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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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Main Authors: Emeline Tan, Norliza Binti Julmohammad, Wee Yin Koh, Muhamad Shirwan Abdullah Sani, Babak Rasti
Format: Article
Language:English
English
Published: Molecular Diversity Preservation International (MDPI) 2023
Subjects:
SF221-250 Dairying
TX341-641 Nutrition. Foods and food supply
Online Access: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
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spelling 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
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic SF221-250 Dairying
TX341-641 Nutrition. Foods and food supply
spellingShingle 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
description 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
score 13.211869

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