Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy

Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics

Sheep meat (encompassing lamb, hogget and mutton) is an important source of animal protein in many countries, with a unique flavour and sensory profile compared to other red meats. Flavour, colour and texture are the key quality attributes contributing to consumer liking of sheep meat. Over the last decades, various factors from 'farm to fork', including production system (e.g., age, breed, feeding regimes, sex, pre-slaughter stress, and carcass suspension), post-mortem manipulation and processing (e.g., electrical stimulation, ageing, packaging types, and chilled and frozen storage) have been identified as influencing different aspects of sheep meat quality. However conventional meat-quality assessment tools are not able to elucidate the underlying mechanisms and pathways for quality variations. Advances in broad-based analytical techniques have offered opportunities to obtain deeper insights into the molecular changes of sheep meat which may become biomarkers for specific variations in quality traits and meat authenticity. This review provides an overview on how omics techniques, especially proteomics (including peptidomics) and metabolomics (including lipidomics and volatilomics) are applied to elucidate the variations in sheep meat quality, mainly in loin muscles, focusing on colour, texture and flavour, and as tools for authentication. SIGNIFICANCE: From this review, we observed that attempts have been made to utilise proteomics and metabolomics techniques on sheep meat products for elucidating pathways of quality variations due to various factors. For instance, the improvement of colour stability and tenderness could be associated with the changes to glycolysis, energy metabolism and endogenous antioxidant capacity. Several studies identify proteolysis as being important, but potentially conflicting for quality as the enhanced proteolysis improves tenderness and flavour, while reducing colour stability. The use of multiple analytical methods e.g., lipidomics, metabolomics, and volatilomics, detects a wider range of flavour precursors (including both water and lipid soluble compounds) that underlie the possible pathways for sheep meat flavour evolution. The technological advancement in omics (e.g., direct analysis-mass spectrometry) could make analysis of the proteins, lipids and metabolites in sheep meat routine, as well as enhance the confidence in quality determination and molecular-based assurance of meat authenticity.

Analysis and prediction of the major fatty acids in vegetable oils using dielectric spectroscopy at 5–30 MHz

A dielectric spectroscopy method was applied to determine major fatty acids composition in vegetable oils. Dielectric constants of vegetable oils were measured in the frequency range of 5–30 MHz. After data pre-treatment, prediction models were constructed using partial least squares (PLS) regression between dielectric spectral values and the fatty acids compositions measured by gas chromatography. Generally, the root means square error of validation (RMSECV) was less than 11.23% in the prediction of individual fatty acids. The determination coefficient (R²) between predicted and measured oleic, linoleic, mono-unsaturated, and poly-unsaturated fatty acids were 0.84, 0.77, 0.84, and 0.84, respectively. These results indicated that dielectric spectroscopy coupled with PLS regression could be a promising method for predicting major fatty acid composition in vegetable oils and has the potential to be used for in-situ monitoring systems of daily consumption of dietary fatty acids.

The Effect of 17α-Ethynilestradiol and GPER1 Activation on Body and Muscle Growth, Muscle Composition and Growth-Related Gene Expression of Gilthead Seabream, Sparus aurata L

Endocrine-disrupting chemicals include natural and synthetic estrogens, such as 17α-ethynilestradiol (EE2), which can affect reproduction, growth and immunity. Estrogen signalling is mediated by nuclear or membrane estrogen receptors, such as the new G-protein-coupled estrogen receptor 1 (GPER1). The present work studies the effect of EE2 and G1 (an agonist of GPER1) on body and muscle parameters and growth-related genes of 54 two-year-old seabreams. The fish were fed a diet containing EE2 (EE2 group) and G1 (G1 group) for 45 days and then a diet without EE2 or G1 for 122 days. An untreated control group was also studied. At 45 days, the shortest body length was observed in the G1 group, while 79 and 122 days after the cessation of treatments, the shortest body growth was observed in the EE2 group. Hypertrophy of white fibers was higher in the EE2 and G1 groups than it was in the control group, whereas the opposite was the case with respect to hyperplasia. Textural hardness showed a negative correlation with the size of white fibers. At the end of the experiment, all fish analyzed in the EE2 group showed a predominance of the gonadal ovarian area. In addition, the highest expression of the mafbx gene (upregulated in catabolic signals) and mstn2 (myogenesis negative regulator) was found in EE2-exposed fish.

Handheld near-infrared spectrometer allows on-line prediction of beef quality traits

The aim of this study was to evaluate the ability of a miniaturized near-infrared spectrometer to predict chemical parameters, technological and quality traits, fatty acids and minerals in intact Longissimus thoracis and Trapezius obtained from the ribs of 40 Charolais cattle. Modified partial least squares regression analysis to correlate spectra information to reference values, and several scatter correction and mathematical treatments have been tested. Leave-one-out cross-validation results showed that the handheld instrument could be used to obtain a good prediction of moisture and an approximate quantitative prediction of fat or protein contents, a*, b*, shear force and purge loss with coefficients of determination above 0.66. Moreover, prediction models were satisfactory for proportions of MUFA, PUFA, oleic and palmitic acids, for Fe and Cu contents. Overall, results exhibited the usefulness of the on-line miniaturized tool to predict some beef quality traits and the possibility to use it with commercial cuts without sampling, carcass deterioration nor grinding and consequent meat products' loss.

Near-Infrared Reflectance Spectroscopy for Predicting the Phospholipid Fraction and the Total Fatty Acid Composition of Freeze-Dried Beef

Research on fatty acids (FA) is important because their intake is related to human health. NIRS can be a useful tool to estimate the FA of beef but due to the high moisture and the high absorbance of water makes it difficult to calibrate the analyses. This work evaluated near-infrared reflectance spectroscopy as a tool to assess the total fatty acid composition and the phospholipid fraction of fatty acids of beef using freeze-dried meat. An average of 22 unrelated pure breed young bulls from 15 European breeds were reared on a common concentrate-based diet. A total of 332 longissimus thoracis steaks were analysed for fatty acid composition and a freeze-dried sample was subjected to near-infrared spectral analysis. 220 samples (67%) were used as a calibration set with the remaining 110 (33%) being used for validation of the models obtained. There was a large variation in the total FA concentration across the animals giving a good data set for the analysis and whilst the coefficient of variation was nearly 68% for the monounsaturated FA it was only 27% for the polyunsaturated fatty acids (PUFA). PLS method was used to develop the prediction models. The models for the phospholipid fraction had a low R²p and high standard error, while models for neutral lipid had the best performance, in general. It was not possible to obtain a good prediction of many individual PUFA concentrations being present at low concentrations and less variable than other FA. The best models were developed for Total FA, saturated FA, 9c18:1 and 16:1 with R²_p greater than 0.76. This study indicates that NIRS is a feasible and useful tool for screening purposes and it has the potential to predict most of the FA of freeze-dried beef.

Lipid Assessment, Cholesterol and Fatty Acid Profile of meat from broilers raised in four different rearing systems

Evaluated lipid and cholesterol concentration and fatty acid profile of raw breast, thigh and drumstick meat from broilers raised in different rearing systems. Were used 200 male broiler carcasses from four different rearing systems (n=50 from conventional intensive; n=50 from organic; n=50 from free-range; and n=50 from antibiotic-free) distributed in a completely randomized design with four rearing systems and 50 replications (carcasses). Breast meat from conventional broilers showed higher lipid (1.47) and cholesterol (34.13) concentration. Thigh and drumstick meat from free-range broilers had higher lipid (7.53/4.73) and cholesterol (45.55/53.65) concentration. Fat contained in breast, thigh and drumstick meat from free-range broilers showed higher levels of polyunsaturated fatty acids. Fat from breast and thigh meat from free-range broilers showed higher total concentration of ω3 and ω6 fatty acids. Fat from thigh meat from organic broilers showed higher levels of EPA (C20:5n3) and DHA (C22:6n3). Fat from drumstick meat from free-range broilers showed higher total concentration of ω3 and ω6 fatty acids. Meat from chickens raised in alternative rearing systems offers less risk to cardiovascular health because it presents lower concentrations of lipids and cholesterol, greater amounts of polyunsaturated fatty acids, which are beneficial for human health.

Near-Infrared Spectroscopy as a Beef Quality Tool to Predict Consumer Acceptance

This study was conducted to evaluate the feasibility of using near-infrared spectroscopy (NIRS) to predict beef consumers’ perceptions. Photographs of 200 raw steaks were taken, and NIRS data were collected (transmittance and reflectance). The steak photographs were used to conduct a face-to-face survey of 400 beef consumers. Consumers rated beef color, visible fat, and overall appearance, using a 5-point Likert scale (where 1 indicated “Dislike very much” and 5 indicated “Like very much”), which later was simplified in a 3-point Likert scale. Factor analysis and structural equation modeling (SEM) were used to generate a beef consumer index. A partial least square discriminant analysis (PLS-DA) was used to predict beef consumers’ perceptions using NIRS data. SEM was used to validate the index, with root mean square errors of approximation ≤0.1 and comparative fit and Tucker–Lewis index values <0.9. PLS-DA results for the 5-point Likert scale showed low prediction (accuracy < 42%). A simplified 3-point Likert scale improved discrimination (accuracy between 52% and 55%). The PLS-DA model for purchasing decisions showed acceptable prediction results, particularly for transmittance NIRS (accuracy of 76%). Anticipating beef consumers’ willingness to purchase could allow the beef industry to improve products so that they meet consumers’ preferences.

Effects of flavonoids from Allium mongolicum Regel as a dietary additive on meat quality and composition of fatty acids related to flavor in lambs

Sixty male Small-tailed Han sheep (initial body weight: 42.5 ± 4.1 kg) were assigned randomly and averagely into four groups to evaluate effects of flavonoids extracted from Allium mongolicum Regel (AMR) on meat quality and composition of fatty acids related to flavor in longissimus dorsi muscle of lambs. Lambs of four groups were fed a basal control diet (C), and basal diet + three different amounts of flavonoids extracted from the AMR; those were 11 mg kg−1 (Flav 11), 22 mg kg−1 (Flav 22), or 33 mg kg−1 (Flav 33), respectively. Dressing percentage, loin eye area, cooking loss, and pressing loss were affected by treatments (P < 0.05). Percentage of intramuscular fat, the concentrations of eicosapentaenoic acid, C18:0, total saturated fatty acids, monounsaturated fatty acid, and 4-methyloctanoic acid in longissimus dorsi muscle were changed by treatments (P < 0.05). Addition of AMR to diet improved the meat quality and had favorable effects on fatty acid composition related to meat flavor in longissimus dorsi muscle.

Feasibility of near infrared transmittance spectroscopy to predict fatty acid composition of commercial processed meat

BACKGROUND

The new European Regulation 1169/2011 concerning nutrition declaration of food products compels the addition of saturated fatty acids, whereas the declaration of monounsaturated and polyunsaturated fatty acids remains voluntary. Therefore, the industry is interested in a more rapid, easy and less cost-effective analysis method for accomplishing this labelling regulation. The present study aimed to evaluate the ability of near infrared transmittance spectroscopy (wavelengths between 850 and 1050 nm) to predict the fatty acid (FA) composition of commercial processed meat samples (n = 310).

RESULTS

Good predictions were achieved for the absolute content of saturated, unsaturated, monounsaturated and polyunsaturated FA, as well as ω-6 groups, and also for a few individual FA (C16:0, C18:0, C18:1n9, C18:2n6 and 18:1n7), with the coefficient of determination in cross-validation being > 0.90 and the residual prediction deviation being > 3.15. Unsatisfactory models were obtained for the relative content of FA.

CONCLUSION

Near infrared transmittance spectroscopy can be considered as a reliable method for predicting the main groups of FA in processed meat products, whereas predictions of individual FA are less reliable. © 2017 Society of Chemical Industry.

On-site evaluation of Wagyu beef carcasses based on the monounsaturated, oleic, and saturated fatty acid composition using a handheld fiber-optic near-infrared spectrometer

The fat quality is an important aspect, especially for Wagyu beef. A handheld fiber-optic near-infrared spectrometer for on-site evaluation of beef fat quality was developed, and the interactance spectra of the intermuscular fat from 833 Wagyu carcasses at 12 markets were measured. The calibration model was transferred to five slave instruments using twenty-six block samples. The performance of one slave instrument was verified at five meat markets (n=360). The coefficients of determination of the slave instrument for monounsaturated, oleic, and saturated fatty acid compositions determined by gas chromatography and near-infrared measurements were 0.69, 0.64, and 0.67, respectively. The standard error of prediction for the slave instrument was approximately 2%. The fiber-optic near-infrared spectrometers were highly accurate in the fat quality evaluation of Wagyu carcasses based on monounsaturated, oleic, and saturated fatty acid composition with easy calibration model transfer.

Recent advances in rapid and nondestructive determination of fat content and fatty acids composition of muscle foods

Conventional methods for determining fat content and fatty acids (FAs) composition are generally based on the solvent extraction and gas chromatography techniques, respectively, which are time consuming, laborious, destructive to samples and require use of hazard solvents. These disadvantages make them impossible for large-scale detection or being applied to the production line of meat factories. In this context, the great necessity of developing rapid and nondestructive techniques for fat and FAs analyses has been highlighted. Measurement techniques based on near-infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance and hyperspectral imaging have provided interesting and promising results for fat and FAs prediction in varieties of foods. Thus, the goal of this article is to give an overview of the current research progress in application of the four important techniques for fat and FAs analyses of muscle foods, which consist of pork, beef, lamb, chicken meat, fish and fish oil. The measurement techniques are described in terms of their working principles, features, and application advantages. Research advances for these techniques for specific food are summarized in detail and the factors influencing their modeling results are discussed. Perspectives on the current situation, future trends and challenges associated with the measurement techniques are also discussed.

A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products

Consumer demand for quality and healthfulness has led to a higher need for quality assurance in meat production. This requirement has increased interest in near-infrared (NIR) spectroscopy due to the ability for rapid, environmentally friendly, and noninvasive prediction of meat quality or authentication of added-value meat products. This review includes the principles of NIR spectroscopy, pre-processing methods, and multivariate analyses used for quantitative and qualitative purposes in the meat sector. Recent advances in portable NIR spectrometers that enable new online applications in the meat industry are shown and their performance evaluated. Discrepancies between published studies and potential sources of variability are discussed, and further research is encouraged to face the challenges of using NIRS technology in commercial applications, so that its full potential can be achieved.

Impact of ground soybean and starch levels on the quality of meat from feedlot young Nellore bulls

Twenty-eight young Nellore bulls (395±32kg initial body weight) were assigned to a completely randomized design (2×2 factorial design with 7 animals per treatment) to evaluate effects on the quality of meat. Diet treatments consisted of high (about 25%) or low (about 16%) starch levels, with or without ground soybean addition. Shear force was decreased in the meat from animals fed a low-starch diet (P=0.0016). Meat from animals fed a high-starch diet had increased total concentrations of unsaturated (P=0.0029) and monounsaturated fatty acids (P=0.0253). Polyunsaturated fatty acid content increased in the meat from animals fed a diet containing soybean (P=0.0121). High starch diets (>25%) decreased the concentration of saturated fatty acids and increased the amount of unsaturated fatty acids in the meat from young Nellore bulls.

New approach to improve the calibration of main fatty acids by near-infrared reflectance spectroscopy in ruminant meat

This study aims to investigate alternative near-infrared reflectance spectroscopy (NIRS) strategies for predicting beef polyunsaturated fatty acids (PUFA) composition, which have a great nutritional interest, and are actually poorly predicted by NIRS. We compared the results of NIRS models for predicting fatty acids (FA) of beef meat by using two databases: a beef database including 143 beef samples, and a ruminant database including 76 lamb and 143 beef samples. For all the FA, particularly for PUFA, the coefficient of determination of cross-validation (R2CV) and the residual predictive deviation (RPD) of models increased when the ruminant muscle samples database was used instead of the beef muscle database. The R2CV values for the linoleic acid, total conjugated linoleic acid and total PUFA increased from 0.44, 0.79 and 0.59 to 0.68, 0.9, 0.8, respectively, and RPD values for these FA increased from 1.33, 2.14, 1.54 to 1.76, 3.11 and 2.24, respectively. RPD above 2.5 indicates calibration model is considered as acceptable for analytical purposes. The use of a universal equation for ruminant meats to predict FA composition seems to be an encouraging strategy.

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.

The application of Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal

Soya bean products are used widely in the animal feed industry as a protein based feed ingredient and have been found to be adulterated with melamine. This was highlighted in the Chinese scandal of 2008. Dehulled soya (GM and non-GM), soya hulls and toasted soya were contaminated with melamine and spectra were generated using Near Infrared Reflectance Spectroscopy (NIRS). By applying chemometrics to the spectral data, excellent calibration models and prediction statistics were obtained. The coefficients of determination (R(2)) were found to be 0.89-0.99 depending on the mathematical algorithm used, the data pre-processing applied and the sample type used. The corresponding values for the root mean square error of calibration and prediction were found to be 0.081-0.276% and 0.134-0.368%, respectively, again depending on the chemometric treatment applied to the data and sample type. In addition, adopting a qualitative approach with the spectral data and applying PCA, it was possible to discriminate between the four samples types and also, by generation of Cooman's plots, possible to distinguish between adulterated and non-adulterated samples.

Prediction of fatty acid composition in Camellia oleifera oil by near infrared transmittance spectroscopy (NITS)

Under the serious circumstances of Camellia oleifera adulteration, the accurate examination for quality trait of C. oleifera oil is extremely urgent. For rapid determination of FA composition in C. oleifera oil, the feasibility of NITS was first studied. The quantitative models for FA were built based on PLS regression. NITS spectra is able to accurately predict for oleic, linoleic, and palmitic acids (R(cv)>0.844, R(2)>0.886). R(cv) are 0.91987, 0.95755, and 0.84447, and R(2) are 0.9424, 0.9682, 0.8862 for NITS models of oleic, linoleic, and palmitic acids, respectively. But models for stearic and unsaturated acids are less accurate, with values of R(cv) from 0.67440 to 0.69114, and R(2) from 0.6834 to 0.7587. These results indicate that NITS will have potential to be used in predicting FA composition of C. oleifera oil.

Influence of inclusion of chestnut in the finishing diet on fatty acid profile of dry-cured ham from Celta pig breed

The effect of the inclusion of chestnut in the finishing diet on fatty acid profile of dry-cured Celta ham was studied. Twelve hams of each type (from three different diets: concentrate, mixed and chestnut) were used. Significant differences between treatments (P<0.001) were found regarding total saturated fatty acids (SFA), monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA). Ham samples from the mixed and chestnut groups had less SFA (35.57% and 35.63%, respectively) with respect to ham samples from the concentrate group (40.33%), while hams from the mixed and chestnut batches showed higher values of MUFA than hams from the concentrate group (50.70 vs. 49.79 vs. 43.85, P<0.001, respectively). From a nutritional point of view, Celta hams from chestnut diets could be considered as healthier regarding their fatty acid profiles (low n-6/n-3 ratio and high hypocholesterolemic/Hypercholesterolemic ratio). Discriminant analysis selected five variables (C(16:0), C(16:1cis-9), C(20:2), C(20:3n-6) and C(20:4n-6)) and calculated two discriminating functions which verifies the presence of chestnut in the finishing diet.

Application of NIRS for predicting fatty acids in intramuscular fat of rabbit

The aim of this study was to evaluate the use of near infrared reflectance spectroscopy (NIRS) for predicting fatty acid content in intramuscular fat to be applied in rabbit selection programs. One hundred and forty three freeze-dried Longissimus muscles (LM) were scanned by NIRS (1100-2498nm). Modified Partial Least Squares models were obtained. Equations were selected according to standard error of cross validation (SECV) and coefficient of determination of cross validation (R(2)(CV)). Residual predictive deviation of cross validation (RPD(CV)) was also studied. Accurate predictions were reported for IMF (R(2)(CV)=0.98; RPD(CV)=7.57), saturated (R(2)(CV)=0.96; RPD(CV)=5.08) and monounsaturated FA content (R(2)(CV)=0.98; RPD(CV)=6.68). Lower accuracy was obtained for polyunsaturated FA content (R(2)(CV)=0.83; RPD(CV)=2.40). Several individual FA were accurately predicted such as C14:0, C15:0, C16:0, C16:1, C17:0, C18:0, C18:1 n-9, C18:2 n-6 and C18:3 n-3 (R(2)(CV)=0.91-0.97; RPD(CV)>3). Long chain polyunsaturated FA and C18:1 n-7 presented less accurate prediction equations (R(2)(CV)=0.12-0.82; RPD(CV)<3).

Evaluation and predictive modeling of shelf life of minced beef stored in high-oxygen modified atmosphere packaging at different temperatures

The aims were: (1) to follow the freshness decay of minced beef stored in high-oxygen modified atmosphere packaging at different temperatures (4.3, 8.1 and 15.5 degrees C) by applying traditional methods (microbiological counts, color evaluation, thiobarbituric acid assay TBA, headspace gas composition) and e-nose; (2) to model the decay kinetics to obtain information about the maximum shelf life as function of storage conditions. The minced beef, packaged in modified atmosphere was supplied by a manufacturer at the beginning of its commercial life. The study demonstrated the ability of the traditional methods to describe the kinetics of freshness decay. The modeling of the experimental data and the comparison with microbiological or chemical thresholds allowed the setting, for each index, of a stability time above which the meat was no longer acceptable. The quality decay of meat was also evaluated by the headspace fingerprint of the same set of samples by means of a commercial e-nose. A clear discrimination between "fresh" and "old" samples was obtained using PCA and CA, determining at each temperature a specific range of stability time. The mean value of the stability times calculated for each index was 9 days at 4.3 degrees C (recommended storage temperature), 3-4 days at 8.1 degrees C (usual temperature in household refrigerators) and 2 days at 15.5 degrees C (abuse temperature). Resolution of the stability times allowed calculation of mean Q(10) values, i.e. the increase in rate for a 10 degrees C increase in temperature. The results show that the Q(10) values from the traditional methods (3.6-4.0 range) overlapped with those estimated with e-nose and color indexes (3.4 and 3.9, respectively).