Discusses the results of studies of the influence of experimental conditions on the information content of the spectra used in the testing of motor fuels by the method of pattern recognition. Considers the possibility of the optical spectra used as the primary images of the studied objects. Multivariate statistical analysis of the results of the dimensional measurement was carried out using principal component, and a relatively high level of informativeness is chosen already during the application of the first two principal components. Shows a marked improvement in the information content of spectroscopic images at the optimal choice of the thickness of the absorbing layer.
motor fuels, optical spectroscopy, pattern recognition method, principal components method
1. Vershinin V.I. Hemometrika v rabotah rossiyskih analitikov // Zhurnal analiticheskoy himii. 2011. T. 66. № 11. S. 1 124-1 134.
2. Rodionova O.E., Pomerancev A.A. Hemometrika: dostizheniya i perspektivy // Uspehi himii. 2006. T. 75. № 4. S. 302-321.
3. Vlasova I.V., Vershinin V.I., Cyupko T.G. Metodologiya spektrofotometricheskogo analiza smesey organicheskih soedineniy. Problema neadditivnosti svetopogloscheniya // Zhurnal analiticheskoy himii. 2011. T. 66. № 1. S. 25-33.
4. Brereton P.G. Chemometric. Data analysis for the Laboratory and chemical plant. Wiley. 2003. 489 p.
5. Massart D.L., Vandeginste B.G., Buydens L.M.L., De Long, Lewi P.J., Smeyers-Verbeke J. Handbook of Chemometrics and Qualimetrics. Part B. Elsevier. Amsterdam, 1998. 710 c.
6. Vershinin V.I., Derendyaev B.G., Lebedev K.S. Komp'yuternaya identifikaciya organicheskih soedineniy. M.: Akademkniga, 2003. 197 s.
7. Vlasova I.V., Vershinin V.I., Shelpakova A.S. Hemometricheskie algoritmy v spektrofotometricheskom analize nerazdelennyh smesey organicheskih veschestv // Vestnik OmGU. 2010. № 2. S. 14-24.
8. Korolev V.N., Marugin A.V., Caregradskiy V.B. Metod opredeleniya detonacionnyh harakteristik nefteproduktov na osnove regressionnogo analiza spektrov pogloscheniya v blizhnem infrakrasnom diapazone // Zhurnal tehnicheskoy fiziki. 2000. T. 70. Vyp. 9. S. 83-88.
9. Kelly J.J., Barlow C.H., Jinguji T.M., Callis J.B. Prediction of gasoline octane numbers from near-infrared spectral features in the range 660-1215 nm // Anal. Chem. 1989. Vol. 61. № 4. P. 313-320.
10. Kelly J.J., Callis J.B. Nondestructive analytical procedure for simultaneous estimation of the major classes of hydrocarbon constituents of finished gasolines // Anal. Chem. 1990. Vol. 62. № 4. P. 1 444-1 451.
11. Dronov S.V. Mnogomernyy statisticheskiy analiz: ucheb. posobie. Barnaul: Altayskiy gosuniversitet, 2003. 213 s.
12. Fukunaga K. Vvedenie v statisticheskuyu teoriyu raspoznavaniya obrazov: per. s angl. M.: Nauka, 1979. 367 s.
13. Balabin R.M., Safieva R.M., Lomakina E.J. Gasoline classif cation using near infrared (NIR) spectroscopy data: Comparison of multivariate techniques // Anal. Chim. Acta. 2010. Vol. 671. № 1-2. P. 27-35.
14. O vozmozhnosti primeneniya metoda glavnyh komponent v analiticheskoy absorbcionnoy spektroskopii / V.V. Bercev [i dr.] // Zavodskaya laboratoriya. Diagnostika materialov. 2002. T. 68. № 12. S. 12-16.
15. Trudy VIII Peterburgskogo mezhdunar. foruma TEK / V.B. Borisov [i dr.]. SPb., 2008. S. 253-255.
16. Hachumov M.V. Rasstoyaniya, metriki i klasternyy analiz // Iskusstvennyy intellekt i prinyatie resheniy. 2012. № 1. S. 81-89.
