METHODS AND OBJECTS OF CHEMICAL ANALYSIS ISSN title: "Metody i obʺekty himičeskogo analiza" An international journal devoted to all aspects of Analytical chemistry ISSN 2413-6166 (Online), ISSN 1991-0290 (Print)
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ЛИТЕРАТУРА

1. Trends Anal. Chem. – 2005. – 24, №6.

2. Milman B.L. Identification of chemical com,pounds // Trends Anal. Chem. – 2005. – 24, № 6. – P. 493–508.

3. Cárdenas S., Valcárcel M. Analytical fea,tures in qualitative analysis // Trends Anal. Chem. – 2005. – 24, № 6. – P. 477–487.

4. Попов А.А. Идентификация // Химическая энциклопедия. Т. 2. – М.: Сов. энциклопедия. – 1990. – С. 346–347.

5. Вершинин В.И., Дерендяев Б.Г., Лебе,дев К.С. Компьютерная идентификация органиче,ских соединений. – М.: Академкнига, 2002. – 197 с.

6. Hardcastle W.A. (Editor). Qualitative analy,sis: a guide to best practice. – Cambridge: Royal So,ciety of Chemistry. 1998. – 24 p. http://www.rsc.org/publishing/ebooks/1998/9780854044627.asp

7. EUROCHEM/CITAC Guide "Quantifying un,certainty in analytical measurements", 2nd ed. 2000. 120 p.; Руководство ЕВРАХИМ/СИТАК "Количе,ственное описание неопределенности в аналити,ческих измерениях", 2,е изд. / Пер. с англ. Р.Л. Кадиса, Г.Р. Нежиховского, В.Б. Симина под ред. Л.А. Конопелько. СПб, 2002. 141 с.

8. Методические указания по оценке под,линности и выявлению фальсификации молочной продукции. Методические указания МУ 4.1./4.2.2484,09 – М.: Федеральный Центр гигиены и эпидемиологии Роспотребнадзора, 2009. – 26 с.

9. Kuchmenko T.A., Lisitskaya R.P., Golova,nova V.A., Arsenova M.S. Detection of synthetic components in food matrices using piezoelectric re,sonators // Anal. Chem. – 2009. – 64, № 4. – P. 338–345.

10. Ivleva N.P., Niessner R., Panne U. Charac,terisation and discriminant of pollen by Raman micro,scopy // Anal. Bioanal. Chem. – 2005. – 381. – P. 261–267.

11. Coetzee P.P., Vanhaecke F. Classifying wine according to geographical origin via quadrupole,based ICP,mass spectrometry measurements of bo,ron isotope ratios // Anal. Bioanal. Chem. – 2005. – 383. – P. 977–984.

12. Alonso,Salces R.M., Guyot S., Herrero C., Berrueta L.A., Drilleau J.F., Gallo B., Vicente F. Che,mometrics characterisation of Basque and French ciders according to their polyphenolic profiles // Anal. Bioanal. Chem. – 2004. – 379. – P. 464–475.

13. Bellorini S., Strathmann S., Baeten V., Fu,miere O., Berben G., Tirendi S., von Holst C. Discri,minating animal fats and their origins: assessing the potentials of Fourier transform infrared spectroscopy, gas chromatography, immunoassay and polymerase chain reaction techniques // Anal. Bioanal. Chem. – 2005. – 382. – P. 1073–1083.

14. Poulli K.I., Mousdis G.A., Georgiou C.A. Synchronous fluorescence spectroscopy for quantita,tive determination of virgin olive oil adulteration with sunflower oil // Anal. Bioanal. Chem. – 2006. – 386. – P. 1571–575.

15. Baeten V., von Holst C., Garrido A., Vancut,sem J., Renier A.M., Dardenne P. Detection of banned meat and bone meal in feedstuffs by near,infrared microscopic analysis of the dense sediment fraction // Anal. Bioanal. Chem. – 2005. – 382. – P. 149,157.

16. Husted S., Mikkelsen B.F., Jensen J., Niel,sen N.E. Elemental fingerprints analysis of barley (Hordeum vulgare) using inductively coupled plasma mass spectrometry, isotope,ratio mass spectrometry,  and multivariate statistics // Anal. Bioanal. Chem. – 2004. – 378. – P. 171–182.

17. Nalda N.M.J., Yague B.J.L., Calva D.J.C., Gómez M.M.T. Classifying honeys from the Soria Province of Spain via multivariate analysis // Anal. Bioanal. Chem. – 2005. – 382. – P. 311,319.

18. Jurado J.M., Ballesteros O., Alcazar A., Pablos F., Martin M.J., Vilchez J.L., Navalon A. Diffe,rentiation of certified brands of origins of Spanish white wines by HS,SPME,GC and chemometrics // Anal. Bioanal. Chem. – 2008. – 390. – P. 961–970.

19. Cuny M., Vigneau E., Le Gall G., Colqu,houn I., Less M., Rutledge D.N. Fruit juice authentica,tion by 1H NMR spectroscopy in combination with dif,ferent chemometrics tools // Anal. Bioanal. Chem. – 2008. – 390. – P. 419–427.

20. Sumar S., Izmail H. Adulteration of foods – past and present // Nutrition & Food Sci. – 1995. – 95, № 4. – P. 11–15.

21. Arvanitoyannis I.S., Vaitsi O.B. A review on tomato authenticity: quality control methods in con,junction with multivariate analysis // Crit. Rev. in Food Sci. and Nutrition – 2007. – 47. – P. 675–699.

22. Рыков С.В., Корягина И.С., Скаковский Е.Д. Принципы контроля экологической и санитар,но,гигиенической безопасности плодовых и ягод,ных соков // Экологические нормы. Правила. Ин,формация. – 2009. – № 4. – С. 34–39.

23. Knödler M., Most M., Schieber A., Carle R. A novel approach to authenticity control of whole grain durum wheat (Triticum durum Desf.) flour and pasta, based on analysis of alkylresorcinol composi,tion // Food Chem. – 2010. – 118, № 1. – P. 177–181.

24. Socaciu C., Ranga F., Fetea F., Leopold L., Dulf F., Parlog R. Complementary advanced tech,niques applied for plant and food authentication // Czech J. of Food Sci. – 2009. – 27. – P. 70–75.

25. Carter R.M., Yan Y., Tomlins K. Digital im,aging based classification and authentication of gra,nular food products // Management Sci. and Technol. – 2006. – 17, № 2. – P. 235–240.

26. Diaz,Maroto M.C., Castillo N., Castro,Vazquez L., De Torres C., Perez,Coello M.S. Authen,ticity evaluation of different mints based on their vola,tile composition and olfactory profile // J. of Essential Oil,Bearing Plants. – 2008. – 11, № 1. – P. 1–16.

27. Winterhalter P. Authentication of food and wine // ACS Symposium Series. – 2007. – 952. – P. 2–12.

28. Martin M., Martin G. Frauds in food prod,ucts: A challenge for analytical chemistry // Actualite Chimique. – 2000. – 11. – P. 18–20.

29. Destaillats F., de Wispelaere M., Joffre F., Golay P.,A., Hug B., Giuffrida F., Fauconnot L., Dio,nisi F. Authenticity of milk fat by fast analysis of tri,acylglycerols. Application to the detection of partially hydrogenated vegetable oils // J. of Chromatography A. – 2006. – 1131, № 1–2. – P. 227–234.

30. Fugel R., Carle R., Schieber A. Quality and authenticity control of fruit purees, fruit preparations and jams – A review // Trends in Food Sci. and Tech,nol. – 2005. – 16, № 10. – P. 433–441.

31. Jee M. Oils and fats – authenticity and adul,teration // Food Sci. and Technol. – 2004. – 18, № 1. – P. 28–29.

32. Zhang Y., Krueger D., Durst R., Lee R., Wang D., Seeram N., Heber D. International multidi,mensional authenticity specification (IMAS) algorithm for detection of commercial pomegranate juice adulte,ration // J. of Agricult. and Food Chem. – 2009. – 57, № 6. – P. 2550–2557.

33. Cotte J.F., Casabianca H., Chardon S., Lhe,ritier J., Grenier,Loustalot M.F. Application of carbo,hydrate analysis to verify honey authenticity // J. of Chromatography A. – 2003. – 1021, № 1–2. – P. 145–155.

 34. Dennis M.J. Establishing food authenticity // Chem. and Ind. (London). – 1997. – 24. – P. 997–1000.

35. Kelly J.F.D., Downey G., Fouratier V. Initial study of honey adulteration by sugar solutions using midinfrared (MIR) spectroscopy and chemometrics // J. of Agricult. and Food Chem. – 2004. – 52, № 1. – P. 33–39.

36. Marini F., Balestrieri F., Bucci R., Ma,gri A.L., Marini D. supervised pattern recognition to discriminate the geographical origin of rice bran oils: a first study // Microchem. J. – 2003. – 74. – P. 239–248.

37. Laursen K.H., Hansen T.H., Persson D.P., Schjoerring J.K., Husted S. Multi,elemental finger,printing of plant tissue by semi,quantitative ICP,MS and Chemometrics // J. of Anal. At. Spectrom. – 2009. – 24, № 9. – P. 1198–1207.

38. Arvanitoyannis I.S., Katsota M.N., Psarra E.P., Soufleros E.H., Kallithraka S. Application of quality control methods for assessing wine authentici,ty: use of multivariate analysis (chemometrics) // Trends in Food Sci. and Technol. – 10, № 10. – P. 321–336.

39. Arvanitoyannis I.S., Tsitsika E.V., Panagi,otaki P. Implementation of quality control methods (physicochemical, microbiological and sensory) in conjunction with multivariate analysis towards fish authenticity // Int. J. of Food Sci. and Technol. – 2005. – 40, № 3. – P. 237–263.

40. Marini F. Artificial neural networks in food analysis: trends and perspectives. A review // Anal. Chim. Acta. – 2009. – 635. – P. 121–131.

41. Marini F., Bucci R., Magrм A.D., Magrм A.L. Authentication of Italian CDO wines by class, Хемометрические методы в контроле подлинности продуктов питания и пищевого сырья Методы и объекты химического анализа 2010, т.5, №3 141 modeling techniques // Chemometr. Intell. Lab. – 2006. – 84. – P. 164–171.

42. Yashin Ya.I., Yashin A.Ya. Analysis of food products and beverages using high,performance liq,uid chromatography and ion chromatography with electrochemical detectors // J. of Anal. Chem. – 2004. – 59, № 12. – P. 1121–1127.

43. Savchuk S.A., Kolesov G.M., Nuzhnyi V.P. Chromatographic study of the chemical composition and potential toxicity of spirits and alcoholic beverag,es // J. of Anal. Chem. – 2007. – 62, № 12. – P. 641–649.

44. Savchuk S.A., Vlasov V.N., Appolonova S.A., Arbuzov V.N., Vedenin A.N., Mezinov A.B., Gri,gor’yan B.R. Application of chromatography and spectrometry to the authentication of alcoholic beve,rages. – J. of Anal. Chem. – 2001. – 56, № 3. – P. 214–231.

45. Arbuzov V.N., Savchuk S.A. Identification of vodkas by ion chromatography and gas chromatogra,phy // J. of Anal. Chem. – 2002. – 57, № 5. – P. 428–433.

46. Rudakov O.B., Polyanskii K.K., Aleksyuk M.P. Qualitative identification of milk fat from chroma,tographic data // J. of Anal. Chem. – 2002. – 57, № 12. – P. 1081–1088. 47. Savchuk S.A., Kolesov G.M. Chromato,graphic determination of phthalic acid esters as an indicator of adulterated cognacs and cognac spirits // J. of Anal. Chem. – 2007. – 62, № 8. – P. 761–772.

48. Kuchmenko T.A., Lisitskaya R.P., Bobrova O.S. Test method for detecting adulterated apple juice using piezoelectric resonators // J. of Anal. Chem. – 2008. – 63, № 6. – P. 596–602.

49. Karoui R., De Baerdemaeker J.A. A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products // Food Chem. – 2007. – 102. – P. 621–640.

50. De Leon,Rodríguez A., Escalante,Minakata P., Jiménez,García M.I., Ordonez,Acevedo L.G., Flores J.L.F., De La Rosa A.P.B. Characteriza,tion of volatile compounds from ethnic Agave alcohol,ic beverages by gas chromatography,mass spectro,metry // Food Technol. and Biotechnol. – 2008. – 46, № 4. – P. 448–455.

 51. Cabanero A.I., Recio J.L., Ruperez M. Liq,uid chromatography coupled to isotope ratio mass spectrometry: a new perspective on honey adultera,tion detection // J. of Agricult. and Food Chem. – 2006. – 54, № 26. – P. 9719–9727.

52. Calderone G., Guillou C., Reniero F., Naulet N. Helping to authenticate sparkling drinks with 13C/12C of CO2 by gas chromatography,isotope ratio mass spectrometry // Food Research Int. – 2007. – 40, № 3. – P. 324–331.

53. Gomez,Ariza J.L., Villegas,Portero M.J., Bernal,Daza V. Characterization and analysis of ami,no acids in orange juice by HPLC,MS/MS for authen,ticity assessment // Anal. Chim. Acta. – 2005. – 540, № 1. – P. 221–230.

54. Nhu,Trang T.,T., Casabianca H., Grenier,Loustalot M.,F. Authenticity control of essential oils containing citronellal and citral by chiral and stable,isotope gas,chromatographic analysis // Anal. and Bioanal. Chem. – 2006. – 386, № 7–8. – P. 2141–2152.

55. Alonso,Salces R.M., Ndjoko K., Queiroz E.F., Ioset J.R., Hostettmann K., Berrueta L.A., Gallo B., Vicente F. On,line characterisation of apple poly,phenols by liquid chromatography coupled with mass spectrometry and ultraviolet absorbance detection // J. of Chromatography A. – 2004. – 1046, № 1–2. – P. 89–100.

56. Csehati T., Forgacs E., Deyl Z., Miksik I. Chromatography in authenticity and traceability tests of vegetable oils and dairy products: A review // Bio,med. Chromatography. – 2005. – 19, № 3. – P. 183–190.

57. Cotte J.F., Casabianca H., Giroud B., Albert M., Lheritier J., Grenier,Loustalot M.F. Characteriza,tion of honey amino acid profiles using high,pressure liquid chromatography to control authenticity // Anal. and Bioanal. Chem. – 2004. – 378, № 5. – P. 1342–1350.

58. von Baer D., Rentzsch M., Hitschfeld M.A., Mardones C., Vergara C., Winterhalter P. Relevance of chromatographic efficiency in varietal authenticity verification of red wines based on their anthocyanin profiles: interference of pyranoanthocyanins formed during wine ageing // Anal. Chim. Acta. – 2008. – 621, № 1. – P. 52–56.

59. Lachenmeier D.W., Richling E., Lopez M.G., Frank W., Schreier P. Multivariate analysis of FTIR and ion chromatographic data for the quality control of tequila // J. of Agricult. and Food Chem. – 2005. – 53, № 6. – P. 2151–2157.

60. Mosandl A. Capillary gas chromatography in quality assessment of flavours and fragrances // J. of Chromatography. – 1992. – 624, № 1–2. – P. 267–292.

61. Cunha S.C., Oliveira M.B.P.P. Discrimina,tion of vegetable oils by triacylglycerols evaluation of profile using HPLC/ELSD // Food Chem. – 2006. – 95, № 3. – P. 518–524.

62. Kvasnicka F. Capillary electrophoresis in food authenticity // J. of Separation Sci. – 2005. – 28, № 9–10. – P. 813–825. 63. Golay P.,A., Giuffrida F., Dionisi F., Destail,lats F. Streamlined methods for the resolution and quantification of fatty acids including trans fatty acid  isomers in food products by gas chromatography // J. of AOAC Int. – 2009. – 92, № 5. – P. 1301–1309.

64. Scharrer A., Mosandl A. Reinvestigation of vanillin contents and component ratios of vanilla ex,tracts using high,performance liquid chromatography and gas chromatography // Deutsche Lebensmittel,Rundschau. – 2001. – 97, № 12. – P. 449–456.

65. Schieber A., Keller P., Carle R. Determina,tion of phenolic acids and flavonoids of apple and pear by high,performance liquid chromatography // J. of Chromatography A. – 2001. – 910, № 2. – P. 265–273.

66. Low N.H., McLaughlin M., Hofsommer H.,J., Hammond D.A. Capillary gas chromatographic detec,tion of invert sugar in heated, adulterated, and adulte,rated and heated apple juice concentrates employing the equilibrium method // J. of Agricult. and Food Chem. – 1999. – 47, № 10. – P. 4261–4266.

67. Liang L., Liu Z.,X., Yang M.,H., Zhang Y.,X., Wang C.,H. Discrimination of variety and authenticity for rice based on visual/near infrared reflection spec,tra // J. of Infrared and Millimeter Waves. – 2009. – 28, № 5. – P. 353–356.

68. Chen L.,Z., Zhao J., Ye Z.,H., Zhong Y.,P. Determination of adulteration in honey using near,infrared spectroscopy // Spectrosc. and Spectral Analysis. – 2008. – 28, № 11. – P. 2565–2568.

69. Nicolaou N., Goodacre R. Rapid and quan,titative detection of the microbial spoilage in milk us,ing Fourier transform infrared spectroscopy and che,mometrics // Analyst. – 2008. – 133. – P. 1424–1431.

70. Woo Y.,Ah, Kim H.,J., Chung H. Classifica,tion of cultivation area of ginseng radix with NIR and Raman spectroscopy // Analyst. – 1999. – 124. – P. 1223–1226.

71. Boffo E.F., Tavares L.A., Ferreira M.M.C., Ferreira A.G. Classification of Brazilian vinegars ac,cording to their 1H NMR spectra by pattern recogni,tion analysis // LWT – Food Sci. and Technol. – 2009. – 42, № 9. – P. 1455–1460.

72. Viggiani L., Morelli M.A.C. Characterization of wines by nuclear magnetic resonance: a work study on wines from the Basilicata region in Italy // J. of Agricult. and Food Chem. – 2008. – 56, № 18. – P. 8273–8279.

73. Casale M., Armanino C., Casolino C., Fori,na M. Combining information from headspace mass spectrometry and visible spectroscopy in the classifi,cation of the Ligurian olive oils // Anal. Chim. Acta. – 2007. – 589, № 1. – P. 89–95.

74. Shintu L., Caldarelli S., Franke B.M. Pre,selection of potential molecular markers for the geo,graphic origin of dried beef by HR,MAS NMR spec,troscopy // Meat Sci. – 2007. – 76, № 4. – P. 700–707.

75. Cabañero A.I., Recio J.L., Rupérez M. Iso,tope ratio mass spectrometry coupled to liquid and gas chromatography for wine ethanol characterization // Rapid Commun. in Mass Spectrom. – 2008. – 22, № 20. – P. 3111–3118. 76. Greule M., Hansel C., Bauermann U., Mo,sandl A. Feed additives: Authenticity assessment us,ing multicomponent,/multielement, isotope ratio mass spectrometry // European Food Research and Tech,nol. – 2008. – 227, № 3. – P. 767–776.

77. Richling E., Appel M., Heckel F., Kahle K., Kraus M., Preston C., Hummer W., Schreier P. Flavor authenticity studies by isotope ratio mass spectrome,try: perspectives and limits // ACS Symposium Series. – 2007. – 952. – P. 75–86.

78. Zeynali F., Tajik H., Asri,Rezaie S., Meshki,ni S., Fallah A.A., Rahnama M. Determination of cop,per, zinc and iron levels in edible muscle of three commercial fish species from iranian coastal waters of the Caspian sea // J. of Animal and Veterinary Ad,vances. – 2009. – 8, № 7. – P. 1285–1288.

79. Khan S., Kazi T.G., Baig J.A., Kolachi N.F., Afridi H.I., Shah A.Q., Kandhro G.A., Kumar S. Sepa,ration and preconcentration of trace amounts of alu,minum ions in surface water samples using different analytical techniques // Talanta. – 2009. – 80, № 1. – P. 158–162.

80. Rizzon L.A., Miele A. Analytical characteris,tics of Merlot wines from the Serra Gaúcha region [Características analíticas de vinhos Merlot da Serra Gaúcha] // Ciencia Rural. – 2009. – 39, № 6. – P. 1913–1916.

81. Scott S.M., James D., Ali Z., O’Hare W.T., Rowell F.J. Total luminescence spectroscopy with pattern recognition for classification of edible oils // Analyst. – 2003. – 128. – P. 966–973.

82. Cuadrado U.M., de Castro L.M.D., Gomez,Nieto M.A. Study of spectral analytical data using fin,gerprints and scaled similarity measurements // Anal. Bioanal. Chem. – 2005. – 381. – P. 953–963.

83. Woodcock T., Downey G., O'Donnell C.P. Near infrared spectral fingerprinting for confirmation of claimed PDO provenance of honey // Food Chem. – 2009. – 114, № 2. – P. 742–746.

84. Vlasov Y., Legin A. Non,selective chemical sensors in analytical chemistry: from "electronic nose" to "electronic tongue" // Fresenius J. Anal. Chem. – 1998. – 361. – P. 255–260.

85. Mielle P., Tarrega A., Gorria P., Liodenot J.J., Liaboeuf J., Andrejewski J.,L., Salles C. From human to artificial mouth, from basics to results // AIP Conference Proceedings. – 2009. –1137.–P.144–147.

86. Leake L.L. Electronic noses and tongues // Food Technol. – 2006. – 60, № 6. – P. 96–102.

87. Lvova L., Kim S.S., Legin A., Vlasov Y., Yang J.S., Cha G.S., Nam H. All,solid,state electronic tongue and its application for beverage analysis // Anal. Chim. Acta. – 2002. – 468. – P. 303–314.

88. Winquist F., Wide P., Lundström I. An elec,tronic tongue based on voltammetry // Anal. Chim. Acta. – 1997. – 357. – P. 21–31.

89. Winquist F., Krantz,Rulcker C., Lundstrom I. Electronic tongues // MRS Bulletin. – 2004. – 29, № 10. – P. 726–731.

90. Ciosek P., Brzozka Z., Wroblewski W. Clas,sification of beverages using a reduced sensor array // Sensors and Actuators B: Chemical. – 2004. – 103, № 1–2. – P. 76–83.

 91. Buratti S., Benedetti S., Scampicchio M., Pangerod E.C. Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue // Anal. Chim. Ac,ta. – 2004. – 525, № 1. – P. 133–139.

92. Tan T., Schmitt V., Isz S. Electronic tongue: a new dimension in sensory analysis: instrument complements use of the electronic nose for sensory analysis of food flavor by measuring nonvolatile flavor components // Food Technol. – 2001. – 55, № 10. – P. 44–50.

93. Bleibaum R.N., Stone H., Tan T., Labreche S., Saint,Martin E., Isz S. Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices // Food Quality and Prefe,rence. – 2002. – 13, № 6. – P. 409–422.

94. Родионова О.Е., Померанцев А.Л. Хемо,метрика в аналитической химии, 2006. – 61 с.

95. Дронов С.В. Многомерный статистиче,ский анализ: Учебное пособие. – Барнаул: Изд,во Алт. гос. ун,та, 2003. – С. 88–89.

 96. Померанцев А.Л. Метод главных компонент(PCA), 2008. 

97. Antti H. Multivariate characterization of wood related materials. PhD Thesis. Umea Universi,ty, 1999. – 86 p. 98. Hopke P.K. The evolution of chemometrics // Anal. Chim. Acta. – 2003. – 500. – P. 365–377.

99. Legin A., Rudnitskaya A., Clapham D., Se,leznev B., Vlasov Y. Electronic tongue for pharma,ceutical analytics: quantification of tastes and mask,ing effects // Anal. Bioanal. Chem. – 2004. – 380. – P. 36–45.

100. Hruskar M., Major N., Krpan M., Krbavcic I.P., Šarić G., Marković K., Vahčić N. Evaluation of milk and dairy products by electronic tongue // Mlje,karstvo. – 2009. – 59, № 3. – P. 193–200. 101. Oliveri P., Baldo M.A., Daniele S., Forina M. Development of a voltammetric electronic tongue for discrimination of edible oils // Anal. Bioanal. Chem. – 2009. – 395. – P. 1135–1143.

102. Cotte J.F., Casabianca H., Chardon S., Lhe,ritier J., Grenier,Loustalot M.F. Chromatographic analysis of sugars applied to the characterization of monofloral honey // Anal. Bioanal. Chem. – 2004. – 380. – P. 698–705.

103. Li H., Takahashi Y., Kumagai M., Fujiwara K., Kikuchi R., Yoshimura N., Amano T., Ogawa N. A chemometrics approach for distinguishing between beers using near infrared spectroscopy // J. of Near Infrared Spectrosc. – 2009. – 17, № 2. – P. 69–76.

104. Shin Y.,S., Bang K.,H., In D.,S., Kim O.,T., Hyun D.,Y., Ahn I.,O., Bon C.K., Choi H.,K. Finger,printing analysis of fresh ginseng roots of different ages using 1H,NMR spectroscopy and principal com,ponents analysis // Archives of Pharmacal Research. – 2007. – 30, № 12. – P. 1625–1628.

105. Urmano M., de Castro M.D.L., Perez P.M., Garcia,Olmo J., Gomez,Nieto M.A. Ultraviolet,visible spectroscopy and pattern recognition methods for differentiation and classification of wines // Food Chem. – 2006. – 97. – P. 166–175.

106. Cozzolino D., Vadell A., Ballesteros F., Ga,lietta G., Barlocco N. Combining visible and near,infrared spectroscopy with Chemometrics to trace muscles from an autochthonous breed of pig pro,duced in Uruguay: a feasibility study // Anal. Bioanal. Chem. – 2006. – 385. – P. 931–936.

107. Родионова О.Е. Задачи классификации и дискриминации. Презентация лекции. Омск, Ом,ский гос. ун,т им. Ф.М. Достоевского, 2007.

108. Кучерявский С.В. Методы многомерной классификации. Презентация лекции. Пятый межд. зимний симпозиум по хемометрике "Совре,менные методы анализа многомерных данных", Самара,2006.

109. Родионова О.Е. Хемометрический подход к исследованию больших массивов химических данных // Рос. хим. журн. (Журн. Рос. хим. об,ва им. Д.И. Менделеева. – 2006. – L, № 2. – С. 128–144.

110. Stanimirova I., Ustun B., Cajka T., Riddelo,va K., Hajslova J., Buydens L.M.C., Walczak B. Trac,ing the geographical origin of honeys based on vola,tile compounds profiles assessment using pattern recognition techniques // Food Chem. – 2010. – 118, № 1. – P. 171–176.

111. Shiroma C., Rodriguez,Saona L. Application of NIR and MIR spectroscopy in quality control of po,tato chips // J. of Food Composition and Analysis. – 2009. – 22, № 6. – P. 596–605.

112. Jaganathan J., Mabud Md. A., Dugar S. Geographic origin of wine via trace and ultra,trace elemental analysis using inductively coupled plasma mass spectrometry and chemometrics // ACS Sym,posium Series. – 2007. – 952. – P. 200–206.

113. Xie L.,J., Ying Y.,B. Use of near,infrared spectroscopy and least,squares support vector ma,chine to determine quality change of tomato juice // J. of Zhejiang University: Science B. – 2009. – 10, № 6. – P. 465–471.

114. Woodcock T., Downey G., Kelly J.D., O'Donnell C. Geographical classification of honey samples by near,infrared spectroscopy: a feasibility study // J. of Agricult. and Food Chem. – 2007. – 55, № 22. – P. 9128–9134.

115. Chen Q., Zhao J., Zhang H., Wang X. Fea,sibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration // Anal. Chim. Acta. – 2006. – 572, № 1. – P. 77–84.

 116. Tian S.,Y., Deng S.,P., Ding C.,H., Yin C.,L., Li H. Discrimination of red wine age using voltam,metric electronic tongue based on multifrequency large,amplitude voltammetry and pattern recognition method // Sensors and Materials. – 2007. – 19, № 5. – P. 287–298.

117. He J., Rodriguez,Saona L.E., Giusti M.M. Midinfrared spectroscopy for juice authentication – rapid differentiation of commercial juices // J. of Agri,cult. and Food Chem. – 2007. – 55, № 11. – P. 4443–4452.

 118. Chen Z.–P., Jiang J.,H., Li Y., Liang Yi,Z., Ru,Qin Yu. Fuzzy linear discriminant analysis for chemical data sets // Chemometr. and Intell. Lab. Systems. – 1999. – 45. – P. 295,302.

119. Факторный, дискриминантный и кластер,ный анализ: Пер. с англ. / Ким Дж.,О., Мьюллер Ч.У., Клекка У.Р. и др.; Под ред. И.С. Енюкова. – М.: Финансы и статистика, 1989. – 215 с.

120. Прикладная статистика: Классификация и снижение размерности: / Айвазян С.А., Бухштабер В.М., Енюков И.С., Мешалкин Л.Д. – М.: Финансы и статистика, 1989. – 607 с.

121. Кендалл М. Многомерный статистический анализ и временные ряды: / Кендалл М., Стьюарт А., Пресман Э.Л., Ротарь В.И., Колмогоров А.Н., Прохоров Ю.В. – М.: Наука, 1976. – 736 с.

122. Дубров А.М. Многомерные статистиче,ские методы. / Дубров А.М., Мхитарян В.С., Тро,шин Л.И. – М.:Финансы и статистика, 2000. – 350 с.

123. Афифи А. Статистический анализ: Под,ход с использованием ЭВМ: Пер. с англ. / Афифи А., Эйзен С., Енюков И.С., Новиков И.Д., Башарин Г.П. – М.: Мир, 1982. – 488 с.

 124. Линейный регрессионный анализ резуль,татов химического эксперимента в системе Statis,tica. Учебно,методическое пособие / Михаль,чук В.М., Михальчук А.В. / Донецк: ДонНУ, 2002. – 66с.

125. Налимов В.В. Применение математиче,ской статистики при анализе вещества. – М.: Гос. изд,во физ.,мат. л,ры, 1960. – 430 с.

126. Paradkar M.M., Irudayaraj J., Sakhamuri S. Discrimination and classification of beet and cane sugars and their inverts in maple syrup by FT,Raman spectroscopy // Applied Eng. in Agricult. – 2002. – 18, № 3. – P. 379–383.

 127. Suchánek M., Filipová H., Volka K., Delga,dillo I., Davies A.N. Qualitative analysis of green cof,fee by infrared spectrometry // Fresenius J. of Anal. Chem. – 1996. – 354, № 3. – P. 327–332.

128. Cozzolino D., Parker M., Dambergs R.G., Herderich M., Gishen M. Chemometrics and visible,near infrared spectroscopic monitoring of red wine fermentation in a pilot scale // Biotechnol. and Bioeng. – 2006. – 95, № 6. – P. 1101–1107.

129. Granitto P.M., Gasperi F., Biasioli F., Trai,notti E., Furlanello C. Modern data mining tools in descriptive sensory analysis: a case study a Random forest approach // Food Quality and Preference. – 2007. – 78. – P. 681–689.

130. Timm H., Borgelt C., Döring C., Kruse R. An extension to possibilistic fuzzy cluster analusis // Fuzzy Sets and Syst. – 2004. – 147. – P. 3–16.

 131. Tsai C.,An, Lee Te,C., Ho I,C., Yang U.,C., Chen C.,H., Chen J.J. Multi,class clustering and pre,diction in the analysis of microarray data // Mathemat. Biosci. – 2005. – 193. – P. 79–100.

132. Leski J. Towards a robust clustering // Fuzzy Sets and Syst. – 2003. – 137. – P. 215–233.

133. Dumitrescu D., Lazzerini B., Jain L.C. Fuzzy sets and their application to clustering and training. – CRC Press LLC: Florida, 2000. – 593 c.

 134. Zahid N., Abouela O., Limouri M., Essaid A. Fuzzy clustering on k,nearest,neighbours rule // Fuzzy Sets and Syst. – 2001. – 120. – P. 239–247.

135. Wu K.,L., Yang M.,S. Alternative c,means clustering algorithms // Pattern Recognition. – 2002. – 35. – P. 2267–2278.

136. Shahin M.A., Verma B.P., Tollner E.W. Fuzzy logic model for predicting peanut maturity // Transactions of the American Soc. of Agricult. Engi,neers. – 2000. – 43, № 2. – P. 483–490.

137. Андреев И.М. Описание алгоритма CART // Методы. Алгоритмы. Программы. – 2004. – 3–4, № 7–8. – С. 48–53.

138. Шитиков В.К., Розенберг Г.С., Зинченко Т.Д. Количественная гидроэкология: методы сис,темной идентификации. – Тольятти: Институт эко,логии Волжского бассейна РАН, 2003. – 463 с.

139. Zhang M. H., Xu Q.S., Daeyaert F., Lewi P.J., Massart D.L. Application of boosting to classifi,cation problems in chemometrics // Anal. Chim. Acta. – 2005. – 544. – P. 167–176.

140. Dobra A.V. Classification and regression tree construction. PhD Thesis. – Cornell University, 2003.–186p. http://www.cise.ufl.edu/~adobra/old/papers/phd,thesis.pdf

141. Шампандер А. Дж. Искусственный интел,лект в компьютерных играх: как обучить виртуаль,ные персонажи реагировать на внешние воздей,ствия. Гл. 26 Деревья классификации и регрессии. – М.: Вильямс, 2007. – 385–401 с. http://www.williamspublishing.com/PDF/978,5,8459,1170,4/part.pdf

142. Khoshgoftaar T.M., Allen E.B., Naik A., Jones W.D., Hudepohl J.P. Using classification trees for software quality models: Lessons learned // Int. J. of Software Eng. and Knowledge Eng. – 1999. – 9, № 2. – P. 217,231.

143. Smeti E.M., Thanasoulias N.C., Lytras E.S., Tzoumerkas P.C., Golfinopoulos S.K. Treated water quality assurance and description of distribution net,works by multivariate сhemometrics // Water Re,search. – 2009. – 43, № 18. – P. 4676–4684.

144. Ortiz M.C., Herrero A., Sanchez M.S., Sara,bia L.A., Iniguez M. Modelling the relation between CieLab parameters and sensory scores for quality control of red,wine colour // Analyst. – 1995. – 120, № 12. – P. 2793–2798.

145. Kokkinofta R.I., Theocharis C.R. Chemome,tric characterization of the cypriot spirit "Zivania" // J. of Agricult. and Food Chem. – 2005. – 53, № 13. – P. 5067–5073.

146. Mutihac L., Mutihac R. Mining in chemome,trics // Anal. Chim. Acta. – 2008. – 612. – P. 1–18.

147. Nobel W.S. What is a support vector ma,chine? // Nature Biotechnol. – 2006. – 24, № 12. – P. 1565–1567.

148. Brereton R.G., Lloyd G.R. support Vector Machines for classification and regression // Analyst. – 2010. – 135. – P. 230–267.

149. Gunn S. support vector machines for classi,fication and regression. ISIS Technical Report. – Uni,versity of Southampton, 1997. – 42 p. http://www.svms.org/tutorials/Gunn1997.pdf

150. Asano A. support vector machine and ker,nel method // Pattern information processing (2004 Autumn Semester) Session 12. – 3 p. http://www.svms.org/tutorials/Asano2004.pdf

151. Лившиц Ю. Метод опорных векторов: лекция № 7 курса "Алгоритмы для Интернета", 2006. – 9 с. http://yury.name/internet/07ianote.pdf

152. Lin Y. On the support vector machines. Technical Report № 1029. – Madison: University of Wisconsin, Department of Statistics, 2000. – 25 p. http://www.svms.org/tutorials/Lin2000.pdf

153. Воронцов К.В. Лекции по методу опорных векторов, 2007. – 18 c. http://www.ccas.ru/voron/download/SVM.pdf

154. Браславский П., Густелев В. Система ав,томатического реферирования новостных сооб,щений на основе машинного обучения. // Элек,тронные библиотеки: перспективные методы и технологии, электронные коллекции. Труды 9,й Всеросс. конф. RCDL'2007. – Переславль,Залесский: Изд,во "Университет города Пере,славля", 2007.– С. 142–147. http://www.kansas.ru/pb/paper/rcdl2007.pdf

155. Erästö P. support vector machines – back,grounds and practice. Dissertation for the Degree of Licentiate of Philosophy. – Helsinki: Rolf Nevanlinna Institute, 2001. – 78 p. http://www.svms.org/tutorials/Erasto2001.pdf

156. Burges C.J.C. A tutorial on support vector machines for pattern recognition // Data Mining and Knowledge Discovery. – 1998. – 2, № 2. – P. 121–167.

157. Burbidge R. support vector machines: some perspectives from probability and statistics // Imperial College Statistics Seminars, 2001. – 53 p. http://www.svms.org/tutorials/Burbidge2001.pdf

158. El Barbri N., Llobet E., El Bari N., Correig X., Bouchikhi B. Electronic nose based on metal oxide semiconductor sensors as an alternative tech,nique for the spoilage classification of red meat // Sensors. – 2008. – 8, № 1. – P. 142–156.

159. Unay D., Gosselin B. Stem and calyx rec,ognition on Jonagold apples by pattern recognition // J. of Food Eng. – 2007. – 78, № 2. – P. 597–605.

160. Yang H., Zhou Z., Zhao R., Wang B. Rec,ognition of wheat varieties by image analysis // Pro,ceedings of SPIE – The Int. Society for Optical Eng. – 2003. – 5286, № 1. – P. 465–468.

161. Setiawan M.A., Rao R.K., Lakshminaraya,nan S. Partial correlation metric based classifier for food product characterization // J. of Food Eng. – 2009. – 90. – P. 146–152.

162. Эсбенсен К. Анализ многомерных дан,ных. Избранные главы / Пер. с англ. С.В. Кучеряв,ского под ред. О.Е. Родионовой. – Черноголовка: Институт проблем химической физики РАН, 2005. – 160 с.

163. Родионова О., Померанцев А. Проекци,онные методы в системе Excel: Учебное пособие // Российское хемометрическое общество. http://www.chemometrics.ru/materials/textbooks/projection.htm#Ch1.3 164. Lakshminarayanan S., Sirish L., Nandaku,mar K. Modelling and control of multivariable process: the dynamic projection to latent structures approach // AIChe J. – 1997. – 43. – P. 2307–2323.

165. Abdi, H. Partial least square regression, pro,jection on latent structure regression (PLS,Regression) // Wiley Interdisciplinary Reviews: Com,putational Statistics, V. 2, Iss. – Wiley, 2010. – P. 97,106. http://www.utdallas.edu/~herve/abdi,wireCS,PLS2010.pdf

166. Nunes A., Martins J., Barros A.S., Galvis,Sánchez A.C., Delgadillo I. Estimation of olive oil acidity using FT,IR and partial least squares regres,sion // Sensing and Instrumentation for Food Quality and Safety. – 2009. – 3, № 3. – P. 187–191.

167. Lachenmeier D.W. Rapid quality control of spirit drinks and beer using multivariate data analysis of Fourier transform infrared spectra // Food Chem. – 2007. – 101, № 2. – P. 825–832.

168. Lin P., Chen Y.M., He Y. Fast discrimination of varieties of sugar based on spectroscopy technolo,gy // Spectrosc. and Spectral Analysis. – 2009. – 29, № 2. – P. 382–385.

169. Armenta S., Garrigues S., de la Guardia M. Determination of edible oil parameters by near infra,red spectrometry // Anal. Chim. Acta. – 2007. – 596, № 2. – P. 330–337.

170. Mizrach A., Schmilovitch Z., Korotic R., Iru,dayaraj J., Shapira R. Yeast detection in apple juice using Raman spectroscopy and chemometric me,thods // Transactions of the ASABE. – 2007. – 50, № 6. – P. 2143–2149.

171. Fagan C.C., O'Donnell C.P., O'Callaghan D.J., Downey G., Sheehan E.M., Delahunty C.M., Everard C., Guinee T.P., Howard V. Application of mid,infrared spectroscopy to the prediction of maturity and sensory texture attributes of cheddar cheese // J. of Food Sci. – 2007. – 72, № 3. – P. E130–E137.

172. Andrade J.M., Gomez,Carracedo M.P., Fer,nandez E., Elbergali A., Kubista M., Prada D. Classi,fication of commercial apple beverages using a mini,mum set of mid,IR wavenumbers selected by Procrustes rotation // Analyst. – 2003. – 128. – P. 1193–1199.

173. Ruth S., Kiers J.L., Akkermans W., Perez R., Koot A.H., Perri E., Pellegrino M., Moreno Rojas J.M., Guillou C., Rossignol,Castera A. Geographical origin, cultivar and harvesting year verification of Eu,ropean and non,European olive oils using proton transfer reaction mass spectrometry with multivariate data analysis // Proceedings of the 5th Intern. Tech,nical Symposium on Food Processing, Monitoring Technol. in Bioprocesses and Food Quality Manage,ment. – Potsdam, Germany. – 2009. – P. 791–797.

174. Ruth S., Koot A., Akkermans W., Araghipour N., Rozijn M., Baltussen M., Wisthaler A., Märk T., Frankhuizen R. Butter and butter oil classification by PTR,MS // European Food Research and Technol. – 2008. – 227, № 1. – P. 307–317.

175. Galtier O., Dupuy N., Le Dréau Y., Ollivier D., Pinatel C., Kister J., Artaud J. Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra // Anal. Chim. Acta. – 2007. – 595, № 1–2. – P. 136–144.

176. Родионова О.Е. Интервальный метод об,работки результатов многоканальных эксперимен,тов. Дис. док. физ.,мат. наук: 01.04.01 / Ин,т хим. физики им. Н.Н. Семенова РАН. – М., 2008. –272 с. www.nsc.ru/interval/Library/ApplDiss/Rodionova.pdf

177. Agatonovic,Kustrin S., Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research // J. of Pharmaceutical and Biomed. Analysis. – 2000. – 22. – P. 717–727.

178. Arbib M.A. The handbook of brain theory and neural networks, 2nd edn. – Cambridge, e.a.: Bradford Book, 2002. – 1290 p.

179. Gupta M.M., Jin L., Homma N. Static and dynamic neural network: from fundamentals to ad,vanced theory. – Wiley, 2003. – 722 p.

180. Braspenning P.J., Thuijsman F., Weijters A.J.M.M. Artificial neural networks. An introduction to ANN theory and practice. – Berlin: Springer,Verlag, 1995. – 136 p.

181. Хайкин С. Нейронные сети: полный курс, 2,е изд., испр.: Пер. с англ. – М.: Вильямс, 2006. – 1104 с.

182. Круглов В.В., Борисов В.В. Искусственные нейронные сети. Теория и практика. 2,е изд., стереотип. – М.: Горячая линия – Телеком, 2002. – 382 с.

183. Осовский С. Нейронные сети для обра,ботки информации / Пер. с. польского И.Д. Рудин,ского. – М.: Финансы и статистика, 2002. – 344 с.

184. Freeman J.A. Neural networks: algorithms, applications and programming techniques. – New York, e.a.: Addison,Wesley, 1991. – 401 p.

185. Mandic D.P., Chambers J.A. Recurrent neural network for prediction: learning algorithms, architectures and stability (adaptive and learning sys,tems for signal processing, communications and con,trol series). – Wiley, 2001. – 285 p.

186. Krogh A. What are artificial neural net,works? // Nature Biotechnol. – 2008. – 26, № 2. – P. 195–197.

187. Smits J.R.M., Melssen W.J., Buydens L.M.C., Kateman G. Using artificial neural networks for solving chemical problems. Part I. Multi,layer feed,forward networks // Chemometr. and Intell. Lab. Sys,tems. – 1994. – 22, № 2. – P. 165–189.

188. Melssen W.J., Smits J.R.M., Buydens L.M.C., Kateman G. Using artificial neural networks for solving chemical problems. Part II. Kohonen self, organising feature maps and Hopfield networks // Chemomemtr. and Intell. Lab. Systems. – 1994. – 23, № 2. – P. 267–291.

189. Marini F. Artificial neural networks in foods,tuff analyses: Trends and perspectives. A review // Anal. Chim. Acta. – 2009. – 635, № 2. – P. 121–131.

190. Llobet E., Hines E.L., Gardner J.W., Bartlett P.N., Mottram T.T. Fuzzy ARTMAP based electronic nose data analysis // Sensors and Actuators B: Chemical. – 1999. – 61, № 1. – P. 183–190.

191. Grošelj N., van der Veer G., Tušar M., Vračko M., Novič M. Verification of the geological ori,gin of bottled mineral water using artificial neural net,works // Food Chem. – 2010. – 118, № 4. – P. 941–947.

192. Wei B.,L., Chen G.,Q., Wang J., Xu J.,C., Yan G.,F., Zhu T., Gao S.,M. A new method for de,termining the concentration of melamine in milk // Gu,angdianzi Jiguang / J. of Optoelectronics Laser. – 2009. – 20, № 12. – P. 1618–1621.

193. da Costa Filho P.A. Rapid determination of sucrose in chocolate mass using near infrared spec,troscopy // Anal. Chim. Acta. – 2009. – 631, № 2. – P. 206–211.

194. Francelin R.A., Gomide F.A.C., Lancas F.M. Use of artificial neural networks for the classification of vegetable oils after CG analysis // Chromatogra,phy. – 1993. – 35, № 3–4. – P. 160–166.

195. Cantero M.C., Perez R.M., Martinez P.J., Aguilar P.L., Plaza J., Plaza A. Analysis of the beha,viour of a neural network model in the identification and quantification of hyperspectral signatures applied to the determination of water quality // Proc. of SPIE. – The Int. Soc. for Optical Eng. – 2004. – 5584, № 23. – P. 174–185.

196. Onea A., Collewet G., Fernandez C., Vertan C., Richard N., Mariette F. Quality analysis of blue,veined cheeses by MRI: a preliminary study // Proc. SPIE. – The Int. Soc. for Optical Eng. – 2003. – 5132. – P. 400–409.