Hostname: page-component-5c6d5d7d68-wtssw Total loading time: 0 Render date: 2024-08-08T05:23:08.490Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction of dry-cured ham weight loss and prospects of use in a pig breeding program

Published online by Cambridge University Press:  04 February 2020

V. Bonfatti Open the ORCID record for V. Bonfatti [Opens in a new window]  and
P. Carnier
V. Bonfatti*
Affiliation:
Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell’Università 16, Legnaro 35020, Italy
P. Carnier
Affiliation:
Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell’Università 16, Legnaro 35020, Italy
*
†E-mail: valentina.bonfatti@unipd.it
Get access

Abstract

Large ham weight losses (WL) in dry-curing are undesired as they lead to a loss of marketable product and penalise the quality of the dry-cured ham. The availability of early predictions of WL may ease the adaptation of the dry-curing process to the characteristics of the thighs and increase the effectiveness of selective breeding in enhancing WL. Aims of this study were (i) to develop Bayesian and Random Forests (RFs) regression models for the prediction of ham WL during dry-curing using on-site infrared spectra of raw ham subcutaneous fat, carcass and raw ham traits as predictors and (ii) to estimate genetic parameters for WL and their predictions (P-WL). Visible-near infrared spectra were collected on the transversal section of the subcutaneous fat of raw hams. Carcass traits were carcass weight, carcass backfat depth, lean meat content and weight of raw hams. Raw ham traits included measures of ham subcutaneous fat depth and linear scores for round shape, subcutaneous fat thickness and marbling of the visible muscles of the thigh. Measures of WL were available for 1672 hams. The best prediction accuracies were those of a Bayesian regression model including the average spectrum, carcass and raw ham traits, with R2 values in validation of 0.46, 0.55 and 0.62, for WL at end of salting (23 days), resting (90 days) and curing (12 months), respectively. When WL at salting was used as an additional predictor of total WL, the R2 in validation was 0.67. Bayesian regressions were more accurate than RFs models in predicting all the investigated traits. Restricted maximum likelihood (REML) estimates of genetic parameters for WL and P-WL at the end of curing were estimated through a bivariate animal model including 1672 measures of WL and 8819 P-WL records. Results evidenced that the traits are heritable (h2 ± SE was 0.27 ± 0.04 for WL and 0.39 ± 0.04 for P-WL), and the additive genetic correlation is positive and high (ra = 0.88 ± 0.03). Prediction accuracy of ham WL is high enough to envisage a future use of prediction models in identifying batches of hams requiring an adaptation of the processing conditions to optimise results of the manufacturing process. The positive and high genetic correlation detected between WL and P-WL at the end of dry-curing, as well as the estimated heritability for P-WL, suggests that P-WL can be successfully used as an indicator trait of the measured WL in pig breeding programs.

Keywords

infrared spectroscopygenetic selectionphenotypingprediction modelsheavy pigs
Type
Research Article
Information
animal , Volume 14 , Issue 6 , June 2020 , pp. 1128 - 1138
Copyright
© The Animal Consortium 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Associazione Nazionale Allevatori Suini 2016. ANAS Notizie: La Selezione per il Prosciutto DOP. Una Sfida Tecnica per un Orizzonte Dilungo Periodo. Retrieved on 8 July 2019 from http://www.anas.it/giornale/201600002.PDF.Google Scholar
Bonfatti, V, Tiezzi, F, Miglior, F and Carnier, P 2017a. Comparison of Bayesian regression models and partial least squares regression for the development of infrared prediction equations. Journal of Dairy Science 100, 7306–7319.CrossRefGoogle ScholarPubMed
Bonfatti, V, Vicario, D, Lugo, A and Carnier, P 2017b. Genetic parameters of measures and population-wide infrared predictions of 92 traits describing the fine composition and technological properties of milk in Italian Simmental cattle. Journal of Dairy Science 100, 5526–5540.CrossRefGoogle ScholarPubMed
Bosi, P and Russo, V 2004. The production of the heavy pig for high quality processed products. Italian Journal of Animal Science 3, 309–321.CrossRefGoogle Scholar
Breiman, L 2001. Random forests. Machine Learning 45, 5–32.CrossRefGoogle Scholar
Buttazzoni, L, Gallo, M, Baiocco, C and Carchedi, G 1993. La selezione per la qualità della carne suina destinata alla trasformazione. Rivista di Suinicoltura 4, 139–145.Google Scholar
Čandek-Potokar, M and Škrlep, M 2012. Factors in pig production that impact the quality of dry-cured ham: a review. Animal 6, 327–338.CrossRefGoogle ScholarPubMed
Carnier, P, Cassandro, M, Knol, E and Padoan, D 1999. Genetic parameters for some carcass and fresh ham traits of crossbred Goland pigs. In Recent progress in animal science (eds. Piva, G, Bertoni, G, Masoero, F, Bani, P and Calamari, L), pp. 221–223. Franco Angeli, Milan, Italy.Google Scholar
Centro Ricerche Produzioni Animali 2010. Suinicoltura italiana e costo di produzione. Brochure No. 1. Centro Ricerche Produzioni Animali, Reggio Emilia, Italy.Google Scholar
Collell, C, Gou, P, Arnau, J and Comaposada, J 2011. Non-destructive estimation of moisture, water activity and NaCl at ham surface during resting and drying using NIR spectroscopy. Food Chemistry, 129, 601–607.CrossRefGoogle ScholarPubMed
Consorzio del Prosciutto di Parma 1992. Prosciutto di Parma (Parma ham) protected designation of origin. Retrieved on 11 Dec 2019 from https://www.prosciuttodiparma.com/wp-content/uploads/2019/07/Parma_Ham_Specifications_Disciplinare_Consolidato_Nov_13.pdf.Google Scholar
Consorzio del prosciutto di San Daniele 2007. Production specification for the protected designation of origin “Prosciutto di San Daniele”. Retrieved on 17 May 2019 from https://www.prosciuttosandaniele.it/content/uploads/2018/05/Production-Specifications-SAN-DANIELE.pdf.Google Scholar
de los Campos, G, Hickey, JM, Pong-Wong, R, Daetwyler, HD and Calus, MPL 2013. Whole genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327–345.CrossRefGoogle ScholarPubMed
de los Campos, G and Perez-Rodriguez, P 2014. BGLR: Bayesian generalized linear regression. Version 1.0.5. Retrieved on 7 May 2019 from https://cran.r-project.org/web/packages/BGLR/index.html.Google Scholar
Gallo, L, Padoan, D, Bondesan, V and Penzo, N 1999. Effect of some carcass and fresh ham traits on curing loss of hams from crossbred Goland pigs. In Recent progress in animal science (eds. Piva, G, Bertoni, G, Masoero, F, Bani, P and Calamari, L), pp. 689–691. Franco Angeli, Milan, Italy.Google Scholar
Groeneveld, E, Kovac, M and Mielenz, N 2010. VCE user’s guide and reference manual v. 6.0. Institute of Farm Animal Genetics, Mariensee, Germany.Google Scholar
Habier, D, Fernando, R, Kizilkaya, K and Garrick, D 2011. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186–197.CrossRefGoogle ScholarPubMed
Heuer, C, Luinge, HJ, Lutz, ETG, Schukken, YH, van der Maas, JH, Wilmink, H and Noordhuizen, JPTM 2001. Determination of acetone in cow milk by Fourier transform infrared spectroscopy for the detection of subclinical ketosis. Journal of Dairy Science 84, 575–582.CrossRefGoogle ScholarPubMed
Ishwaran, H and Kogalur, UB 2018. Random Forests for Survival, Regression, and Classification (RF-SRC), R package version 2.7.0. Retrieved on 7 May 2019 from https://cran.r-project.org/web/packages/randomForestSRC/randomForestSRC.pdf.Google Scholar
Liaw, A and Wiener, M 2002. Classification and regression by random forest. R News 2, 18–22.Google Scholar
Pérez, P and de los Campos, G 2014. Genome-wide regression and prediction with the BGLR statistical package. Genetics 198, 483–495.CrossRefGoogle ScholarPubMed
Prevolnik, M, Andronikov, D, Žlender, B, Font-i-Furnols, M, Novič, M, Škorjanc, D and Čandek-Potokar, M 2014. Classification of dry-cured hams according to the maturation time using near infrared spectra and artificial neural networks. Meat Science 96, 14–20.CrossRefGoogle ScholarPubMed
Prieto, N, Pawluczyk, O, Dugan, MER and Aalhus, JL 2017. A review of the principles and applications of near-infrared spectroscopy to characterized meat, fat, and meat products. Applied Spectroscopy 7, 1403–1426.CrossRefGoogle Scholar
R Development Core Team 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Rostellato, R, Sartori, C, Bonfatti, V, Chiarot, G and Carnier, P 2015. Direct and social genetic effects on body weight at 270 days and carcass and ham quality traits in heavy pigs. Journal of Animal Science 93, 1–10.CrossRefGoogle ScholarPubMed
Russo, V, Davoli, R and Nanni Costa, L 1992. Recenti acquisizioni nel miglioramento genetico della qualità della carne suina. Rivista di suinicoltura 4, 47–55.Google Scholar
Russo, V, Nanni Costa, L, Lo Fiego, DP and De Grossi, A 1991. Early estimation of seasoning loss in Parma ham production. In Proceedings of the 37th International Congress of Meat Science and Technology, 1–6 September 1991, Kulmbach, Germany, pp. 926–929.Google Scholar
Sturaro, E 2004. Caratteristiche tecnologiche e qualitative di cosce suine fresche destinate alla trasformazione in prodotti tipici stagionati: aspetti genetici e relazioni con la qualità del prodotto finale. PhD thesis, University of Padua, Padua, Italy.Google Scholar