source of origin of unknown printed documents, i.e., whether it belongs to the laser or inkjet or photocopier devices as well as to visualize the intra-variations present in the same types of printed documents.
研究成果
ATR-FTIR spectroscopy combined with multivariate methods is utilized for the examination of printed documents. The methodology developed in the present research helps in the accurate classification of unknown printed documents, i.e., whether it belongs to the laser or inkjet or photocopier devices and also, is able to discriminate the intra-variations present in the same types of printed documents. The present methodology provides robust and non-destructive simultaneous identification of all three types of printed samples.
研究不足
The study is limited to the analysis of black printed toner samples collected from laser, inkjet printers and photocopiers machines of different sources of origins. The methodology may require further validation for other colors and types of printing inks.
1：Experimental Design and Method Selection:
ATR-FTIR spectroscopy combined with chemometric methods was used for the rapid and no-destructive forensic investigation of inkjet, laser and photocopier printed documents.
2：Sample Selection and Data Sources:
Printouts were collected from various offices, banks, educational institutes, business firms, and local markets in the forms of lines/text and squared boxes on a standard A4 size office paper of 100 gsm quality.
3：List of Experimental Equipment and Materials:
'Spectrum Two' ATR-FTIR spectrophotometer (Perkin Elmer) containing ATR diamond crystal was used for scanning the printouts.
4：Experimental Procedures and Operational Workflow:
The samples were scanned with 4 cm-1 resolution having accumulations of 16 times. A constant gauge pressure was applied on printed samples with the help of knob and the cleaning of a diamond crystal was done by dry methanol after each scanning.
5：Data Analysis Methods:
The obtained dataset from spectroscopic methods was interpreted by using multivariate statistical methods including hierarchal cluster analysis, principal component analysis, and linear discriminant analysis.
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