The objective of this work was to reduce the predictor dimensionality and to develop a model able to forecast Clostridium tyrobutyricum contamination in corn silages. A survey on 33 dairy farms was performed, and samples from core, lateral, and apical parts of the feed-out face of corn silage bunkers were analyzed for chemical, biological (digestible and indigestible NDF), fermentative (pH, ammonia nitrogen, lactic acid, VFA, and ethanol), and microbiological (yeasts and molds) traits. Corn silage samples were analyzed for C. tyrobutyricum cell and spore counts by adoption of a molecular DNA–based method. A partial least squares (PLS) regression with a leaveone- out cross-validation method was used to reduce the dimensionality of the original predictors (n = 30) by projecting the independent variables into latent constructs. In a first step of the model development, the importance of independent variables in predicting C. tyrobutyricum contamination was assessed by plotting factor loadings of both dependent and independent variables on the first 2 components and by verifying for each predictor the variable influence on projection values adopting the Wold’s criterion as well as the entity of standardized regression coefficients. Three ensiling characteristics (bunker type, presence of lateral wrap plastic film, and penetration resistance as a measurement of the ensiled mass density), a chemical trait (DM), 9 characterizations of the fermentative profile (pH, ammonia nitrogen, acetic acid, butyric acid, isobutyric acid, valeric acid, isovaleric acid, ethanol, and lactic acid), and 2 microbiological traits (yeasts and molds) were retained as important terms in the PLS model. Three reduced-variable PLS (rPLS) regressions—the first based on ensiling, chemical, fermentative, and microbiological retained important variables (rPLSecfm); the second based on chemical, fermentative, and microbiological retained important traits (rPLScfm); and the last based on only chemical and fermentative retained important variables (rPLScf)—were performed. The model that best fit the C. tyrobutyricum measurements was rPLSecfm. The rPLScfm and rPLScf models had similar regression performances but higher mean square errors of prediction than rPLSecfm. However, all tested models seemed adequate to rank corn silages for low, medium, and high risks of C. tyrobutyricum contamination. To avoid the visit on farm by trained people required to measure penetration resistance, the use of the rPLScf model is suggested as a useful tool to assess the risk of C. tyrobutyricum in corn silage.

Gallo, A., Bassi, D., Esposito, R., Moschini, M., Cocconcelli, P. S., Masoero, F., Relationships among ensiling, nutritional, fermentative, microbiological traits and Clostridium tyrobutyricum contamination in corn silages addressed with partial least squares regression, <<JOURNAL OF ANIMAL SCIENCE>>, 2016; 2016 (94): 4346-4359. [doi:10.2527/jas2016-0479] [http://hdl.handle.net/10807/91551]

Relationships among ensiling, nutritional, fermentative, microbiological traits and Clostridium tyrobutyricum contamination in corn silages addressed with partial least squares regression

Gallo, Antonio
Primo
;
Bassi, Daniela
Secondo
;
Esposito, Roberta;Moschini, Maurizio;Cocconcelli, P. S.;Masoero, Francesco
Ultimo
2016

Abstract

The objective of this work was to reduce the predictor dimensionality and to develop a model able to forecast Clostridium tyrobutyricum contamination in corn silages. A survey on 33 dairy farms was performed, and samples from core, lateral, and apical parts of the feed-out face of corn silage bunkers were analyzed for chemical, biological (digestible and indigestible NDF), fermentative (pH, ammonia nitrogen, lactic acid, VFA, and ethanol), and microbiological (yeasts and molds) traits. Corn silage samples were analyzed for C. tyrobutyricum cell and spore counts by adoption of a molecular DNA–based method. A partial least squares (PLS) regression with a leaveone- out cross-validation method was used to reduce the dimensionality of the original predictors (n = 30) by projecting the independent variables into latent constructs. In a first step of the model development, the importance of independent variables in predicting C. tyrobutyricum contamination was assessed by plotting factor loadings of both dependent and independent variables on the first 2 components and by verifying for each predictor the variable influence on projection values adopting the Wold’s criterion as well as the entity of standardized regression coefficients. Three ensiling characteristics (bunker type, presence of lateral wrap plastic film, and penetration resistance as a measurement of the ensiled mass density), a chemical trait (DM), 9 characterizations of the fermentative profile (pH, ammonia nitrogen, acetic acid, butyric acid, isobutyric acid, valeric acid, isovaleric acid, ethanol, and lactic acid), and 2 microbiological traits (yeasts and molds) were retained as important terms in the PLS model. Three reduced-variable PLS (rPLS) regressions—the first based on ensiling, chemical, fermentative, and microbiological retained important variables (rPLSecfm); the second based on chemical, fermentative, and microbiological retained important traits (rPLScfm); and the last based on only chemical and fermentative retained important variables (rPLScf)—were performed. The model that best fit the C. tyrobutyricum measurements was rPLSecfm. The rPLScfm and rPLScf models had similar regression performances but higher mean square errors of prediction than rPLSecfm. However, all tested models seemed adequate to rank corn silages for low, medium, and high risks of C. tyrobutyricum contamination. To avoid the visit on farm by trained people required to measure penetration resistance, the use of the rPLScf model is suggested as a useful tool to assess the risk of C. tyrobutyricum in corn silage.
Inglese
Gallo, A., Bassi, D., Esposito, R., Moschini, M., Cocconcelli, P. S., Masoero, F., Relationships among ensiling, nutritional, fermentative, microbiological traits and Clostridium tyrobutyricum contamination in corn silages addressed with partial least squares regression, <>, 2016; 2016 (94): 4346-4359. [doi:10.2527/jas2016-0479] [http://hdl.handle.net/10807/91551]
File in questo prodotto:
File Dimensione Formato  
4346.pdf

non disponibili

Tipologia file ?: Versione Editoriale (PDF)
Licenza: Non specificato
Dimensione 497.84 kB
Formato Unknown
497.84 kB Unknown   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/91551
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact