Preliminary investigation for the prediction of intramuscular fat content of lamb in-situ using a hand- held NIR spectroscopic device

Continued development of a tool to measure intramuscular fat of Australian lamb carcases in commercial situations

Despite the importance of intramuscular fat (IMF) to eating quality, as yet no methodology has been widely adopted by the whole of industry in Australia to measure it routinely. Thus, a study was conducted to investigate the potential for a Near Infra-Red (NIR) device to predict the IMF content of the loin from spectra collected on the topside which is externally located on a hanging carcase and therefore easily accessible. To this end, NIR spectra were collected from topsides (m. semimembranosus) of 258 lamb carcases over 5 data collections and a sample of muscle was collected from the loin and the topside for IMF determination using a wet chemistry method. Subsequent Partial Least Square (PLS) models suggested the ability to predict the absolute IMF content of loins was poor (R2 = 0.28, RMSE = 1.26), yet there was a moderate ability to predict the IMF content of the topside (R2 = 0.56, RMSE = 0.82). Partial Least Square Discrimination Analysis (PLS-DA) models to classify cuts based on the IMF eating quality threshold of 4.5% yielded better predictive outcomes with accuracies of 66.7% and 76.7% for loin and topside respectively. However, further research to assess the relationship between the IMF of the loin and topside and reduce the impact of differences in overall absorbance between data collections will improve predictive outcomes.

Saturated Fatty Acids in Wool as Markers Related to Intramuscular Fat Content in Lambs

The aim of this study was to identify Longissimus lumborum (LL) muscle from lambs with high IMF content (≥3%) using wool FA. The FA composition was characterized in two sections of wool from Suffolk Down lambs, and its relationship with IMF content assessed. One hundred and forty lambs of similar body weight, body condition score, and age were selected. IMF content from LL muscle, and the wool fat and FA composition of the wool were determined. The proportions of FAs in the wool of lambs with low IMF were 51.96 ± 7.3% of saturated, 31.96 ± 7.7% polyunsaturated, and 16.08 ± 2.3% monounsaturated. Similar proportions of FAs were observed in wool samples of lambs with high IMF. Significant differences were found only in the SFA proportion between groups (p < 0.05). The predominant FAs in the wool samples were C16:0, C17:0, C18:0, C18:1n9c and C18:2n6c, representing over 60% of total FA methyl esters. Correlations were found between the wool FAs C16:1, C17:0, C18:0, C18:1n9c, C24:0, and C22:6n3 in wool samples divided into two sections. Discriminant analysis identified SFAs, particularly FAs C22:0 and C24:0, as potential candidate for predicting lambs with high IMF content with an accuracy of over 92%. These results provide to our knowledge evidence that IMF content could potentially be determined using wool FAs as a non-invasive method.

Validation of a handheld near-infrared spectrophotometer for measurement of chemical intramuscular fat in Australian lamb

The objective of the study was to independently validate a calibrated commercial handheld near infrared (NIR) spectroscopic device and test its repeatability over time using phenotypically diverse populations of Australian lamb. Validation testing in eight separate data sub-groups (n = 1591 carcasses overall) demonstrated that the NIR device had moderate precision (R2 = 0.4-0.64, RMSEP = 0.70-1.22%) but fluctuated in accuracy between experimental site demonstrated by variable slopes (0.50-0.94) and biases (-0.86-0.02). The repeatability experiment (n = 10 carcasses) showed that time to scan post quartering affected NIR measurement from 0 to 24 h (P < 0.001). On average, NIR IMF% was 0.97% lower (P < 0.001) at 24 h (4.01% ± 0.166), compared to 0 h. There was no difference (P > 0.05) between Time 0 and 1 h or Time 0 and 4 h or between replicate scans within each time point. This study demonstrated the SOMA NIR device could predict lamb chemical IMF% with moderate precision and accuracy, however additional work is required to understand how loin preparation, blooming and surface hydration affect NIR measurement.

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.

Potential of in-plant intramuscular fat predictions to enable sheep breeders to incorporate consumer preferences in breeding programmes

The inclusion of eating quality traits in sheep genetic improvement programmes is desirable. Intramuscular fat (IMF) plays a key role in ensuring consumer satisfaction when eating lamb, but genetic progress for IMF is constrained by a lack of routine data collection. This study investigated the potential for IMF predictor traits to substitute for measured IMF in genetic improvement programmes. Carcass and predicted IMF (near-infrared estimated IMF and marbling score) data were available on 10,113 New Zealand lambs, 1678 of which also had measured chemical IMF on a slice of M. longissimus lumborum on which the predictions of IMF had been made. Genetic antagonisms were observed between carcass lean traits and IMF. The genetic correlation between the predictors and measured IMF approached one, indicating that predictors of IMF can be used in genetic improvement programmes. Through using selection indexes, simultaneous increases in IMF and the existing terminal selection index are possible, provided all traits are measured. This study highlights the importance and potential of predicted IMF to achieve genetic improvement in traits of importance to consumers.

Rapid Nondestructive Prediction of Multiple Quality Attributes for Different Commercial Meat Cut Types Using Optical System

There are differences of spectral characteristics between different types of meat cut, which means the model established using only one type of meat cut for meat quality prediction is not suitable for other meat cut types. A novel portable visible and near-infrared (Vis/NIR) optical system was used to simultaneously predict multiple quality indicators for different commercial meat cut types (silverside, back strap, oyster, fillet, thick flank, and tenderloin) from Small-tailed Han sheep. The correlation coefficients of the calibration set (R c) and prediction set (R p) of the optimal prediction models were 0.82 and 0.81 for pH, 0.88 and 0.84 for L*, 0.83 and 0.78 for a*, 0.83 and 0.82 for b*, 0.94 and 0.86 for cooking loss, 0.90 and 0.88 for shear force, 0.84 and 0.83 for protein, 0.93 and 0.83 for fat, 0.92 and 0.87 for moisture contents, respectively. This study demonstrates that Vis/NIR spectroscopy is a promising tool to achieve the predictions of multiple quality parameters for different commercial meat cut types.

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.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

Partial least squares and machine learning for the prediction of intramuscular fat content of lamb loin

Given the paucity of lamb carcase grading tools, there is a distinct need for the development of rapid, non-destructive grading tools for Australian lamb carcases, particularly fat content given its importance to meat and eating quality. The aim of the current study was to determine the potential for Near Infrared (NIR) spectroscopy to predict IMF using Partial Least Squares (PLS) and machine learning analysis methods. As such, 299 lamb loins were measured using a NIR fibre optic device, a sample was excised for Soxhlet determination of IMF content and prediction models were created using either PLS or machine learning analyses methods. IMF prediction model outcomes were similar between analysis methods with an R² = 0.6 and RMSE = 0.84 and R² = 0.65 and RMSE = 0.72, respectively. This study highlighted that spectra from one slaughter varied greatly from the two succeeding slaughters and wavelengths selected between studies are not consistent.

Comparison of smartphone and lab-grade NIR spectrometers to measure chemical composition of lamb and beef

Context Near-infrared reflectance spectroscopy (NIRS) has been extensively investigated for non-destructive and rapid determination of pH and chemical composition of meat including water, crude protein, intramuscular fat (IMF) and stable isotopes. Smaller, cheaper NIRS sensors that connect to a smartphone could enhance the accessibility and uptake of this technology by consumers. However, the limited wavelength range of these sensors could restrict the accuracy of predictions compared with benchtop laboratory NIRS models. Aims To compare the precision and accuracy metrics of predicting pH, water, crude protein and IMF of three sample presentations and two sensors. Methods Fresh intact (FI) store-bought beef and lamb steak samples (n = 43) were ground and freeze-dried (FD), and then oven-dried to create freeze-dried oven-dried (FDOD) samples. All three forms of sample presentation (FI, FD, FDOD) were scanned using the smartphone and benchtop NIRS sensors. Key results The IMF was the best predicted trait in FD and FDOD forms by the smartphone NIRS (R2 &gt;0.75; RPD &gt;1.40) with limited differences between the two sensors. However, predictions on FI meat were poorer for all traits regardless of the NIRS scanner used (R2 ≤ 0.67; RPD ≤ 1.58) and not suitable for use in research or industry. Conclusion The smartphone NIRS sensor showed accuracy and precision comparable to benchtop NIRS to predict meat composition. However, these preliminary results found that neither of the two sensors reliably predicted quality attributes for industry or consumer applications. Implications Miniaturised NIRS sensors connected to smartphones could provide a practical solution to measure some meat quality attributes such as IMF, but the accuracy depends on sample presentation.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

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

In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.