Abstract
Bread is a major source of human’s diet especially in developing countries and has significant role in human's healthy lifestyle. The addition of sodium dithionite (Na2S2O4) to flour for whitening has dangerous effects on human's health. In this research Fourier Transform-Infrared (FT-IR) spectroscopy with chemometric techniques is used to identify this compound in flour. After acquiring spectral data some preprocessing methods for removing uninformative effects and improving model results were applied. Principle Component Analysis (PCA) as unsupervised and Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) as supervised models were used. By comparing different preprocessing methods with models, The SVM model had the best classification result with 100% and 79.62% for two (pure and adulterated) and five (pure and adulterated with adulteration levels) class classification, respectively. This study showed the applicability of FT-MIR spectroscopy technology with chemometrics methods for detection of fraud in wheat flour.
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References
M.L. Adams, E. Lombi, F.J. Zhao, S.P. McGrath, Evidence of low selenium concentrations in UK bread-making wheat grain. J. Sci. Food Agric. 82(10), 1160–1165 (2002). https://doi.org/10.1002/jsfa.1167
P.R. Shewry, The HEALTHGRAIN programme opens new opportunities for improving wheat for nutrition and health. Nutr. Bull. 34(2), 225–231 (2009). https://doi.org/10.1111/j.1467-3010.2009.01747.x
P.R. Shewry, S. Powers, J.M. Field, R.J. Fido, H.D. Jones, G.M. Arnold et al., Comparative field performance over 3 years and two sites of transgenic wheat lines expressing HMW subunit transgenes. Theor. Appl. Genet. 113(1), 128–136 (2006). https://doi.org/10.1007/s00122-006-0279-1
D. Topping, Cereal complex carbohydrates and their contribution to human health. J. Cereal Sci. 46(3), 220–229 (2007)
M. Malakootian, S. Dowlatshahi, The quality of the manufactured bread and hygienic conditions of bakeries. J. Environ. Health Sci. Eng. 2(2), 72–78 (2005)
M. Sabeghi, Interview with dean of faculity of flour and bread. J. Iran Dough-Baked 3, 5–6 (2004)
J.B. Weinrach, D.R. Meyer, J.T. Guy, P.E. Michalski, K.L. Carter, D.S. Grubisha, D.W. Bennett, A structural study of sodium dithionite and its ephemeral dihydrate: a new conformation for the dithionite ion. J. Crystallogr. Spectrosc. Res. 22(3), 291–301 (1992)
J. Chitra, M. Ghosh, H. Mishra, Rapid quantification of cholesterol in dairy powders using Fourier transform near infrared spectroscopy and chemometrics. Food Control 78, 342–349 (2017)
B. Gaspardo, S. Del Zotto, E. Torelli, S. Cividino, G. Firrao, G. Della Riccia, B. Stefanon, A rapid method for detection of fumonisins B1 and B2 in corn meal using Fourier transform near infrared (FT-NIR) spectroscopy implemented with integrating sphere. Food Chem. 135(3), 1608–1612 (2012)
T. Leng, F. Li, L. Xiong, Q. Xiong, M. Zhu, Y. Chen, Quantitative detection of binary and ternary adulteration of minced beef meat with pork and duck meat by NIR combined with chemometrics. Food Control 113, 107203 (2020)
J.A.L. Pallone, E.T. dos Santos Caramês, P.D. Alamar, Green analytical chemistry applied in food analysis: alternative techniques. Curr. Opin. Food Sci. 22, 115–121 (2018)
S. Lohumi, S. Lee, H. Lee, B.-K. Cho, A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends Food Sci. Technol. 46(1), 85–98 (2015)
A. De Girolamo, M.C. Arroyo, S. Cervellieri, M. Cortese, M. Pascale, A.F. Logrieco, V. Lippolis, Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: a case study. LWT 127, 109368 (2020)
A. De Girolamo, S. Cervellieri, E. Mancini, M. Pascale, A.F. Logrieco, V. Lippolis, Rapid authentication of 100% Italian durum wheat pasta by FT-NIR spectroscopy combined with chemometric tools. Foods 9(11), 1551 (2020)
X.X. Guo, W. Hu, Y. Liu, D.C. Gu, S.Q. Sun, C.H. Xu, X.C. Wang, Rapid analysis and quantification of fluorescent brighteners in wheat flour by Tri-step infrared spectroscopy and computer vision technology. J. Mol. Struct. 1099, 393–398 (2015)
S.D. Rodríguez, G. Rolandelli, M.P. Buera, Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods. Food Chem. 274, 392–401 (2019)
W. Yuan, B. Xiang, L. Yu, J. Xu, A non-invasive method for screening sodium hydroxymethanesulfonate in wheat flour by near-infrared spectroscopy. Food Anal. Methods 4(4), 550–558 (2011)
Å. Rinnan, F. Van Den Berg, S.B. Engelsen, Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal. Chem. 28(10), 1201–1222 (2009)
H.I. Kademi, B.H. Ulusoy, C. Hecer, Applications of miniaturized and portable near infrared spectroscopy (NIRS) for inspection and control of meat and meat products. Food Rev. Intl. 35(3), 201–220 (2019)
A. López-Maestresalas, K. Insausti, C. Jarén, C. Pérez-Roncal, O. Urrutia, M.J. Beriain, S. Arazuri, Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control 98, 465–473 (2019)
A. Savitzky, M.J. Golay, Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)
K.A. Bakeev, Process analytical technology: spectroscopic tools and implementation strategies for the chemical and pharmaceutical industries (Wiley, Hoboken, 2010)
M. Khojastehnazhand, M.H. Khoshtaghaza, B. Mojaradi, M. Rezaei, M. Goodarzi, W. Saeys, Comparison of visible–near infrared and short wave infrared hyperspectral imaging for the evaluation of rainbow trout freshness. Food Res. Int. 56, 25–34 (2014)
G. Destefanis, M.T. Barge, A. Brugiapaglia, S. Tassone, The use of principal component analysis (PCA) to characterize beef. Meat Sci. 56(3), 255–259 (2000)
M. Peyvasteh, A. Popov, A. Bykov, I. Meglinski, Meat freshness revealed by visible to near-infrared spectroscopy and principal component analysis. J. Phys. Commun. 4(9), 095011 (2020)
C. Syms, Principal components analysis (Elsevier, Amsterdam, 2008)
F. Pan, G. Song, X. Gan, Q. Gu, Consistent feature selection and its application to face recognition. J. Intell. Inf. Syst. 43(2), 307–321 (2014)
A. Tharwat, T. Gaber, A. Ibrahim, A.E. Hassanien, Linear discriminant analysis: a detailed tutorial. AI Commun. 30(2), 169–190 (2017)
K.H. Esbensen, B. Swarbrick, Multivariate data analysis, 6th edn. (IMPublising, Chichester, 2018)
K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, B. Scholkopf, An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181–201 (2001)
D. Ballabio, R. Todeschini, Multivariate classification for qualitative analysis. Infrared Spectrosc. Food Qual. Anal. Control 83, e102 (2009)
M. Rashvand, M. Omid, H. Mobli, M.S. Firouz, Adulteration detection in olive oil using dielectric technique and data mining. Sens. Bio-Sens. Res. 11, 33–36 (2016)
M. Blanco, I. Villarroya, NIR spectroscopy: a rapid-response analytical tool. TrAC Trends Anal. Chem. 21(4), 240–250 (2002)
D.F. Roa, P.R. Santagapita, M.P. Buera, M.P. Tolaba, Ball milling of Amaranth starch-enriched fraction. Changes on particle size, starch crystallinity, and functionality as a function of milling energy. Food Bioprocess Technol. 7(9), 2723–2731 (2014)
F.A. Guzmán-Ortiz, H. Hernández-Sánchez, H. Yee-Madeira, E. San Martín-Martínez, M. del Carmen Robles-Ramírez, M. Rojas-López et al., Physico-chemical, nutritional and infrared spectroscopy evaluation of an optimized soybean/corn flour extrudate. J. Food Sci. Technol. 52(7), 4066–4077 (2015)
L. Hu, C. Yin, S. Ma, Z. Liu, Assessing the authenticity of black pepper using diffuse reflectance mid-infrared Fourier transform spectroscopy coupled with chemometrics. Comput. Electron. Agric. 154, 491–500 (2018)
S. Wold, H. Martens, H. Wold, in The Multivariate Calibration Problem in Chemistry Solved by the PLS Method, eds. by B. Kågström, A. Ruhe. Matrix Pencils Lecture Notes in Mathematics, vol. 973 (Springer, Cham, 1983), pp. 286–293
F.N. Arslan, G. Akin, ŞN. Karuk Elmas, B. Üner, I. Yilmaz, H.G. Janssen, A. Kenar, FT-IR spectroscopy with chemometrics for rapid detection of wheat flour adulteration with barley flour. J. Consum. Prot. Food Saf 15(3), 245–261 (2020)
F. Tao, L. Liu, C. Kucha, M. Ngadi, Rapid and non-destructive detection of cassava flour adulterants in wheat flour using a handheld MicroNIR spectrometer. Biosys. Eng. 203, 34–43 (2021)
L.C. Padierna, M. Carpio, A. Rojas-Dominguez, H. Puga, H. Fraire, A novel formulation of orthogonal polynomial kernel functions for SVM classifiers: the Gegenbauer family. Pattern Recogn. 84, 211–225 (2018)
L.F. Siqueira, R.F.A. Júnior, A.A. de Araújo, C.L. Morais, K.M. Lima, LDA vs. QDA for FT-MIR prostate cancer tissue classification. Chemom. Intell. Lab. Syst. 162, 123–129 (2017)
S.J. Dixon, R.G. Brereton, Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure. Chemom. Intell. Lab. Syst. 95(1), 1–17 (2009)
M. Khanmohammadi, K. Ghasemi, A.B. Garmarudi, Genetic algorithm spectral feature selection coupled with quadratic discriminant analysis for ATR-FTIR spectrometric diagnosis of basal cell carcinoma via blood sample analysis. RSC Adv. 4(78), 41484–41490 (2014)
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This study was done by central laboratory of university of Tabriz. The authors thank the personnel of this laboratory for their friendly cooperation during the experiments.
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Kazemi, A., Mahmoudi, A. & Khojastehnazhand, M. Detection of sodium hydrosulfite adulteration in wheat flour by FT-MIR spectroscopy. Food Measure 17, 1932–1939 (2023). https://doi.org/10.1007/s11694-022-01763-x
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DOI: https://doi.org/10.1007/s11694-022-01763-x