دورية أكاديمية
Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms
| العنوان: | Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms |
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| المؤلفون: | Chu Chu, Haitong Wang, Xuelu Luo, Yikai Fan, Liangkang Nan, Chao Du, Dengying Gao, Peipei Wen, Dongwei Wang, Zhuo Yang, Guochang Yang, Li Liu, Yongqing Li, Bo Hu, Abula Zunongjiang, Shujun Zhang |
| المصدر: | Heliyon, Vol 10, Iss 12, Pp e32720- (2024) |
| بيانات النشر: | Elsevier, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Science (General) LCC:Social sciences (General) |
| مصطلحات موضوعية: | Pasteurized milk, Fourier transform infrared (FTIR) spectroscopy, Modern statistical machine learning algorithms, Adulteration, Science (General), Q1-390, Social sciences (General), H1-99 |
| الوصف: | There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2405-8440 |
| العلاقة: | http://www.sciencedirect.com/science/article/pii/S2405844024087516Test; https://doaj.org/toc/2405-8440Test |
| DOI: | 10.1016/j.heliyon.2024.e32720 |
| الوصول الحر: | https://doaj.org/article/9e8bb926785d42e5aac4befce3295dd2Test |
| رقم الانضمام: | edsdoj.9e8bb926785d42e5aac4befce3295dd2 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24058440 |
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| DOI: | 10.1016/j.heliyon.2024.e32720 |