Variable selection procedures before partial least squares regression enhance the accuracy of milk fatty acid composition predicted by mid-infrared spectroscopy.

Abstract excerpt

Mid-infrared spectroscopy is a high-throughput technique that allows the prediction of milk quality traits on a large-scale. The accuracy of prediction achievable using partial least squares (PLS) regression is usually high for fatty acids (FA) that are more abundant in milk, whereas it decreases for FA that are present in low concentrations. Two variable selection methods, uninformative variable elimination or a...

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