Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods
| Autor: | Maria Frizzarin, A. Lynch, Thomas Brendan Murphy, Donagh P. Berry, Isobel Claire Gormley, Alessandro Casa, Sinead McParland |
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| Rok vydání: | 2020 |
| Předmět: |
Elastic net regularization
Lactoglobulins Machine learning computer.software_genre Machine Learning 03 medical and health sciences Lasso (statistics) Partial least squares regression Genetics Animals 030304 developmental biology Mathematics 0303 health sciences business.industry 0402 animal and dairy science Caseins 04 agricultural and veterinary sciences Linear discriminant analysis Milk Proteins 040201 dairy & animal science Random forest Support vector machine Projection pursuit regression Milk Phenotype Principal component regression Animal Science and Zoology Cattle Female Artificial intelligence business computer Food Science |
| Zdroj: | Journal of dairy science. 104(7) |
| ISSN: | 1525-3198 |
| Popis: | Numerous statistical machine learning methods suitable for application to highly correlated features, as those that exist for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN), and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated [curd firmness at 60 min, αS1-casein (CN), αS2-CN, κ-CN, α-lactalbumin, and β-lactoglobulin B], whereas NN and RR were the best algorithms for 3 traits each (rennet coagulation time, curd-firming time, and heat stability, and curd firmness at 30 min, β-CN, and β-lactoglobulin A, respectively), PLSR was best for pH, and LASSO was best for CN micelle size. When traits were divided into 2 classes, SVM had the greatest accuracy for the majority of the traits investigated. Although the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error compared with PLSR from between 0.18% (κ-CN) to 3.67% (heat stability). The use of modern statistical machine learning methods for trait prediction from mid-infrared spectroscopy may improve the prediction accuracy for some traits. |
| Databáze: | OpenAIRE |
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