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Metamerism phenomenon is an important problem in spectral reflectance reconstruction and color reproduction. In this paper, a 3-primary color CCD camera is used to acquire spectral information in CIE standard illuminant D65 and a nonlinear composite model is established, including principal component analysis and neural network method (PCA-NET) to modify the Matrix R Method based on the Metameric Black theory. The standard Munsell color card is used in spectral reflectance reconstruction experiment and the results are evaluated and discussed. The experimental results verified that the PCA-NET algorithm can accurately fit the nonlinear relationship between the output signal of the camera and the principal component coefficients; and it can be used in the R matrix algorithm instead of the linear algorithm; the new method can serve as a promising technique for building a spectral image database whihc is better than the original Matrix R Method. In the fixed illumination environment, the mean RMS of the test set is 0.76 improved, and the mean STD of the test set is 0.85 improved, which can effectively improve the accuracy of spectral reflectance reconstruction. The modified matrix R method has the advantages of higher accuracy and easy implementation, and it can be used in the field of color reproduction and spectral reflectance reconstruction.
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PMID:Spectral Reflectance Reconstruction with Nonlinear Composite Model of the Metameric Black. 3014 48

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.
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PMID:Identification of traditional East Asian handmade papers through the multivariate data analysis of pyrolysis-GC/MS data. 3053 84