Version 1
: Received: 4 November 2024 / Approved: 4 November 2024 / Online: 4 November 2024 (14:47:12 CET)
How to cite:
Agiomavriti, A.-A.; Nikolopoulou, M. P.; Bartzanas, T.; Chorianopoulos, N.; Demestichas, K.; Gelasakis, A. I. Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants. Preprints2024, 2024110204. https://doi.org/10.20944/preprints202411.0204.v1
Agiomavriti, A.-A.; Nikolopoulou, M. P.; Bartzanas, T.; Chorianopoulos, N.; Demestichas, K.; Gelasakis, A. I. Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants. Preprints 2024, 2024110204. https://doi.org/10.20944/preprints202411.0204.v1
Agiomavriti, A.-A.; Nikolopoulou, M. P.; Bartzanas, T.; Chorianopoulos, N.; Demestichas, K.; Gelasakis, A. I. Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants. Preprints2024, 2024110204. https://doi.org/10.20944/preprints202411.0204.v1
APA Style
Agiomavriti, A. A., Nikolopoulou, M. P., Bartzanas, T., Chorianopoulos, N., Demestichas, K., & Gelasakis, A. I. (2024). Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants. Preprints. https://doi.org/10.20944/preprints202411.0204.v1
Chicago/Turabian Style
Agiomavriti, A., Konstantinos Demestichas and Athanasios I. Gelasakis. 2024 "Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants" Preprints. https://doi.org/10.20944/preprints202411.0204.v1
Abstract
Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made feasible the development of analytical tools and re-al-time monitoring and prediction systems in the dairy ruminant sector. The objectives of the cur-rent review were i) to describe the most widely applied spectroscopy-based and supervised ma-chine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drugs residues, ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
