Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk
Urea is naturally present in milk, yet urea is added intentionally to increase milk’s nitrogen content and shelf life. In this study, a total of 50 Ultra heat treatment (UHT) milk samples were spiked with known urea concentrations (0–5 w/v%). Attenuated total reflectance–Fourier transform infrared (...
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Molecular Diversity Preservation International (MDPI)
2023
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my.ums.eprints.375122023-10-13T08:01:11Z https://eprints.ums.edu.my/id/eprint/37512/ Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk Emeline Tan Norliza Binti Julmohammad Wee Yin Koh Muhamad Shirwan Abdullah Sani Babak Rasti SF221-250 Dairying TX341-641 Nutrition. Foods and food supply Urea is naturally present in milk, yet urea is added intentionally to increase milk’s nitrogen content and shelf life. In this study, a total of 50 Ultra heat treatment (UHT) milk samples were spiked with known urea concentrations (0–5 w/v%). Attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy with principal component analysis (PCA), discriminant analysis (DA), and multiple linear regression (MLR) were used for the discrimination and quantification of urea. The PCA was built using 387 variables with higher FL > 0.75 from the first PCA with cumulative variability (90.036%). Subsequently, the DA model was built using the same variables from PCA and demonstrated the good distinction between unadulterated and adulterated milk, with a correct classification rate of 98% for cross-validation. The MLR model used 48 variables with p-value < 0.05 from the DA model and gave R2 values greater than 0.90, with RMSE and MSE below 1 for cross-validation and prediction. The DA and MLR models were then validated externally using a test dataset, which shows 100% correct classification, and the t-test result (p > 0.05) indicated that the MLR could determine the percentage of urea in UHT milk within the permission limit (70 mg/mL). In short, the wavenumbers 1626.63, 1601.98, and 1585.5534 cm−1 are suitable as fingerprint regions for detecting urea in UHT milk. Molecular Diversity Preservation International (MDPI) 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/37512/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/37512/2/FULL%20TEXT.pdf Emeline Tan and Norliza Binti Julmohammad and Wee Yin Koh and Muhamad Shirwan Abdullah Sani and Babak Rasti (2023) Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk. Foods, 12. pp. 1-18. https://doi.org/10.3390/foods12152855 |
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SF221-250 Dairying TX341-641 Nutrition. Foods and food supply Emeline Tan Norliza Binti Julmohammad Wee Yin Koh Muhamad Shirwan Abdullah Sani Babak Rasti Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk |
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Urea is naturally present in milk, yet urea is added intentionally to increase milk’s nitrogen content and shelf life. In this study, a total of 50 Ultra heat treatment (UHT) milk samples were spiked with known urea concentrations (0–5 w/v%). Attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy with principal component analysis (PCA), discriminant analysis (DA), and multiple linear regression (MLR) were used for the discrimination and quantification of urea. The PCA was built using 387 variables with higher FL > 0.75 from the first PCA with cumulative variability (90.036%). Subsequently, the DA model was built using the same variables from PCA and demonstrated the good distinction between unadulterated and adulterated milk, with a correct classification rate of 98% for cross-validation. The MLR model used 48 variables with p-value < 0.05 from the DA model and gave R2 values greater than 0.90, with RMSE and MSE below 1 for cross-validation and prediction. The DA and MLR models were then validated externally using a test dataset, which shows 100% correct classification, and the t-test result (p > 0.05) indicated that the MLR could determine the percentage of urea in UHT milk within the permission limit (70 mg/mL). In short, the wavenumbers 1626.63, 1601.98, and 1585.5534 cm−1 are suitable as fingerprint regions for detecting urea in UHT milk. |
format |
Article |
author |
Emeline Tan Norliza Binti Julmohammad Wee Yin Koh Muhamad Shirwan Abdullah Sani Babak Rasti |
author_facet |
Emeline Tan Norliza Binti Julmohammad Wee Yin Koh Muhamad Shirwan Abdullah Sani Babak Rasti |
author_sort |
Emeline Tan |
title |
Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk |
title_short |
Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk |
title_full |
Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk |
title_fullStr |
Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk |
title_full_unstemmed |
Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk |
title_sort |
application of atr-ftir incorporated with multivariate data analysis for discrimination and quantification of urea as an adulterant in uht milk |
publisher |
Molecular Diversity Preservation International (MDPI) |
publishDate |
2023 |
url |
https://eprints.ums.edu.my/id/eprint/37512/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/37512/2/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/37512/ https://doi.org/10.3390/foods12152855 |
_version_ |
1781705922341502976 |