Detection and Quantification of Formalin Adulteration in Cow Milk Using Near Infrared Spectroscopy Combined with Multivariate Analysis
- *Corresponding Author:
- Fazal Mabood
Department of Biological Sciences & Chemistry
College of Arts and Sciences
University of Nizwa
Sultanate of Oman
E-mail: [email protected]
Received date: November 15, 2016; Accepted date: December 15, 2016; Published date: December 23 2016
Citation: Mabood F, Hussain J, Al Nabhani MMO, Gilani SA, et al. (2017) Detection and Quantification of Formalin Adulteration in Cow Milk Using Near Infrared Spectroscopy Combined with Multivariate Analysis. J Adv Dairy Res 5:167. doi:10.4172/2329-888X.1000167
Copyright: © 2017 Mabood F, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In order to increase the shelf life for long distance transportation of milk, formalin is added as an adulterant to milk. That is highly toxic causes liver and kidney damages. A new Near Infrared spectroscopy (NIR) combined with multivariate analysis was developed to detect as well as to quantify the level of formalin adulteration in cow milk. In this study four different types of cow milk samples were collected from Nizwa regions of Sultanate of Oman and were investigated. Those cow milk samples were then adulterated with formalin at eight different percentage levels: 0%, 1%, 3%, 5%, 7%, 9%, 11%, 13% and 17% of formalin. All samples were measured using NIR spectroscopy in absorption mode in the wavelength range from 700-2500 nm, at 2 cm-1 resolution and using a 0.2 mm path length CaF2 sealed cell. The multivariate methods like Principle component analysis (PCA), partial least discriminant analysis (PLS-DA) and partial least regression analysis (PLS) were applied for statistical analysis of the obtained NIR spectral data. PLS-DA model was used to check the discrimination between the pure and formalin adulterated milk samples. For PLSDA model the R-square value obtained was 0.969 with 0.086 RMSE (Root mean square error). Furthermore, PLS regression model was also built to quantify the levels formalin adulterant in cow milk samples. The PLS regression model was obtained with the R-square 93% and with 1.38 RMSECV(Root mean square error of cross validation) value having good prediction with RMSEP (Root mean square error of prediction) value 1.50 and correlation of 0.95. This newly developed method is non-destructive, cheap, no need of much sample preparation and having sensitivity level less than 2% level of formalin adulteration.