کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5141916 | 1495967 | 2017 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
FT-NIRS coupled with chemometric methods as a rapid alternative tool for the detection & quantification of cow milk adulteration in camel milk samples
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کلمات کلیدی
PLSPLS-DARMSECVRMSEPPLSDAFourier transform near-infrared spectroscopyFT-NIRSPCA - PCAPartial least squares discriminant analysis - تجزیه و تحلیل خرده مقیاس حداقل مربعات جزئیPrincipal component analysis - تحلیل مولفههای اصلی یا PCAroot mean square error of prediction - خطای متوسط مربع خطای پیش بینیPartial least squares regression - رگرسیون حداقل مربعات جزئیRoot mean square error of cross validation - ریشه میانگین خطای مربع اعتبارسنج متقابل است
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In the current study FT-NIRS combined with multivariate methods was developed as a novel alternative tool for the rapid determination of cow milk adulteration in camel milk samples. For this investigation the camel milk as well as cow milk samples were collected from Aldhahira and Sharqia regions of Sultanate of Oman. FT-NIR spectroscopy was used for the measurement of all milk samples with a scanning range from 4000 to 10000Â cmâ1 in absorption mode at 2Â cmâ1 resolution and using a 0.2Â mm path length CaF2 sealed cell. The chemometric methods like Principle component analysis (PCA), partial least discriminant analysis (PLS-DA) and partial least regression analysis (PLS) were used for statistical analysis of the obtained NIR spectral data. PLS-DA model was built to check the discrimination between the pure and adulterated camel milk samples. For PLS-DA model the R-square value obtained was 0.97 with 0.080 RMSEC (Root mean square error). Furthermore, PLS regression model was also built to quantify the levels of cow milk adulterant in camel milk samples. The PLS regression model was obtained with the R-square value of 92%. Root mean square error of cross validation (RMSECV) value for PLSR was found 1.76% showing good prediction with root mean square error of prediction (RMSEP) value of 1.32%. This newly developed method is non-destructive, quick, no need of much sample preparation and having detection limit less than 1.5%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Vibrational Spectroscopy - Volume 92, September 2017, Pages 245-250
Journal: Vibrational Spectroscopy - Volume 92, September 2017, Pages 245-250
نویسندگان
Fazal Mabood, Farah Jabeen, Javid Hussain, Ahmed Al-Harrasi, Ahmad Hamaed, Saaida A.A. Al Mashaykhi, Zainb M.A. Al Rubaiey, Suryyia Manzoor, Ajmal Khan, Q.M. Imranul. Haq, S.A. Gilani, Alamgir Khan,