Ternary mixtures of sugar solutions containing maple syrup were studied quantitatively using Fourier transform infrared (FTIR) attenuated total reflectance (ATR) technique coupled with partial least squares regression (PLS) and selection of spectral variables. Two ternary mixtures were analyzed; first ternary mixture contained maple syrup, white sugar solution, and fully inverted sugar solution; second ternary mixture comprised maple syrup, white, and brown sugar solutions. In this paper, a procedure for selection of spectral variables with PLS, called first break forward interval PLS (FB-FiPLS), is tested on maple syrup adulteration. The method achieved almost exactly the same performance as synergy interval PLS (SiPLS) but with much shorter computational time. The upper limit of number of latent variables (LVs), which is the critical factor for both interval PLS methods, was determined using repeated double cross-validation on whole spectral region of calibration set for each analyzed component in each analyzed ternary mixture set. FB-FiPLS procedure for selection of spectral variables, using only root mean square error of cross validation (RMSECV) values for whole optimization of spectral variables, is fast and robust. After spectral variables and LVs for each particular model had been selected with minimum RMSECV of FB-FiPLS procedure, final results in terms of RMSECV and RMSEP for FB-FiPLS were in most cases statistically significantly better than PLS on whole spectral region and on selected spectral regions. Predictions of each component in analyzed ternary mixture set is promising (R 2(training set) > 0.98, R 2(test set) > 0.97), especially for fully inverted sugar solution (RMSEP = 0.142 % w/w).
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Al-Jowder O, Defernez M, Kemsley EK, Wilson RH (1999) Mid-infrared spectroscopy and chemometrics for the authentication of meat products. J Agric Food Chem 47:3210–3218. doi:10.1021/jf981196d
Arnholt, AT (2012) Package ‘PASWR’. Probability and Statistics with R. http://cran.r-project.org/web/packages/PASWR/PASWR.pdf pp. 53-55
Bonvehi JS, Pajuelo AG (1986) Aliment Equipos Tecnol 5:143–147
Bruce EM (1907) Detection of common food adulterants. D. Van Nostrand Company, New York
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 128:1110–1114
Davis A (2005) New England Agricultural Statistics. http://www.nass.usda.gov/nh/mapleconf2005.pdf
De Jong S (1993) SIMPLS: an alternative approach to partial least squares regression. Chemometr Intell Lab Syst 18:251–263. doi:10.1016/0169-7439(93)85002-X
Fairchild GF, Capps O Jr, Nichols JP (2000) Impacts of economic adulteration on the U.S. honey Industry, Paper presented at the Western Agricultural Economics Association Annual Meetings. Vancouver, British Columbia
Figoni PI (2010) How baking works: exploring the fundamentals of baking science. Wiley, New York, p 171
Filzmoser P, Varmuza K (2015) Package ‘chemometrics’ http://cran.r-project.org/web/packages/chemometrics/index.html, reference manual: http://cran.r-project.org/web/packages/chemometrics/chemometrics.pdf pages 20 − 22
Filzmoser P, Liebmann B, Varmuza K (2009) Repeated double cross validation. J Chemometrics 23:160–171
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. doi:10.1016/j.foodchem.2009.02.068
Heil C (2008) Application note 51705, multi-component analysis of fructose syrup using the antaris FT-NIR analyzer. Thermo Fisher Scientific, Madison
Hemmateenejad B, Akhond M, Samari F (2007) A comparative study between PCR and PLS in simultaneous spectrophotometric determination of diphenylamine, aniline, and phenol: effect of wavelength selection. Spectrochim Acta A 67:958–965. doi:10.1016/j.saa.2006.09.014
Ibrahim M, Alaam M, El-Haes H, Jalbout AF, Leon A (2006) Eclet Quím 31:15–21
Kalivas JH (1997) Two Data Sets of Near Infrared Spectra. Chemometr Intell Lab 37:255–259. http://cran.r-project.org/web/packages/pls/pls.pdf page 11
Kemsley EK, Holland JK, Defernez M, Wilson RH (1996) Detection of adulteration of raspberry purees using infrared spectroscopy and chemometrics. J Agric Food Chem 44:3864–3870. doi:10.1021/jf960089l
Lai YW, Kemsley EK, Wilson RH (1995) Quantitative analysis of potential adulterants of extra virgin olive oil using infrared spectroscopy. Food Chem 53:95–98. doi:10.1016/0308-8146(95)95793-6
Maggio RM, Cerretani L, Chiavaro E, Kaufman TS, Bendini A (2010) A novel chemometric strategy for the estimation of extra virgin olive oil adulteration with edible oils. Food Control 21:890–895. doi:10.1016/j.foodcont.2009.12.006
Mata-Espinosa P, Bosque-Sendra JM, Bro R, Cuadros-Rodriguez L (2011) Olive oil quantification of edible vegetable oil blends using triacylglycerols chromatographic fingerprints and chemometric tools. Talanta 85:177–182. doi:10.1016/j.talanta.2011.03.049
Moreland C, Heil C (2011) Application note 52269, quantitative analysis of wheat flour using FT-NIR. Thermo Fisher Scientific, Madison
Müller ALH, Flores EMM, Müller EI, Silva FEB, Ferrão MF (2011) Attenuated total reflectance with Fourier transform infrared spectroscopy (ATR/FTIR) and different PLS algorithms for simultaneous determination of clavulanic acid and amoxicillin in powder pharmaceutical formulation. J Braz Chem Soc 22:1903. doi:10.1590/S0103-50532011001000011
Nicolaou N, Xu Y, Goodacre R (2010) Fourier transform infrared spectroscopy and multivariate analysis for the detection and quantification of different milk species. J Dairy Sci 93:5651–5660. doi:10.3168/jds.2010-3619
Nørgaard L, Saudland A, Wagner J, Nielsen JP, Munck L, Engelsen SB (2000) Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Appl Spectrosc 54:413–419. doi:10.1366/0003702001949500
Paradkar MM, Irudayaraj J (2002) A rapid FTIR spectroscopic method for estimation of caffeine in soft drinks and total methylxanthines in tea and coffee. J Food Sci 67:2507–2511. doi:10.1111/j.1365-2621.2002.tb08767.x
Paradkar MM, Sivakesava S, Irudayaraj J (2002a) Discrimination and classification of adulterants in maple syrup with the use of infrared spectroscopic techniques. J Sci Food Agric 83:714–721. doi:10.1002/jsfa.1332
Paradkar MM, Sakhamuri S, Irudayaraj J (2002b) Comparison of FTIR, FT‐raman, and NIR spectroscopy in a maple syrup adulteration study. J Food Sci 67:2009–2015
Qiang L, Mingjie T, Jianrong C, Huazhu L, Chaitep S (2010) Selection of efficient wavelengths in NIR spectrum for determination of dry matter in kiwi fruit. Maejo Int J Sci Tech 4:113–124
Rios-Corripio MA, Rios-Leal E, Rojas-López M, Delgado-Macuil R (2011) FTIR characterization of Mexican honey and its adulteration with sugar syrups by using chemometric methods. J Phys Conf Ser 274:012098. doi:10.1088/1742-6596/274/1/012098
Rohman A, Che Man YB (2011) Potential use of FTIR-ATR spectroscopic method for determination of virgin coconut oil and extra virgin olive oil in ternary mixture systems. Food Anal Methods 4:155–162
Sarrafi AHM, Konoz E, Ghiyasvand M (2011) Simultaneous detemination of atorvastatin calcium and amlodipine besylate by spectrophotometry and multivariate calibration methods in pharmaceutical formulations. E-J Chem 8:1670–1679. doi:10.1155/2011/292346
Sivakesava S, Irudayaraj J (2001) A rapid spectroscopic technique for determining honey adulteration with corn syrup. J Food Sci 66:787–792. doi:10.1111/j.1365-2621.2001.tb15173.x
Spectral Database for Organic Compounds (SDBS) (2011) IR spectra of malic acid, SDBS No.: 1611, RN 6915-15-7; fumaric acid, SDBS No.: 1339, RN 110-17-8; riboflavin, SDBS No.: 2254, RN 83-88-5; nicotinamide, SDBS No.: 553, RN 98-92-0; Ca DL-pantothenate, SDBS No.: 19583 RN 6381-63-1; Na D-pantothenate, SDBS No.: 3136 RN 867-81-2; Ca pantothenate, SDBS No.: 1780 RN 137-08-6
Stuckel JG, Low NH (1995) Maple syrup authenticity analysis by anion-exchange liquid chromatography with pulsed amperometric detection. J Agric Food Chem 43:3046–3051. doi:10.1021/jf00060a011
Tewari JC, Malik K (2007) In situ laboratory analysis of sucrose in sugarcane bagasse using attenuated total reflectance spectroscopy and chemometrics. Int J Food Sci Technol 42:200–207. doi:10.1111/j.1365-2621.2006.01209.x
USDA (2014) U.S. Department of Agriculture, Agricultural Research Service. National Nutrient Database for Standard Reference, Release, Nutrient Data Laboratory Home Page. http://www.ars.usda.gov/nutrientdata
Wang L, Lee FSC, Wang X, He Y (2006) Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chem 95:529–536. doi:10.1016/j.foodchem.2005.04.015
Wehrens R (2011) Chemometrics with R, Multivariate data analysis in the natural sciences and life sciences. Springer-Verlag, Berlin, Heidelberg
Whetstone K (2014) Maple Syrup Production. http://www.nass.usda.gov/Statistics_by_State/New_England_includes/Publications/0605mpl.pdf
Wojciechowski C, Dupuy N, Ta CD, Huvenne JP, Legrand P (1998) Quantitative analysis of water-soluble vitamins by ATR-FTIR spectroscopy. Food Chem 63:133–140. doi:10.1016/S0308-8146(97)00138-6
Zuo X, Jiewen Z, Yanxiao L (2007) Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of ‘Fuji’ apple based on BiPLS and FiPLS models. Vib Spectrosc 44:220–227. doi:10.1016/j.vibspec.2006.11.005
Zuo X, Fang S, Liang X (2014) Synergy interval partial least square (siPLS) with potentiometric titration multivariate calibration for the simultaneous determination of amino acids in mixtures. Adv J Food Sci Technol 6:1209–1218
This work was supported by the Ministry of Science of Croatia under research project spectroscopic analysis of unsaturated systems and metal compounds MSES 119-1191342-2959.
Conflict of Interest
Ozren Jović declares that he has no conflict of interest with the Ministry of Science who supported this research under mentioned research project. This article does not contain any studies with human or animal subjects.
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Jović, O. First Break Forward Interval PLS (FB-FiPLS) Procedure as Potential Tool in Analysis of FTIR Data for Fast and Robust Quantitative Determination of Food Adulteration. Food Anal. Methods 9, 281–291 (2016). https://doi.org/10.1007/s12161-015-0201-z
- Repeated double cross-validation
- Number of latent variables