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Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

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Volume 19, Issue 4 (Dec 2012)

Issues

Measurement Data Processing in Spectrophotometric Analysers of Food

Roman Z. Morawski
  • Warsaw University of Technology, Faculty of Electronics and Information Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland; +48 22 234 7721
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  • De Gruyter OnlineGoogle Scholar
Published Online: 2012-12-28 | DOI: https://doi.org/10.2478/v10178-012-0056-1

Abstract

Spectrometry, especially spectrophotometry, is getting more and more often the method of choice not only in laboratory analysis of (bio)chemical substances, but also in the off-laboratory identification and testing of physical properties of various products, in particular - of various organic mixtures including food products and ingredients. Specialised spectrophotometers, called spectrophotometric analysers, are designed for such applications. This paper is on the state of the art in the domain of data processing in spectrophotometric analysers of food (including beverages). The following issues are covered: methodological background of food analysis, physical and metrological principles of spectrophotometry, the role of measurement data processing in spectrophotometry. General considerations are illustrated with examples, predominantly related to wine and olive oil analysis.

: Keywords spectrophotometry; chemometrics; spectral data processing; food analysis; wine analysis.

  • [1] Heldman, D.R. (2006). President's message: IFT and the Food Science Profession. Food Technol. Biotech., 60(10), 11.Google Scholar

  • [2] Application of spectroscopy for non-destructive ‘in-bottle’ measurement of wine composition and quality. Report of The Australian Wine Research Institute, June 2009, www.awri.com.au[2012.09.22].Google Scholar

  • [3] Casale, M., Oliveri, P., Armanino, C., Lanteri, S., Forina, M. (2010). NIR and UV-Vis spectroscopy, artificial nose and tongue: comparison of four fingerprinting techniques for the characterisation of Italian red wines. Anal. Chim. Acta, 668(2), 143-148.Google Scholar

  • [4] Cosme, F., Ricardo-Da-Silva, J.M., Laureano, O. (2009). Tannin profiles of Vitis vinifera L. cv. red grapes growing in Lisbon and from their monovarietal wines. Food Chem., 112, 197-204.CrossrefGoogle Scholar

  • [5] Cozzolino, D., Kwiatkowski, M.J., Dambergs, R.G., Cynkat, W.U., Janik, L.J., Skouroumounis, G., Gishen, M. (2008). Analysis of elements in wine using near infrared spectroscopy and partial least squares regression. Talanta, 74, 711-716.CrossrefGoogle Scholar

  • [6] Cozzolino, D., Cynkar, W.U., Dambergs, R.G., Mercurio, M.D., Smith, P.A. (2006). Measurement of Condensed Tannins and Dry Matter in Red Grape Homogenates Using Near Infrared Spectroscopy and Partial Least Squares. J. Agr. Food Chem., 56(20), 7631-7636.Google Scholar

  • [7] Cozzolino, D., Cowey, G., Lattey, K.A., Godden, P., Cynkar, W.U., Dambergs, R.G., Janik, L., Gishen, M. (2008). Relationship between wine scores and visible-near-infrared spectra of Australian red wines. Anal. Bioanal. Chem., 391, 975-981.Google Scholar

  • [8] Cozzolino, D., Cynkar, W. U., Shah, N., Dambergs, R. G., Smith, P.A. (2009). A brief introduction to multivariate methods in grape and wine analysis. Int. J. Wine Res., 1, 123-130.CrossrefGoogle Scholar

  • [9] Cozzolino, D., Cynkar, W.U., Shah, N., Smith, P.A. (2011). Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand. Food. Chem., 126(2), 673-678.CrossrefGoogle Scholar

  • [10] Cozzolino, D., Cynkar, W., Shah, N., Smith, P. (2011). Quantitative analysis of minerals and electric conductivity of red grape homogenates by near infrared reflectance spectroscopy. Comput. Electron. Agr., 77(1), 81-85.CrossrefGoogle Scholar

  • [11] Cozzolino, D., Cynkar, W., Shah, N., Smith, P. (2011). Technical solutions for analysis of grape juice, must, and wine: the role of infrared spectroscopy and chemometrics. Anal. Bioanal Chem., 401(5), 1475-1484.CrossrefGoogle Scholar

  • [12] Cynkar, W., Dambergs, R., Smith, P., Cozzolino, D. (2010). Classification of Tempranillo wines according to geographic origin: Combination of mass spectrometry based electronic nose and chemometrics. Anal. Chim. Acta, 660, 227-231.Google Scholar

  • [13] Fernández-Novales, J., López, M.-I., Sánchez, M.-T., Morales, J., González-Caballero, V. (2009). Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening, winemaking, and aging of white and red wines. Food Res. Int., 42, 285-291.CrossrefGoogle Scholar

  • [14] Fernández-Novales, J., Sánchez, M.-T., López, M.-I., García-Mesa, J.-A., Ramírez, P. (2011). Feasibility of using a miniature fiber optic UV-Vis-NIR spectrometer to assess total polyphenol index, color intensity and volumic mass in red wine fermentations. J. Food Process Eng., 34, 1028-1045.CrossrefGoogle Scholar

  • [15] Ferreira, M.L., Costa, a. M., Ribeiro, N., Simões, T., Barros, P. (2009). Quality control in FTIR wine analysis - acceptance of analytical results. Ciência e Técnica Vitivinícola, 24(1), 47-53.Google Scholar

  • [16] Ferrer-Gallego, R., Hernández-Hierro, J.M., Rivas-Gonzalo, J.C., Escribano-Bailón, M.T. (2011). Determination of phenolic compounds of grape skins during ripening by NIR spectroscopy. Food Sci. Technol. - LEB, 44(4), 847-853.Google Scholar

  • [17] Geraudie, V., Roger, J.M., Ferrandis, J.L., Gialis, J.M., Barbe, P., Bellon Maurel, V., Pellenc, R. (2009). A revolutionary device for predicting grape maturity based on NIR spectrometry. In Proc. 8th Fruit Nut and Vegetable Production Engineering Symposium, Concepcion, Chile.Google Scholar

  • [18] González-Caballero, V., Sánchez, M.-T., López, M.-I., Pérez-Marín, D. (2010). First steps towards the development of a non-destructive technique for the quality control of wine grapes during on-vine ripening and on arrival at the winery. J. Food Eng., 101(2), 158-165.CrossrefGoogle Scholar

  • [19] Li, H., Wang, X., Li, Y., Li, P., Wang, H. (2009). Polyphenolic compounds and antioxidant properties of selected China wines. Food Chem., 112, 454-460.CrossrefGoogle Scholar

  • [20] Kruzlicova, D., Mocak, J., Balla, B., Petka, J., Farkova, M., Havel, J. (2009). Classification of Slovak white wines using artificial neural networks and discriminant techniques. Food Chem., 112, 1046-1052.CrossrefGoogle Scholar

  • [21] Larrain, M., Guesalaga, A.R., Agosin, E. (2008). A Multipurpose Portable Instrument for Determining Ripeness in Wine Grapes Using NIR Spectroscopy. IEEE T. Instrum. Meas., 57(2), 294-302.CrossrefGoogle Scholar

  • [22] Lorenzo, C., Garde-Cerdán, T., Pedroza, M.A., Alonso, G.L., Salinas, M.R. (2009). Determination of fermentative volatile compounds in aged red wines by near-infrared spectroscopy. Food Res. Int., 42(9), 1281-1286.CrossrefGoogle Scholar

  • [23] Versari, A., Boulton, R.B., Parpinello, G.P. (2008). A comparison of analytical methods for measuring the color components of red wines. Food Chem., 106, 397-402.CrossrefGoogle Scholar

  • [24] Niu, X., Shen, F., Yu, Y., Yan, Z., Xu, K., Yu, H., Ying, Y. (2008). Analysis of Sugars in Chinese Rice Wine by Fourier Transform Near-Infrared Spectroscopy with Partial Least-Squares Regression. J. Agr. Food Chem., 56(16), 7271-7278.CrossrefGoogle Scholar

  • [25] Yanez, L., Saavedra, J., Martınez, C., Cordova, A., Ganga, M.A. (2012). Chemometric Analysis for the Detection of Biogenic Amines in Chilean Cabernet Sauvignon Wines: A Comparative Study between Organic and Nonorganic Production. J. Food Sci., 77(8), T143-T150.CrossrefGoogle Scholar

  • [26] Yu, H.Y., Niu, X.Y., Lin, H.J., Ying, Y.B., Li, B.B., Pan, X.X. (2009). A feasibility study on on-line determination of rice wine composition by Vis-NIR spectroscopy and least-squares support vector machines. Food Chem., 113, 291-296.CrossrefGoogle Scholar

  • [27] de O. Alves, J., Neto, W. B., Mitsutake, H., Alves, P.S.P., Augusti, R. (2010). Extra virgin (EV) and ordinary (ON) olive oils: distinction and detection of adulteration (EV with ON) as determined by direct infusion electrospray ionization mass spectrometry and chemometric approaches. Rapid Commun. Mass Sp., 24(13), 1875-1880.CrossrefGoogle Scholar

  • [28] Barros, A.S., Nunes, A., Martins, J., Delgadillo, I. (2009). Determination of oil and water in olive and olive pomace by NIR and multivariate analysis. Sensing Instrum. Food Qual. Safety, 3(3), 180-186.Google Scholar

  • [29] Bellincontro, A., Taticchi, A., Servili, M., Esposto, S., Farinelli, D., Mencarelli, F. (2012). Feasible Application of a Portable NIR-AOTF Tool for On-Field Prediction of Phenolic Compounds during the Ripening of Olives for Oil Production. J. Agr. Food Chem., 60, 2665-2673.CrossrefGoogle Scholar

  • [30] Cajka, T., Riddellova, K., Klimankova, E., Cerna, M., Pudil, F., Hajslova, J. (2010). Traceability of olive oil based on volatiles pattern and multivariate analysis. Food Chem., 121(1), 282-289.CrossrefGoogle Scholar

  • [31] Calvano, C.D., Aresta, A., Zambonin, C.G. (2010). Detection of hazelnut oil in extra-virgin olive oil by analysis of polar components by micro-solid phase extraction based on hydrophilic liquid chromatography and MALDI-ToF mass spectrometry. J. Mass Spectrom., DOI: 10.1002/jms.1753.CrossrefGoogle Scholar

  • [32] Casale, M., Sinelli, N., Oliveri, P., Di Egidio, V., Lanteri, S. (2010). Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification. Talanta, 80(5), 1832-1837.CrossrefGoogle Scholar

  • [33] Casale, M., Zunin, P., Cosulich, M.E., Pistarino, E., Perego, P., Lanteri, S. (2010). Characterisation of table olive cultivar by NIR spectroscopy. Food Chem., 122(4), 1261-1265.CrossrefGoogle Scholar

  • [34] Casale, M., Casolino, C., Oliveri, P., Forina, M. (2010). The potential of coupling information using three analytical techniques for identifying the geographical origin of Liguria extra virgin olive oil. Food Chem., 118(1), 163-170.CrossrefGoogle Scholar

  • [35] Casale, M., Oliveri, P., Casolinoa, C., Sinelli, N., Zunina, P., Armaninoa, C., Forina, M., Lanteria, S. (2012). Characterisation of PDO olive oil Chianti Classico by non-selective (UV-visible, NIR and MIR spectroscopy) and selective (fatty acid composition) analytical techniques. Anal. Chim. Acta, 712, 56-63.Google Scholar

  • [36] Concha-Herrera, V., Lerma-García, M.a.J.s., Herrero-Martínez, J.M., Simó-Alfonso, E.F. (2009). Prediction of the Genetic Variety of Extra Virgin Olive Oils Produced at La Comunitat Valenciana, Spain, by Fourier Transform Infrared Spectroscopy. J. Agr. Food Chem., 57(21), 9985-9989.CrossrefGoogle Scholar

  • [37] Dupuy, N., Galtier, O., Le Dréau, Y., Pinatel, C., Kister, J., Artaud, J. (2010). Chemometric analysis of combined NIR and MIR spectra to characterize French olives. Eur. J. Lipid Sci. Tech., 112(4), 463-475.CrossrefGoogle Scholar

  • [38] Dupuy, N., Galtier, O., Ollivier, D., Vanloot, P., Artaud, J. (2010). Comparison between NIR, MIR, concatenated NIR and MIR analysis and hierarchical PLS model. Application to virgin olive oil analysis. Anal. Chim. Acta, 666(1-2), 23-31.Google Scholar

  • [39] Frankel, E.N. (2010). Chemistry of Extra Virgin Olive Oil: Adulteration, Oxidative Stability, and Antioxidants. J. Agr. Food Chem., 58(10), 5991-6006.CrossrefGoogle Scholar

  • [40] Galtier, O., Abbas, O., Le Dréau, Y., Rebufa, C., Kister, J., Artaud, J., Dupuy, N. (2011). Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions. Vib. Spectrosc., 55, 132-140.Google Scholar

  • [41] Gómez-Robledo, L., Melgosa, M., Huertas, R., Roa, R., Moyano, M.J., Heredia, F.J. (2008). Virgin-Olive-Oil Color in Relation to Sample Thickness and the Measurement Method. J. Am. Oil Chem. Soc., 85(11), 1063-1071.CrossrefGoogle Scholar

  • [42] Gurdeniz, G. Ozen, B. (2009). Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data. Food Chem., 116, 519-525.CrossrefGoogle Scholar

  • [43] Gurdeniz, G., Ozen, B., Tokatli, F. (2010). Comparison of fatty acid profiles and mid-infrared spectral data for classification of olive oils. Eur. J. Lipid Sci. Tech., 112(2), 218-226.CrossrefGoogle Scholar

  • [44] Harrington, P.d.B., Kister, J., Artaud, J., Dupuy, N. (2009). Automated Principal Component-Based Orthogonal Signal Correction Applied to Fused NI-MIR Spectra of French Olive Oils. Anal. Chem., 81(17), 7160-7169.CrossrefGoogle Scholar

  • [45] Hennessy, S., Downey, G., O'Donnell, C.P. (2009). Confirmation of Food Origin Claims by Fourier Transform Infrared Spectroscopy and Chemometrics: Extra Virgin Olive Oil from Liguria. J. Agr. Food Chem., 57, 1735-1741.CrossrefGoogle Scholar

  • [46] Kavdir, I., Buyukcan, M.B., Lu, R., Kocabiyik, H., Seker, M. (2009). Prediction of olive quality using FT-NIR spectroscopy in reflectance and transmittance modes. Biosystems Eng., 103(3), 304-312.CrossrefGoogle Scholar

  • [47] Kružlicová, D., Mrázová, V., Šnuderl, K., Mocák, J., Lankmayr, E. (2008). Chemometric classification of edible oils. Acta Chim. Slovaca, 1(1), 165-174.Google Scholar

  • [48] Lerma-García, M.J., Ramis-Ramos, G., Herrero-Martínez, J.M., Simó-Alfonso, E.F. (2010). Authentication of extra virgin olive oils by Fourier-transform infrared spectroscopy. Food Chem., 118(1), 78-83.CrossrefGoogle Scholar

  • [49] Lerma-García, M.J., Simó-Alfonso, E.F., Bendini, A., Cerretani, L. (2011). Rapid evaluation of oxidised fatty acid concentration in virgin olive oil using Fourier-transform infrared spectroscopy and multiple linear regression. Food Chem., 124(2), 679-684.CrossrefGoogle Scholar

  • [50] Maggio, R.M., Valli, E., Bendini, A., Gómez-Caravaca, A.M., Toschi, T.G., Cerretani, L. (2011). A spectroscopic and chemometric study of virgin olive oils subjected to thermal stress. Food Chem., 127, 216-221.CrossrefGoogle Scholar

  • [51] Morales-Sillero, A., Fernández-Cabanás, V.-M., Casanova, L., Jiménez, M.-R., Suárez, M.-P., Rallo, P. (2011). Feasibility of NIR spectroscopy for non-destructive characterization of table olive traits. J. Food Eng., 107(1), 99-106.CrossrefGoogle Scholar

  • [52] Nunes, A., Martins, J., Barros, A.S., Galvis-Sánchez, A.C., Delgadillo, I. (2009). Estimation of olive oil acidity using FT-IR and partial least squares regression. Sensing Instrum. Food Qual. Safety, 3(3), 187-191.Google Scholar

  • [53] Obeidat, S.M., Khanfar, M.S., Obeidat, W.M. (2009). Classification of Edible Oils and Uncovering Adulteration of Virgin Olive Oil Using FTIR with the Aid of Chemometrics. Aust. J. Basic Appl. Sci., 3(3), 2048-2053.Google Scholar

  • [54] Rohman, A., Che Man, Y.B., Ismail, A., Hashim, P. (2010). Application of FTIR Spectroscopy for the Determination of Virgin Coconut Oil in Binary Mixtures with Olive Oil and Palm Oil. J. Am. Oil Chem. Soc., 87(6), 601-606.CrossrefGoogle Scholar

  • [55] Rohman, A. Man, Y.B.C. (2010). Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil. Food Res. Int., 43(3), 886-892.CrossrefGoogle Scholar

  • [56] Sinelli, N., Cerretani, L., Egidio, V.D., Bendini, A., Casiraghi, E. (2010). Application of near (NIR) infrared and mid- (MIR) infrared spectroscopy as a rapid tool to classify extra virgin olive oil on the basis of fruity attribute intensity. Food Res. Int., 43(1), 369-375.CrossrefGoogle Scholar

  • [57] Sinelli, N., Casale, M., Di Egidio, V., Oliveri, P., Bassi, D., Tura, D., Casiraghi, E. (2010). Varietal discrimination of extra virgin olive oils by near- and mid-infrared spectroscopy. Food Res. Int., 43, 2126-2131.CrossrefGoogle Scholar

  • [58] Torrecilla, J.S., Ro o, ., Domínguez, J.C., Rodríguez, F. (2010). A Novel Method To Quantify the Adulteration of Extra Virgin Olive Oil with Low-Grade Olive Oils by UV-Vis. J. Agr. Food Chem., 58(3), 1679-1684.CrossrefGoogle Scholar

  • [59] Bobelyn, E., Serban, A.-S., Nicu, M., Lammertyn, J., Nicolai, B.M., Saeys, W. (2010). Postharvest quality of apple predicted by NIR-spectroscopy: Study of the effect of biological variability on spectra and model performance. Postharvest Biol. Tech., 55(3), 133-143.CrossrefGoogle Scholar

  • [60] Bureau, S., Ruiz, D., Reich, M., Gouble, B., Bertrand, D., Audergon, J.-M., Renard, C.M.G.C. (2009). Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. Food Chem., 113, 1323-1328.CrossrefGoogle Scholar

  • [61] Cayuela, J.A. Weiland, C. (2010). Intact orange quality prediction with two portable NIR spectrometers. Postharvest Biol. Tec., 58(2), 113-120.CrossrefGoogle Scholar

  • [62] Clement, A., Dorais, M., Vernon, M. (2008). Nondestructive Measurement of Fresh Tomato Lycopene Content and Other Physicochemical Characteristics Using Visible-NIR Spectroscopy. J. Agr. Food Chem., 56, 9813-9818.CrossrefGoogle Scholar

  • [63] Cozzolino, D., Cynkar, W.U., Shah, N., Smith, P. (2011). Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality. Food Res. Int., 44(7), 1888-1896.CrossrefGoogle Scholar

  • [64] Hongjian, L., Yibin, Y. (2009). Theory and application of near infrared spectroscopy in assessment of fruit quality: a review. Sensing Instrum. Food Qual. Safety, 3, 130-141.Google Scholar

  • [65] Judd, M.J., Meyer, D.H., Meekings, J.S., Richardson, A.C., Walton, E.F. (2010). An FTIR study of the induction and release of kiwifruit buds from dormancy. J. Sci. Food Agr., 90, 1071-1080.Google Scholar

  • [66] Lijuan, X., Xingqian, Y., Donghong, L., Yibin, Y. (2009). Quantification of glucose, fructose and sucrose in bayberry juice by NIR and PLS. Food Chem., 114, 1135-1140.Google Scholar

  • [67] Lijuan, X., Yibin, Y., Tiejin, Y. (2009). Rapid determination of ethylene content in tomatoes using visible and short-wave near-infrared spectroscopy and wavelength selection. Chemometr. Intell. Lab., 97, 141-145.Google Scholar

  • [68] Lijuan, X., Xingqian, Y., Donghong, L., Yibin, Y. (2011). Prediction of titratable acidity, malic acid, and citric acid in bayberry fruit by near-infrared spectroscopy. Food Res. Int., 44(7), 2198-2204.Google Scholar

  • [69] Magwaza, L.S., Opara, U.L., Nieuwoudt, H., Cronje, P.J.R., Saeys, W., Nicolaï, B. (2012). NIR Spectroscopy Applications for Internal and External Quality Analysis of Citrus Fruit - A Review. Food Bioprocess Technol., 5, 425--444.CrossrefGoogle Scholar

  • [70] Makino, Y., Ichimura, M., Oshita, S., Kawagoe, Y., Yamanaka, H. (2010). Estimation of oxygen uptake rate of tomato (Lycopersicon esculentum Mill.) fruits by artificial neural networks modelled using near-infrared spectral absorbance and fruit mass. Food Chem., 121(2), 533-539.CrossrefGoogle Scholar

  • [71] Moghimi, A., Aghkhani, M.H., Sazgarnia, A., Sarmad, M. (2010). Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosystems Eng., 106(3), 295-302.CrossrefGoogle Scholar

  • [72] Pérez-Marín, D., Paz, P., Guerrero, J.-E., Garrido-Varo, A., Sánchez, M.-T. (2010). Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums. J. Food Eng., 99, 294-302.CrossrefGoogle Scholar

  • [73] Tewari, J.C., Dixit, V., Cho, B.-K., Malik, K.A. (2008). Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy. Spectrochim. Acta A, 71, 1119-1127.CrossrefGoogle Scholar

  • [74] Tong, S., Kang, H., Huirong, X., Yibin, Y. (2010). Research advances in nondestructive determination of internal quality in watermelon/melon: A review. J. Food Eng., 100(4), 569-577.Google Scholar

  • [75] Neo, Y.P., Ariffin, A., Tan, C.P., Tan, Y.A. (2008). Determination of oil palm fruit phenolic compounds and their antioxidant activities using spectrophotometric methods. International J. Food Sci. & Technology, 43(10), 1832-1837.CrossrefGoogle Scholar

  • [76] Fei, L., Li, W., Yong, H. (2008). Determination of acetic acid of fruit vinegars using near infrared spectroscopy and least squares-support vector machine. In Proc. of 7th International Conference on Machine Learning and Cybernetics, Kunming, China, 1232-1237.Google Scholar

  • [77] Fei, L., Yong, H., Li, W., Guangming. S. (2009). Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration. Food Bioprocess Technol., 4(8), 1331-1340.Google Scholar

  • [78] Guerrero, .D., Me ías, R.C., Marín, R.N., Lovillo, M.P., Barroso, C.G. (2010). A new FT-IR method combined with multivariate analysis for the classification of vinegars from different raw materials and production processes. J. Sci. Food Agr., 90, 712-718.Google Scholar

  • [79] Zunyi, W., Fei, Li., Yong, H. (2009). Comparison and Determination of Acetic Acid of Plum Vinegar Using Vis/Near Infrared Spectroscopy and Multivariate Calibration. In Proc. World Congress onComputer Science and Information Engineering, Los Angeles, USA, 201-204.Google Scholar

  • [80] Posudin, Y.I. (2007). Practical spectroscopy in agriculture and food science. Enfield, NH, USA: Science Publishers.Google Scholar

  • [81] Andersen, C.M., Frøst, M.B., Viereck, N. (2010). Spectroscopic characterization of low- and non-fat cream cheeses. Int. Dairy J., 20(1), 32-39.CrossrefGoogle Scholar

  • [82] González-Martín, I., Hernández-Hierro, J.M., Vivar-Quintana, A., Revilla, I., González-Pérez, C. (2009). The application of near infrared spectroscopy technology and a remote reflectance fibre-optic probe for the determination of peptides in cheeses (cow's, ewe's and goat's) with different ripening times. Food Chem., 114, 1564-1569.CrossrefGoogle Scholar

  • [83] González-Martín, I., Hernández-Hierro, J.M., Salvador- steban, J., González-Pérez, C., Revillab, I., Vivar-Quintanab, A. (2011). Discrimination of seasonality in cheeses by near-infrared technology. J. Sci. Food Agr., 91, 1064-1069.CrossrefGoogle Scholar

  • [84] González-Martín, M.I., Severiano-Pérez, P., Revilla, I., Vivar-Quintana, A.M., Hernández-Hierro, J.M., González-Pérez, C., Lobos-Ortega, I.A. (2011). Prediction of sensory attributes of cheese by near-infrared spectroscopy. Food Chem., 127(1), 256-263.CrossrefGoogle Scholar

  • [85] Subramanian, A., Harper, W.J., Rodriguez-Saona, L.E. (2009). Rapid Prediction of Composition and Flavor Quality of Cheddar Cheese Using ATR-FTIR Spectroscopy. J. Food Sci., 74(3), 292-297.CrossrefGoogle Scholar

  • [86] Balabin, R.M. Smirnov, S.V. (2011). Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: a quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder. Talanta, 85(1), 562-568.CrossrefGoogle Scholar

  • [87] Carleos Artime, C.E., de la Fuente, J.A.B., Garcia, M.A.P., Vega, R.M., Blanco, N.C. (2008). On-line Estimation of Fresh Milk Composition by means of VIS-NIR Spectrometry and Partial Least Squares Method (PLS). In Proc. IEEE Instrumentation and Measurement Technology Conference, Victoria, Vancouver Island, Canada.Google Scholar

  • [88] Coppa, M., Ferlay, A., Leroux, C., Jestin, M., Chilliard, Y., Martin, B., Andueza, D. (2010). Prediction of milk fatty acid composition by near infrared reflectance spectroscopy. Int. Dairy J., 20(3), 182-189.CrossrefGoogle Scholar

  • [89] Muniz, R., Pérez, M.A., de la Torre, C., Carleos, C.E., Corral, N., Baro, J.A. (2009). Comparison of Principal Component Regression (PCR) and Partial Least Square (PLS) methods in prediction of raw milk composition by Vis-NIR spectrometry. application to development of on-line sensors for fat, protein and lactose contents. In Proc. XIX IMEKO World Congress 'Fundamental and Applied Metrology', Lisbon, Portugal, 2564-2568.Google Scholar

  • [90] Woodcock, T., Fagan, C.C., O’Donnell, C.P., Downey, G. (2008). Application of Near and Mid-Infrared Spectroscopy to Determine Cheese Quality and Authenticity. Food Bioprocess Technol., 1, 117-129.Google Scholar

  • [91] Hao, L., Jiewen, Z., Li, S., Quansheng, C., Fang, Z. (2011). Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis. Innov Food Sci. Emerging Technol., 12(2), 182-186.Google Scholar

  • [92] Jiewen, Z., Hao, L., Quansheng, C., Xingyi, H., Zongbao, S., Fang, Z. (2010). Identification of egg’s freshness using NIR and support vector data description. J. Food Eng., 98(4), 408-414.Google Scholar

  • [93] González-Martín, M.I., Berme o, C.F., Hierro, J.M.H., González, C.I.S. (2009). Determination of hydroxyproline in cured pork sausages and dry cured beef products by NIRS technology employing a fibre-optic probe. Food Control, 20(8), 752-755.CrossrefGoogle Scholar

  • [94] Guy, F., Prache, S., Thomas, A., Bauchart, D., Andueza, D. (2011). Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS). Food Chem., 127(3), 1280-1286.CrossrefGoogle Scholar

  • [95] Haibo, H., Haiyan, Y., Huirong, X., Yibin, Y. (2008). Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: A review. J. Food Eng., 87, 303-313.Google Scholar

  • [96] Kapper, C., Klont, R.E., Verdonk, J.M.A.J., Urlings, H.A.P. (2012). Prediction of pork quality with near infrared spectroscopy (NIRS) - 1. Feasibility and robustness of NIRS measurements at laboratory scale. Meat Sci., 91, 294-299.CrossrefGoogle Scholar

  • [97] Kapper, C., Klont, R.E., Verdonk, J.M.A.J., Urlings, H.A.P. (2012). Prediction of pork quality with near infrared spectroscopy (NIRS) - 2. Feasibility and robustness of NIRS measurements under production plant conditions. Meat Sci., 91, 300-305.Google Scholar

  • [98] del Moral, F.G., Guillén, A., del Moral, L.G., O’Valle, F., Martinez, L., del Moral, R.G. (2009). Duroc and Iberian pork neural network classification by visible and near infrared reflectance spectroscopy. J. Food Eng., 90, 540-547.Google Scholar

  • [99] Pérez-Marín, D., Fearn, T., Guerrero, J. ., Garrido-Varo, A. (2010). Robustness in pig fat NIRS calibrations by orthogonal projection. Chemometr. Intell. Lab., 100(1), 36-40.CrossrefGoogle Scholar

  • [100] Quansheng, C., Jianrong, C., Xinmin, W., Jiewen, Z. (2011). Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy. Food Sci. Technol. - LEB, 44(10), 2053-2058.Google Scholar

  • [101] Ritthiruangdej, P., Ritthiron, R., Shinzawa, H., Ozaki, Y. (2011). Non-destructive and rapid analysis of chemical compositions in Thai steamed pork sausages by near-infrared spectroscopy. Food Chem., 129(2), 684-692.CrossrefGoogle Scholar

  • [102] Liao, Y.T., Fan, Y.X., Cheng, F. (2010). On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy. Meat Sci., 86(4), 901-907.CrossrefGoogle Scholar

  • [103] Alexandrakis, D., Downey, G., Scannell, A.G.M. (2011). Detection and identification of selected bacteria, inoculated on chicken breast, using near infrared spectroscopy and chemometrics. Sensing Instrum. Food Qual. Safety, 5(2), 57-62.Google Scholar

  • [104] Etzold, E. Lichtenberg-Kraag, B. (2007). Determination of the botanical origin of honey by Fourier-transformed infrared spectroscopy: an approach for routine analysis. Eur. Food Res. Technol., 227(2), 579-586.Google Scholar

  • [105] Gallardo-Velázquez, T., Osorio-Revilla, G., Zuňiga-de Loa, M., Rivera-Espinoza, Y. (2009). Application of FTIR-HATR spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys. Food Res. Int., 42, 313-318.CrossrefGoogle Scholar

  • [106] Chen, L., Xue, X., Ye, Z., Zhou, J., Chen, F., Zhao J. (2011). Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy. Food Chem., 128(4), 1110-1114.CrossrefGoogle Scholar

  • [107] Xiangrong, Z., Shuifang, L., Yang, S., Zhuoyong, Z., Gaoyang, L., Donglin, S., Feng, L. (2010). Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics. J. Food Eng., 101(1), 92-97.Google Scholar

  • [108] da Costa-Filho, P.A. (2009). Rapid determination of sucrose in chocolate mass using near-infrared spectroscopy. Anal. Chim Acta, 631, 206-211.Google Scholar

  • [109] Chena, Q., JZhao, J., Liu, M., Cai, J., Liu, J. (2008). Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms. J. Pharamaceut. Biomed., 46, 568-573.Google Scholar

  • [110] Quansheng Chen, Jiewen Zhao, Sumpunchaitep, Zhiming Guo (2009). Simultaneous analysis of main catechins contents in green tea (Camellia sinensis (L.)) by Fourier transform near-infrared reflectance (FT-NIR) spectroscopy. Food Chem., 113, 1272-1277.CrossrefGoogle Scholar

  • [111] Morawski, R.Z. (2008). On food, spectrophotometry, and measurement data processing. In Proc. of 12th IMEKO TC1 & TC7 Joint Symposium 'Man Science & Measurement', Annecy, France, 7-20.Google Scholar

  • [112] Examples of mini- and micro-spectrophotometers: http://www.oceanoptics.com; http://www.controldevelopment.com; http://www.arcoptix.com/Spectrometers.htm; http://www.horiba.com; http://www.microspectralanalysis.com; http://www.stellarnet.us[2012.09.22].Google Scholar

  • [113] Morawski, R.Z. (1994). Unified Approach to Measurand Reconstruction. IEEE T. Instrum. Meas., 43(2), 226-231.CrossrefGoogle Scholar

  • [114] Mroczka, J. Szczuczyński, D. (2009). Inverse problems formulated in terms of first-kind Fredholm integral equations in indirect measurements. Metrol. Meas. Syst., 26(3), 333-357.Google Scholar

  • [115] Yongni, S., Yong, H. (2009). Measurement of Soluble Solids Content and pH of Yogurt Using Visible/Near Infrared Spectroscopy and Chemometrics. Food Bioprocess. Technol., 2(2), 229-233.Google Scholar

  • [116] Morawski, R.Z. (2006). Spectrophotometric applications of digital signal processing. Meas. Sci. Technol., 17, R.117-R.144.CrossrefGoogle Scholar

  • [117] Wisniewski, M.P., Morawski, R.Z., Barwicz, A. (1999). Modeling the Spectrometric Microtransducer. IEEE T. Instrum. Meas., 48(3), 747-752.CrossrefGoogle Scholar

  • [118] van Cittert, P.H. (1931). Zum influss der Spaltbreit auf die Intensitätsverteilung in Spectrallinien II. Z. Physik, 69, 298-311.CrossrefGoogle Scholar

  • [119] Kalivas, J. (1999). Interrelationships of multivariate regression methods using eigenvector basis sets. J.Chemometr., (13), 111-132.CrossrefGoogle Scholar

  • [120] Geladi, P., Dabakk, E. (1999). Computational methods of analysis and chemometrics in near-infrared spectroscopy. Encyclopedia of Spectroscopy and Spectrometry, Lindon, J., et al, Eds., London: Academic Press.Google Scholar

  • [121] Morawski, R.Z., Miękina, A., Wagner, J. (2012). A method of weighing matrix for spectrophotometric analysis of oil mixtures. In Advance d Mathematical and Computationol Tools in Metrology and Testing IX, Pavese, F., et al., Eds., Singapore-Hackensack-London: World Scientific Publishing Company.Google Scholar

  • [122] Cardaliaguet, P., Euvrard, G. (1992). Approximation of a Function and its Derivative with a Neural Network. Neural Networks, 5, 207-220.CrossrefGoogle Scholar

  • [123] Geva, S., Sitte, J. (1992). A Constructive Method for Multivariate Function Approximation by Multilayer Perceptrons. IEEE T. Neural Networ., 3(4), 621-624.CrossrefGoogle Scholar

  • [124] Höskuldsson, A. (Nov. 2003). Selection of subsets of variables in linear regression. Homepage of Chemometrics, http://www.acc.umu.se/~tnkjtg/Chemometrics/Editorial[2012.09.20].Google Scholar

  • [125] Swierenga, H., Wülfert, F., deNoord, O.E., de Weijer, A.P., Smilde, A.K., Buydens, L.M.C. (2000). Development of robust calibration models in near infra-red spectrometric applications. Anal Chim. Acta, 411, 121-135.Google Scholar

  • [126] Wold, S. (Jan. 2004). PAC, PAT and Variable Selection. Homepage of Chemometrics, http://www.acc.umu.se/~tnkjtg/Chemometrics/Editorial[2012.09.20].Google Scholar

  • [127] Xueguang Shao, Fang Wang, Da Chen, Qingde Su (2004). A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables. Anal. Bioanal. Chem., 378(5), 1382-1387.CrossrefGoogle Scholar

  • [128] Francois, N., Govaerts, B., Boulanger, B. (2004). Optimal designs for inverse prediction in univariate nonlinear calibration models. Chemometr. Intell. Lab., 74, 283-292.CrossrefGoogle Scholar

  • [129] Brereton, R.G. (2005). Chemometrics - Data Analysis for the Laboratory and Chemical Plant. Chichester, J. Wiley & Sons.Google Scholar

  • [130] Kruse, R., M. Steinbrecher, M. (2010). Visual data analysis with computational intelligence methods. Bull. Pol. Acad. Sci.-Te., 58(4), 393-401.Google Scholar

  • [131] Alciaturi, C.E., Escobar, M.E., Esteves, I. (2005). The use of the autocorrelation function in modeling of multivariate data. Anal. Chim. Acta, 553, 134-140.Google Scholar

  • [132] Benoudjit, N., Francois, D., Meurens, M., Verleysen, M. (2004). Spectrophotometric variable selection by mutual information. Chemometr. Intell. Lab., 74, 243-251.CrossrefGoogle Scholar

  • [133] Benoudjit, N., Cools, E., Meurens, M., Verleysen, M. (2004). Chemometric Calibration of Infrared Spectrometers: Selection and Validation of Variables by Non-Linear Models. Chemometr. Intell. Lab., 70, 47-53.CrossrefGoogle Scholar

  • [134] Pontes, M.J.C., Galvao, R.K.H., Araujo, M.C.N., Moriera, P.N.T., Neto, O.D.P., Jose, G.E., Saldanha, T.C.B. (2005). The Successive projections algorithm for spectral variable selection in classification problems. Chemometr. Intell. Lab., 78, 11-18.CrossrefGoogle Scholar

  • [135] Naes, T., Mevik, B.-H. (2001). Understanding the collinearity problem in regression and discriminant analysis. J. Chromatometr., 15, 413-426.Google Scholar

  • [136] De Juan, A., Casassas, E., Tauler, R. (2000). Soft modeling of analytical data. In Encyclopedia of Anal. Chem., Myers, R. A., Ed., Chichester, Wiley and Sons.Google Scholar

  • [137] Jackson, J.E. (1991). A User's Guide To Principal Components. New York - Singapore: John Wiley & Sons, Inc.Google Scholar

  • [138] Lavine, B.K. (2000). Chemometrics: Fundamental review. Anal. Chem., 72, 91R-98R.CrossrefGoogle Scholar

  • [139] Bro, R. (1998). Multi-way Analysis in the Food Industry - Models, Algorithms, and Applications. Ph.D. Thesis, Universiteit van Amsterdam, Amsterdam, The Netherlands.Google Scholar

  • [140] Lomillo, M.A.A., Renedo, O.D., Martinez, M.J.A. (2004). Resolution of Binary Mixtures of Rifamycin SV and Rifampicin by UV/Vis Spectroscopy and Partial Least-Squares Method (PLS). Chem. Biodivers., 1, 1336-1343.CrossrefGoogle Scholar

  • [141] Azzouz, T., Puigdoménech, A., Aragay, M., Tauler, R. (2003). Comparison between different data pre-treatment methods in the analysis of forage samples using near-infrared diffuse reflectance spectroscopy and partial least-squares multivariate calibration method. Anal. Chim. Acta, 484(1), 121-134.Google Scholar

  • [142] Cozzolino, D., Kwiatkowski, M.J., Parker, M., Cynkar, W.U., Dambergs, R.G., Gishen, M., Herdrich, M.J. (2004). Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Anal. Chim. Acta, 513, 73-80.Google Scholar

  • [143] Du, Y.P., Liang, Y.Z., Jiang, J.H., Berry, R.J., Ozaki, Y. (2004). Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares. Anal. Chim. Acta, 501(2), 183-191.Google Scholar

  • [144] Fernandez-Pierna, J.A., Jin, L., Wahl, F., Faber, N.M., Massart, D.L. (2003). Estimation of partial least squares regression prediction uncertainty when the reference values carry a sizeable measurement error. Chemometr. Intell. Lab., 65, 281-291.CrossrefGoogle Scholar

  • [145] Ghasemi, J., Shahabadi, N., Seraji, H.R. (2004). Spectrophotometric simultaneous determination of cobalt, copper and nickel using nitroso-R-salt in alloys by partial least squares. Anal. Chim. Acta, 510(1), 121-126.CrossrefGoogle Scholar

  • [146] Hiroaki, I., Toyonori, N., Eiji, T. (2002). Measurement of Pesticide Residues in Food Based on Diffuse Reflectance IR Spectroscopy. IEEE T. Instrum. Meas., 51(5), 886-890.CrossrefGoogle Scholar

  • [147] Huang, J., Brennan, D., Sattler, L., Alderman, J., Lane, B., O’Mathuna, C. (2002). A Comparison of Calibration Methods Based on Calibration Data Size and Robustness. Chemometr. Intell. Lab., 62, 25-35.CrossrefGoogle Scholar

  • [148] Kasemsumran, S., Du, Y.P., Murayama, K., Huehne, M., Ozaki, Y. (2004). Near-infrared spectroscopic determination of human serum albumin g-globulin, and glucose in a control serum solution with searching combination moving window partial least squares. Anal. Chim. Acta, 512, 223-230.Google Scholar

  • [149] Khajehsharifi, H., Mousavi, M.F., Ghasemi, J., Shamsipur, M. (2004). Kinetic spectrophotometric method for simultaneous determination of selenium and tellurium using partial least squares calibration. Anal. Chim. Acta, 512(2), 369-373.Google Scholar

  • [150] Muik, B., Lendl, B., Molina-Diaz, A., Villarejo, L.P., Ayora-Canada, M.J. (2004). Determination of oil and water content in olive pomace using near infrared and Raman. Anal. Bioanal. Chem., 379, 35-41.CrossrefGoogle Scholar

  • [151] Pizarro, C., Esteban-Díez, I., Nistal, A.-J., González-Sáiz, J.-M. (2004). Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy. Anal. Chim. Acta, 509(2), 217-227.Google Scholar

  • [152] Shen, Q., Jiang, J.H., Shen, G.L., Yu, R.Q. (2003). Variable selection by an evolution algorithm using modified Cp based on MLR and PLS modeling: QSAR studies of carcinogenicity of aromatic amines. Anal. Bioanal. Chem., 375(2), 248-254.Google Scholar

  • [153] Abdi, H. (2010). Partial least squares regression and projection on latent structure regression (PLS Regression). WIREs Comput. Stat., 2(1), 97-106.CrossrefGoogle Scholar

  • [154] Ashok, P.C., Praveen, B.B., Dholakia, K. (2011). Near infrared spectroscopic analysis of single malt Scotch whisky on an optofluidic chip. Opt. Express, 19(23).CrossrefGoogle Scholar

  • [155] Balabin, R.M., Safieva, R.Z. (2011). Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data. Anal. Chim. Acta, 689(2), 190-197.Google Scholar

  • [156] Brunt, K., Drost, W.C. (2010). Design, Construction, and Testing of an Automated NIR In-line Analysis System for Potatoes. Part I: Off-line NIR Feasibility Study for the Characterization of Potato Composition. Potato Res., 53(1), 25-39.CrossrefGoogle Scholar

  • [157] Brunt, K., Smits, B., Holthuis, H. (2010). Design, Construction, and Testing of an Automated NIR In-line Analysis System for Potatoes. Part II. Development and Testing of the Automated Semi-industrial System with In-line NIR for the Characterization of Potatoes. Potato Res., 53(1), 41-60.CrossrefGoogle Scholar

  • [158] Cai, C.B., Han, Q.J., Tang, L.J., Nie, J.F., Ouyang, L.Q., Yu, R.Q. (2008). Treating NIR data with orthogonal discrete wavelet transform: Predicting concentrations of a multi-component system through a small-scale calibration set. Talanta, 77, 822-826.CrossrefGoogle Scholar

  • [159] Costa Filho, P.A.D. (2009). Rapid determination of sucrose in chocolate mass using near infrared spectroscopy. Anal. Chim. Acta, (631), 206-211.CrossrefGoogle Scholar

  • [160] Costa-Pereira, A.F., Coelho Pontes, M.J., Gambarra Neto, F.F., Bezerra Santos, S.R., Harrop Galvao, R.K., Ugulino Araú o, M.C. (2008). NIR spectrometric determination of quality parameters in vegetable oils using iPLS and variable selection (review). Food Res. Int., 41, 341-348.Google Scholar

  • [161] Cozzolino, D., Chree, A., Murray, I., Scaife, J.R. (2009). Usefulness of near infrared spectroscopy to monitor the extent of heat treatment in fish meal. Int. J. Food Sci. Technol., 44(8), 1579-1584.CrossrefGoogle Scholar

  • [162] Cozzolino, D., Restaino, E., Fassio, A. (2010). Discrimination of yerba mate (Ilex paraguayensis St. Hil.) samples according to their geographical origin by means of near infrared spectroscopy and multivariate analysis. Sensing Instrum. Food Qual. Safety, 4(2), 67-72.Google Scholar

  • [163] Di Egidio, V., Sinelli, N., Giovanelli, G., Moles, A., Casiraghi, E. (2010). NIR and MIR spectroscopy as rapid methods to monitor red wine fermentation. Eur. Food Res. Technol., 230(6), 947-955.CrossrefGoogle Scholar

  • [164] Fei, L., Yong, H., Li, W., Guangming, S. (2009). Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration. Food Bioprocess Technol., 14(8), 1331-1340.Google Scholar

  • [165] Horikawa, Y., Imai, T., Takada, R., Watanabe, T., Takabe, K., Kobayashi, Y., Sugiyama, J. (2011). Near-infrared chemometric approach to exhaustive analysis of rice straw pretreated for bioethanol conversion. Appl. Biochem. Biotech., 164(2), 194-203.CrossrefGoogle Scholar

  • [166] Huazhou, C,, Tao, P., Jiemei, C., Qipeng, L. (2011). Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods. Chemometr. Intell. Lab., 107, 139-146.Google Scholar

  • [167] Hyonho, C., Keles, S. (2010). Sparse PLS regression for simultaneous dimension reduction and variable selection. J. Roy. Stat. Soc. B Met., 72(Part 1), 3-25.Google Scholar

  • [168] Jin, H.L., Choung, M.G. (2011). Nondestructive determination of herbicide-resistant genetically modified soybean seeds using near-infrared reflectance spectroscopy. Food Chem., 126(1), 368-373.Google Scholar

  • [169] Xie, L.J., Ying, J.B. (2009). Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice. J. Zhejiang Univ. Sci. B, 10(6), 465-71.CrossrefGoogle Scholar

  • [170] Moros, J., Llorca, I., Cervera, M.L., Pastor, A., Garrigues, S., de la Guardia, M. (2008). Chemometric determination of arsenic and lead in untreated powdered red paprika by diffuse reflectance near-infrared spectroscopy. Anal. Chim. Acta, 613, 196-206.Google Scholar

  • [171] Po ić, M., Mastilović, J., Palić, D., Pestorić, M. (2010). The development of near-infrared spectroscopy (NIRS) calibration for prediction of ash content in legumes on the basis of two different reference methods. Food Chem., 123(3), 800-805.Google Scholar

  • [172] Quansheng, C., Pei, J., Jiewen, Z. (2010). Measurement of total flavone content in snow lotus (Saussurea involucrate) using near infrared spectroscopy combined with interval PLS and genetic algorithm. Spectrochim. Acta. A, 76(1), 50-55.Google Scholar

  • [173] Queji, M.D., Wosiacki, G., Cordeiro, G.A., Peralta-Zamora, P.G., Nagata, N. (2010). Determination of simple sugars, malic acid and total phenolic compounds in apple pomace by infrared spectroscopy and PLSR. Int. J. Food Sci. Technol., 45, 602-609.CrossrefGoogle Scholar

  • [174] Sherazi, S.T.H., Kandhro, A., Mahesar, S.A., Bhanger, M.I., Younis Talpur, M., Arain, S. (2009). Application of transmission FT-IR spectroscopy for the trans fat determination in the industrially processed edible oils. Food Chem., 114, 323-327.CrossrefGoogle Scholar

  • [175] Shetty, N., Gislum, R. (2011). Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR. Field Crops Res., 120(1), 31-37.Google Scholar

  • [176] Shiroma, C., Rodriguez-Saona, L. (2009). Application of NIR and MIR spectroscopy in quality control of potato chips. J. Food Compos. Anal., 22(6), 596-605.CrossrefGoogle Scholar

  • [177] Smyth, H.E., Cozzolino, D., Cynkar, W.U., Dambergs, R.G., Sefton, M., Gishen, M. (2008). Near infrared spectroscopy as a rapid tool to measure volatile aroma compounds in Riesling wine: possibilities and limits. Anal. Bioanal. Chem., 390(7), 1911-1916.CrossrefGoogle Scholar

  • [178] Soares, I.P., Rezende, T.F., Silva, R.C., Castro, E.V.R., Fortes, I.C.P. (2008). Multivariate Calibration by Variable Selection for Blends of Raw Soybean Oil/Biodiesel from Different Sources Using Fourier Transform Infrared Spectroscopy (FTIR) Spectra Data. Energ. Fuels, 22, 2079-2083.CrossrefGoogle Scholar

  • [179] Ulissi, V., Antonucci, F., Benincasa, P., Farneselli, M., Tosti, G., Guiducci, M., Tei, F., Costa, C., Pallottino, F., Pari, L., Menesatti, P. (2011). Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy. Sensors, 11(6), 6411-6424.CrossrefGoogle Scholar

  • [180] Zou Xiaobo, Zhao Jiewen, Povey, M. J., Holmes, M., Mao Hanpin (2010). Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta, 667(1-2), 14-32.Google Scholar

  • [181] Martens, H., Naes, T. (1989). Multivariate Calibration. Chichester - Singapore, J.Wiley&Sons.Google Scholar

  • [182] Varmuza, K., Filzmoser, P. (2009). Introduction to Multivariate Statistical Analysis in Chemeometrics. Boca Raton: CRC Press - Taylor & Francis Group.Google Scholar

  • [183] Gustafsson, M.G. (2001). A Probabilistic Derivation of the Partial Least-Squares Algorithm. J. Chem. Inf. Comp. Sci., 41, 288-294.CrossrefGoogle Scholar

  • [184] Website of Eigenvector Research, Inc., http://www.eigenvector.com/software/pls_toolbox.htm [2012.09.22].Google Scholar

  • [185] Morawski, R.Z., Miękina, A. (2012). A comparative study of forty algorithms for spectrophotometric analysis of edible oil mixtures. In Proc. of XXth IMEKO World Congress 'Metrology for Green Growth', Busan, Republic of Korea.Google Scholar

  • [186] Frank, I., Friedman, J. (1993). A statistical view of some chemometric regression tools. Technometrics, 35, 109-148.CrossrefGoogle Scholar

  • [187] Sundberg, R. (1993). Continuum Regression and Ridge Regression. Food Qual. Prefer., 55(3), 653-659.Google Scholar

  • [188] Vigneau, E., Devaux, M., Qannari, M., Robert, P. (1997). Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration. J. Chemometr., (11), 239-249.CrossrefGoogle Scholar

  • [189] Li-Xian, S., Reddy, A.M., Matsuda, N., Takatsu, A., Kato, K., Okada, T. (2003). Simultaneous determination of methylene blue and new methylene blue by slab optical waveguide spectroscopy and artificial neural networks. Anal. Chim. Acta, 487(1), 109-116.Google Scholar

  • [190] Petersen, L., Minkkinen, P., Esbensen, K.H. (2005). Representative sampling for reliable data analysis: Theory of Sampling. Chemometr. Intell. Lab., 77, 261-277.CrossrefGoogle Scholar

  • [191] Serneels, S., Croux, C., van Espen, P.J. (2004). Influence properties of partial least squares regression. Chemometr. Intell. Lab., 71, 13-20.CrossrefGoogle Scholar

  • [192] Corona, F., Reinikainen, S.-P., Aal oki, K., Perkiö, A., Liitiäinen, ., Baratti, R., Simula, O., Lendasse, A. (2008). Wavelength selection using the measure of topological relevance on the self-organizing map. J. Chemometr., 22, 610-620.CrossrefGoogle Scholar

  • [193] Heng, X., Zhichao, L., Wensheng, C., Xueguang, S. (2009). A wavelength selection method based on randomization test for near-infrared spectral analysis. Chemometr. Intell. Lab., 97(2), 189-193.Google Scholar

  • [194] Igne, B., Hurburgh, C. R. (2010). Local chemometrics for samples and variables: optimizing calibration and standardization processes. J. Chemometr., 24, 75-86.Google Scholar

  • [195] McLeod, G., Clelland, K., Tapp, H., Kemsley, E.K., Wilson, R.H., Poulter, G., Coombs, D., Hewitt, C.J. (2009). A comparison of variate pre-selection methods for use in partial least squares regression: A case study on NIR spectroscopy applied to monitoring beer fermentation. J. Food Eng., 90, 300-307.CrossrefGoogle Scholar

  • [196] Han, Q.J., Wu, H.L., Cai, C.B., Xu, L., Yu, R.Q. (2008). An ensemble of Monte Carlo uninformative variable elimination for wavelength selection. Anal. Chim. Acta, 612, 121-125.Google Scholar

  • [197] Roger, J.M., Palagos, B., Bertrand, D. (2009). Variable selection for highly multivariate and multi-response calibration. In Proc. of 14th International Conference on Near Infrared Spectroscopy, Bangkok, Thailand.Google Scholar

  • [198] Wensheng, C., Yankun, L., Xueguang, S. (2008). A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemometr. Intell. Lab., 90, 188-194.Google Scholar

  • [199] Gilbert, A. (1991). The Resolution of Bands in Spectroscopy. In Analytical Applications of Spectroscopy II, Davies, A.M.C. and Creaser, C.S., Eds., Cambridge, Royal Soc. Chem.Google Scholar

  • [200] Morawski, R.Z., Miękina, A. (1996). Combined Use of Tikhonov Deconvolution and Curve Fitting for Spectrogram Interpretation. Instrum. Sci. Technolog., 24(3), 155-167.CrossrefGoogle Scholar

  • [201] Miękina, A., Morawski, R.Z., Barwicz, A. (1997). The Use of Deconvolution and Iterative Optimization for Spectrogram Interpretation. IEEE T. Instrum. Meas., 46(4), 1049-1053.CrossrefGoogle Scholar

  • [202] Miękina, A., Morawski, R.Z. (1998). Industrial Application of Algorithms for Interpretation of Spectrometric Data. In Proc. 6th IMEKO Symp. ‘Metrology for Quality Control in Production’, Vienna, Austria, 441-447.Google Scholar

  • [203] Benjathapanun, N., Boyle, W.J.O., Grattan, K.T.V. (1997). Binary Encoded 2nd-Differential Spectrometry Using UV-Vis Spectral Data and neural Networks in teh Estimation of Spieces Type and Concentration. IEE P.- Sci. Meas. Tech., 144(2), 73-80.CrossrefGoogle Scholar

  • [204] Trygg, J. (Mar. 2002). Everything you need to know about Orthogonal Signal Correction (OSC) filters - and how they can improve interpretation of your data. Homepage of Chemometrics, http://www.acc.umu.se/~tnkjtg/Chemometrics/Editorial [2012/09.20].Google Scholar

  • [205] Hoy, M. (2002). Building simple, reliable and relevant multivariate data-analysis tools. Ph.D. Thesis, NTNU, Trondheim, Norway.Google Scholar

  • [206] Morton, S. (2006). Lecture course covering all practical, and interpretive aspects of electron spectroscopy, http://www.uksaf.org/tutorials.html[2011.09.11].Google Scholar

  • [207] Trygg, J. (May 2002). Wavelets in Chemometrics - compression, denoising and feature extraction. Homepage of Chemometrics, http://www.acc.umu.se/~tnkjtg/Chemometrics/Editorial[2012/09.20].Google Scholar

  • [208] Xiuqi, Z., Jiye, J., Jianbin, Z., Hong, G. (2003). Genetic algorithms based on wavelet transform for resolving simulated overlapped spectra. Anal. Bioanal. Chem., 377(7-8), 1153-1158.Google Scholar

  • [209] Mark, H., Workman, J. (2000-2003). Chemometrics in Spectroscopy - Analysis of Noise, I-XIV. Spectroscopy on line, www.spectroscopyonline.com2011.08.23].Google Scholar

  • [210] Brown, C.D. (2000). Rational Approaches to Data Preprocessing in Multivariate Calibration, Ph.D. Thesis, Dalhousie University, Halifax, Canada.Google Scholar

  • [211] Bosch-Ojeda, C., Sanchez-Rojas, F. (2004). Recent developments in derivative ultraviolet/visible absorption spectrophotometry. Anal Chim. Acta, 518(1-2), 1-24.Google Scholar

  • [212] Da, C., Xueguang, S., Bin, H., Qingde, S. (2004). A background and noise elimination method for quantitative calibration of near infrared spectra. Anal. Chim. Acta, 511(1), 37-45.Google Scholar

  • [213] Mazet, V., Carteret, C., Brie, D., Idier, J., Humbert, B. (2005). Background removal from spectra by designing and minimizing a non-quadratic cost function. Chemometr. Intell. Lab., 76, 121-133.CrossrefGoogle Scholar

  • [214] Qu, H.B., Ou, D.L, Cheng, Y.Y. (2005). Background correction in near-infrared spectra of plant extracts by orthogonal signal correction. J. Zhejiang Univ. Sci. B, 6(8), 838-843.CrossrefGoogle Scholar

  • [215] Morawski, R.Z., Miękina, A. (2009). Improving Absorbance Spectrum Reconstruction via Spectral Data Decomposition and Pseudo-Baseline Optimization. IEEE T. Instrum. Meas., 58(3), 691-697.CrossrefGoogle Scholar

  • [216] Mark, H.,Workman Jr., J. (I, 18(4), 2003; II, 18, (9), 2003; III, 18(12), 2003; 19(1), 2004). Chemometrics - Derivatives in Spectroscopy. Spectroscopy on line, www.spectroscopyonline.com[2011.08.23]Google Scholar

  • [217] Badea, I., Moja, D., Vladescu, L. (2002). Determination of para-aminobenzoic acid, a degradation product of procaine hydrochloride, by zero-crossing first-derivative spectrometry. Anal. Bioanal. Chem., 374(1), 51-53.CrossrefGoogle Scholar

  • [218] Ergun, E., Demirata, B., Gumus, G., and Apak, R. (2004). Simultaneous determination of chlorophyll a and chlorophyll b by derivative spectrophotometry. Anal. Bioanal. Chem., 379(5-6), 803-811.Google Scholar

  • [219] Kharintsev, S. S. and Salakhov, M.K. (2004). A simple method to extract spectral parameters using fractional derivative spectrometry. Spectrochim. Acta A, 60, 2125-2133.CrossrefGoogle Scholar

  • [220] Moldovan, Z., Alexandrescu, L. (2002). Derivative spectrophotometry in the determination of phenyl-β-naphthylamine used as an antioxidant in rubber mixtures. Anal. Bioanal. Chem., 374(1), 46-50.Google Scholar

  • [221] Ait Kaddour, A., Cuq, B. (2009). In line monitoring of wet agglomeration of wheat flour using near infrared spectroscopy. Powder Technol., 190(1-2), 10-18.CrossrefGoogle Scholar

  • [222] Yande, L., Xudong, S., Jianmin, Z., Hailiang, Z., Chao, Y. (2010). Linear and nonlinear multivariate regressions for determination sugar content of intact Gannan navel orange by Vis-NIR diffuse reflectance spectroscopy. Math. Comp. Model., 51(11-12), 1438-1443.Google Scholar

  • [223] Jansson, P.A. (1997). Deconvolution of Images and Spectra. London: Academic Press.Google Scholar

  • [224] Crilly, P.B. (1991). A Quantitative Evaluation of Various Iterative Deconvolution Algorithms. IEEE T. Instrum. Meas., 40(3), 558-562.CrossrefGoogle Scholar

  • [225] Milewski, M.S., Morawski, R.Z., Szczeciński, L. (1993). Comparative Study of Some Iterative and Variational Algorithms of Measurand Reconstruction. In Proc. IMEKO-TC1&TC7 Colloq. London, UK, 305-310.Google Scholar

  • [226] Miękina, A., Morawski, R.Z., Podgórski, A. (1994). Dynamic Reconstrtuction of Measurands and Calibration of Measuring Systems. Measurement, (14), 63-72.CrossrefGoogle Scholar

  • [227] Morawski, R.Z., Szczeciński, L., Barwicz, A. (1995). Deconvolution Algorithms for Instrumental Applications: A Comparative Study. J. Chemometr., 9, 3-20.CrossrefGoogle Scholar

  • [228] (2006). Introducing a Personal Robot with a Sense of Taste. NEC System Technologies Ltd. News, http://www.necst.co.jp/english/news/20060801/index.htm [2012.09.20].Google Scholar

  • [229] Examples of spectrophotometric wine analysers: http://www.foss.dk; http://www.randoxfooddiagnostics.com; http://www.mikochem.gr; http://www.brukeroptics.com; http://www.tridentinstrumentation.co.za[2012.09.22].Google Scholar

  • [230] Application of spectroscopy for non-destructive 'in-bottle' measurement of wine composition and quality. CRCV Technology Application Note, April 2007. www.crcv.com.au/viticare/vitinotes/Viti-Notes/2012.10.11].Google Scholar

  • [231] Cozzolino, D., Cynkar, W.U., Dambergs, R.G., Janik, L., Gishen, M. (2007). Near infrared spectroscopy in the Australian grape and wine industry. CRCV Technology Application Note, www.crcv.com.au/viticare/vitinotes/Viti-Notes/ [2012.10.11].Google Scholar

  • [232] Araú o, A.N., Lima, J.F.L.C., Rangel, A.O.S.S., Segundo, M.A. (2000). Sequential injection system for the spectrophotometric determination of reducing sugars in wines. Talanta, 52, 59-66.CrossrefGoogle Scholar

  • [233] Bevin, C.,J., Dambergs, R.G., Fergusson, A.J., Cozzolino, D. (2008). Varietal discrimination of Australian wines by means of mid-infrared spectroscopy and multivariate analysis. Anal Chim Acta, (621), 19-23.CrossrefGoogle Scholar

  • [234] Cozzalino, D., Parker, M., Dambergs, R.G., Herderich, M., Gishen, M. (2006). Chemometrics and Visible-Near Infrared Spectroscopic Monitoring of Red Wine Fermentation in a Pilot Scale. Biotechnology and Bioengineering, 95(6), 1101-1107.Google Scholar

  • [235] Cozzolino, D. (2007). Non-Destructive Analysis by Vis-NIR Spectroscopy of Fluid (S) in its Original Container, International Patent No WO2007006099.Google Scholar

  • [236] Fei, L., Yong, H., Li, W., Hongming, P. (2007). Feasibility of the use of visible and near infrared spectroscopy to assess soluble solids content and pH of rice wines. J. Food Eng., 83, 430-435.Google Scholar

  • [237] Ferreira, A.P., Alves, T.P., Menezes, J.C. (2005). Monitoring Complex Media Fermentations with Near-Infrared Spectroscopy: Comparison of Different Variable Selection Methods. Biotechnol. Bioeng, 91(4), 474-481.CrossrefGoogle Scholar

  • [238] Herrera, J., Guesalaga, A., Agosin, E. (2003). Shortwave-near infrared spectroscopy for nondestructive determination of maturity of wine grapes. Meas. Sci. Technol., 14, 689-697.CrossrefGoogle Scholar

  • [239] Liu, L., Cozzolino, D., Cynkar, W.U., Dambergs, R.G., Janik, L., O'Neill, B.K., Colby, C.B., Gishen, M. (2008). Preliminary study on the application of visible-near infrared spectroscopy and chemometrics to classify Riesling wines from di.erent countries. Food Chem., 106, 781-786.CrossrefGoogle Scholar

  • [240] Moreira, J.L. Santos, L. (2004). Spectroscopic interferences in Fourier transform infrared wine analysis. Anal. Chim Acta, 513, 263-268.Google Scholar

  • [241] Riganakos, K.A., Veltsistas, P.G. (2003). Comparative spectrophotometric determination of the total iron content in various white and red Greek wines. Food Chem., 82, 637-643.CrossrefGoogle Scholar

  • [242] Sáiz-Aba o, M.J., González-Sáiz, J.M., Pizarro, C. (2006). Prediction of organic acids and other quality parameters of wine vinegar by near-infrared spectroscopy. A feasibility study. Food Chem., 99, 615-621.CrossrefGoogle Scholar

  • [243] Serapinas, P., Venskutonis, P.R., Aninkevičius, V., zerinskis, Ž., Galdikas, A., Juzikině, V. (2008). Step by step approach to multi-element data analysis in testing the provenance of wines. Food Chem., 107, 1652-1660.CrossrefGoogle Scholar

  • [244] Skogerson, K., Downey, M., Mazza, M., Boulton, R. (2007). Rapid Determination of Phenolic Components in Red Wines from UV-Visible Spectra and the Method of Partial Least Squares. Am. J. Enol. Viticult., 58(3), 318-325.Google Scholar

  • [245] Soriano, A., Perez-Juan, P.M., González, J.M., Perez-Coello, M.S. (2007). Determination of anthocyanins in red wine using a newly developed method based on Fourier transform infrared spectroscopy. Food Chem., 104, 1295-1303.CrossrefGoogle Scholar

  • [246] Foca, G., Cocchi, M., Li Vigni, M., Caramanico, R., Corbellini, M., Ulrici, A. (2009). Different feature selection strategies in the wavelet domain applied to NIR-based quality classification models of bread wheat flours. Chemometr. Intell. Lab., 99, 91-100.CrossrefGoogle Scholar

  • [247] Niedziński, C., Morawski, R.Z. (2004). Estimation of low concentrations in presence of high concentrations using Bayesian algorithms for interpretation of spectrophotometric data. J. Chemometr., 18, 217-230.CrossrefGoogle Scholar

  • [248] Morawski, R. Z., Niedziński, C. (2008). Application of a Bayesian estimator for identification of edible oils on the basis of spectrophotometric data. Metrol. Meas. Syst., 15(3), 247-266.Google Scholar

  • [249] Pérez-Marín, D., Fearn, T., Guerrero, J. ., Garrido-Varo, A. (2012). Improving NIRS predictions of ingredient composition in compound feedingstuffs using Bayesian non-parametric calibrations. Chemometr. Intell. Lab., 110, 108-112.CrossrefGoogle Scholar

  • Google Scholar

About the article

Published Online: 2012-12-28

Published in Print: 2012-12-01


Citation Information: Metrology and Measurement Systems, ISSN (Online) , ISSN (Print) 0860-8229, DOI: https://doi.org/10.2478/v10178-012-0056-1.

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