At-line prediction of fatty acid profile in chicken breast using near infrared reflectance spectroscopy

An innovative application developed to determine the blood output of chickens and its impact on the meat quality in poultry slaughtering

Optimizing blood loss during the slaughtering process is crucial for obtaining high-quality meat, as the presence of meat blood can lead to a reduction in shelf life and negative sensory evaluation by consumers. Moreover, the high water and nutritional content of the meat, along with its appropriate pH value, necessitate careful consideration of the remaining blood, as it can support microbial spoilage of chicken meat. In this context the effects of making an extra cut on conventionally halal-slaughtered broiler's leg which had an extra cut at the cartilage point where the drumstick and the claw part meet, before the bloodletting process were analysed. The results of the analysis indicate that making an additional cut on the drumsticks does not adversely affect the overall quality of chicken meat. Determination of peroxide value (PV) and thiobarbituric acid reactive substance (TBARS) analyses were performed to analyze the degree of lipid oxidation. The PV and the TBARS value were higher in the drumsticks which have extra cut compared to the uncut samples. L*, a*, and b* values of extra cut thigh meats are higher. However, considering the storage period, the ninth day values in the cut thigh meats were found to be lower than the first day results of the chickens drumsticks do not have an extra cut procedure. As the storage period of chicken drumsticks progressed, as expected, the L* value decreased, while a* value and b* value increased over time. As regards sexes of broilers, it was observed that the Pseudomonas spp of female broilers with extra cut in the cartilage tissue of their legs on d 1 was significantly higher than the male broilers. These findings suggest that this innovative method holds significant potential for widespread application.

Near-Infrared Reflectance Spectroscopy for Quantitative Analysis of Fat and Fatty Acid Content in Living Tenebrio molitor Larvae to Detect the Influence of Substrate on Larval Composition

Several studies have shown that mealworms (Tenebrio molitor L.) could provide animals and humans with valuable nutrients. Tenebrio molitor larvae were studied to determine whether their rearing diets affected their fat and fatty acid content and to ascertain if it is possible to detect the changes in the larval fat composition using near-infrared reflectance spectroscopy (NIRS). For this reason, a standard control diet (100% wheat bran) and an experimental diet, consisting of wheat bran and the supplementation of a different substrate (coconut flour, flaxseed flour, pea protein flour, rose hip hulls, grape pomace, or hemp protein flour) were used. The results showed lesser weight gain and slower growth rates for larvae raised on diets with a high fat content. A total of eight fatty acids were identified and quantified, where palmitic, oleic, and linoleic acids were the most prevalent and showed a correlation between larval content and their content in the rearing diets. There was a high content of lauric acid (3.2-4.6%), myristic acid (11.4-12.9%), and α-linolenic acid 8.4-13.0%) in mealworm larvae as a result of the high dietary content of these fatty acids. NIR spectra were also influenced by the fat and fatty acid composition, as larval absorbance values differed greatly. The coefficient of the determination of prediction (R²_P) was over 0.97, with an RPD value of 8.3 for the fat content, which indicates the high predictive accuracy of the NIR model. Furthermore, it was possible to develop calibration models with great predictive efficiency (R²_P = 0.81-0.95, RPD = 2.6-5.6) for all fatty acids, except palmitoleic and stearic acids which had a low predictive power (R²_P < 0.5, RPD < 2.0). The detection of fat and fatty acids using NIRS can help insect producers to quickly and easily analyze the nutritional composition of mealworm larvae during the rearing process.

FTIR-PCA Approach on Raw and Thermally Processed Chicken Lipids Stabilized by Nano-Encapsulation in β-Cyclodextrin

This study evaluated similarities/dissimilarities of raw and processed chicken breast and thigh lipids that were complexed by β-cyclodextrin, using a combined FTIR-PCA technique. Lipid fractions were analyzed as non-complexed and β-cyclodextrin-complexed samples via thermogravimetry, differential scanning calorimetry and ATR-FTIR. The lipid complexation reduced the water content to 7.67-8.33%, in comparison with the β-cyclodextrin hydrate (~14%). The stabilities of the complexes and β-cyclodextrin were almost the same. ATR-FTIR analysis revealed the presence of important bands that corresponded to the C=O groups (1743-1744 cm^-1) in both the non-complexed and nano-encapsulated lipids. Furthermore, the bands that corresponded to the vibrations of double bonds corresponding to the natural/degraded (cis/trans) fatty acids in lipids appeared at 3008-3011 and 938-946 cm^-1, respectively. The main FTIR bands that were involved in the discrimination of raw and processed chicken lipids, and of non-complexed and complexed lipids, were evaluated with PCA. The shifting of specific FTIR band wavenumbers had the highest influence, especially vibrations of the α(1→4) glucosidic bond in β-cyclodextrin for PC₁, and CH_2/3 groups from lipids for PC₂. This first approach on β-cyclodextrin nano-encapsulation of chicken lipids revealed the possibility to stabilize poultry fatty components for further applications in various ingredients for the food industry.

Use of Near-Infrared Spectroscopy to Discriminate DFD Beef and Predict Meat Quality Traits in Autochthonous Breeds

The potential of near-infrared reflectance spectroscopy (NIRS) to discriminate Normal and DFD (dark, firm, and dry) beef and predict quality traits in 129 Longissimus thoracis (LT) samples from three Spanish purebreeds, Asturiana de los Valles (AV; n = 50), Rubia Gallega (RG; n = 37), and Retinta (RE; n = 42) was assessed. The results obtained by partial least squares-discriminant analysis (PLS-DA) indicated successful discrimination between Normal and DFD samples of meat from AV and RG (with sensitivity over 93% for both and specificity of 100 and 72%, respectively), while RE and total sample sets showed poorer results. Soft independent modelling of class analogies (SIMCA) showed 100% sensitivity for DFD meat in total, AV, RG, and RE sample sets and over 90% specificity for AV, RG, and RE, while it was very low for the total sample set (19.8%). NIRS quantitative models by partial least squares regression (PLSR) allowed reliable prediction of color parameters (CIE L*, a*, b*, hue, chroma). Results from qualitative and quantitative assays are interesting in terms of early decision making in the meat production chain to avoid economic losses and food waste.

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.

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.

The Role of Coffee Silver Skin against Oxidative Phenomena in Newly Formulated Chicken Meat Burgers after Cooking

Coffee Silver Skin (CSS) is the unique by-product discarded after the roasting of coffee beans. This research aimed to evaluate the effect of two levels of CSS (1.5% and 3%) added as a natural ingredient in new formulations of chicken meat burgers. This is one of the first studies proposing a "formulation approach" to control the emergence of off flavours after meat cooking. Physical, chemical, and sensory analyses were carried out, within the CSS content and the evolution of volatile organic compounds in different samples. Newly formulated chicken burgers could limit food waste, while also becoming a source of fibres, minerals, and bioactive molecules. CSS limited weight losses (after cooking process) to 10.50% (1.5% addition) and 11.05% (3% addition), significantly lower (p < 0.01) than the control (23.85%). In cooked burgers, the occurrence of hexanal was reduced from 55.1% (CTRL T₀) to 11.7% (CSS T₀ 1.5%) to 0 (CSS T₀ 3%). As for the limitation of off-flavours, CSS also showed good activity, contrasting with the emergence of octanal, alcohols and other markers of lipid oxidation. From the sensory test carried out, the volatile profile of CSS does not seem to impair the flavour of burgers, though at higher percentages hydrocarbons and pyrazines are traceable. The thiobarbituric acid reactive substances (TBARS assay confirmed the protective effect of CSS against oxidation.

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.

Combination of spectra and texture data of hyperspectral imaging for prediction and visualization of palmitic acid and oleic acid contents in lamb meat

The feasibility of combining spectral and textural information from hyperspectral imaging to improve the prediction of the C16:0 and C18:1 n9 contents for lamb was explored. 29 and 22 optimal wavelengths were selected for the C16:0 and C18:1 n9 contents, respectively, by conducting the variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) algorithm. To extract the textural features of images, a gray-level co-occurrence matrix (GLCM) analysis was implemented in the first principal component image. The least squares support vector machine (LSSVM) model and the partial least squares regression (PLSR) model were developed to predict the C16:0 and C18:1 n9 contents from the spectra and the fusion data. The distribution map was visualized using the best model with the imaging process. The results showed that the combination of the spectral and textural information of hyperspectral imaging coupled with the VCPA-IRIV algorithm had strong potential for the prediction and visualization of the C16:0 and C18:1 n9 contents of lamb.

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the "time from slaughter" parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.

Differentiation between Normal and White Striped Turkey Breasts by Visible/Near Infrared Spectroscopy and Multivariate Data Analysis

The appearance of white striations over breast meat is an emerging and growing problem. The main purpose of this study was to employ the reflectance of visible-near infrared (VIS/NIR) spectroscopy to differentiate between normal and white striped turkey breasts. Accordingly, 34 turkey breast fillets were selected representing a different level of white striping (WS) defects (normal, moderate and severe). The findings of VIS/NIR were analyzed by principal component (PC1) analysis (PCA). It was found that the first PC1 for VIS, NIR and VIS/NIR region explained 98%, 97%, and 96% of the total variation, respectively. PCA showed high performance to differentiate normal meat from abnormal meat (moderate and severe WS). In conclusion, the results of this research showed that VIS/NIR spectroscopy was satisfactory to differentiate normal from severe WS turkey fillets by using several quality traits.

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.

Influence of storage and packaging method on chicken breast meat chemical composition and fat oxidation

The aim of this study was to determine the influence of storage conditions and packaging methods on chicken breast meat chemical composition and fat oxidation. In this study, chicken breasts available in retail sale were used for evaluation. The meat packed by three different methods (air packaging-AP, modified atmosphere packaging-MAP, and vacuum packaging-VP) was stored in a cooling room or a commercial display case for 9 D and tested for basic chemical composition, lipid oxidation (TBARS), and fatty acid composition. Meat in MAP during storage in the cooling room was characterized by higher (P ≤ 0.05) TBARS values as compared to meat packaged in AP and VP, respectively. The highest share in the fatty acid profile in chicken breasts was demonstrated by monounsaturated fatty acid, including cis C18:1 (n9 + n11) acid in the range from 31.86 to 34.66%. A high share of polyunsaturated acids was observed, including linoleic (C18:2 cis, cis) acid from 24.50 to 31.22% of all fatty acids. The simple relationship between the changes in the profile of fatty acids in meat packaged using different methods and in storage time was not determined, likely due to the variability of the composition of fatty acids in chicken breasts depending on the sample and the general low level of fat found in the meat.

A near-infrared reflectance spectroscopic method for the direct analysis of several fodder-related chemical components in drumstick (Moringa oleifera Lam.) leaves

The drumstick tree has traditionally been used as foodstuff and fodder in several countries. Due to its high nutritional value and good biomass production, interest in this plant has increased in recent years. It has therefore become important to rapidly and accurately evaluate drumstick quality. In this study, we addressed the optimization of Near-infrared spectroscopy (NIRS) to analyze crude protein, crude fat, crude fiber, iron (Fe), and potassium (K) in a variety of drumstick accessions (N = 111) representing different populations, cultivation programs, and climates. Partial least-squares regression with internal cross-validation was used to evaluate the models and identify possible spectral outliers. The calibration statistics for these fodder-related chemical components suggest that NIRS can predict these parameters in a wide range of drumstick types with high accuracy. The NIRS calibration models developed in this study will be useful in predicting drumstick forage quality for these five quality parameters.

Non-Destructive Spectroscopic Techniques and Multivariate Analysis for Assessment of Fat Quality in Pork and Pork Products: A Review

Fat is one of the most important traits determining the quality of pork. The composition of the fat greatly influences the quality of pork and its processed products, and contribute to defining the overall carcass value. However, establishing an efficient method for assessing fat quality parameters such as fatty acid composition, solid fat content, oxidative stability, iodine value, and fat color, remains a challenge that must be addressed. Conventional methods such as visual inspection, mechanical methods, and chemical methods are used off the production line, which often results in an inaccurate representation of the process because the dynamics are lost due to the time required to perform the analysis. Consequently, rapid, and non-destructive alternative methods are needed. In this paper, the traditional fat quality assessment techniques are discussed with emphasis on spectroscopic techniques as an alternative. Potential spectroscopic techniques include infrared spectroscopy, nuclear magnetic resonance and Raman spectroscopy. Hyperspectral imaging as an emerging advanced spectroscopy-based technology is introduced and discussed for the recent development of assessment for fat quality attributes. All techniques are described in terms of their operating principles and the research advances involving their application for pork fat quality parameters. Future trends for the non-destructive spectroscopic techniques are also discussed.

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.

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.

Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging

Hyperspectral imaging (HSI) is a novel technology for the food sector that enables rapid non-contact analysis of food materials. HSI was applied for the first time to whole green coffee beans, at a single seed level, for quantitative prediction of sucrose, caffeine and trigonelline content. In addition, the intra-bean distribution of coffee constituents was analysed in Arabica and Robusta coffees on a large sample set from 12 countries, using a total of 260 samples. Individual green coffee beans were scanned by reflectance HSI (980–2500 nm) and then the concentration of sucrose, caffeine and trigonelline analysed with a reference method (HPLC-MS). Quantitative prediction models were subsequently built using Partial Least Squares (PLS) regression. Large variations in sucrose, caffeine and trigonelline were found between different species and origin, but also within beans from the same batch. It was shown that estimation of sucrose content is possible for screening purposes (R² = 0.65; prediction error of ~ 0.7% w/w coffee, with observed range of ~ 6.5%), while the performance of the PLS model was better for caffeine and trigonelline prediction (R² = 0.85 and R² = 0.82, respectively; prediction errors of 0.2 and 0.1%, on a range of 2.3 and 1.1% w/w coffee, respectively). The prediction error is acceptable mainly for laboratory applications, with the potential application to breeding programmes and for screening purposes for the food industry. The spatial distribution of coffee constituents was also successfully visualised for single beans and this enabled mapping of the analytes across the bean structure at single pixel level.

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NIR hyperspectral imaging was applied to green coffee beans on a single seed basis.

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Sucrose, caffeine and trigonelline were analysed from single beans using HPLC-MS.

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PLS regression models were built to quantify for the coffee constituents.

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Prediction models were applied to single pixels to visualise the compound distribution.

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HSI was shown to be useful for rapid non-destructive screening of single coffee beans.

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.

Prediction of chemical composition and peroxide value in unground pet foods by near-infrared spectroscopy

The massive development of the pet food industry in recent years has lead to the formulation of hundreds of canine and feline complete extruded foods with the objective of meeting both the needs of the animals and numerous demands from pet owners. In the meantime, highly variable raw material compositions and the industry's new production techniques oblige manufacturers to monitor all phases of the extrusion process closely in order to ensure the targeted composition and quality of the products. This study aimed at evaluating the potential of infrared technology (visible and near-infrared spectrophotometer; 570-1842 nm) in predicting the chemical composition and peroxide value (PV) of unground commercial extruded dog foods. Six hundred and forty-nine commercial extruded dog foods were collected. For each product, an unground aliquot was analysed by infrared instrument while a second aliquot was sent to a laboratory for proximate analysis and PV quantification. The wide range of extruded dog food typologies included in the study was responsible for the wide variability observed within each nutritional trait, especially crude fibre and ash. The mean value of the 208 pet foods sampled for PV quantification was 17.49 mEq O₂ /kg fat (min 2.2 and max 94.10 mEq O₂ /kg fat). The coefficients of determination in cross-validation of NIRS prediction models were 0.77, 0.97, 0.83, 0.86, 0.78 and 0.94 for moisture, crude protein, crude fat, crude fibre, ash and nitrogen-free extract (NFE) respectively. PV prediction was less precise, as demonstrated by the coefficient of determination in cross-validation (0.66). The results demonstrated the potential of NIRS in predicting chemical composition in unground samples, with lower accuracy for moisture and ash, while PV prediction models suggest use for screening purposes only.

Characterization of muscle fiber type in the pectoralis major muscle of slow-growing local and commercial chicken strains

The study aimed to characterize muscle fiber type of the pectoralis major muscle of slow-growing chickens belonging to the Padovana local breed, the commercial strain Berlanda gaina, and their cross. Forty-five chickens (both males and females) from the different genotypes were grown up to 180 d. Histochemical and morphometrical analyses were performed to characterize muscle fiber types, myofiber density, and myofiber size of the different genotypes. The effects of genotype, sex, and their interaction were estimated. Muscle samples appeared almost entirely made up of IIB fiber type, whereas a low percentage of area (5 to 6%) was composed of hypercontracted fiber. Myofiber density was significantly higher (P < 0.05) in Padovana strains and cross-sectional area was significantly lower (P < 0.01) than in Berlanda strain. Muscle fiber characteristics appeared not to be affected by the interaction of genotype × sex.