IDENTIFICATION OF COUNTERFEIT ALCOHOLIC BEVERAGES USING CLUSTER ANALYSIS IN PRINCIPAL-COMPONENT SPACE

被引：4

作者：

Khodasevich, M. A. ^{[1
]}

Sinitsyn, G. V. ^{[1
]}

Gres'ko, M. A. ^{[2
]}

Dolya, V. M. ^{[2
]}

Rogovaya, M. V. ^{[1
]}

Kazberuk, A. V. ^{[1
]}

机构：

[1] Natl Acad Sci Belarus, BI Stepanov Inst Phys, 68 Nezavisimost Ave, Minsk 220072, BELARUS

[2] BIOSAN Alkos Ltd, Moscow, Russia

来源：

JOURNAL OF APPLIED SPECTROSCOPY | 2017年 / 84卷 / 03期

关键词：

UV-Vis spectroscopy; principal component analysis; cluster analysis; NEAR-INFRARED SPECTROSCOPY; PARTIAL LEAST-SQUARES;

D O I：

10.1007/s10812-017-0503-6

中图分类号：

O433 [光谱学];

学科分类号：

0703 ; 070302 ;

摘要：

A study of 153 brands of commercial vodka products showed that counterfeit samples could be identified by introducing a unified additive at the minimum concentration acceptable for instrumental detection and multivariate analysis of UV-Vis transmission spectra. Counterfeit products were detected with 100% probability by using hierarchical cluster analysis or the C-means method in two-dimensional principal-component space.

引用

页码：517 / 520

页数：4

共 50 条

[31] L1-Norm Principal-Component Analysis of Complex Data
Tsagkarakis, Nicholas
Markopoulos, Panos P.
Sklivanitis, George
Pados, Dimitris A.
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2018, 66 (12) : 3256 - 3267
[32] Principal-component analysis for raindrops and its application to the remote sensing of rain
Meagher, JP
Haddad, ZS
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2002, 128 (580) : 559 - 571
[33] APPLICATION OF PRINCIPAL-COMPONENT ANALYSIS TO THE INTERPRETATION OF BROWN-COAL PROPERTIES
TESCH, S
OTTO, M
FUEL, 1995, 74 (07) : 978 - 982
[34] COMPUTER DISPLAY GRAPHICS FOR PRINCIPAL-COMPONENT ANALYSIS AND VEGETATION ORDINATION STUDIES
KERSHAW, KA
SHEPARD, RK
CANADIAN JOURNAL OF BOTANY, 1972, 50 (11): : 2239 - &
[35] Detection of hidden structures in a photoexcited semiconductor via principal-component analysis
Shiau, YH
SOLID-STATE ELECTRONICS, 2006, 50 (02) : 191 - 198
[36] FUNCTIONAL CONNECTIVITY - THE PRINCIPAL-COMPONENT ANALYSIS OF LARGE (PET) DATA SETS
FRISTON, KJ
FRITH, CD
LIDDLE, PF
FRACKOWIAK, RSJ
JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, 1993, 13 (01): : 5 - 14
[37] OPTIMAL SPARSE L1-NORM PRINCIPAL-COMPONENT ANALYSIS
Chamadia, Shubham
Pados, Dimitris A.
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2017, : 2686 - 2690
[38] Novel Algorithms for Lp-Quasi-Norm Principal-Component Analysis
Chachlakis, Dimitris G.
Markopoulos, Panos P.
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020), 2021, : 1045 - 1049
[39] Principal-component analysis of multiscale data for process monitoring and fault diagnosis
Yoon, S
MacGregor, JF
AICHE JOURNAL, 2004, 50 (11) : 2891 - 2903
[40] Assessment of Sustainable Development Using Cluster Analysis and Principal Component Analysis
Drastichova, Magdalena
Filzmoser, Peter
PROBLEMY EKOROZWOJU, 2019, 14 (02): : 7 - 24

← 1 2 3 4 5 →