Monitoring NIRS calibrations for use in routine meat analysis as part of Iberian pig-breeding programs

Candidate SNPs for meat quality and carcass composition in free-range Iberian pigs

Several genetic markers, previously associated with meat quality traits, have been proposed to be included in Iberian pig breeding programs. However, before being implemented, effects of these candidate SNPs on premium cuts' yield should be evaluated to avoid potential undesirable antagonistic effects. Therefore, the main goals of this study were to evaluate the effects of a set of 26 polymorphisms on premium cuts weights and to corroborate their effects on meat quality in a larger population. Phenotypic data of approximately 1550 Iberian pigs were recorded. The PRKAG3_rs319678464C and PRKAG3_rs1108399077G alleles were associated with an increase of shear force, water losses and color values and a reduction of average daily gain (ADG). The CAST_rs196949783G > A and ADIPOQ_rs3476515794T > G SNPs mainly affected IMF content, with this last SNP being the only one with significant effects on any of the carcass measures, specifically Longissimus thoracis et lumborum (LTL) weight. The ELOVL6_rs3473714672A, FASN_rs331694510A, MTTP_rs335896411C and ACACA_rs340781986C alleles were linked to a higher percentage of oleic acid and monounsaturated FA and a decrease in palmitic, palmitoleic and saturated FA. Besides, suggestive effects were observed between ELOVL6_rs3473714672A > G and ham and shoulder weights, and between MTTP_rs335896411T > C and LTL muscle weight. Finally, the NR6A1_rs326780270T allele was associated with a significant increase in ADG and tended to reduce the ham weight. The SNPs mapped to PRKAG3 (rs319678464G > C), ACACA, FASN and CAST genes could be implemented in breeding programs to improve meat quality traits without undesirable effects on carcass composition. The SNPs mapped to PRKAG3 (rs1108399077G > A) ELOVL6, MTTP and NR6A1 should continue to be tested in a larger number of animals.

SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology

This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bísaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BÜCHI) over a NIR spectral range of 4000-10,000 cm^-1 with a resolution of 4 cm^-1. The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE ≤ 0.5% and R² ≥ 0.95) except for the RT variable (RMSE of 0.891% and R² of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R² of 0.993 and 0.996; slope of 0.985 ± 0.006 and 0.925 ± 0.006). The results showed NIRs capacity to predict the meat quality traits of Bísaro pig breed in order to guarantee its characterization.

Rapid and Simultaneous Measurement of Fat and Moisture Contents in Pork by Low-Field Nuclear Magnetic Resonance

In order to improve the efficiency of Soxhlet extraction and oven drying, low-field nuclear magnetic resonance (LF-NMR) technology was used to detect fat and moisture contents in pork. The transverse relaxation time (T2) distribution curves were constructed by Carr−Purcell−Meiboom−Gill (CPMG) experiments. In addition, the optimal conditions of adding MnCl2 aqueous solution was explored to separate water and fat signal peaks. Finally, the reliability of this method for the determination of fat and moisture contents in pork was verified. The present study showed that adding 1.5 mL of 20% MnCl2 aqueous solution solution at 50 °C can isolate and obtain a stable peak of fat. The lard and 0.85% MnCl2 aqueous solution were used as the standards for fat and moisture measurements, respectively, and calibration curves with R2 = 0.9999 were obtained. In addition, the repeatability and reproducibility of this method were 1.71~3.10%. There was a significant correlation (p < 0.05) between the LF-NMR method and the conventional methods (Soxhlet extraction and oven drying), and the R2 was 0.9987 and 0.9207 for fat and moisture, respectively. All the results proved that LF-NMR could determine fat and moisture contents in pork rapidly and simultaneously.

Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System

The visible and near-infrared spectroscopy (Vis/NIRS) models for sheep meat quality evaluation using only one type of meat cut are not suitable for other types. In this study, a novel portable Vis/NIRS system was used to simultaneously detect physicochemical properties (pH, color L*, a*, b*, cooking loss, and shear force) for different types of sheep meat cut, including silverside, back strap, oyster, fillet, thick flank, and tenderloin cuts. The results show that the predictive abilities for all parameters could be effectively improved by spectral preprocessing. The coefficient of determination (R_p²) and residual predictive deviation (RPD) of the optimal prediction models for pH, L*, a*, b*, cooking loss, and shear force were 0.79 and 3.50, 0.78 and 2.28, 0.68 and 2.46, 0.75 and 2.62, 0.77 and 2.19, and 0.83 and 2.81, respectively. The findings demonstrate that Vis/NIR spectroscopy is a useful tool for predicting the physicochemical properties of different types of meat cut.

Carbon stable isotopes, fatty acids and the use of NIRS to differentiate IBERIAN pigs

This study explores the viability of the application of Near Infrared Spectrometry (NIR) for the rapid prediction of the ratio of ¹³C/¹²C stable isotopes and fatty acid composition in Iberian pigs. The potential use of this technique for distinguishing samples according to the duration of the montanera period was also studied. Subcutaneous fat samples from 50% and 100% Iberian pigs allowed to feed freely during different montanera periods were analyzed: 24 biopsies were taken prior to the montanera and 106 samples were taken after this feeding period. The results show significant correlations between δ¹³C (‰) and several fatty acids. Furthermore, it is possible to differentiate samples taken from pigs reared using different feeding regimes by analyzing the data obtained from the NIR spectra or by applying an Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) on data on δ¹³C (‰) and fatty acids in subcutaneous fat.

Routine NIRS analysis methodology to predict quality and safety indexes in spinach plants during their growing season in the field

Cultural practices and harvesting in spinach plants should be based not only on subjective indexes such as freshness and green colour, which are both related with the visual appearance of the plants, but also on objective indexes that can be quantified non-destructively. The aim of this research was to develop a methodology based on the use of near infrared spectroscopy to monitor routinely the growth process of the spinach plants in the field. Using the MicroNIR™ OnSite-W spectrophotometer, which is ideally suited for in situ analysis, 261 spinach plants were analysed. Initially, calibration models for dry matter, soluble solid and nitrate contents were developed using 1 spectrum per plant for dry matter content, and nine spectra per plant for the other two parameters. These models were then validated using the same number of spectra per plant as for calibration purposes. After that, to establish a procedure more suitable to routine analysis in the field, the models were validated with only one spectrum per plant and the suitability of the predictions was measured considering the global and neighbourhood Mahalanobis distances, whose control limit values were defined as inferior to 4.0 and 1.0, respectively. The results showed that once the calibration models were developed, only one spectrum per plant was enough to predict dry matter and nitrate contents successfully. Therefore, the methodology developed will allow us to monitor in real time the complete growth process and to take decisions about spinach cultivation based on internal quality and safety indexes.

Near-infrared spectroscopy-based analysis to study sensory parameters on pork loins as affected by cooking methods and conditions

BACKGROUND

The main objectives of this study were to evaluate the use of near infrared spectroscopy (NIRS) to classify pork loins under different methods and cooking conditions, and to predict sensory attributes of this product.

RESULTS

Samples were oven cooked at two temperatures (150 and 180 °C) for different times (45, 60 and 75 min) and confit cooked for different times (120, 180 and 240 min). All cooked loin samples were subjected to a Quantitative Descriptive Analysis by a trained panel. For classification, principal component analysis was performed based on the NIRS database, showing a good discrimination between loins samples subjected to different cooking conditions. Regarding prediction, a data mining technique (multiple linear regression) was applied on a database constructed with data from NIRS and sensory analysis.

CONCLUSION

The correlation coefficient and the mean absolute error obtained suggest that the calculated prediction equations of this study are valid to predict the changes in the sensory attributes depending on the cooking method and conditions used for pork loins. © 2018 Society of Chemical Industry.

Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression (iSPA-PLS)

Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12mgkg^-1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (ww^-1). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59mgkg^-1, REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis.

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.

Carbon monoxide stunning of Atlantic salmon (Salmo salar L.) modifies rigor mortis and sensory traits as revealed by NIRS and other instruments

BACKGROUND

Methods of stunning used in salmon slaughter are still the subject of research. Fish quality can be influenced by pre-, ante- and post-mortem conditions, including handling before slaughter, slaughter methods and storage conditions. Carbon monoxide (CO) is known to improve colour stability in red muscle and to reduce microbial growth and lipid oxidation in live fish exposed to CO. Quality differences in Atlantic salmon, Salmo salar L., stunned by CO or percussion, were evaluated and compared by different techniques [near infrared reflectance spectroscopy (NIRS), electronic nose (EN), electronic tongue (ET)] and sensory analysis.

RESULTS

Thawed samples, freeze-dried preparates and NIRS devices proved to be the most efficient combinations for discriminating the treatments applied to salmon, i.e. first the stunning methods adopted, then the back-prediction of the maximum time to reach rigor mortis and finally to correlate some sensory attributes. A trained panel found significant differences between control and CO-stunned salmon: reduced tactile crumbliness, reduced odour and aroma intensities, and reduced tenderness of CO-treated fillets. CO stunning reduced radiation absorbance in spectra of thawed and freeze-dried fillets, but not fillet samples stored in ethanol, where it may have interacted with myoglobin and myosin.

CONCLUSIONS

The good results in a rapid discrimination of thawed samples detected by NIRS suggest suitable applications in the fish industry. CO treatment could mitigate sensory perception, but consumer tests are needed to confirm our findings. © 2015 Society of Chemical Industry.

Characterization and quantification of 4-methylsterols and 4,4-dimethylsterols from Iberian pig subcutaneous fat by gas chromatography-mass spectrometry and gas chromatography-flame ionization detector and their use to authenticate the fattening systems

4-Methylsterols and 4,4-dimethylsterols of 47 samples of subcutaneous fat from Iberian pigs reared on two different fattening systems, "Extensive" and "Intensive", have been analyzed by GC-MS and GC-FID. The lipids were extracted by melting the subcutaneous fat in a microwave oven. The unsaponifiable matter was fractionated by thin layer chromatography. Then, the analysis was performed on a capillary SPB-5 column (30 m × 0.25 mm i.d., 0.15 μm film thickness), with hydrogen as a carrier gas and using a flame ionization detector. n-eicosanol was used as internal standard for quantification of individual methylsterols. These compounds have been analyzed by GC-MS for their identification. The full scan of free and trimethyl silyl ethers was used as acquisition mode. Six compounds have been identified for the first time in this type of samples: (3β,4α,5α)-4-methyl-cholesta-7-en-3-ol, (3β,4α,5α)-4-methyl-cholesta-8(14)-en-3-ol, (3β,5α)-4,4-dimethyl-cholesta-8(14),24-dien-3-ol, (3β)-lanosta-8,24-dien-3-ol, (3β, 5α)-4,4-dimethyl-cholesta-8,14-dien-3-ol and (3β)-lanost-9(11),24-dien-3-ol. The samples were derivatized as trimethyl silyl ethers before their analysis by GC-FID. By using these compounds as chemical descriptors, pattern recognition techniques were applied to differentiate between extensive and intensive fattening systems of Iberian pig. Several pattern recognition techniques, such as principal component analysis, linear discriminant analysis, support vector machines, artificial neural networks, soft independent modeling of class analogy and k nearest neighbors, have been used in order to find out a suitable classification model. A multilayer perceptron artificial neural network based on the contents of the above mentioned compounds allowed the differentiation of the two fattening systems with an overall classification performance of 91.7%.