Measurement Duration but Not Distance, Angle, and Neighbour-Proximity Affects Precision in Enteric Methane Emissions when Using the Laser Methane Detector Technique in Lactating Dairy Cows

SIMPLE SUMMARY: Methane that is breathed out and eructed from ruminants is a potent greenhouse gas that contributes to climate change. Although metabolic chambers are the “gold standard” for measuring methane from livestock, their application in production farms is very limited. There is a need to develop proxy methods that can be applied in such production environments. The proprietary Laser Methane Detector (LMD) has been trialed for the previous decade and has demonstrated its usefulness as a non-invasive and portable instrument to determine methane output from ruminants. In validating the reliability and stability of the data generated by the LMD, the current study gives answers to some very practical assumptions used in the use of the LMD and enhances the confidence in its use in ruminants. ABSTRACT: The laser methane detector (LMD), is a proprietary hand-held open path laser measuring device. Its measurements are based on infrared absorption spectroscopy using a semiconductor laser as a collimated excitation source. In the current study, LMD measurements were carried out in two experiments using 20 and 71 lactating dairy cows in Spain and Scotland, respectively. The study aimed at testing four assumptions that may impact on the reliability and repeatability of the LMD measurements of ruminants. The study has verified that there is no difference in enteric methane measurements taken from a distance of 3 m than from those taken at a distance of 2 m; there was no effect to the measurements when the measurement angle was adjusted from 90° to 45°; that the presence of an adjacent animal had no effect on the methane measurements; and that measurements lasting up to 240 s are more precise than those taken for a shorter duration. The results indicate that angle, proximity to other animals, and distance had no effects and that measurements need to last a minimum of 240 s to maintain precision

Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows

International audienceAbstractBackgroundMilk quality in dairy cattle is routinely assessed via analysis of mid-infrared (MIR) spectra; this approach can also be used to predict the milk’s cheese-making properties (CMP) and composition. When this method of high-throughput phenotyping is combined with efficient imputations of whole-genome sequence data from cows’ genotyping data, it provides a unique and powerful framework with which to carry out genomic analyses. The goal of this study was to use this approach to identify genes and gene networks associated with milk CMP and composition in the Montbéliarde breed.ResultsMilk cheese yields, coagulation traits, milk pH and contents of proteins, fatty acids, minerals, citrate, and lactose were predicted from MIR spectra. Thirty-six phenotypes from primiparous Montbéliarde cows (1,442,371 test-day records from 189,817 cows) were adjusted for non-genetic effects and averaged per cow. 50 K genotypes, which were available for a subset of 19,586 cows, were imputed at the sequence level using Run6 of the 1000 Bull Genomes Project (comprising 2333 animals). The individual effects of 8.5 million variants were evaluated in a genome-wide association study (GWAS) which led to the detection of 59 QTL regions, most of which had highly significant effects on CMP and milk composition. The results of the GWAS were further subjected to an association weight matrix and the partial correlation and information theory approach and we identified a set of 736 co-associated genes. Among these, the well-known caseins, PAEP and DGAT1, together with dozens of other genes such as SLC37A1, ALPL, MGST1, SEL1L3, GPT, BRI3BP, SCD, GPAT4, FASN, and ANKH, explained from 12 to 30% of the phenotypic variance of CMP traits. We were further able to identify metabolic pathways (e.g., phosphate and phospholipid metabolism and inorganic anion transport) and key regulator genes, such as PPARA, ASXL3, and bta-mir-200c that are functionally linked to milk composition.ConclusionsBy using an approach that integrated GWAS with network and pathway analyses at the whole-genome sequence level, we propose candidate variants that explain a substantial proportion of the phenotypic variance of CMP traits and could thus be included in genomic evaluation models to improve milk CMP in Montbéliarde cows

Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows

Background Milk quality in dairy cattle is routinely assessed via analysis of mid-infrared (MIR) spectra; this approach can also be used to predict the milk’s cheese-making properties (CMP) and composition. When this method of high-throughput phenotyping is combined with efficient imputations of whole-genome sequence data from cows’ genotyping data, it provides a unique and powerful framework with which to carry out genomic analyses. The goal of this study was to use this approach to identify genes and gene networks associated with milk CMP and composition in the Montbéliarde breed. Results Milk cheese yields, coagulation traits, milk pH and contents of proteins, fatty acids, minerals, citrate, and lactose were predicted from MIR spectra. Thirty-six phenotypes from primiparous Montbéliarde cows (1,442,371 test-day records from 189,817 cows) were adjusted for non-genetic effects and averaged per cow. 50 K genotypes, which were available for a subset of 19,586 cows, were imputed at the sequence level using Run6 of the 1000 Bull Genomes Project (comprising 2333 animals). The individual effects of 8.5 million variants were evaluated in a genome-wide association study (GWAS) which led to the detection of 59 QTL regions, most of which had highly significant effects on CMP and milk composition. The results of the GWAS were further subjected to an association weight matrix and the partial correlation and information theory approach and we identified a set of 736 co-associated genes. Among these, the well-known caseins, PAEP and DGAT1, together with dozens of other genes such as SLC37A1, ALPL, MGST1, SEL1L3, GPT, BRI3BP, SCD, GPAT4, FASN, and ANKH, explained from 12 to 30% of the phenotypic variance of CMP traits. We were further able to identify metabolic pathways (e.g., phosphate and phospholipid metabolism and inorganic anion transport) and key regulator genes, such as PPARA, ASXL3, and bta-mir-200c that are functionally linked to milk composition. Conclusions By using an approach that integrated GWAS with network and pathway analyses at the whole-genome sequence level, we propose candidate variants that explain a substantial proportion of the phenotypic variance of CMP traits and could thus be included in genomic evaluation models to improve milk CMP in Montbéliarde cows.info:eu-repo/semantics/publishedVersio

Etude de l'organisation spatiale du tissu conjonctif par analyse d'images basée sur une approche multiéchelles. Application à la prédiction de la tendreté de la viande bovine

The aim of this work is to characterize muscular tissue by evaluating its quality from imaging data. More precisely, we intend to develop prediction tools of bovine meat tenderness based on arti cial vision process, by studing intramuscular connective tissue, which contributes in a signi cant manner to the intrinsic toughness of meat. The images of muscle slices were made using two types of lighting: polarized white light and ultraviolet. Our contribution to analyze the meat images is based on a multiscale approach. Two methods of segmentation were proposed, both based on discrete wavelet transform, in particular the "à trous" algorithm. The rst one is based on the universal thresholding and the second, on the k-means algorithm applied on the image resulting from the summation of wavelet planes. Another section of this work deals with parameters extraction and decision. Information retained is the distribution of considered object sizes of the meat connective network. Statistical tools which are the linear regression and neural networks were applied on data issued from stages of image processing. The nal model retained for tenderness prediction was determined according to a maximization criterion of R2. The choice of the number of parameters was based on a cross validation criterion (Leave one out). Prediction results, derived from data base of the study, are very encouraging highlighting an undoubtedly correlation between the images parameters and sensory quality of meat particulary tenderness.L'objectif de ce travail est de caractériser le tissu musculaire en évaluant sa qualité à partir de données d'imagerie. Plus précisement, on se propose de développer des outils de prédiction de la tendreté de la viande bovine, basés sur le processus de vision artificielle, en étudiant le tissu conjonctif intramusculaire qui contribue de manière significative à la dureté intrinsèque de la viande. Les images des coupes de muscles, ont été acquises avec deux types d'éclairage : lumière blanche polarisée et ultraviolet. Notre contribution pour analyser ces images est basée sur une approche multiéchelle. Deux méthodes de segmentation ont été proposées, elles sont basées sur la transformée en ondelettes discrète, notamment l'algorithme "à trous". La première repose sur le seuillage universel et la seconde sur l'algorithme de K-moyennes appliqué à l'image résultante d'une sommation sur les plans d'ondelettes. Un autre volet de ce travail concerne l'extraction des paramètres et la décision. L'information retenue est la distribution des tailles d'objets éléments de la trame conjonctive de viande. Les outils statistiques que sont la régression linéaire et les réseaux de neurones ont été appliqués aux données issues des étapes de traitement des images. Le modèle final qui a été retenu pour la prévision de la tendreté a été déterminé selon un critère de maximisation du R2. Le choix du nombre de paramètres a été basé sur un critère de validation croisée (Leave one out). Les résultats de prédiction, issus de la base de données d'étude, sont très encourageants, mettant en évidence une corrélation certaine entre les paramètres d'images et la qualité sensorielle de la viande en particulier la tendreté

Etude de l'organisation spatiale du tissu conjonctif par analyse d'images basée sur une approche multiéchelles (Application à la prédiction de la tendreté de la viande bovine)

L'objectif de ce travail est de caractériser le tissu musculaire en évaluant sa qualité à partir de données d'imagerie. Plus précisement, on se propose de développer des outils de prédiction de la tendreté de la viande bovine, basés sur le processus de vision artificielle, en étudiant le tissu conjonctif intramusculaire qui contribue de manière significative à la dureté intrinsèque de la viande. Les images des coupes de muscles, ont été acquises avec deux types d'éclairage : lumière blanche polarisée et ultraviolet. Notre contribution pour analyser ces images est basée sur une approche multiéchelle. Deux méthodes de segmentation ont été proposées, elles sont basées sur la transformée en ondelettes discrète, notamment l'algorithme "à trous". La première repose sur le seuillage universel et la seconde sur l'algorithme de K-moyennes appliqué à l'image résultante d'une sommation sur les plans d'ondelettes. Un autre volet de ce travail concerne l'extraction des paramètres et la décision. L'information retenue est la distribution des tailles d'objets éléments de la trame conjonctive de viande. Les outils statistiques que sont la régression linéaire et les réseaux de neurones ont été appliqués aux données issues des étapes de traitement des images. Le modèle final qui a été retenu pour la prévision de la tendreté a été déterminé selon un critère de maximisation du R2. Le choix du nombre de paramètres a été basé sur un critère de validation croisée (Leave one out). Les résultats de prédiction, issus de la base de données d'étude, sont très encourageants, mettant en évidence une corrélation certaine entre les paramètres d'images et la qualité sensorielle de la viande en particulier la tendretéCLERMONT FD-BCIU Sci.et Tech. (630142101) / SudocSudocFranceF

Image analysis study of the perimysial connective network, and its relationship with tenderness and composition of bovine meat

International audienceImage processing method was developed to predict beef tenderness, collagen and lipids contents. The study was carried out on the semimembranosus muscle (SM). Images of sM slices were acquired under visible and ultraviolet lighting, In this work statistical technique was implemented as a method to relate the distribution of intramuscular connective tissue (IMCT), characterized by image analysis, to sensory tenderness evaluated by a trained panel and collagen and total lipids contents assessed chemically. Using Multiple Linear Regression (MLR) combining visible and ultraviolet lighting, IMCT image parameters were found to be good predictors of beef tenderness (R2 = 0.89), collagen and lipids contents (respectively R2 = 0.82 and R2 = 0,91)

A software for Risk-Benefit based probabilistic assessment for heat processed foods

National audienceOBJECTIVE(S) The preservation of food quality during food processing is a key condition for food industries to remain competitive and respond to consumer demand. For that purpose, food industry must mediate among two objectives: preserve the nutritional benefits and ensure a high level of microbiological risk control. . The aim of this study is to develop a user friendly probabilistic tool able to assess quantitatively the risk and benefit of heat processed foods. The tool allows to appraise the risk (food spoilage) associated to survival Geobacillus Stearothermophilus and the benefit related to Vitamin C. In the end it proposes the best compromise to preserve vitamins and to control the microbiological risk of food. METHODS(S) The industrial risk considered in this study was that of the thermophilic bacterium Geobacillus stearothermophilus, recognized as a major source of spoilage in canned foods and frequently detected in cans presenting defects after 7-day incubation at 55°C (André et al., 2012). G. stearothermophilus heat resistance parameters (Dref, ZT and ZpH), used in this study, were estimated using hierarchical Bayesian modeling (Rigaux et al., 2012). Furthermore, nutritional benefit was that of vitamine C. The two bioactive forms of vitamin C were taken into account: DeHydroascorbic Acid (DHA) and Ascorbic Acid (AA). For both cases, activation Energy (Ea) and reaction rate (K) were considered. Monte Carlo based simulations were used in order to set up the destruction of G. Stearothermophilus and both AA and DHA of vitamin C (Rigaux et al., 2013). RESULTS The decision making tool developed in this study allows to quantify the risk due to G. stearothermophilus and displays the distribution based Monte-Carlo simulation of the decimal reduction number of the considered bacteria. The distribution is given with a target level user-defined. The probability to reach this known threshold is also calculated. At the same time, the tool displays the distribution based Monte-Carlo simulation of the vitamin C percentage reduction ([AA] and [DHA] concentrations). This distribution is given also with a user-specified allegation threshold for vitamin C. The probability to reach the allegation threshold is also given. Color codes are used to help users to decide on the risk-benefit compromise associated to a given heat processed food. CONCLUSIONS AND IMPACT OF THE STUDY A user friendly risk-benefit based probabilistic assessment tool for heat processed foods was developed in this study. The software allows to accurately evaluate and find the best compromise between nutritional quality and microbiological safety for heat processed food products. Monte-Carlo simulation method was performed in order to estimate the probability to reach both user-defined target levels of bacteria and allegation threshold of vitamin C. This statistical tool could have a significant industrial impact to better assess temperature profiles applied during heat processing, because not only a microbiological risk is put forth but also the nutritional benefit. This tool will be integrated to the heat inactivation module of Sym’Previus

Meat@ppli - application smartphone pour déterminer la teneur en gras de la viande bovine en temps réel

Fat has a major economic importance in the beef industry. It affects all the meat food chain steps: fromthe farmer to the consumer, through the slaughterer-processor or the distributor. At the beginning of theproject, no tool was able to measure fat in meat in real time, in a reliable, economical and non-destructiveway. The Meat@ppli project aimed to predict the fat content of beef from its photo, both at the carcassand sliced beef stage, based on image analysis methods. The results are encouraging, with correlationswith reference methods varying from 0.5 to 0.9. The prediction models were embedded in the Meat@ppliapplication, developed for fat measurement at the carcass stage. It remains a proof-of-concept that, inthe future, could be used by the beef industry to route carcasses to the most suitable distribution channelsand to perform massive phenotyping for the selection of bovines with appropriate marbling.L’importance économique du gras est majeure dans la filière viande bovine. Il impacte tous les maillonsde la filière : de l’éleveur au consommateur, en passant par l’abatteur-transformateur ou le distributeur.Or jusqu’à présent, aucun outil ne permettait de mesurer le gras dans la viande en temps réel, de façonfiable, économe et non destructive. Le projet Meat@ppli s’est attaché à prédire les teneurs en gras d’unmorceau de viande à partir de sa photo, au stade de la carcasse comme à celui du morceau tranché, ense basant sur des techniques d’analyse d’image. Les résultats sont encourageants, avec des corrélationsplus ou moins étroites avec les méthodes de références (0,5 < r < 0,9). Les modèles de prédiction ont étéembarqués dans l’application Meat@ppli, développée pour la mesure du gras au stade de la carcasse.Elle est encore à l’état de preuve de concept mais à l’avenir, elle pourrait être utilisée par la filière pourorienter les carcasses vers les circuits de distribution les plus adaptés et pour réaliser du phénotypagemassif en vue de la sélection des animaux de demai

An open-access computer image analysis (CIA) method to predict meat and fat content from an android smartphone-derived picture of the bovine 5th-6th rib

International audienceMarbling and rib composition are important attributes related to carcass yields and values, beef quality, consumer satisfaction and purchasing decisions. An open-access computer image analysis method based on a fresh beef rib image captured under nonstandardized and uncontrolled conditions was developed to determine the intramuscular, intermuscular and total fat content. For this purpose, cross-section images of the 5th-6th rib from 130 bovine carcasses were captured with a Galaxy S8 smartphone. The pictures were analyzed with a program developed using ImageJ open source software. The 17 processed image features that were obtained were mined relative to gold standard measures, namely, intermuscular fat, total fat and muscles dissected from a rib and weighed, and intramuscular fat content (IMF - marbling) determined by the Soxhlet method. The best predictions with the lowest prediction errors were obtained by the sparse partial least squares method for both IMF percent and rib composition and from a combination of animal and image analysis features captured from the caudal face of the 6th rib captured on a table. These predictions were more accurate than those based on animal and image analysis features captured from the caudal face of the 5th rib on hanging carcasses. The external-validated prediction precision was 90% for IMF and ranged from 71 to 86% for the total fat, intermuscular and muscle rib weight ratios. Therefore, an easy, low-cost, user-friendly and rapid method based on a smartphone picture from the 6th rib of bovine carcasses provides an accurate method for fat content determination

Meat@ppli - application smartphone pour déterminer la teneur en gras de la viande bovine en temps réel

Fat has a major economic importance in the beef industry. It affects all the meat food chain steps: fromthe farmer to the consumer, through the slaughterer-processor or the distributor. At the beginning of theproject, no tool was able to measure fat in meat in real time, in a reliable, economical and non-destructiveway. The Meat@ppli project aimed to predict the fat content of beef from its photo, both at the carcassand sliced beef stage, based on image analysis methods. The results are encouraging, with correlationswith reference methods varying from 0.5 to 0.9. The prediction models were embedded in the Meat@ppliapplication, developed for fat measurement at the carcass stage. It remains a proof-of-concept that, inthe future, could be used by the beef industry to route carcasses to the most suitable distribution channelsand to perform massive phenotyping for the selection of bovines with appropriate marbling.L’importance économique du gras est majeure dans la filière viande bovine. Il impacte tous les maillonsde la filière : de l’éleveur au consommateur, en passant par l’abatteur-transformateur ou le distributeur.Or jusqu’à présent, aucun outil ne permettait de mesurer le gras dans la viande en temps réel, de façonfiable, économe et non destructive. Le projet Meat@ppli s’est attaché à prédire les teneurs en gras d’unmorceau de viande à partir de sa photo, au stade de la carcasse comme à celui du morceau tranché, ense basant sur des techniques d’analyse d’image. Les résultats sont encourageants, avec des corrélationsplus ou moins étroites avec les méthodes de références (0,5 < r < 0,9). Les modèles de prédiction ont étéembarqués dans l’application Meat@ppli, développée pour la mesure du gras au stade de la carcasse.Elle est encore à l’état de preuve de concept mais à l’avenir, elle pourrait être utilisée par la filière pourorienter les carcasses vers les circuits de distribution les plus adaptés et pour réaliser du phénotypagemassif en vue de la sélection des animaux de demai