Opening book details…
Can I read A Comparison of 4 Different Machine Learning Algorithms to Predict Lactoferrin Content in Bovine Milk from Mid-infrared Spectra on EtoBox?
A Comparison of 4 Different Machine Learning Algorithms to Predict Lactoferrin Content in Bovine Milk from Mid-infrared Spectra by Soyeurt, H.; Grelet, C.; McParland, S.; Calmels, M.; Coffey, M.; Tedde, A.; Delhez, P.; Dehareng, F.; Gengler, N. is a Agricultural and Biological Sciences article available to read on EtoBox.
What is A Comparison of 4 Different Machine Learning Algorithms to Predict Lactoferrin Content in Bovine Milk from Mid-infrared Spectra about?
Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best fo
Who reads A Comparison of 4 Different Machine Learning Algorithms to Predict Lactoferrin Content in Bovine Milk from Mid-infrared Spectra?
It is typically read by researchers, students, and practitioners in Agricultural and Biological Sciences.
- Author
- Soyeurt, H.; Grelet, C.; McParland, S.; Calmels, M.; Coffey, M.; Tedde, A.; Delhez, P.; Dehareng, F.; Gengler, N.
- Publisher
- American Dairy Science Association; Elsevier Ltd; Elsevier BV (ISSN 0022-0302)
- Published
- 2020
- Language
- EN
- Field
- Agricultural and Biological Sciences (Life Sciences)