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Titolo:
RECOGNITION OF VISUAL CHARACTERISTICS OF INFRARED-SPECTRA BY ARTIFICIAL NEURAL NETWORKS AND PARTIAL LEAST-SQUARES REGRESSION
Autore:
VISSER T; LUINGE HJ; VANDERMAAS JH;
Indirizzi:
NATL INST PUBL HLTH & ENVIRONM PROTECT,ORGAN ANALYT CHEM LAB,POB 1 3720 BA BILTHOVEN NETHERLANDS UNIV UTRECHT 3508 TB UTRECHT NETHERLANDS
Titolo Testata:
Analytica chimica acta
fascicolo: 2, volume: 296, anno: 1994,
pagine: 141 - 154
SICI:
0003-2670(1994)296:2<141:ROVCOI>2.0.ZU;2-W
Fonte:
ISI
Lingua:
ENG
Soggetto:
EXPERT SYSTEM;
Keywords:
INFRARED SPECTROMETRY; COMPUTERIZED INTERPRETATION; MULTIVARIATE METHODS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
28
Recensione:
Indirizzi per estratti:
Citazione:
T. Visser et al., "RECOGNITION OF VISUAL CHARACTERISTICS OF INFRARED-SPECTRA BY ARTIFICIAL NEURAL NETWORKS AND PARTIAL LEAST-SQUARES REGRESSION", Analytica chimica acta, 296(2), 1994, pp. 141-154

Abstract

The potentials of artificial neural networks and partial least squares regression for computerized interpretation of infrared spectra are studied. Experiments are carried out to establish the capabilities of these methods to recognize characteristic band shapes and spectral patterns, commonly used by experienced spectroscopists for interpretation. Classification is performed on (i) organic, inorganic and polyaromatic compounds using the entire spectral profiles, (ii) organophosphorus and non-organophosphorus compounds using specific absorption patterns of the O-P and P=S bands, and (iii) alcohols, carbamates and terminal alkynes using the shape of the individual O-H, N-H and =C-H bands. Results are compared with the information obtained from classification using frequency/intensity-structure correlation tables, and with interpretation as performed by experts. Classification by skilled interpreters is found to be superior in all cases. The multivariate methods give a significant improvement of the results compared to the predictions obtained from frequency/intensity data. Differences between artificial neural networks and partial least squares regression are small when full spectra or spectral regions are considered. Networks score better in recognising individual bands. The band width and the absorption frequency play an important role in the recognition process. The results prove to be practically insensitive to reduction of the number of spectral data points by a factor 16.

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Documento generato il 30/11/20 alle ore 07:08:10