AI Article Synopsis
- Researchers explored using NIR spectroscopy and multivariate analysis to identify and measure goat milk adulteration in camel milk from Oman.
- The study utilized various multivariate methods like PCA, PLS-DA, and PLS regression to analyze the NIR spectral data, achieving a high R-square value of 0.974 for distinguishing pure from adulterated samples.
- The PLS regression model effectively quantified adulteration levels, showing a low detection limit of 0.5% and making the method reliable and sensitive for quality control.
Article Abstract
New NIR spectroscopy combined with multivariate analysis for detection and quantification of camel milk adulteration with goat milk was investigated. Camel milk samples were collected from Aldhahira and Sharqia regions of Sultanate of Oman and were measured using NIR spectroscopy in absorption mode in the wavelength range from 700 to 2500nm, at 2cm resolution and using a 0.2mm path length CaF sealed cell. The multivariate methods like PCA, PLS-DA and PLS regression were used for interpretation of NIR spectral data. PLS-DA was used to detect the discrimination between the pure and adulterated milk samples. For PLSDA model the R-square value obtained was 0.974 with 0.08 RMSE. Furthermore, PLS regression model was used to quantify the levels of adulteration from, 0%, 2%, 5%, 10%, 15% and 20%. The PLS model showed the RMSEC=1.10% with R=94%. This method is simple, reproducible, having excellent sensitivity. The limit of detection was found 0.5%, while the limit of quantification was 2%.
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| http://dx.doi.org/10.1016/j.foodchem.2016.11.109 | DOI Listing |
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