Volume 5, Issue 2, March 2017, Page: 51-56
Chemometrics Assisted Method for Classification of Mango Juice as Adulterated or Safe with over Use of Artificial Colours by UV Spectroscopic Data
Mohammad Nashir Uddin, BCSIR Laboratories, Dhaka, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
Ajit Kumar Majumder, Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh
Abu Tareq Mohammad Abdullah, Institute of Food Science and Technology (IFST), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
Md. Alamgir Kabir, Institute of Food Science and Technology (IFST), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
Received: Dec. 10, 2016;       Accepted: Mar. 15, 2017;       Published: Mar. 28, 2017
DOI: 10.11648/j.jfns.20170502.15      View  2141      Downloads  108
Tartazine, Sunset Yellow and Beta Carotine are commonly used artificial colours in commercial mango juices in order to make them attractive to the consumers, even though these synthetic colours have hazardous effect on health. Therefore, it is very often necessary to classify juices as adulterated with heavy use of these colours or not. In the present study, two chemometric techniques, Artificial Neural Network (ANN) and Partial Least Square-Discrimination Analysis (PLS-DA) have been assessed for their efficiencies for classification. Here, UV spectroscopic data are used as input. Three techniques, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Savitzky–Golay (S-G) filtering have been evaluated for their do-noising performance, and select the best one. Before calibration, spectral data are de-noised with MSC as is proved most efficient for de-noising UV spectral data. Spectral range 371-533 nm has been used for calibration ultimately. ANN shows better classification results than PLS-DA for all colours. Finally, the study is proposing a simpler and cheaper method for classification of mango juice as adulterated or safe with over use of artificial colours by applying ANN in de-noised spectroscopic data.
Artificial Colours, Chemometrics, De-Noising, Classification, ANN, PLS-DA
To cite this article
Mohammad Nashir Uddin, Ajit Kumar Majumder, Abu Tareq Mohammad Abdullah, Md. Alamgir Kabir, Chemometrics Assisted Method for Classification of Mango Juice as Adulterated or Safe with over Use of Artificial Colours by UV Spectroscopic Data, Journal of Food and Nutrition Sciences. Vol. 5, No. 2, 2017, pp. 51-56. doi: 10.11648/j.jfns.20170502.15
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Babu S and Shenoliker IS (1995). Health and Nutritional Implications of Food Colors. Indian Journal of Medical Research, 102: 245-255.
Sarah Chapman (2011). Guidelines on approaches to the replacement of Taretazine, Allura RED, Ponceau 4R, Quinoline Yellow, Sunset Yellow and Carmoisine in food and beverages.https://www.food.gov.uk/sites/default/files/multimedia/pdfs/publication/ guide linessoton six colours. pdf.
“Food additives”. CBC News, 29 September, 2008. http://www.cbc.ca/news2/background/foodsafety/additives.html.
“The additives which could be banned”. The Telegraph, 2009. http:// www.telegraph.co.uk/ news/health/news/3453522/The-additives-which-could-be-banned.html.
Sahar SAS and Manal MEMS (2012).The Effects of Using Color Foods of Children on Immunity Properties and Liver, Kidney on Rats. Food and Nutrition Sciences, 3: 897-904.
Garcia-Falcon MS and Simal-Gandara J (2005). Determination of food dyes in soft drinks containing natural pigments by liquid chromatography with minimal clean-up. Food Control, 16: 293–297.
Chen Q, Mou S, Hou X, Riviello JM and Ni Z (1998). Determination of eight synthetic food colourants in drinks by high performance ion chromatography. Journal of Chromatography A, 827: 73–81.
Chou SS, Lin YH, Cheng CC and Hwang DF (2002). Determination of synthetic colours in soft drinks and confectioneries by micellarelectrokinetic capillary chromatography. Journal of Food Science, 67(4): 1314–1318.
González M, Lobo MG, Méndez J and Carnero A (2005). Detection of colour adulteration in cochineals by spectrophotometric determination of yellow and red pigment groups. Food Control, 16(2): 105-112.
Rong Li, Jiang Z and Liu Y (2008). Direct Solid-phase Spectrophotometric Determination of Tartrazine in Soft Drinks Using β-Cyclodextrin Polymer as Support. Journal of Food and Drug Analysis, 16(5): 91-96.
Ou S, Xiashi Z, Yanli F and Weixing M (2014). Determination of Sunset Yellow and Tartrazine in Food Samples by Combining Ionic Liquid-Based Aqueous Two-Phase System with High Performance Liquid Chromatography. Journal of Analytical Methods in Chemistry, 2014: 1-8.
Bachalla N (2016). Identification of synthetic food colors adulteration by paper chromatography and spectrophotometric methods. International Archives of Integrated Medicine, 3(6): 182-191.
Kirali R and Ferreira MMC (2006). The past, present, and future of chemometrics worldwide: some etymological, linguistic, and bibliometric investigations. Journal of Chemometrics, 20: 247-271.
Rasmus Bro (2013). Chemometrics in Food Chemistry. Elsevier, the Boulevard, Langford Lane, Kidlington, Oxford, OX5 1GB, UK. pp: 171-227.
Carolina VDA, Serena R, Liliana A and Rodríguez MS (2016). UV-Visible Spectroscopy and Multivariate Classification as a Screening Tool for Determining the Adulteration of Sauces. Food Analytical Methods, 9(11): 3117–3124.
Nicolai BM, Beullens K, Bobelyn E, Peirs A, Saeys W and Theron KI (2007). Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46: 99−118.
Paradkar MM, Sivakeasva S and Irudayaraj J (2003). Discrimination and classification of adulterants in maple syrup with the use of infrared spectroscopic techniques. Journal of the Science of Food and Agriculture, 83: 714-721.
Jha SN, Gunasekaran S (2010). Authentication of sweetness of mango juice using Fourier transform infrared-attenuated total reflection spectroscopy. Journal of Food Engineering, 101: 337-342.
Nikbakht M, Tavakkoli HT, Malekfar R and Ghobadian B (2011). Nondestructive Determination of Tomato Fruit Quality Parameters Using Raman Spectroscopy. Journal of Agricultural Science and Technology, 13(4): 517-526.
Dinc E, Baydan E, Kanbur B and Onur F (2002). Spectrophotometric multicomponent determination of sunset yellow, tartrazine and allura red in soft drink powder by double divisor-ratio spectra derivative, inverse least-squares and principal component regression methods. Talanta, 58: 579-594.
Dinc E, Aktas AH, Baleanu D and Ustundag O (2006). Simultaneous Determination of Tartrazine and Allura Red in Commercial Preparation by Chemometric HPLC Method. Journal of Food and Drug Analysis, 14 (3): 284-291.
Karaoglan GK, Gumrukcu G, Ozgur MU, Bozdogan A and Asci B (2007). Abilities of Partial Least-Squares (PLS-2) Multivariate Calibration in the Analysis of Quaternary Mixture of Food Colours (E-110, E-122, E-124, E-131). Analytical Letters, 40: 1893-1903.
Arvantoyannis I, Katsota MN, Psarra P, Soufleros E andKallinthraka S (1999). Application of quality control methods for assessing wine authenticity: Use of multivariate analysis (chemometrics). Trends Food SciTechnol, 10: 321–336.
Buratti S, Bendetti S, Scampicchio M and Pangerod EC (2004). Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue. Anal ChimActa, 525: 133–139.
Cynkar WU, Cozzolino D, Dambergs RG, Janik L and Gishen M. (2007). Feasibility study on the use of a head space mass spectrometry electronic nose (MS e-nose) to monitor red wine spoilage induced by Brettanomyces yeast. Sensor Actuat, 124: 167–171.
Aleixandre M, Lozano J, Gutierrez J, Sayago I, Fernandez MJ, Horrillo MC (2008). Portable e-nose to classify different kinds of wine. Sensors and Actuators, 131: 71–76.
Rumelhart DE, Hinton GE and Williams RJ (1986). Learning representations by back-propagating error. Nature, 323: 533–536.
Kosko B (1992). Neural Networks for Signal Processing. Prentice-Hall, London, pp. 399.
David R, Sovan L, Ioannis D, Jean J and Jacques LSA (1997). Artificial neural networks as a classification method in the behavioural sciences. Behavioural Processes, 40: 35–43.
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