The problem with traditional evaluation is structural rather than technical. Physicochemical analyses of pH, water-holding capacity and lipid oxidation, microbiological cultures that require days of incubation, and Warner–Bratzler shear force tests that destroy the very sample being judged are all accurate but slow, labor-intensive and fundamentally incompatible with a processing line moving thousands of carcasses per hour. An abnormal postmortem pH decline, for example, can produce pale, soft, exudative meat or dark, firm, dry meat, both of which damage appearance, processing yield and shelf life, yet conventional pH probes sample only tiny regions of a carcass. Water-holding capacity, which governs juiciness and drip loss, is measured destructively. Tenderness is assessed by cutting. By the time results arrive, the product has often already been shipped.

Nondestructive sensing technologies were the first step toward solving this, and the review catalogs them in detail. Near-infrared spectroscopy exploits the absorption of radiation by C–H, O–H and N–H molecular bonds, making it possible to estimate moisture, protein and fat content in seconds. Raman spectroscopy probes molecular vibrations to reveal protein secondary structure, lipid composition and water distribution in muscle, all of which correlate with tenderness and water-holding capacity. Hyperspectral imaging, the technology the authors treat as most promising, merges spectroscopy with photography: each pixel of an image carries hundreds of spectral bands, so a single scan can map pH, color, tenderness and intramuscular fat simultaneously across an entire cut. Electronic noses detect the volatile organic compounds emitted by growing bacteria and oxidizing lipids, while electronic tongues mimic taste perception to track flavor changes during storage.

What these sensors produce, however, is not an answer but a deluge. A hyperspectral image of a single beef steak can contain hundreds of thousands of spectral measurements, far too many for classical statistics to disentangle. This is where machine learning enters. Support vector machines construct optimal separating hyperplanes and, through kernel functions, capture nonlinear relationships in spectral data; they have been used to detect adulteration, differentiate meat species and predict microbial spoilage from electronic nose signals. Random forests build ensembles of decision trees and, crucially, can rank which variables matter most, a feature the review highlights for identifying influential quality markers in high-dimensional datasets. K-nearest neighbors offers a simpler instance-based approach widely applied to species identification and freshness classification, though it degrades as feature counts grow.

Deep learning pushes the capability further. Convolutional neural networks automatically extract spatial features from images using convolutional filters, and the review describes their use in automated carcass grading, marbling detection and authentication, including a study that identified duck meat adulteration in beef and lamb slices from digital photographs alone. Artificial neural networks, the older and simpler architecture, remain workhorses for predicting lipid oxidation and other physicochemical properties from spectral inputs. Hybrid strategies are increasingly favored: principal component analysis compresses hyperspectral data before modeling, and deep networks serve as feature extractors feeding into support vector machines or random forests. Notably, the review cautions that fancier is not always better, citing cases where partial least squares regression outperformed deep learning on hyperspectral data depending on how features were extracted.

The applications described read like a quality-control wish list being checked off in real time. Hyperspectral imaging paired with regression models has predicted tenderness in lamb and beef nondestructively, sidestepping the destructive shear force test, though the authors note that postmortem muscle transformation remains biochemically complex enough to keep prediction challenging. XGBoost models analyzing carcass images can estimate marbling scores and classify quality grades with accuracy rivaling trained inspectors, removing a major source of subjectivity from grading. Electronic noses coupled with support vector machines have classified freshness levels and estimated remaining shelf life from spoilage volatiles, and when such models incorporate temperature and humidity data through Internet of Things networks, shelf life becomes a dynamic, supply-chain-wide prediction rather than a static label date.

Food safety and authenticity emerge as perhaps the most consequential applications. Conventional microbiological testing requires incubation periods of hours to days, useless for rapid decisions on a processing line. Hyperspectral imaging combined with machine learning classifiers can detect the biochemical signatures of microbial growth, and electronic noses have identified spoilage-related volatile compounds produced by bacteria such as Pseudomonas and Brochothrix thermosphacta. On the fraud front, Fourier-transform near-infrared spectroscopy with chemometric analysis has accurately detected turkey adulteration in minced beef, and hyperspectral imaging has differentiated meat species, offering rapid alternatives to DNA-based methods. The review is careful to note the limits: microbial signals are hard to separate from the intrinsic variability of the meat itself, and authentication models are only as reliable as the reference datasets behind them.

The industrial vision extends beyond the laboratory into what the authors frame as Industry 4.0 for meat. Smart processing plants would integrate sensor networks, computer vision and machine learning to monitor quality continuously, adjusting chilling, aging and packaging parameters in real time. Digital twin technology, still nascent in the food sector, would create virtual replicas of physical processing systems fed by live sensor data, allowing a plant to simulate, for instance, how a temperature fluctuation during storage will accelerate microbial growth and intervene before quality deteriorates. The review also points toward generative artificial intelligence, which could simulate processing conditions or design novel product formulations with optimized sensory and nutritional profiles, including alternative protein systems, though it acknowledges this frontier remains in its infancy.

What keeps the review from being pure boosterism is its frank accounting of the obstacles. Meat is biologically heterogeneous, and most published models are trained on small laboratory datasets that fail to capture the noise of a real plant, so robustness suffers. High-dimensional sensor data demand intensive preprocessing, and mishandled preprocessing introduces bias. Deep learning’s black-box nature clashes with regulatory contexts where transparency about contamination or adulteration decisions is mandatory, which is why the authors call for explainable AI tools such as SHAP and LIME and for hybrid models that balance predictive power with statistical accountability. Infrastructure costs, sensor calibration, data interoperability and workforce training round out the practical barriers. The authors’ conclusion is measured but ambitious: machine learning is already transformative for predicting tenderness, marbling, shelf life and microbial safety, but its full promise depends on standardized datasets, interpretable models and the digital plumbing of next-generation smart factories. If those pieces fall into place, the steak on your plate may one day carry a quality verdict rendered not by a lab technician with a knife, but by an algorithm that saw it coming weeks earlier.

Subject of Research: Machine learning applications for prediction and quality control of meat and meat products

Article Title: Machine learning–based prediction and quality control of meat and meat products: applications for intelligent quality management in the meat industry

Article References: Kim, Y.-J., Kwon, H. C., Cha, J. Y., Lee, S., Kim, T.-K., Kang, M.-C., Park, M. K., & Choi, Y.-S. (2026). Machine learning–based prediction and quality control of meat and meat products: applications for intelligent quality management in the meat industry. Food Science of Animal Resources, 46(1), Article 85. https://doi.org/10.1007/s44463-026-00085-6

Image Credits: AI Generated

DOI: 10.1007/s44463-026-00085-6

Keywords: machine learning, meat quality, hyperspectral imaging, food safety, spectroscopy, electronic nose, deep learning, shelf life prediction, food authenticity, digital twin, Industry 4.0, meat industry

The researchers, Anthony Pius Bassey, Wangang Zhang, and Guanghong Zhou of Nanjing Agricultural University’s State Key Laboratory of Meat Quality Control and Cultured Meat Development, synthesized evidence from systematic searches across Scopus, Web of Science, PubMed, and CAB Abstracts, supplemented by policy documents and technical reports. Their geographic focus centered on China, Japan, South Korea, Singapore, Thailand, and Vietnam, the countries with the richest peer-reviewed literature, though they acknowledge that South Asia and smaller Southeast Asian nations remain underrepresented in the evidence base. What emerges is a portrait of an industry under compound pressure from four directions at once: environmental degradation, biological fragility, resource dependence, and eroding consumer trust.

The environmental toll of concentrated animal feeding operations has moved from peripheral concern to the center of regulatory and public conflict. Massive volumes of animal manure create nutrient hotspots that seep into groundwater, and in Thailand’s Chao Phraya River Basin, dense concentrations of poultry and swine farms have been repeatedly linked to eutrophication and fish kills driven by nitrogen- and phosphorus-rich runoff. Slaughterhouses compound the problem: in the Indian city of Chennai, a typical facility generates 8 to 10 tonnes of waste daily, and untreated effluent discharged into sewers or water bodies can create anaerobic dead zones that devastate aquatic life. In Vietnam’s Mekong Delta, expanding farms have degraded air quality through ammonia and hydrogen sulfide emissions, causing odor pollution and health worries for nearby communities. These impacts are steadily eroding the social license of intensive livestock production across the region.

Biological vulnerability may be the most dramatic weakness. The hyper-intensification of Asian livestock has created what the authors describe as a perfect epidemiological storm, in which extreme animal density amplifies pathogens and long-distance transport networks ferry disease across borders with ease. The 2018 African Swine Fever panzootic demonstrated the stakes catastrophically: China’s swine herd fell by roughly 40 percent, and the outbreak inflicted an estimated 0.78 percent loss in national GDP in 2019. The crisis was not a random accident but, the review argues, a direct consequence of the system’s own operational logic. The very concentration and connectivity that generated efficiency became its greatest liability, exposing a biosecurity weakness that threatened the entire region’s food supply.

Resource dependence adds a geopolitical dimension. China imported around 105 million metric tonnes of soybeans in 2024, with Brazil supplying roughly 76 percent of that total on average over recent years, and drought-driven price spikes in 2020 and 2021 significantly raised costs for Chinese hog farmers. Japan, which imports nearly all of its feed corn from the United States, saw feed costs hit a decade high after the 2022 surge in global corn prices triggered by the war in Ukraine, pushing numerous mid-sized operators into bankruptcy. Vietnam’s rapidly expanding pork and aquaculture sectors depend critically on imported soy and fishmeal, a vulnerability exposed when pandemic-era freight costs spiked and producers lost export market share. In Indonesia, fewer than 20 million cattle scattered across thousands of islands cannot keep pace with demand. The review concludes that this reliance on foreign inputs has transformed the meat industry from a purely economic sector into a matter of national security.

Consumer trust, meanwhile, has been battered by a history of food safety scandals. China’s 2008 melamine contamination of infant formula and the 2015 “Zombie Meat” scandal involving long-expired frozen products continue to shape perceptions. In India, a Food Safety and Standards Authority investigation found significant proportions of meat samples from processed food outlets contained pork or horse DNA despite being labeled as chicken or mutton. Vietnam has seen repeated seizures of smuggled and chemically treated meat, while Malaysian importers have flagged safety concerns over pork shipments from African Swine Fever-affected Thailand. These scandals, the authors argue, are symptoms of systemic oversight failures in opaque industrialized supply chains, and their cumulative effect is pushing consumers toward alternatives promising better traceability, ethics, and safety.

Against this backdrop, three technological niches are reshaping the industry. Precision livestock farming deploys artificial intelligence, Internet of Things sensors, and blockchain to monitor animal health and supply chains. In Japan, where aging farmer populations strain conventional husbandry, Fujitsu has partnered with the government on an AI system that analyzes video footage of Wagyu cattle to detect subtle changes in gait, behavior, and feeding that signal early disease or stress. India’s Licious platform runs a vertically controlled farm-to-fork model with IoT-enabled cold-chain monitoring, while Stellapps Technologies uses smart wearable collars to track the activity, rumination, and health of millions of cattle. After the ASF crisis, Walmart China implemented blockchain-based pork tracking, recording farm origin, slaughter date, processing batch, and logistics on an immutable ledger that consumers can verify by scanning a QR code.

Circular economy innovations are turning waste liabilities into assets. Since 2020, China’s National Development and Reform Commission has mandated biogas digesters on large-scale livestock and poultry farms, capturing methane from manure for electricity and heat while converting nutrient-rich digestate into organic fertilizer. Across Southeast Asia, black soldier fly larvae are being used to upcycle organic waste into protein meal for aquaculture and poultry feed; a Philippine startup called Insiklo converts roughly 500 kilograms of household and market waste daily from Los Baños municipality, and a vertical modular setup boosted its conversion yield fivefold compared with traditional concrete beds. In Vietnam, projects supported by the International Rice Research Institute promote larvae processing of rice bran and residues into protein for small-scale aquaculture, cutting feed costs and improving farm-level circularity.

Biological alternatives face the steepest technical hurdles, particularly in a region defined by demanding culinary traditions. Asian cooking techniques such as stir-frying, braising, and high-heat wok grilling impose specific structural requirements, and regional dishes prize the umami richness delivered by glutamic acid and inosine-5′-monophosphate. Comparative analyses show cultivated meat falls short on both counts, with glutamic acid lower in cultivated chicken and IMP markedly reduced in both chicken and cattle tissues compared with conventional meat. Plant-based proteins carry volatile compounds such as hexanal and 1-octen-3-ol that produce “beany” and “grassy” off-flavors, though heme proteins like leghemoglobin can bind and neutralize them through hydrogen bonding and hydrophobic interactions. Nutritional analyses of 27 animal and alternative products in Asian markets found alternatives generally lower in lysine and methionine and less digestible. Hybrid strategies offer a promising compromise: research incorporating 10 percent cultured porcine fat into plant-based meatballs expanded fatty acid diversity from 20 to 26 types and produced taste profiles closest to conventional meat.

Commercial momentum is nonetheless accelerating. Singapore’s 2020 regulatory approval of cultivated meat created a global first, and its “30 by 30” food security goal is aggressively catalyzing alternative protein ecosystems. China’s Joe’s Future Foods has completed 2,000-liter pilot production of cultivated meat, Hong Kong’s OmniFoods has introduced plant-based pork through food service partnerships, and South Korean startups Cellmeat and Space F have developed cultured seafood and pork prototypes. Companies such as Singapore’s Ants Innovate are engineering cell-based ingredients for dumplings and grilled skewers, while Karana reformulates jackfruit products for halal certification across Muslim-majority markets. The review’s central insight is that this transition succeeds or fails on coordination among three mutually reinforcing forces: corporate-led investment providing capital and market access, state-backed orchestration opening regulatory windows, and digital intermediation integrating value chains. That hybrid model carries inherent tensions, between corporate consolidation and equitable access, technological efficiency and smallholder livelihoods, environmental metrics and cultural legitimacy, and the authors warn that whether Asia’s reconfigured meat system proves sustainable, resilient, and fair will depend on how deliberately those contradictions are managed.

Subject of Research: Sustainability transitions and technological reconfiguration of the meat industry in Asia

Article Title: Technological landscapes and sustainable transitions: reconfiguring the meat industry in Asia

Article References: Bassey, A. P., Zhang, W., & Zhou, G. (2026). Technological landscapes and sustainable transitions: reconfiguring the meat industry in Asia. Food Science of Animal Resources, 46(1), Article 94. https://doi.org/10.1007/s44463-026-00078-5

Image Credits: AI Generated

DOI: 10.1007/s44463-026-00078-5

Keywords: meat industry, Asia, sustainable transitions, precision livestock farming, cultivated meat, circular economy, food security, African Swine Fever, blockchain traceability, plant-based alternatives, consumer trust, halal certification