Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk. (1st March 2023)

• Record Type: Journal Article
• Authors: Stocco, Giorgia; Dadousis, Christos; Pazzola, Michele; Vacca, Giuseppe M.; Dettori, Maria L.; Mariani, Elena; Cipolat-Gotet, Claudio
• Is Part Of: Food chemistry. Volume 403(2023)
• Publication Date: 2023-03-01
• Subjects: Goat -- Cheese yield -- Nutrient recovery -- Farm -- Infrared spectra; %CY cheese yield -- %REC recovery of milk nutrients -- FTIR Fourier-transform infrared spectroscopy -- REP repeatability -- CV Calibration-Validation procedures -- SCV Stratified Calibration-Validation procedure -- CAL calibration set -- VAL validation set -- RMSE root mean squared error -- R2 coefficient of determination -- SWIR short wave infrared spectral region -- MWIR mid-infrared spectral region -- MWIR1 and MWIR2 first and second mid-infrared spectral region -- LWIR long-infrared spectral region -- MWIR-LWIR transition between MWIR and LWIR; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.134403
• Languages: English
• ISSNs: 0308-8146

Abstract

Graphical abstract: Highlights: Fourier-transform infrared spectroscopy (FTIR) can predict cheese-making traits. Cheese yield of total solids was the best-predicted trait of goat milk. Variability of the prediction performance was found among and within farms and across the cheese-making traits analyzed. Specific procedures for FTIR calibrations are necessary along the dairy goat chain. 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 (SCV80 ). 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.