201. Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk.
- Author
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Stocco G, Dadousis C, Pazzola M, Vacca GM, Dettori ML, Mariani E, and Cipolat-Gotet C
- Subjects
- Humans, Animals, Milk chemistry, Bayes Theorem, Goats, Spectroscopy, Fourier Transform Infrared, Cheese analysis
- Abstract
The objectives of this study were to explore the use of Fourier-transform infrared (FITR) spectroscopy on 458 goat milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. Calibration equations were developed using a Bayesian approach with three different scenarios: i) a random cross-validation (CV) [80% calibration (CAL); 20% validation (VAL) set], ii) a stratified CV [(SCV), 13 farms used as CAL, and the remaining one as VAL set], and iii) a SCV where 20% of the goats randomly selected from the VAL farm were included in the CAL set (SCV
80 ). The best prediction performance was obtained for cheese yield solids, justifying for its practical application at population level. Overall results were similar to or outperformed those reported for bovine milk. Our results suggest considering specific procedures for calibration development to propose reliable tools applicable along the dairy goat chain., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)- Published
- 2023
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