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

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

On-site visible-near IR prediction of iodine number and fatty acid composition of subcutaneous fat of raw hams as phenotypes for a heavy pig breeding program.

Abstract The quality of subcutaneous fat of raw hams is a trait of interest in selective breeding programs for pig lines used in dry-cured ham production, and rapid, non-invasive methods for its assessment are available. However, the efficacy of such methods to provide indicator traits for breeding programs needs to be proven. The study investigated the accuracy of on-site visible–near IR spectroscopy predictions of iodine number and fatty acid (FA) composition of raw ham subcutaneous fat, and it evaluated their effectiveness as indicator traits of ham fat quality in a pig breeding program. Prediction equations were developed using visible–near IR spectra acquired at the slaughterhouse from five sites in subcutaneous fat of raw hams of 1025 crossbred pigs. Pigs were raised, under standardized rearing and feeding conditions, in the sib-testing program of the Goland C21 boar line and slaughtered at nine months of age and average body weight of 166 ± 15 kg. Accuracy was generally relatively poor, but R2 in external validation was > 0.7 for iodine number and concentration of C18:2n-6, polyunsaturated FAs and omega-6 FAs. To assess the effectiveness of the on-site predictions as indicator traits in a breeding program, (co)variance components of the measured traits (OBS) and of their predictions using in-lab (in-lab-PR) or on-site (on-site-PR) spectrometers were estimated. Available records for OBS were 6814 and 2048, for iodine number and FA composition, respectively. Predictions using in-lab were available for pigs slaughtered between 2006 and 2014, for a total of 10 153 records. Predictions using on-site were obtained from spectra collected since 2011, for a total of 10 296 records. The estimated heritabilities for the investigated traits ranged from 0.34 to 0.50 and were greater for on-site-PR than for OBS. Genetic correlations between OBS and in-lab-PR were very close to 1.00 for all the investigated traits, whereas those between OBS and on-site-PRED ranged from 0.86 to 0.94. On-site visible-IR predictions are accurate enough to support the use of this technique for large-scale phenotyping of raw ham fat quality, even when dealing with animals of a single genetic line raised in standardized conditions, and may be implemented as indicator traits in breeding programs

Effects of κ-CN Glycosylation on Rennet Coagulation Properties of Milk in Simmental Cattle

Contents of casein fractions are known to affect coagulation properties and cheese yield of milk, but studies on the effects of κ-CN composition on variation of coagulation properties of milk are still very scarce. Effects exerted by κ-CN composition on variation of milk coagulation properties (MCP) were investigated using 2,084 individual milk samples of Simmental cows. Rennet coagulation time (RCT), and curd firmness (A30) were measured using a computerized renneting meter. Milk protein composition and genotypes at CSN2, CSN3 and BLG were obtained by reversed-phase HPLC. The percentage ratios of κ-CN (κCN%), of Glycosylated-κ-CN (G-κCN%), and Unglycosylated-κ-CN (U-κCN%) to total casein were measured. The degree of glycosylation (GD) was measured as the percentage ratio of glycosylated-κ-CN to total κ-CN. A difference of 1.7 min (corresponding to 0.37 SD of the trait) was observed for the average RCT of the two extreme classes of κCN% content. RCT decreased when κCN% and G-κCN% increased, whereas U-κCN% exhibited a slightly unfavourable effect on the onset of the coagulation process. A slight decrease of RCT was also observed for high GD, although this effect was less clear than that of G-κCN%. A favourable effect of κCN%, G-κCN% and GD on A30 was also detected

Prediction of protein composition of individual cow milk using mid-infrared spectroscopy.

This study investigated the application of mid-infrared spectroscopy for the prediction of protein composition in individual milk samples (n=1,336) of Simmental cows. Protein fractions were quantified by RP-HPLC and MIR data were recorded over the spectral range from 4,000 to 900 cm-1. Models were developed by partial least squares regression using untreated spectra. The most successful predictions were for protein, casein, αS1-casein, whey protein, and β-lactoglobulin contents. The models could discriminate between high and low values of protein composition (R2=0.50 to 0.58). The root mean square errors of cross-validation were 3.11g/l for protein (range 39.91g/l), 2.76g/l for casein (range 35.16g/l), 1.07 g/l for αS1-casein (range 12.82g/l), 0.51 g/l for whey protein (range 4.97g/l), and 0.43 for β-lactoglobulin (range 4.37). Application of MIR spectroscopy is possible for the routinely assessment of protein, casein, αS1-casein, whey protein, and β-lactoglobulin and its implementation might be in future a tool for improving protein composition of bovine milk through breeding programs

Investigation on variability of candidate genes for meat quality traits in Piemontese cattle

This study aimed to investigate the variability of 15 genes chosen according to their function as markers of meat quality traits in Piemontese cattle. Meat samples of Longissimus thoracis muscle and data on carcass weight (CW), shear force (SF), cooking loss (CL), and pH collected on 1,208 Piemontese young bulls progeny of 109 AI sires were available for this investigation. For each trait considered (CW, SF, CL, pH), 48 samples were chosen from each tail of normal distribution, and one or more single nucleotide polymorphisms (SNPs) were determined for the following loci: growth hormone (GH), growth hormone receptor (GHR), pro-opiomelanocortin (POMC), pituitary-specific positive transcription factor 1 (POU1F1), melanocortin-4 receptor (MC4R), corticotrophin-realising hormone (CRH), insulin-like growth factor binding protein- 3 (IGFBP3), diacylglycerol acyltransferase 1 (DGAT1), thyroglobulin (TG), carboxypeptidase E (CPE), calpain 1 (CAPN1), calpastatin (CAST), cathepsin B and D (CATB, CATD), and protein kinase adenosine monophosphate-activated γ3-subunit (PRKAG3). Eight SNPs were characterized by a high, 4 by an intermediate and 2 by a low variability; 6 may be almost fixed. Based on these results, variable loci will be investigated on the entire available data set in order to study their effects on meat quality traits

Comparison between Direct and Competitive Models to Investigate Variation of Carcass and Ham Quality Traits in Heavy Pigs

Social interactions among animals raised in pens can affect their performance. In this study direct and competitive models were compared to investigate the influence of social genetic effects on variation of carcass weight (CW), carcass lean meat content (LM) and ham round shape (RS) in heavy pigs. Four sequential models including, in addition to sex and slaughter group fixed effects, the random effects of the social group, litter (full-sibs family), direct and social genetic effects of pigs were evaluated. Social group and litter effects accounted for about 4 and 3% of the phenotypic variance, respectively. When social genetic effects were added to model, a small social heritability was estimated for all traits (from 0.3 to 0.7% of the phenotypic variance). A negative correlation between direct and social effects was estimated for LM and RS, reducing the total heritable variance available for selection. Model comparison showed that the best fit was provided by the model including only direct additive genetic effects of pigs. So, this model seems still preferable for the genetic evaluation of the investigated traits

Effects of κ-CN Glycosylation on Rennet Coagulation Properties of Milk in Simmental Cattle

Contents of casein fractions are known to affect coagulation properties and cheese yield of milk, but studies on the effects of κ-CN composition on variation of coagulation properties of milk are still very scarce. Effects exerted by κ-CN composition on variation of milk coagulation properties (MCP) were investigated using 2,084 individual milk samples of Simmental cows. Rennet coagulation time (RCT), and curd firmness (A30) were measured using a computerized renneting meter. Milk protein composition and genotypes at CSN2, CSN3 and BLG were obtained by reversed-phase HPLC. The percentage ratios of κ-CN (κCN%), of Glycosylated-κ-CN (G-κCN%), and Unglycosylated-κ-CN (U-κCN%) to total casein were measured. The degree of glycosylation (GD) was measured as the percentage ratio of glycosylated-κ-CN to total κ-CN. A difference of 1.7 min (corresponding to 0.37 SD of the trait) was observed for the average RCT of the two extreme classes of κCN% content. RCT decreased when κCN% and G-κCN% increased, whereas U-κCN% exhibited a slightly unfavourable effect on the onset of the coagulation process. A slight decrease of RCT was also observed for high GD, although this effect was less clear than that of G-κCN%. A favourable effect of κCN%, G-κCN% and GD on A30 was also detected

Genetic analysis of milk protein composition and of its relationship with renneting properties of individual cow milk

Milk coagulation properties (MCP) are a fundamental aspect in cheese production, but un unfavorable trend over year on MCP have been observed in several countries. The cheese yield has decreased, accentuating the necessity to provide dairies with milk well suited for dairy products manufacture. During the past decades the focus of milk production has been kg’s of milk protein, but total milk protein content is a poor indicator of MCP, and the lack of an appropriate high-throughput analysis for routine determination of milk coagulation is currently limiting the opportunity to improve MCP by direct selection. Milk protein composition has long been a subject of interest for worldwide dairy researchers. As a consequence, information on milk protein genotype could be utilized to improve milk protein composition and MCP trough marker assisted selection without having to phenotype large progeny groups. Considering such options, it would be desirable to gain further knowledge about effects of milk protein genetic variants on milk protein composition and on MCP. Aims of the study were to investigate the effects of CSN2-CSN3 haplotypes (β-κ-casein) and BLG (β-Lactoglobulin, β-LG) genotypes on milk production traits, contents of protein fractions and detailed protein composition; to investigate the effects of CSN2-CSN3 haplotypes, BLG genotypes, contents of milk protein fractions and protein composition on MCP; to investigate the effect exerted by the relative ratio of κ-CN A to κ-CN B content on MCP and industrial cheese yield of three Italian cheese varieties. The final aim was to estimate genetic parameters of major milk protein fractions and estimate genetic and phenotypic correlation between milk protein fractions and MCP. A new reversed-HPLC method for the separation and quantification of the most common genetic variants of bovine milk proteins was developed and validated testing linearity, repeatability, reproducibility and accuracy. Contents of major protein fractions were measured by this new method in individual milk samples of 2,167 Simmental cows. Protein composition was measured as weight percentage of each casein (CN) fraction to total casein (TCN) and as weight percentage of β-LG to total whey protein (WH). Genotypes at CSN2, CSN3 and BLG loci were also assessed by HPLC and CSN2-CSN3 haplotype probabilities were estimated for each cow. Rennet coagulation time (RCT) and curd firmness (a30) were measured using a computerized renneting meter. Effects of haplotypes and BLG genotypes on yields were weak or trivial. Haplotypes carrying CSN2 B and CSN3 B exhibited greater TCN and casein number (CI), in comparison with all other haplotypes. Genotype BB at BLG was associated with increased protein, TCN and CI, when compared to genotype AA. Haplotypes including CSN3 B were associated with greater κ-CN content and percentage. Allele CSN2 B was associated with an increase of β-CN content, which occurred at the expense of content of αS1-CN. Haplotypes including allele CSN2 A1 exhibited decreased β-, αS2- and γ-CN concentrations and increased αS1- and κ-CN contents, whereas CSN2 I exerted positive effects on β-CN concentration, without altering other protein fractions content. Effects exerted by haplotypes on CN composition were similar to those exhibited on CN fractions contents. Allele BLG A increased β-LG concentration and altered the β-LG to α-Lactalbumin (α-LA) ratio. When protein fractions contents or protein composition were not included in the statistical model, haplotypes carrying CSN3 B allele exhibited shorter RCT and greater a30, in comparison with those carrying CSN3 A, and haplotypes carrying CSN2 B allele were responsible for a noticeable decrease of RCT and for an increase of a30, when compared to haplotype A2A. When effects of protein fractions contents or of protein composition were added to the model, no difference across haplotypes due to CSN3 and CSN2 alleles was observed for MCP, with the exception of the effect of CSN2 B on RCT, which remained markedly favorable. Also, the favorable effect exerted by CSN2 B on a30 was mediated by the increase of β-CN B in milk. Conversely, β-CN B is likely to exert a molecular effect on RCT, which does not depend upon variation of β-CN content associated to allele B. To test if the lack of effect of κ-CN genetic variant would have been observed also on cheese yield, milks with different κ-CN A to κ-CN B content ratios were separately manufactured to produce Montasio, Asiago and Caciotta cheese. Milk was characterized by having similar composition in terms of protein, TCN, CI, CN composition, β-CN composition and pH. Milk with the higher proportion of κ-CN B (HIGHB) exhibited similar coagulation properties but a higher cheese yield in all the investigated cheese in comparison with milk with a lower proportion of κ-CN B (LOWB). However, the increment of yield observed for HIGHB milk in Montasio cheese was ascribed to a greater fat content of HIGHB milk in comparison with LOWB milk. The probability of HIGHB milk giving a cheese yield 5 % greater than that of LOWB milk ranged from 51 to 67 % for Montasio cheese, but was lower than 21 % for Asiago and Caciotta cheeses. Thus, the ratio of κ-CN A to κ-CN B content did not relevantly affect industrial cheese yield when milks of similar CN composition were processed, and an indirect effect due to the higher κ-CN content of κ-CN B milk on cheese yield is to be suggested. Values of heritability for αS1-CN%, κ-CN% and β-CN% were similar and ranging from 0.61 to 0.70, whereas heritability of αS2-CN%, γ-CN% and β-LG% were 0.28, 0.29 and 0.33, respectively. When CSN2-CSN3 haplotype and BLG genotype were accounted for by the model, heritability estimates of all the protein fractions became similar suggesting that proteins synthesis is regulated by specific genes which control the overall production of milk protein. Genetic correlations among the contents of the five CN fractions and between CN fractions and WH fractions were generally low. Generally, all the CN fractions were also moderately positively correlated with WH. When data where adjusted for CSN2-CSN3 haplotype and BLG genotype, genetic correlations among the contents of protein fractions markedly increased confirming that all the fractions undergone a common regulation. The content and the relative proportion of κ-CN were not genetically correlated with RCT, αS1- and αS2-CN were unfavourately correlated with RCT, but increasing the content of β-CN in milk would result in a shorter RCT. Stronger curds were associated with higher κ-CN and β-CN, and with lower αS1-, αS2-, and γ-CN contents and proportions. Results confirm the lack of favorable associations between TCN and MCP indicating that other traits, i.e. milk protein fractions, should be used for the genetic improvement of cheese-making properties.Le proprietà di coagulazione del latte (MCP) sono un aspetto fondamentale nella produzione di formaggio, tuttavia, negli ultimi anni, è stato registrato un andamento sfavorevole della coagulazione del latte in diversi Paesi. La resa in formaggio è diminuita, accentuando la necessità di fornire i caseifici con latte più adatto per la trasformazione in formaggio. Nel corso degli ultimi decenni il miglioramento genetico si è focalizzato sui kg di proteina del latte, ma il contenuto totale di proteina non sembra essere un buon indicatore delle MCP, e la mancanza di un metodo di analisi che consenta la determinazione delle MCP su larga scala attualmente limita la possibilità di migliorare le MCP attraverso una selezione diretta. La composizione proteica del latte è stato a lungo oggetto di interesse per i ricercatori di tutto il mondo. Di conseguenza, le informazioni sul genotipo delle proteine del latte potrebbero essere utilizzate per migliorare la composizione della proteina oppure nella selezione assistita da marcatori per migliorare le MCP, senza dover fenotipizzare grandi gruppi di progenie. Alla luce di tali possibilità, sarebbe auspicabile poter acquisire ulteriori conoscenze sugli effetti delle varianti genetiche delle proteine sulla composizione proteica del latte e sulle MCP. Obiettivi di questa tesi sono stati: studiare gli effetti dell’aplotipo CSN2-CSN3 (β-κ-caseina) e del genotipo al locus BLG (β-lattoglobulina, β-LG) su caratteri produttivi, contenuto di frazioni proteiche e composizione proteica; studiare gli effetti dell’aplotipo CSN2-CSN3 e del genotipo al locus BLG, del contenuto di frazioni proteiche e della composizione proteica sulle MCP, studiare l'effetto esercitato dal rapporto relativo tra κ-CN A e B sulle MCP e sulla resa industriale in tre varietà di formaggi italiani. Inoltre, ultimo obiettivo del lavoro è stato la stima dei parametri genetici delle principali frazioni proteiche del latte e delle correlazioni genetiche e fenotipiche tra le frazioni proteiche e le MCP. Un nuovo metodo di analisi HPLC a fase inversa per la separazione e la quantificazione delle più comuni varianti genetiche delle proteine del latte bovino è stato sviluppato e validato attraverso test di linearità, ripetibilità, riproducibilità e accuratezza. Il contenuto delle principali frazioni proteiche è stato misurato con questo nuovo metodo in campioni di latte individuale di 2,167 bovine di razza Simmental. La composizione proteica è stata espressa come percentuale in peso di ogni frazione caseinica rispetto al contenuto totale di caseina (TCN) e come percentuale del peso della β-LG sul totale di proteine del siero (WH). Il genotipo ai loci CSN2, CSN3 e BLG è stato determinato tramite HPLC e le probabilità aplotipiche per gli aplotipi CSN2-CSN3 sono state stimate per ogni animale. Tempo di coagulazione (RCT) e consistenza del coagulo (a30) sono stati misurati utilizzando un lattodinamografo. Gli effetti dell’aplotipo delle caseine e del genotipo al locus BLG sui caratteri produttivi sono stati limitati o trascurabili. Gli aplotipi contenenti gli alleli CSN2 B e CSN3 B hanno mostrato valori più elevati di TCN e un indice caseinico (CI) superiore, rispetto a tutti gli altri aplotipi. Il genotipo BB al locus BLG è stato associato ad un aumento del contenuto proteico e ad un CI superiore rispetto al genotipo AA. Gli aplotipi contenenti l’allele CSN3 B sono stati associati a contenuti e percentuali di κ-CN maggiori. L’allele CSN2 B è risultato associato con un aumento del contenuto di β-CN, che si è verificato a scapito del contenuto di αS1-CN. Gli aplotipi che includevano la variante CSN2 A1 hanno mostrato una diminuzione del contenuto di β-, αS2- e γ-CN e un aumento del contenuto di αS1- e κ-CN, mentre la variante CSN2 I ha esercitato effetti positivi sulla concentrazione di β-CN, senza alterare il contenuto delle altre frazioni proteiche. L’allele A al locus BLG è stato associato ad una maggiore concentrazione di β-LG e ad un più elevato rapporto tra β-LG e α-lattoalbumina (α-LA). Quando il contenuto delle frazioni proteiche o la composizione della proteina non erano inclusi nel modello statistico, gli aplotipi contenenti l’allele CSN3 B erano associati ad RCT più brevi ed a30 maggiori, rispetto a quelli che includevano l’allele CSN3 A, e gli aplotipi contenenti la variante CSN2 B erano responsabili di una notevole diminuzione dei valori di RCT e per valori di a30 maggiori, rispetto agli aplotipi contenente la variante A2. Quando gli effetti del contenuto delle frazioni proteiche o della composizione proteica sono stati inclusi nel modello statistico, nessuna differenza tra aplotipi riconducibile agli alleli ai loci CSN3 e CSN2 è stata osservata per le MCP, con l'eccezione dell’effetto della CSN2 B su RCT, che è rimasto molto favorevole. L'effetto favorevole esercitato dall’allele CSN2 B su a30 è risultato mediato dall’aumento di β-CN B nel latte. Al contrario, la β-CN B esercita probabilmente un effetto diretto su RCT, che non dipende dalla variazione del contenuto di β-CN associato all’allele B. Per verificare se la mancanza di effetto diretto delle varianti genetiche di κ-CN sarebbe stato osservato anche sulla resa in formaggio, latte con differenti rapporti tra κ-CN A e B sono stati lavorati separatamente per la produzione di Montasio, Asiago e Caciotta. Il latte lavorato aveva composizione simile in termini di proteina, TCN, CI, composizione caseinica, composizione della β-CN e pH simile. Il latte con la percentuale maggiore di κ-CN B (HIGHB) ha presentato valori di MCP simili, ma una resa superiore in tutti i tipi di formaggio esaminati, rispetto al latte con una percentuale inferiore di κ-CN B (LOWB). Tuttavia, l'incremento di resa osservato per il formaggio Montasio è stato attribuito a un maggior contenuto di grasso del latte HIGHB in confronto con il latte LOWB. La probabilità del latte HIGHB di dare un formaggio con una resa del 5% superiore a quella del latte LOWB variava dal 51 al 67% per il Montasio, ma è stata inferiore al 21% per Asiago e Caciotta. Il rapporto tra le varianti A e B di κ-CN non ha quindi influito in modo rilevante sulla resa casearia industriale, quando la composizione del latte era bilanciata per la composizione caseinica, ed è possibile supporre pertanto che vi sia un effetto indiretto delle varianti di κ-CN sulla resa casearia, a causa del più elevato contenuto di κ-CN associato alla variante B. I valori di ereditabilità per αS1-CN%, κ-CN% e β-CN% erano simili e variabili da 0.61 al 0.70, mentre l’ereditabilità di αS2-CN%, γ-CN% e β-LG% erano 0.28, 0.29 e 0.33, rispettivamente. Quando l’effetto dell’aplotipo CSN2-CSN3 e del genotipo al locus BLG sono stati inclusi nel modello, le stime di ereditabilità di tutte le frazioni proteiche sono divenute simili suggerendo che la sintesi di proteine del latte sia sottoposta a un controllo genetico da parte di geni specifici che controllano il livello generale di proteina del latte. Le correlazioni genetiche tra il contenuto delle 5 frazioni caseiniche e tra le frazioni caseiniche e le frazioni sieriche erano generalmente basse. In generale, tutte le frazioni caseiniche erano anche moderatamente positivamente correlata con WH, suggerendo che vi sia una regolazione generale del livello di proteina del latte che coinvolge contemporaneamente TCN e WH. Quando l’effetto dell’aplotipo CSN2-CSN3 e del genotipo al locus BLG sono stati inclusi nel modello, le correlazioni genetiche tra i contenuti delle frazione proteiche sono aumentate significativamente, supportando l’ipotesi che tutte le frazioni siano oggetto di una regolazione generale. Il contenuto di κ-CN del latte non è risultato essere geneticamente correlato con RCT, αS1- and αS2-CN hanno mostrato una correlazione sfavorevole con RCT, mentre un aumento della β-CN nel latte sarebbe a favore di RCT più brevi. Coaguli più consistenti sono stati associati ad un maggior contenuto di κ-CN e β-CN e ad un minor contenuto di αS1-, αS2-, e γ-CN. I risultati ottenuti confermano la mancanza di un’associazione favorevole tra TCN e MCP, sottolineando l’esigenza di utilizzare altri caratteri, come il contenuto delle frazioni proteiche, per il miglioramento genetico delle proprietà casearie del latte