Use of Chemometrics: Principal Component Analysis (PCA) and Principal Component Regression (PCR) for the Authentication of Orange Juice
作者:
Stella Vaira,
Víctor. E. Mantovani,
Juan C. Robles,
Juan C. Sanchis,
Héctor C. Goicoechea,
期刊:
Analytical Letters
(Taylor Available online 1999)
卷期:
Volume 32,
issue 15
页码: 3131-3141
ISSN:0003-2719
年代: 1999
DOI:10.1080/00032719908543031
出版商: Taylor & Francis Group
关键词: Chemometrics;Principal component analysis;Principal component regression;Authentication;Orange juice
数据来源: Taylor
摘要:
Principal Component Analysis (PCA) was applied to a set of physico-chemical variables obtained from 41 samples of summer orange juice, in order to reduce the number of variables. Working with the covariance matrix, three components (which explained 98.27% of the variance) were taken. With the correlation matrix, four components which explained: 85.65% of the variance were taken. With the scores corresponding to both matrixes a principal component regression (PCR) was carried out against the dependent variable of Brix grades, so as to obtain two statistical models that would allow the detection of adulterations in pure orange juice, based on dilution and later masking by the addition of sugar. The models were tested with simulated dilutions of 41 samples of juice, to assess the effectiveness of each for the detection of adulterations. Both models turned out to be equally effective, detecting adulterations starting from about 15 % of dilution.
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