期刊
JOURNAL OF DAIRY SCIENCE
卷 102, 期 8, 页码 6943-6958出版社
ELSEVIER SCIENCE INC
DOI: 10.3168/jds.2019-16320
关键词
Montbeliarde; cheese-making property; mid-infrared spectroscopy; Bayesian method; partial least squares regression
资金
- French Ministry of Agriculture, Agro-Food and Forest (Paris, France)
- French Dairy Interbranch Organization (CNIEL, Paris, France)
- Regional Union of Protected Designation cheeses of Franche-Comte (URFAC, Poligny, France)
- Regional Council of Bourgogne Franche-Comte (Besancon, France), under the project FROM'MIR
Assessing the cheese-making properties (CMP) of milks with a rapid and cost-effective method is of particular interest for the Protected Designation of Origin cheese sector. The aims of this study were to evaluate the potential of mid-infrared (MIR) spectra to estimate coagulation and acidification properties, as well as curd yield (CY) traits of Montbeliarde cow milk. Samples from 250 cows were collected in 216 commercial herds in Franche-Comte with the objectives to maximize the genetic diversity as well as the variation in milk composition. All coagulation and CY traits showed high variability (10 to 43%). Reference analyses performed for soft (SC) and pressed cooked (PCC) cheese technology were matched with MIR spectra. Prediction models were built on 446 informative wavelengths not tainted by the water absorbance, using different approaches such as partial least squares (PLS), uninformative variable elimination PLS, random forest PLS, Bayes A, Bayes B, Bayes C, and Bayes RR. We assessed equation performances for a set of 20 CMP traits (coagulation: 5 for SC and 4 for PCC; acidification: 5 for SC and 3 for PCC; laboratory CY: 3) by comparing prediction accuracies based on cross-validation. Overall, variable selection before PLS did not significantly improve the performances of the PLS regression, the prediction differences between Bayesian methods were negligible, and PLS models always outperformed Bayesian models. This was likely a result of the prior use of informative wavelengths of the MIR spectra. The best accuracies were obtained for curd yields expressed in dry matter (CYDM) or fresh (CYFRESH) and for coagulation traits (curd firmness for PCC and SC) using the PLS regression. Prediction models of other CMP traits were moderately to poorly accurate. Whatever the prediction methodology, the best results were always obtained for CY traits, probably because these traits are closely related to milk composition. The CYDM predictions showed coefficient of determination (R-2) values up to 0.92 and 0.87, and RSy, x values of 3 and 4% for PLS and Bayes regressions, respectively. Finally, we divided the data set into calibration (2/3) and validation (1/3) sets and developed prediction models in external validation using PLS regression only. In conclusion, we confirmed, in the validation set, an excellent prediction for CYDM [R-2 = 0.91, ratio of performance to deviation (RPD) = 3.39] and a very good prediction for CYFRESH (R-2 = 0.84, RPD = 2.49), adequate for analytical purposes. We also obtained good results for both PCC and SC curd firmness traits (R-2 >= 0.70, RPD >= 1.8), which enable quantitative prediction.
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