Validation of two laboratory methods for beef intramuscular fat quantification.

Many studies on beef nutritional qualities require the quantification of intramuscular fat. To reduce the sample amount, solvent use and time of analysis, two alternative methods to the Folch et al. (1957) reference method were studied: a miniaturised Folch's method and a near-infrared spectroscopic method. Performances and acceptability limits were evaluated with accuracy profiles for each of the methods. Equations to correct bias between the alternative and reference methods were calculated. Uncertainties associated with measurements were determined, and the validity domains were defined. From a previous set of studies, the ability of each method to discriminate samples from bovines of different breeds or receiving diverse treatments was tested. The validity domain of the miniaturised Folch's method ranged from 1.9 to 13.8 g of total lipids/100 g of tissue, and that of the near-infrared spectroscopic method ranged from 4.8 to 13.8 g of total lipids/100 g of tissue, with less than 20% difference from the reference method's results. Thus, the two alternative methods could be used depending on the research objectives: the miniaturised Folch's method could be used for detailed quantification of intramuscular fat and the near-infrared spectroscopic method for a quick classification of a large number of muscles. The precise knowledge of uncertainties associated with each measurement was determined, and perfect continuity with the results obtained so far with the reference Folch's method was confirmed.

Contribution of connective tissue components, muscle fibres and marbling to beef tenderness variability in longissimus thoracis, rectus abdominis, semimembranosus and semitendinosus muscles.

BACKGROUND: The present study aimed to identify relationships between components of intramuscular connective tissue, proportions of the different fiber types, intramuscular fat and sensory tenderness of beef cooked at 55 °C. Accordingly, four muscles differing in their metabolic and contractile properties, as well as in their collagen content and butcher value, were obtained from dairy and beef cattle of several ages and sexes and were then used to create variability. RESULTS: Correlation analyses and/or stepwise regressions were applied on Z-scores to identify the existing and robust associations. Tenderness scores were further categorized into tender, medium and tough classes using unsupervised learning methods. The findings revealed a muscle-dependant role with respect to tenderness of total and insoluble collagen, cross-links, and type IIB + X and IIA muscle fibers. The longissimus thoracis and semitendinosus muscles that, in the present study, were found to be extreme in their tenderness potential were also very different from each other and from the rectus abdominis (RA) and semimembranosus (SM). RA and SM muscles were very similar regarding their relationship for muscle components and tenderness. A relationship between marbling and tenderness was only present when the results were analysed irrespective of all factors of variation of the experimental model relating to muscle and animal type. CONCLUSION: The statistical approaches applied in the present study using Z-scores allowed identification of the robust associations between muscle components and sensory beef tenderness and also identified discriminatory variables of beef tenderness classes. © 2020 Society of Chemical Industry.

Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging.

The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).

Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse.

Only few studies have used Near-Infrared (NIR) spectroscopy to assess meat quality traits directly in the chiller. The aim of this study was therefore to investigate the ability of a handheld NIR spectrometer to predict marbling scores on intact meat muscles in the chiller. A total of 829 animals from 2 slaughterhouses in France and Italy were involved. Marbling was assessed according to the 3G (Global Grading Guaranteed) protocol using 2 different scores. NIR measurements were collected by performing 5 scans at different points of the Longissimus thoracis. An average MSA marbling score of 330-340 was obtained in the two countries. The prediction models provided a R2 in external validation between 0.46 and 0.59 and a standard error of prediction between 83.1 and 105.5. Results did provide a moderate prediction of the marbling scores but can be useful in the European industry context to predict classes of MSA marbling.

Vitamin B2 concentration in cow milk: Quantification by a new UHPLC method and prediction by visible and near-infrared spectral analysis.

Until now, there are few information on vitamin B2 concentration variability in milk. In this study, a novel analytical method to quantify total vitamin B2 in milk was developed and applied on 676 samples. In parallel, spectral analysis (colorimetry and near infrared spectroscopy) were performed to develop prediction models of vitamin B2 concentration in milk. The analytical method includes an acid and enzymatic extraction followed by vitamin B2 quantification by Ultra High Performance Liquid Chromatography coupled with fluorimetry. Samples analysis showed a wide range of concentration from 0.78 to 4.58 mg/L with a mean of 2.09 ± 0.48 mg/L. Two prediction models based on colorimetric analysis allow estimation of vitamin B2 concentration in milk. Thus, this work shows an analytical method and, for the first time, a prediction method to enable enhancement of researches on vitamin B2 content of milk and its variation factors.

The role of plant traits and their plasticity in the response of pasture grasses to nutrients and cutting frequency.

BACKGROUND AND AIMS: Although plant functional traits (PFTs) appear to be important indicators of species' responses to land use changes, there is no clear understanding of how the variations in traits and their plasticity determine variations in species performance. This study investigated the role of functional shoot traits and their plasticity for variation in above-ground net primary productivity (ANPP) due to changes in N supply and in cutting frequency for 13 native perennial C(3) grass species. METHODS: Monocultures of the grass species were grown in a fully factorial block design combining plant species, cutting frequency and N supply as factors. KEY RESULTS: Four major trait associations were obtained by reducing the dimensions of 14 PFTs with a principal component analysis (PCA).Variations in species' productivity in response to an increase in cutting frequency was mainly explained by traits linked to the first PCA axis, opposing high plant stature from lower shoot cellulose and lignin contents and high leaf N content. Variation in species productivity in response to change in N supply was mainly explained by a set of predictor variables combining traits (average flowering date) and a trait's plasticity (tiller density per unit land area and leaf dry matter content, i.e. mg dry matter g fresh mass(-1)). These traits involved are linked to the second PCA axis ('nutrient acquisition-conservation'), which opposes distinct strategies based on response to nutrient supply. CONCLUSIONS: Variations in ANPP of species in response to an increase in cutting frequency and a decrease in N supply are controlled by a group of traits, rather than by one individual trait. Incorporating plasticity of the individual traits into these trait combinations was the key to explaining species' productivity responses, accounting for up to 89 % of the total variability in response to the changes in N supply.

Carotenoid, Tocopherol, and Phenolic Compound Content and Composition in Cover Crops Used as Forage.

Secondary compounds of grassland and forage plant species such as vitamins or phenolic compounds are involved in different health-promoting effects in animals. However, information on their concentration and composition in forage plant species remains scarce. The objective of this study was to characterize the composition of secondary compounds of seven grazed cover crop plant species harvested at two stages of growth. Carotenoids and tocopherols were characterized and quantified using ultraperformance liquid chromatography with a photodiode array, and soluble phenolic compounds were characterized using high-performance liquid chromatography with diode-array detection. All species were rich in carotenoids, especially at the vegetative stage, even if the concentrations varied between plant species. Variations in tocopherol concentrations and phenolic composition were more important between plant species than between stages within species. Among the plant species tested, sainfoin (Onobrychis viciifolia Scop) contained the most secondary metabolites.

Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles.

The objective of this study was to determine the potential of multispectral imaging (MSI) data recorded in the visible and near infrared electromagnetic regions to predict the structural features of intramuscular connective tissue, the proportion of intramuscular fat (IMF), and some characteristic parameters of muscle fibers involved in beef sensory quality. In order to do this, samples from three muscles (Longissimus thoracis, Semimembranosus and Biceps femoris) of animals belonging to three breeds (Aberdeen Angus, Limousine, and Blonde d'Aquitaine) were used (120 samples). After the acquisition of images by MSI and segmentation of their morphological parameters, a back propagation artificial neural network (ANN) model coupled with partial least squares was applied to predict the muscular parameters cited above. The results presented a high accuracy and are promising (R2 test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R2P = 0.99 and RMSEP = 0.103 g × 100 g-1 DM.

Comparison of the Potential Abilities of Three Spectroscopy Methods: Near-Infrared, Mid-Infrared, and Molecular Fluorescence, to Predict Carotenoid, Vitamin and Fatty Acid Contents in Cow Milk.

The objective of this work is to compare the ability of three spectroscopy techniques: molecular fluorescence, near-infrared (NIR), and mid-infrared with attenuated total reflectance (MIR-ATR) spectroscopy to predict the concentrations of 8 carotenoids, 6 vitamins and 22 fatty acids (FA) in cow's milk. A dataset was built through the analysis of 242 frozen milk samples from different experiments. The milk compounds were analysed using reference methods and by NIR, MIR-ATR, and fluorescence to establish different predictive models. NIR spectroscopy allowed for better prediction of cis9-ß-carotene, ß-cryptoxanthin and the sum of carotenoids than the other techniques, with a coefficient of cross-validation in calibration (R2CV) > 0.60 and a coefficient of determination in validation (R2V) > 0.50. Their standard errors of prediction (SEP) were equal to 0.01, except for the sum of carotenoids (SEP = 0.15). However, MIR-ATR and fluorescence seem usable for the prediction of lutein and all-trans-ß-carotene, respectively. These three spectroscopy methods did not allow us to predict (R2CV < 0.30) vitamin contents except, for vitamin A (the best R²CV = 0.65 with NIR and SEP = 0.15) and α-tocopherol (the best R²CV = 0.56 with MIR-ATR and SEP = 0.41), but all R²V were <0.30. NIR spectroscopy yielded the best prediction of the selected milk FA.

Predicting the Quality of Meat: Myth or Reality?

This review is aimed at providing an overview of recent advances made in the field of meat quality prediction, particularly in Europe. The different methods used in research labs or by the production sectors for the development of equations and tools based on different types of biological (genomic or phenotypic) or physical (spectroscopy) markers are discussed. Through the various examples, it appears that although biological markers have been identified, quality parameters go through a complex determinism process. This makes the development of generic molecular tests even more difficult. However, in recent years, progress in the development of predictive tools has benefited from technological breakthroughs in genomics, proteomics, and metabolomics. Concerning spectroscopy, the most significant progress was achieved using near-infrared spectroscopy (NIRS) to predict the composition and nutritional value of meats. However, predicting the functional properties of meats using this method-mainly, the sensorial quality-is more difficult. Finally, the example of the MSA (Meat Standards Australia) phenotypic model, which predicts the eating quality of beef based on a combination of upstream and downstream data, is described. Its benefit for the beef industry has been extensively demonstrated in Australia, and its generic performance has already been proven in several countries.

Fecal Near-Infrared Reflectance Spectroscopy Prediction of the Feed Value of Temperate Forages for Ruminants and Some Parameters of the Chemical Composition of Feces: Efficiency of Four Calibration Strategies.

The forage feed value determined by organic matter digestibility (OMD) and voluntary intake (VI) is hard and expensive. Thus, several indirect methods such as near infrared reflectance (NIR) spectroscopy have been developed for predicting the feed value of forages. In this study, NIR spectra of 1040 samples of feces from sheep fed fresh temperate forages were used to develop calibration models for the prediction of fecal crude ash (CA), fecal crude protein (CP), fresh forage OMD, and VI. Another 136 samples of feces were used to assess these models. Four calibration strategies were compared: (1) species-specific calibration; (2) family-specific calibration; (3) a global procedure; and (4) a LOCAL approach. The first three strategies were based on classical regression models developed on different sample populations, whereas the LOCAL approach is based on the development models from selected samples spectrally similar to the sample to be predicted. The first two strategies use feces-samples grouping based on the species or the family of the forage ingested. Forage calibration data sets gave value ranges of 79-327 g/kg dry matter (DM) for CA, 65-243 g/kg DM for CP, 0.52-0.85 g/g for OMD, and 34.7-100.5 g DM/kg metabolic body weight (BW0.75) for VI. The prediction of CA and CP content in feces by species-specific fecal NIR (FNIR) spectroscopy models showed lower standard error of prediction (SEP) (CA 15.03 and CP 7.48 g/kg DM) than family-specific (CA 21.93 and CP 7.69 g/kg DM), global (CA 19.83 and CP 7.98 g/kg DM), or LOCAL (CA 30.85 and CP 8.10 g/kg DM) models. For OMD, the LOCAL procedure led to a lower SEP (0.018 g/g) than the other approaches (0.023, 0.024, and 0.023 g/g for species-specific, family-specific, and global models, respectively). For VI, the LOCAL approach again led to a lower SEP (6.15 g/kg BW0.75) than the other approaches (7.35, 8.00, and 8.13 g/kg BW0.75 for the species-specific, family-specific, and global models, respectively). LOCAL approach performed on FNIR spectroscopy samples gives more precise models for predicting OMD and VI than species-specific, family-specific, or global approaches.

Exploration of Biological Markers of Feed Efficiency in Young Bulls.

The efficiency with which ruminants convert feed to desirable products is difficult to measure under normal commercial settings. We explored the use of potential biological markers from easily obtainable samples, that is, blood, hair, and feces, to characterize potential causes of divergent efficiency when considered as residual feed intake (RFI) or feed conversion efficiency (FCE). A total of 54 Charolais bulls, 20 in period 1 and 34 in period 2, were examined for individual dry matter intake (DMI) and growth. Bulls were offered a diet of 70:30 wrapped grass silage to concentrate for 99 d. At the conclusion of the test period, blood samples were collected for the determination of vitamins B2 and B6, and plasma used for the determination of metabolites, natural isotopic 15N abundance (15N NIA, expressed as δ15N ‰) and fractionation (Δ15Nplasma proteins-diet and Δ13Cplasma proteins-diet) and near-infrared spectroscopy (NIRS). Feces were analyzed by NIRS. Bulls were slaughtered at 15-17 months of age and carcass characteristics determined. Bulls were ranked according to RFI with extremes (SD ± 0.5; n = 31) classified as either efficient (Neg-RFI) or inefficient (Pos-RFI). Extreme bulls were then classified for FCE (high vs low FCE), changing the groups. Pos-RFI bulls consumed 14% more feed than Neg-RFI bulls for the same level of weight gain. Low FCE bulls tended to eat more, but had lower weight gains than high FCE bulls. No differences were detected in carcass conformation, fat scores, hot carcass weight, or dressing percentage. Yet, heart and bladder weights were heavier in Pos-RFI, and rumen weight tended to be heavier in Pos-RFI bulls. RFI did not affect bulk 15N or 13C fractionation. A negative correlation was observed between FCE and Δ15Nplasma proteins-diet. Inefficient bulls (Pos-RFI) had higher δ15N in glycine compared to Neg-RFI bulls. Similarly, metabolomic analysis showed a tendency for concentrations of glycine and sarcosine to be elevated in Pos-RFI bulls, whereas aspartic acid and carnosine tended to be elevated, and serine tended to be lower in High FCE. Among vitamins, only flavin adenine dinucleotide concentration was higher in the blood of bulls with High FCE. These results suggest that the two feed efficiency metrics differ in the underlying mechanisms of metabolism, where RFI is driven by differences in the energetic requirements of visceral organs and the extent of AA catabolism.

Near-Infrared Spectroscopy Calibrations Performed on Oven-Dried Green Forages for the Prediction of Chemical Composition and Nutritive Value of Preserved Forage for Ruminants.

Predicting forage feed value is a vital part of estimating ruminant performances. Most near-infrared (NIR) reflectance calibration models have been developed on oven-dried green forages, but preserved forages such as hays or silages are a significant part of real-world farm practice. Fresh and preserved forages give largely similar fodder, but drying or ensiling processes could modify preserved forage spectra which would make the oven-dried green forage model unsuitable to use on preserved forage samples. The aim of this study was to monitor the performance of oven-dried green forage calibration models on a set of hay and silage to predict their nutritive value. Local and global approaches were tested and 1025 green permanent grassland forages, 46 types of hay, and 27 types of silage were used. The samples were scanned by NIR spectroscopy and analyzed for nitrogen, neutral detergent fiber, acid detergent fiber, and pepsin-cellulase dry matter digestibility (PCDMD). Local and global calibrations were developed on 975 oven-dried green forage spectra and tested on 50 samples of oven-dried green forages, 46 samples of hay, and 27 samples of silage. For oven-dried green forage and hay validation sets, Mahalanobis distance (H) between these samples and the calibration population center was lower than 3. No significant standard error of prediction differences was obtained when calibration models were applied to oven-dried green forage and hay validation sets. For silage, the H-distance was higher than 3, meaning that calibration models built from oven-dried green forages cannot be applied to silage samples. We conclude that local calibration outperforms global strategy on predicting the PCDMD of oven-dried green forages and hay.

Early pregnancy diagnosis in sheep using near-infrared spectroscopy on blood plasma.

The objective of this study was to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to discriminate between pregnant and nonpregnant ewes in early stages of pregnancy after artificial insemination (AI) from blood plasma. Samples were collected using jugular puncture at 18 and 25 days after AI from 188 Rasa Aragonesa and Ansotana ewes. Plasma samples were analyzed for pregnancy-associated glycoprotein (PAG) and progesterone (P4) using ELISA commercial kits. The spectra of plasma samples were recorded in the visible and near-infrared ranges. The performance of these tests were compared, using as criterion standard the pregnancy status determined using transabdominal ultrasonography at 45 days after AI. Pregnancy rate was 47.9% (90/188). At Day 18, sensitivity was similar in NIRS and P4 tests (98.9% vs. 100%; not significant) and greater than PAG (32.2%; both P < 0.001). Specificity was similar in NIRS and PAG tests (both 100%) and greater than that of P4 (84.7%; P < 0.001). At Day 25, sensitivity and specificity of NIRS and PAG were both 100%. It can be concluded that NIRS was an accurate method of diagnosis of pregnancy at Days 18 and 25 after AI in ewes.

Structural and biochemical characteristics of bovine intramuscular connective tissue and beef quality.

The aim of the study was to evaluate the impact of structural and biochemical characteristics of muscle intramuscular connective tissue on beef quality. The experimental design was based on three muscles of three breeds sampled as fresh material and cooked at 55°C (Longissimus thoracis and Semimembranosus) or at 70°C (Semimembranosus and Biceps femoris) for quality assessment. The results showed that muscle characteristics influence beef quality differently from one muscle to another. In grilled LT, proteoglycan content contributed negatively to juiciness, and intramuscular lipids were linked positively to tenderness, flavour, residues and overall liking scores. In grilled SM, cross-link and lipid contents were involved in beef quality. In BF cooked to 70°C, perimysial branch points were negatively linked to tenderness. In SM cooked to 70°C, perimysial area was involved in beef quality. These results should allow a better understanding of the factors involved in background toughness, in juiciness and flavour of meat.

Effects of nitrogen source and water availability on stem carbohydrates and cellulosic bioethanol traits of alfalfa plants.

Symbiotic association of legumes with rhizobia frequently results in higher photosynthesis and soluble carbohydrates in comparison with nitrate-fed plants, which might improve its potential for biomass conversion into bioethanol. A greenhouse experiment was conducted to examine the effects of nitrogen source and water availability on stem characteristics and on relationships between carbohydrates, phenolic metabolism activity and cell wall composition in alfalfa (Medicago sativa L. cv. Aragón). The experiment included three treatments: (1) plants fed with ammonium nitrate (AN); (2) plants inoculated with rhizobia (R); and (3) plants inoculated with rhizobia and amended with sewage sludge (RS). Two levels of irrigation were imposed: (1) well-watered and (2) drought stress. Under well-watered conditions, nitrogen-fixing plants have increased photosynthesis and stem fermentable carbohydrate concentrations, which result in higher potential for biomass conversion to bioethanol than in AN plants. The latter had higher lignin due to enhanced activities of phenolic metabolism-related enzymes. Under drought conditions, the potential for bioethanol conversion decreased to a similar level in all treatments. Drought-stressed nitrogen-fixing plants have high concentrations of fermentable carbohydrates and cell wall cellulose, but ammonium nitrate-fed plants produced higher plant and stem biomass, which might compensate the decreasing stem carbohydrates and cellulose concentrations.

Prediction of dry matter, fat, pH, vitamins, minerals, carotenoids, total antioxidant capacity, and color in fresh and freeze-dried cheeses by visible-near-infrared reflectance spectroscopy.

Visible-near-infrared reflectance spectroscopy was used to predict dry matter, fat, pH, retinol, alpha-tocopherol, beta-carotene, xanthophylls, sodium chloride, calcium, potassium, magnesium, zinc, total antioxidant capacity, brightness, redness, and yellowness in both fresh and freeze-dried cheeses. A total of 445 cheeses of four cheese varieties were investigated. Composition of samples was analyzed by reference methods. Samples were scanned (400-2500 nm) and predictive equations were developed using modified partial least-squares with both cross-validation and external validation. Coefficient of determination (R(2)) and residual predictive deviation (RPD) in external validation were satisfactory for dry matter (0.97; 5.99), fat (0.90; 3.22), beta-carotene (0.92; 3.43), sodium chloride (0.89; 2.94), calcium (0.95; 4.62), Zinc (0.93; 3.75), brightness (0.96; 4.88), redness (0.96; 5.23), and yellowness (0.93; 3.73) in fresh cheeses. Poor predictions were obtained for pH, retinol, alpha-tocopherol, xanthophylls, potassium, magnesium, and total antioxidant capacity (R(2) < 0.81; RPD < 3).