Principal Component Analysis of Spectral Data: A Contribution to the Knowledge of the Materials Constituting Works of Art

1996 ◽  
Vol 462 ◽  
Author(s):  
M. Bacci ◽  
S. Baronti ◽  
A. Casini ◽  
F. Lotti ◽  
M. Picollo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Spectral Data ◽  
Laboratory Tests ◽  
Principal Component ◽  
Component Analysis ◽  
Work Of Art ◽  
Works Of Art ◽  
Image Spectroscopy ◽  
Non Destructive ◽  
Non Destructive Techniques

ABSTRACTThe use of totally non-destructive techniques such as image spectroscopy for diagnosing paintings makes it possible to obtain a large amount of spectral data that provides information concerning the composition of works of art. Here, we stress how statistical treatments, such as principal component analysis (PCA), applied to 2-D data, can contribute to a better knowledge of the work of art itself and of the distribution of the materials that constitute it.Laboratory tests, as well as applications to actual paintings, will be presented and discussed.

New Method for Spectral Data Classification: Two-Way Moving Window Principal Component Analysis

2006 ◽  
Vol 60 (8) ◽  
pp. 884-891 ◽  
Author(s):  
Hideyuki Shinzawa ◽  
Shigeaki Morita ◽  
Yukihiro Ozaki ◽  
Roumiana Tsenkova
Keyword(s):  
Principal Component Analysis ◽  
Spectral Data ◽  
Principal Component ◽  
Data Classification ◽  
Component Analysis ◽  
New Method ◽  
Moving Window

scholarly journals Combination of ICP-OES, XRF and XRD techniques for analysis of several dental ceramics and their identification using chemometrics

2011 ◽  
Vol 26 (2) ◽  
pp. 141-149 ◽  
Author(s):  
Safwan M. Obeidat ◽  
Idrees Al-Momani ◽  
Asma'a Haddad ◽  
Motasem Bani Yasein
Keyword(s):  
Principal Component Analysis ◽  
Elemental Composition ◽  
Spectral Data ◽  
Principal Component ◽  
Component Analysis ◽  
Dental Ceramics ◽  
Icp Oes ◽  
Dental Ceramic ◽  
Ceramic Samples ◽  
Xrd Techniques

In this paper dental ceramic samples from seven vendors were studied. The elemental composition for each type was investigated using the ICP-OES and the XRF. Assessment of the seven types of ceramic was also successfully achieved using the XRD spectral data and processed with Principal Component Analysis (PCA). Detecting possible adulteration in different mass percentages of ceramic was also possible by applying the XRD data for the adulterated samples to the original PCA model.

scholarly journals PENERAPAN ALGORITMA DISKRIMINASI MENGGUNAKAN METODE PRINCIPAL COMPONENT ANALYSIS (PCA) DAN Vis-SWNIR SPECTROSCOPY PADA BUAH CABAI RAWIT DOMBA BERBAGAI TINGKAT KEMATANGAN

2021 ◽  
Vol 4 (1) ◽  
pp. 40-46
Author(s):  
Ine Elisa Putri ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Absorbance Spectrum ◽  
Pepper Fruit ◽  
Destructive Measurement ◽  
Non Destructive ◽  
Destructive Methods ◽  
Spectrum Data

Cayenne pepper fruit can be used for health because it is a source of antioxidants. Detection of quality fruit can use non-destructive methods as an alternative method. Visible short wavelength near infrared (Vis-SWNIR) spectroscopy is non-destructive measurement. This method can be used to discriminate fruit by using the principal component analysis (PCA). This research aimed to discriminate between Cayenne pepper with various maturity by using Vis-SWNIR spectroscopy with a wavelength of 300-1065 nm and principal component analysis (PCA). Cayenne pepper fruit was devided into three groups, namely green, orange and red. The spectrum used the absorbance spectrum data (original). The research was carried out from March to June 2020. The result showed that the use of Vis-SWNIR and PCA were able to discriminate various maturity of cayenne pepper with a 100% success rate.

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