Robustness of hyperspectral imaging and PLSR model predictions of intramuscular fat in lamb M. longissimus lumborum across several flocks and years

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.

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection.

Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications

As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.

Fortification of diets with omega-3 long-chain polyunsaturated fatty acids enhances feedlot performance, intramuscular fat content, fat melting point, and carcass characteristics of Tattykeel Australian White MARGRA lambs

Meat eating quality indices such as intramuscular fat content (IMF) and fat melting point (FMP) of the Longissimus thoracis et lumborum muscle and the feedlot performance, carcass traits, and commercial wholesale cuts of lot-fed Tattykeel Australian White (TAW) MARGRA lambs as a result of dietary fortification of the diet with omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA) were evaluated. A total of 75 TAW MARGRA lambs at 6 months of age with an average liveweight of 30 ± 1.2 kg were used. The lambs were randomly allocated to the following three dietary treatments of 25 lambs each in a 47-day feeding trial using a completely randomized experimental design: (1) control diet of hay plus pellets without omega-3 oil, (2) hay plus commercial whole grain pellets (MSM) without omega-3 oil, and (3) hay plus pellets fortified with omega-3 oil. It was hypothesized that dietary supplementation with omega-3 fortified pellets will improve feedlot performance, meat-eating quality indices of IMF, FMP, and carcass characteristics. Lot-fed lambs on the MSM whole grain had the highest feed intake of 1.69 kg/day, followed by the control at 1.57 kg/day and the lowest in the omega-3 diet at 1.01 kg/day (p = 0.0001). However, the omega-3 diet had the highest average daily gain of 230 g/head/day (p = 0.0001), indicating the greatest feed efficiency since it had the best growth response with minimal feed intake. Post-slaughter evaluation of the Longissimus thoracis et lumborum muscle revealed significant treatment variations in IMF (p = 0.0001), FMP (p = 0.0001), pH (p = 0.0380), and wholesale French rack primal cut (p = 0.0001). Strong correlations (p < 0.05) between liveweight, temperature, pH, FMP, and IMF were observed. Similarly, significant correlations between carcass characteristics of total saleable meat yield, lean trim, fat trims, bones, and leg shank were evident (p < 0.05). However, there were no treatment differences in the final liveweight, GR fat depth, hot standard carcass weight, or dressing percentage. The findings indicate that feedlot performance, meat-eating quality traits such as IMF and FMP, and commercial wholesale French rack cuts can be further improved during feedlot finishing of TAW MARGRA lambs through dietary supplementation with omega-3 oils, and hence the tested hypothesis of improved meat quality attributes is partially confirmed.

The application of computer vision systems in meat science and industry - A review

Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisition devices make it technically feasible to obtain information about both the external features and internal structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used in research related to assessing the composition of animal carcasses, which might help determine the impact of cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are under constant development an improvement. The present review covers computer vision system techniques, stages of measurements, and possibilities for using these to assess carcass and meat quality.

Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends

Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues.

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HSI has shown promising early signs in the non-destructive analysis of meat quality and safety.

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Hyperspectral imaging (HSI) is now widely applied in research studies for different meat products with the help of machine learning methods.

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With a fully adapted factory setting and robust machine learning of HSI, the inspection coverage can reach 100% of the target meat.

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HSI can change the meat industry to become from reactive to predictive when facing issues, this will be translated into fewer recalls, less meat fraud, and less waste.

Prediction of carcass composition and meat and fat quality using sensing technologies: A review

Consumer demand for high-quality healthy food is increasing, &nbsp;thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.

Objective measurement technologies for transforming the Australian & New Zealand livestock industries

This paper introduces the special edition of Meat Science focused upon the development, calibration and validation of technologies that measure traits influencing meat eating quality, or carcass fat and lean composition. These papers reflect the combined research efforts of groups in Australia, through the Advanced Livestock Measurement Technologies project, and New Zealand through AgResearch. We describe the various technologies being developed, how these devices are being trained upon common gold-standard measurements, and how their outputs are being simultaneously integrated into existing industry systems. We outline how this enhances the industry uptake and adoption of these technologies, and how this is further accelerated by education programs and strategic industry investment into their commercialisation.