Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

Contribution to Characterizing the Meat Quality of Protected Designation of Origin Serrana and Preta de Montesinho Kids Using the Near-Infrared Reflectance Methodology

The aims of this study were to describe and compare the meat quality characteristics of male and female kids from the "Serrana" and "Preta de Montesinho" breeds certified as "Cabrito Transmontano" and reinforce the performance of near-infrared reflectance (NIR) spectra in predicting these quality characteristics and discriminating among breeds. Samples of Longissimus thoracis (n = 32; sixteen per breed; eight males and eight females) were used. Breed significantly affected meat quality characteristics, with only color and fatty acid (FA) (C12:0) being influenced by sex. The meat of the "Serrana" breed proved to be more tender than that of the "Preta de Montesinho". However, the meat from the "Preta de Montesinho" breed showed higher intramuscular fat content and was lighter than that from the "Serrana" breed, which favors its quality of color and juiciness. The use of NIR with the linear support vector machine regression (SVMR) classification model demonstrated its capability to quantify meat quality characteristics such as pH, CIELab color, protein, moisture, ash, fat, texture, water-holding capacity, and lipid profile. Discriminant analysis was performed by dividing the sample spectra into calibration sets (75 percent) and prediction sets (25 percent) and applying the Kennard-Stone algorithm to the spectra. This resulted in 100% correct classifications with the training data and 96.7% accuracy with the test data. The test data showed acceptable estimation models with R2 > 0.99.

Sheep and Goat Meat Processing and Quality

Sheep and goat meat production includes the increased demand for grass-fed and organic meat and value-added products such as sausages, meatballs, and burgers [...].

Honey authentication and adulteration detection using emission - excitation spectra combined with chemometrics

The aim of this study was to evaluate the usefulness of emission-excitation matrices for honey authentication and adulteration detection. For this purpose, 4 types of authentic honeys (tilia, sunflower, acacia and rape) and samples adulterated with different adulteration agents (agave, maple, inverted sugar, corn and rice in different percentages - 5%, 10% and 20%) were analysed. Each honey type and each adulteration agent exhibit unique emission-excitation spectra that can be used for the classification according to the botanical origin and for the detection of adulteration. The principal component analysis clearly separated the rape, sunflower and acacia honeys. The partial least squares - discriminant analysis (PLS-DA) and support vector machines (SVM) were used in a binary mode to separate the authentic honeys from the adulterated ones, and the SVM proved to separate much better than PLS-DA.

Estimation of carcass chemical composition in beef-on-dairy cattle using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut

The aim of the present study was to estimate the chemical composition (water, lipid, protein, mineral, and energy contents) of carcasses measured postmortem using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut. One hundred and twenty beef-on-dairy (dam: Swiss Brown, sire: Angus, Limousin, or Simmental) bulls (n = 66), heifers (n = 42), and steers (n = 12) were included in the study. The reference carcass composition measured after grinding, homogenization, and chemical analyses was estimated from DXA variables using simple or multiple linear regressions with model training on 70% (n = 84) and validation on 30% (n = 36) of the observations. In the validation step, the estimates of water and protein masses from the half-carcass (R2 = 0.998 and 0.997; root mean square error of prediction [RMSEP], 1.0 and 0.5 kg, respectively) and 11th rib DXA scans (R2 = 0.997 and 0.996; RMSEP, 1.5 and 0.5 kg, respectively) were precise. Lipid mass was estimated precisely from the half-carcass DXA scan (R2 = 0.990; RMSEP = 1.0 kg) with a slightly lower precision from the 11th rib DXA scan (R2 = 0.968; RMSEP = 1.7 kg). Mineral mass was estimated from half-carcass (R² = 0.975 and RMSEP = 0.3 kg) and 11th rib DXA scans (R2 = 0.947 and RMSEP = 0.4 kg). For the energy content, the R2 values ranged from 0.989 (11th rib DXA scan) to 0.996 (half-carcass DXA scan), and the RMSEP ranged from 36 (half-carcass) to 55 MJ (11th rib). The proportions of water, lipids, and energy in the carcasses were also precisely estimated (R2 ≥ 0.882) using either the half-carcass (RMSEP ≤ 1.0%) or 11th rib-cut DXA scans (RMSEP ≤ 1.3%). Precision was lower for the protein and mineral proportions (R2 ≤ 0.794, RMSEP ≤ 0.5%). The cattle category (sex and breed of sire) effect was observed only in some estimative models for proportions from the 11th rib cut. In conclusion, DXA imaging of either a cold half-carcass or 11th rib cut is a precise method for estimating the chemical composition of carcasses from beef-on-dairy cattle.

Growth Performance, Carcass and Meat Traits of Autochthonous Arouquesa Weaners Raised on Traditional and Improved Feeding Systems

Arouquesa is an autochthonous bovine breed known for its Arouquesa PDO beef labeling. There are several production systems under the definition of PDO labeling. This study aimed to compare the effect of different production systems on carcass and meat traits for the Arouquesa breed. Two trials differing in diet and weaning age were conducted. The first trial included a TF group fed the traditional way and weaned at 9 months; a TF + S1 group, equal to TF, but with a starter supplement; and finally, a S1 + S2 group that was fed with a starter and a growth supplement and weaned at 5 months. The second trial was composed of a TF + S3 group fed like the TF + S1 group but reared until 12 months with a finishing supplement, and finally, the S3 group fed like the S1 + S2 group but reared until 12 months. In the first trial, the TF + S1 and S1 + S2 groups showed higher final live weight and average daily gain. In the second trial, we observed differences in the subcutaneous fat that was higher in the S3 group. Regarding meat traits, we observed differences in exudative and cooking losses in the first trial. In general, supplementation improved meat production without affecting meat quality parameters.

In Vivo Ultrasound Prediction of the Fillet Volume in Senegalese Sole (Solea senegalensis)

Simple Summary

The ability to obtain in vivo information on characteristics related to fish composition is necessary for aquaculture. In addition, there is growing interest in production traits, such as growth, feed efficiency or fillet weight, but it remains difficult to precisely record in vivo individual fish traits that report to these production traits, which can increase edible fish meat production and decrease the environmental impact. In the present study, we performed an ultrasound approach for the in vivo prediction of fillet volume of the Senegalese sole (Solea senegalensis), a species considered a promising flatfish species for marine fish farming. The results show that models based on ultrasound fillet volume measurements explain above 95% of the variation observed in fillet volume. However, for fillet yield estimation, the results were modest. Therefore, further studies are necessary to better understand the potential of the ultrasound approach to this trait. Nevertheless, this work allows us to conclude that the approach with ultrasound is promising for measuring in vivo fish composition traits.

Abstract

Senegalese sole (Solea senegalensis) has been considered a promising new flatfish species for Mediterranean marine fish farming. Accurate prediction of fillet traits in live animals may allow for more efficient control of muscle deposition in fish. In this sense, this study was undertaken to develop a non-invasive method to predict in vivo fish fillet volume and yield using real-time ultrasonography (RTU). The trial was conducted with 44 market weight Senegalese sole (298.54 ± 87.30 g). Fish were scanned with an Aloka SSD 500V with a 7.5 MHz probe. Ten RTU cross-sectional images were taken from the operculum to the caudal fin at regular intervals. These images were analyzed using Fiji software. These data were then used to estimate the partial volumes of the fillet. Actual fillet volume was determined using Archimedes’ principle. Simple and stepwise multiple regression analyses were then used to develop prediction models of fillet volume and yield. The most cranial RTU sections of the fish fillet were the best single predictors of both fillet volume and fillet yield and were the ones included in the best stepwise models. The best RTU slice area explained 82% of the variation observed in fillet volume, but the other RTU slice areas used as predictors of fillet volume showed poor to moderate accuracy (0.035 ≤ R² ≤ 0.615). Single RTU partial volumes showed poor to very high accuracy (0.395 ≤ R² ≤ 0.970) as predictors of fillet volume. The best stepwise model based on the RTU slice areas included three independent variables and explained 88.3% of the observed variation. The best stepwise models based on RTU partial volumes (single volumes and/or combinations of single volumes) explained about 97% of the variation observed in fillet volume. Two RTU volume traits, V_1–5 + V_6–9, and V₁₊₍₎₊₉, showed to be practically direct predictors of the actual fillet volume, explaining, respectively, 97% and 96% of the variation observed in the actual fillet volume. The fillet yields show lower correlations with slice areas (r between 0.044 and 0.601) than with volumes (r between 0.288 and 0.637). While further studies are clearly necessary to better understand the potential of RTU for the estimation of fillet yield in fish in general and Senegalese sole in particular, the present results showed that RTU traits can be very good predictors of Senegalese sole’s fillet volume, either used in regression models or as direct predictors.

Rapid Determination of Mixed Pesticide Residues on Apple Surfaces by Surface-Enhanced Raman Spectroscopy

Chlorpyrifos (CPF) and 2,4-dichlorophenoxyacetic acid (2,4-D) are insecticides and herbicides which has been widely used on farms. However, CPF and 2,4-D residues on corps can bring high risks to human health. Accurate detection of pesticide residues is important for controlling health risks caused by CPF and 2,4-D. Therefore, we developed a fast, sensitive, economical, and lossless surface-enhanced Raman spectroscopy (SERS)-based method for pesticide detection. It can rapidly and simultaneously determine the CPF and 2,4-D mixed pesticide residues on an apple surface at a minimum of 0.001 mg L⁻¹ concentration, which is far below the pesticide residue standard in China and the EU. The limits of detection reach down to 1.28 × 10⁻⁹ mol L⁻¹ for CPF and 2.47 × 10⁻¹⁰ mol L⁻¹ for 2,4-D. The limits of quantification are 4.27 × 10⁻⁹ mol L⁻¹ and 8.23 × 10⁻¹⁰ mol L⁻¹ for CPF and 2,4-D. This method has a great potential for the accurate detection of pesticide residues, and may be applied to other fields of agricultural products and food industry.

Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume

This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. The trial was performed on carcasses of twenty-two male lambs (17.6 ± 1.8 kg, body weight). The carcasses were split into eight cuts, divided into three groups according to their commercial value: high-value, medium value, and low-value group. Linear, area, and volume of leg measurements were obtained to predict carcass and cuts composition. The leg volume was acquired by two different methodologies: 3D image reconstruction using a Microsoft Kinect sensor and Archimedes principle. The correlation between these two leg measurements was significant (r = 0.815, p < 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R² of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor.

Fluorescence-based characterisation of selected edible insect species: Excitation emission matrix (EEM) and parallel factor (PARAFAC) analysis

Fluorescence spectroscopy coupled with chemometric tools is a powerful analytical method, largely used for rapid food quality and safety evaluations. However, its potential has not yet been explored in the novel food sector. In the present study, excitation emission matrices (EEMs) of 15 insect powders produced by milling insects belonging to 5 Orthoptera species (Acheta domesticus, Gryllus assimilis, Gryllus bimaculatus, Locusta migratoria, Schistocerca gregaria) from 3 different origins were investigated. Parallel factor (PARAFAC) analysis performed on the overall averaged dataset was validated for five components, highlighting the presence of five different fluorescence peaks. The presence of these peaks was confirmed on each species, suggesting that fluorescence compounds of edible insects are the same in several species. PARAFAC analysis performed on the overall averaged dataset after alternatively adding the EEM recorded from one standard compound allowed to speculate that edible insects fluorescence raises from mixtures of: tryptophan + tyrosine (PARAFAC component-1), tryptophan + tyrosine + tocopherol (PARAFAC component-2), collagen + pyridoxine + pterins (PARAFAC component-3). This study suggests that fluorescence spectroscopy may represent a powerful method for investigating composition and quality of insect-based foods.

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Fluorescence landscape of edible insects comprises of 5 different peaks.

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Similar fluorescence compounds are present among several Orthoptera species.

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Fluorescence peaks of edible insects result from several chemical molecules.

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Fluorescence intensity of edible insects depends on their species and origin.

Using segmental bioimpedance analysis to estimate soft tissue and chemical composition of retail cuts and carcasses of lambs

This study evaluated the potential of segmental bioimpedance analysis (SBIA) to estimate the composition of retail cuts and their predictability to infer on the carcass composition in lambs. Leg, rib, shoulder, neck, and loin from thirty-one lamb carcasses were evaluated. A single-frequency bioelectrical impedance analyzer at 50 kHz was used to perform measurements. The models for estimating soft tissue showed the highest accuracy in the retail cuts. Lean and fat weight of the lamb cuts or of the carcasses were predicted with R² of calibration ranging from 86.6 to 99.1% and from 67.5 to 95.4%, respectively. Segmental bioimpedance analysis is an accurate technology to assess physical and chemical components in retail cuts of lamb. Despite that, shoulder was the most representative cut; all cuts evaluated through SBIA were valuable to estimate the components of the edible portion of lamb carcasses.

Evaluation of an Image Analysis Approach to Predicting Primal Cuts and Lean in Light Lamb Carcasses

Simple Summary

The traditional way of estimating the carcass composition by complete dissection of muscle, fat and bone is an expensive, time-consuming and inconsistent process. The purpose of this study was to evaluate the accuracy of a simple video image analysis (VIA) system to predict the composition and primal cuts using light lamb carcasses. The six cuts of the carcasses were grouped according to their commercial value: high-value cuts (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). Results showed the ability of the VIA system to estimate the weight and yield of the groups of carcass joints.

Abstract

Carcass dissection is a more accurate method for determining the composition of a carcass; however, it is expensive and time-consuming. Techniques like VIA are of great interest once they are objective and able to determine carcass contents accurately. This study aims to evaluate the accuracy of a flexible VIA system to determine the weight and yield of the commercial value of carcass cuts of light lamb. Photos from 55 lamb carcasses are taken and a total of 21 VIA measurements are assessed. The half-carcasses are divided into six primal cuts, grouped according to their commercial value: high-value (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). K-folds cross-validation stepwise regression analyses are used to estimate the weights of the cuts in the groups and their lean meat yields. The models used to estimate the weight of AllC, HVC, MVC and LVC show similar results and a k-fold coefficient of determination (k-fold-R²) of 0.99 is achieved for the HVC and AllC predictions. The precision of the weight and yield of the three prediction models varies from low to moderate, with k-fold-R² results between 0.186 and 0.530, p < 0.001. The prediction models used to estimate the total lean meat weight are similar and low, with k-fold-R² results between 0.080 and 0.461, p < 0.001. The results confirm the ability of the VIA system to estimate the weights of parts and their yields. However, more research is needed on estimating lean meat yield.

Near-infrared hyperspectral imaging for detection and visualization of offal adulteration in ground pork

Hyperspectral imaging (HSI) technique was investigated to explore a feasible protocol for detecting the potential offal (lung) adulteration in ground pork. Tested samples (176 adulterated and 2 controls) were first prepared with adulterant of ground lung in range of 0%-100% (w/w) at 10% intervals. After hyperspectral images were acquired and calibrated in reflectance mode (400-1000 nm), full spectra were extracted from identified regions of interests (ROIs) and then transformed into absorbance and Kubelka-Munck spectral units, respectively. Partial least squares regression (PLSR) models based on full spectra showed that raw reflectance spectra with no preprocessings performed best with coefficient of determination (R_p²) of 0.98, root mean square error (RMSEP) of 4.25%, and ratio performance deviation (RPD) of 7.53 in prediction set. To reduce the high dimensionality of spectra, data was further explored using principal component loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC), respectively. The optimal performance of established simplified PLSR model were acquired using eleven featured wavelengths selected by PC loadings with R_p² of 0.98, RMSEP of 4.47% and RPD of 7.16. Finally, the limit of detection (LOD) was calculated to be a satisfactory 7.58%, and readily discernible visualization procedure using preferred simplified PLSR model yielded satisfactory spatial distribution of adulteration situation. Control samples with known distribution were also visualized to successfully prove the validity. Consequently, this research offers an alternative assay for visually and rapidly detecting offal of lung adulteration in ground pork.

Simultaneously prediction of sheep and goat carcass composition and body fat depots using in vivo ultrasound measurements and live weight

The present study established multiple linear regression models using two ultrasound in vivo measurements (at lumbar and sternal regions, with different real-time ultrasonography machines and probes) and live weight, to predict simultaneously carcass composition and body fat depots of different breeds of sheep and goat. This study is important for the small ruminant industry, considering the feasibility of using the ultrasound methodology in field conditions, as well as an online system of the carcass evaluation. The multiple linear regression models were obtained by selecting the best subset of variables between using the in vivo measurements (raw variables), their second degree and interactions, evaluated in terms of prediction performance using cross-validation "K-folds" and validated by a test group. Overall, high accuracy (adj R²) was obtained from the linear relationship between predicted and experimental values of the group test for each of the nine dependent variables, with values varying between adj R² 0.88 and 0.98.