W. Terouzi, Team of Applied Spectro- Chemometrics and environment, Faculty of Science and Technology of Beni Mellal, University of Sultan Moulay slimane, 21000- Beni Mellal, Morocco; Laboratory of Engineering and Applied Technologies, Hight School of Technology of Beni Mellal, University of Sultan Moulay slimane, 21000- Beni Mellal, Morocco.

F. Kzaiber, Team of Applied Spectro- Chemometrics and environment, Faculty of Science and Technology of Beni Mellal, University of Sultan Moulay slimane, 21000- Beni Mellal, Morocco; Laboratory of Engineering and Applied Technologies, Hight School of Technology of Beni Mellal, University of Sultan Moulay slimane, 21000- Beni Mellal, Morocco.

A. Oussama, Team of Applied Spectro- Chemometrics and environment, Faculty of Science and Technology of Beni Mellal, University of Sultan Moulay slimane, 21000- Beni Mellal, Morocco.

Abstract
Authenticity is the most important criterion for food quality. In fact, food industries, consumers and regulatory agencies are increasingly demanding fast and effective methods to confirm authenticity or detect adulterations. The goal of present study is to use Attenuated total reflectance-Fourier transform mid-infrared (ATR-FTMIR) spectroscopy coupled with chemometrics, as rapid and green analytical method for the detection of the traditional butter adulteration. In this case, pure traditional butter and blends of traditional cow’s butter with different percentages of vegetable butter (3.8–40%) and of mashed potatoes (13–36%) were measured using ATR-FTMIR spectroscopy. The spectral data were subjected to a preliminary derivative elaboration based on the Gap algorithm to reduce the noise and extract a largest number of analytical information from spectra. Firstly, cluster analysis (CA) and principal component analysis (PCA) were applied, and three distinctive clusters were recognized. Then, Linear discriminant analysis (LDA) and support vector machines (SVM), were elaborated as classification methods. The obtained classification results showed that this approach could identify and detect, easily, traditional butter adulteration with an accuracy value of 97,22 and 100%.

Keywords
Adulteration ; ATR-FTIR spectroscopy ; Authentication ; Chemometrics tools ; mashed potatoes ; traditional cow’s butter ; vegetable butter

Cite this paper
W. Terouzi, F. Kzaiber, A. Oussama, Use of Infrared Spectroscopy and chemometrics for rapid authentication and detection of Moroccan traditional butter adulteration , SCIREA Journal of Chemical Engineering. Volume 3, Issue 1, February 2019 | PP. 1-19.

References

[ 1 ]	Chia-Lin Lee, Huei-Lin Liao, Wan-Chen Lee, Chao-Kai Hsu, Fwu-Chyn Hsueh, Jyh-Quan Pan, Chih-Hsuan Chu, Chieh-Ting Wei, Ming-Ju Chen (2018). Standards and labeling of milk fat and spread products in different countries. Journal of food and drug analysis, 26, 4 69 - 480.
[ 2 ]	Terouzi, W., Kzaiber, F., Gorfti, A., Oussama, A (2017). Application of multivariate analysis to predict adulteration of Moroccan traditional butter by mashed potatoes. International Journal of Engineering Research and Allied Sciences (IJERAS) ISSN : 2455-9660, 01(10), 8-12.
[ 3 ]	Nikhil M. Jirankalgikar, Subrata De (2014). Detection of tallow adulteration in cow ghee by derivative spectrophotometry. Journal of Natural Science, Biology and Medicine, 5 (2), 317-319.
[ 4 ]	Terouzi, W., Ait Elkadi, Y., Gorfti, A., Kzaiber, F., Oussama, A (2018). Multivariate regression methods with infrared spectroscopy to detect the falsification of traditional butter. Invention Journal of Research Technology in Engineering & Management (IJRTEM) ISSN: 2455-3689, 2 (7), 87-96.
[ 5 ]	Esslinger, S., Riedl, J., Fauhl-Hassek, C (2014). Potential and limitations of non-targeted fingerprinting for authentication of food in official control. Food Research International, 60, 189–204.
[ 6 ]	G. Dobson (2001). Lipid analysis by vibrational spectroscopy. European Journal of Lipid Science and Technology, 103, 815–840.
[ 7 ]	Y. W. Lai, E. K. Kemsley & R. H. Wilson (1994). Potential of Fourier transform infrared spectroscopy for the authentication of vegetable oils. Journal of Agricultural and Food Chemistry, 42, 1154–1159.
[ 8 ]	D. B. Dahlberg, S. M. Lee, S. J. Wenger & J. A. Vargo (1997). Classification of vegetable oils by FT-IR.Applied Spectroscopy. Applied Spectroscopy, 51, 1118–1124.
[ 9 ]	N. Dupuy, L. Duponchel, J. P. Huvenne, B. Sombret & P. Legrand (1996). Classification of edible fats and oils by principal component analysis of Fourier transform infrared spectra. Food Chemistry, 57, 245–251.
[ 10 ]	B. F. Ozen, I. Weiss & L. J. Mauer (2003). Dietary Supplement Oil Classification and Detection of Adulteration Using Fourier Transform Infrared Spectroscopy. Journal of Agricultural and Food Chemistry, 57, 5871–5876.
[ 11 ]	M. Safar, D. Bertrand, P. Robert, M. F. Devaux & C. Genot (1994). Characterization of edible oils, butters and margarines by Fourier transform infrared spectroscopy with attenuated total reflectance. Chemists- Society. 71, 371–377.
[ 12 ]	M. De Luca, W. Terouzi, G. Ioele, F. Kzaiber, A. Oussama, F. Oliverio, R. Tauler, G. Ragno (2011). Derivative FTIR spectroscopy for cluster analysis and classification of morocco olive oils. Food Chemistry, 124, 1113–1118.
[ 13 ]	N. Nicolaou, Y. Xu, R.Goodacre (2010). Fourier transform infrared spectroscopy and multivariate analysis for the detection and quantification of different milk species. J. Dairy Sci., 93, 5651-5660.
[ 14 ]	N. Nicolaou and R.Goodacre (2008). Rapid and quantitative detection of the microbial spoilage in milk using Fourier transform infrared spectroscopy and chemometrics. Analyst, 133, 1424-1431.
[ 15 ]	G. Downey (1996). Authentication of food and food ingredients by near infrared spectroscopy. J. Near Infrared Spectrosc. 4 (1), 47–61.
[ 16 ]	G. Downey (1998). Food and food ingredient authentication by mid-infrared spectroscopy and chemometrics. Trends Anal. Chem. 17, 418–424.
[ 17 ]	Y. Roggo, P. Chalus, L.Maurer, C. Lema-Martinez, A. Edmond & N. Jent (2007). Areview of near infrared spectroscopy and chemometrics in pharmaceutical technologies.Journal of Pharmaceutical and Biomedical Analysis, 44, 683–700.
[ 18 ]	J. Yang & J. Yu Yang (2003). Why can LDA be performed in PCA transformed space. Pattern Recogn, 36, 563-566.
[ 19 ]	Terouzi, W., Platikanov, S., Anna de Juan Capdevila, A., and Oussama, A. (2013). Classification of olives from Moroccan regions by using direct FT-IR analysis: Application of support vector machines (SVM). International Journal of Innovation and Applied Studies. ISSN 2028-9324, 3 (2), 493-503.
[ 20 ]	Terouzi, W., Ait yacine, Z., Kzaiber, F., Oussama, A. (2016). Determining the geographical origin of olives by chemometric tools applied to ATR-FTIR spectra of mesocarp. Asian Academic Research Journal of Multidisciplinary, 3 (6), ISSN: 2319-2801, 176-192.
[ 21 ]	W.Terouzi, A.Oussama (2016). Characterization And Discrimination of Monocultivar Loquat (Eriobotrya Japonica) Fruit From two Areas in Beni Mellal-Khenifra Region. International Journal of Research in Engineering and Science (IJRES), ISSN (Print): 2320-9356, 4 (7), 40-48.
[ 22 ]	Belousov AI, Verzakov SA, von Frese J (2002). Applicational aspects of support vector machines. J. Chemometrics, 16, 482–489.
[ 23 ]	Broun, M.P.S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T.S., Manuel Ares, Jr. and Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS, 97 (1), 262-267.
[ 24 ]	A. P. Reynolds, G. Richards, B. dela Iglesia and V. J. Rayward-Smith (2006). Clustering rules: A comparison of partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms, 5, 475–504.
[ 25 ]	B.G.M. Vandeginste, D.L. Massart, L.M.C. Buydens, S.D.E. Jong, P.J., Lewi. Smeyers Verbeke, J. Handbook of Chemometrics and Qualimetrics: Part B, Elsevier, Amsterdam, The Netherlands, 1998, p. 88.
[ 26 ]	S. Wold, K.Esbensen & P. Geladi. Principal component analysis. Chemom. Intell. Lab. Syst., 2, 1987, 37-52.
[ 27 ]	R. Garrido-Delgado, L. Arce, A.V. Guamán, A.. Pardo, S. Marco, M. Valcárcel (2011). Direct coupling of a gas–liquid separator to an ion mobility spectrometer for the classification of different white wines using chemometrics tools. Talanta 84, 471–479.
[ 28 ]	A.Gori, R. M. Maggio, L. Cerretani, M. Nocetti, M. Fiorenza Caboni (2012). Discrimination of grated cheeses by Fourier transform infrared spectroscopy coupled with chemometric techniques. International Dairy Journal 23, 115-120.
[ 29 ]	Xu, L., Shao, X.G. Chemometric Methods. Beijing: Science Press, 2006, 130 -133.
[ 30 ]	Vapnik, V., Chervonenkis, A. Theory of Pattern Recogn, Nauka, Moscow, 1974.
[ 31 ]	Vapnik, V. The nature of statistical learning theory, Springer Verlag, 1995.
[ 32 ]	Vapnik, V.The Nature of Statistical Learning Theory. Second edition. New York: Springer, 1999.
[ 33 ]	Drucker, H., Burges, CJ., Kaufman, L., Smola, A (1997). and Vapnik, V. Support Vector Regression Machines. Adv Neural Inf Process Syst, 9, 155-161.
[ 34 ]	Savitzky, A. & Golay, M.J.E. Smoothing and differentiation of data by simplified least-squares procedures. Analytical Chemistry, 36, 1964, 1627–1639.
[ 35 ]	Norris, K.H. & Williams, P.C. Optimization of mathematical treatments of raw near infrared signal in the measurement of protein in hard Red Spring wheat, I: influence of particle size. Cereal Chemistry, 62, 1984,158–165.
[ 36 ]	Iñón, F. A., Garrigues, J. M., Garrigues, S., Molina, A., and de la Guardia, M. (2003). Selection of calibration set samples in determination of olive oil acidity by partial least squares-attenuated total reflectance-Fourier transform infrared spectroscopy. Analytica Chimica Acta, 489, 59–75.
[ 37 ]	KH. Esbensen. Multivariate data analysis-in practice. An introduction to multivariate data analysis and experimental desing.Oslo: CAMO. 2000.
[ 38 ]	Scott, I.M., Lina, W., Liakata, M., Wood, J.E., Vermeer, C.P., Allaway, D., Ward, J.L., Draper, J., Beale, M.H., Corol, D.I., Baker, J.M., King, R.D. (2013). Merits of random forests emerge in evaluation of chemometricclassifiers by external validation. Analytica Chimica Acta 801, 22– 33.
[ 39 ]	Ankit Bansal, Vikas Chhabra, Ravindra K. Rawal, Simant Sharma (2014). Chemometrics: A new scenario in herbal drug standardization. Journal of Pharmaceutical Analysis, http://dx.doi.org/ 10.1016/j.jpha.2013.12.001.