The biochemical basis of spoilage is well understood, and the review uses it as a map of what sensors should be hunting. Refrigerated meat is dominated by spoilage microorganisms such as Pseudomonas, Shewanella, Brochothrix thermosphacta, and lactic acid bacteria, which metabolize carbohydrates, amino acids, and lipids into ammonia, hydrogen sulfide, short-chain fatty acids, alcohols, and biogenic amines like putrescine and cadaverine. These metabolites generate volatile organic compounds, or VOCs, that accumulate in the package headspace and are among the earliest detectable chemical signatures of decline. Meanwhile, lipid and protein oxidation produce aldehydes and ketones responsible for rancid flavors, and the conversion of oxymyoglobin to metmyoglobin drives the discoloration that shoppers instinctively reject. Endogenous enzymes play a subtler role, releasing substrates that accelerate microbial growth and shifting pH, water-holding capacity, and texture along the way.

Crucially, the review emphasizes that these pathways are interconnected and non-linear. Enzymatic degradation feeds microbes with fresh substrates, microbial metabolism alters pH and redox conditions that in turn modulate oxidation kinetics, and the whole system behaves differently in beef than in poultry, and differently again in fish. This is why the authors argue that no single marker can reliably capture freshness. Total viable counts correlate only weakly and inconsistently with sensory rejection, and indices like pH or total volatile basic nitrogen each reflect just one facet of a multifactorial process. The practical implication is that effective monitoring increasingly depends on multi-parameter sensing, sensor arrays, and data-fusion strategies that integrate complementary signals rather than betting on one.

The conventional toolkit, for all its regulatory weight, is poorly suited to this dynamic picture. Sensory evaluation is subjective, fatigued by assessor variability, and typically flags spoilage only after microbial loads have surpassed acceptable levels. Physicochemical indices such as TVB-N and TBARS require destructive sampling and hours of laboratory processing, and they lack universal thresholds across species and packaging systems. Microbiological culture, the gold standard for safety verification, takes 24 to 72 hours to yield a result, an eternity in fast-moving logistics. These methods were designed to confirm spoilage after the fact, the review notes, not to manage freshness proactively across storage, transport, and retail.

Into this gap have rushed the emerging technologies. Electronic noses sniffing VOC profiles report classification accuracies of 85 to 98 percent, electronic tongues and biosensors detect amines and hypoxanthine with high specificity, and colorimetric indicators embedded in packaging shift color as volatile amines accumulate. Hyperspectral imaging achieves R-squared values of 0.85 to 0.98 against spoilage indices, while infrared and Raman spectroscopy can detect molecular changes before they become visible or smellable. Yet the review delivers a sobering caveat: most of these figures come from controlled laboratory conditions with constant temperature, fixed humidity, artificially inoculated samples, and small single-batch datasets validated by internal cross-validation rather than independent external testing. The 85 to 98 percent range, the authors warn, should be read as evidence of laboratory feasibility, not field readiness.

Sensor drift, cross-sensitivity, and calibration instability, rather than detection limits, emerge as the dominant barriers to industrial adoption. In real cold chains, sensors face fluctuating temperatures, mechanical vibration, and shifting headspace composition that validation studies rarely reproduce. The review suggests that sensor-based systems may be most useful when treated as relative-change instruments tracking freshness evolution over time, rather than devices claiming absolute freshness measurements. Intelligent packaging faces parallel challenges: indicator responses can be skewed by ambient humidity and headspace gas composition, colorimetric readings depend on lighting and human color perception, and printing indicators onto film at production-line speeds remains an unsolved engineering problem. The authors also draw a sharp distinction between active packaging, which intervenes chemically to slow spoilage, and intelligent packaging, which merely reports on it, noting that many studies conflate the two and overstate the impact of color-change labels.

The digital layer is where the review sees the most transformative potential. Internet-of-Things networks can stream environmental and freshness data in near real time, machine-learning models can convert complex sensor arrays into predicted remaining shelf life, and smartphones can standardize the interpretation of colorimetric labels using built-in cameras, reducing the subjectivity that has long undermined visual indicators. Smartphone-readable freshness labels and dual-modal colorimetric-fluorescent systems have already been demonstrated as low-cost formats compatible with existing packaging lines. But machine-learning performance comes with its own traps: many published models rely on fewer than 100 samples, and accuracies of 85 to 97 percent often collapse when applied across different meat species, packaging formats, or temperature-abuse scenarios. The review calls for standardized benchmarking protocols, transparent reporting of training and validation splits, and large-scale field trials before predictive systems can be trusted.

Beyond the laboratory, the translational hurdles are economic, regulatory, and even environmental. Sensor arrays, wireless modules, and smart packaging elements raise per-unit costs that small and medium processors may not recoup, and few studies account for cost-benefit trade-offs or maintenance. Regulatory approval for packaging incorporating nanomaterials or migrating chemical indicators demands migration testing and toxicological assessment that early-stage research routinely ignores. There is also an uncomfortable irony: smart packaging promoted as a waste-reduction solution may worsen environmental impact if electronic or non-biodegradable components lack end-of-life planning. The review insists that life-cycle analysis and early engagement with regulators must become design requirements, not afterthoughts, and that freshness outputs must match what each stakeholder actually needs, from quantitative shelf-life scores for processors to traffic-light warnings for retailers and temperature-threshold alerts for logistics operators.

The review’s ultimate contribution is conceptual rather than technical: it reframes meat freshness monitoring as a systems-level challenge spanning sensing, data analytics, and decision-making. The future, the authors argue, lies not in discovering new spoilage markers but in integrating existing ones into robust, redundant, and interpretable architectures, combining VOC profiling with optical signals and temperature history to span the full range of spoilage pathways. They call for shared data schemas so that heterogeneous sensors can talk to enterprise systems, edge computing to reduce latency and cloud dependence, and externally validated predictive models that estimate remaining shelf life and recommend concrete interventions such as rerouting, discounting, or withdrawal. One caution runs throughout: freshness is not safety. Meat contaminated with Salmonella, Listeria, or pathogenic E. coli can appear perfectly fresh, so freshness-monitoring systems must complement, never replace, targeted pathogen testing. If the field can close the gap between laboratory accuracy and cold-chain reality, the payoff is substantial: less food waste, sharper supply-chain decisions, and a food system that finally knows, in real time, how fresh its meat really is.

Subject of Research: Emerging sensing, packaging, and digital technologies for monitoring meat freshness across the supply chain

Article Title: Meat Freshness Monitoring in the Modern Supply Chain: Analytical Advances, Smart Packaging, and Digital Integration

Article References: Rayhan, M. A., Nabi, M. H. B., Rahman, T., Mia, M. S., & Zzaman, W. (2026). Meat Freshness Monitoring in the Modern Supply Chain: Analytical Advances, Smart Packaging, and Digital Integration. Food Science & Nutrition, 14(10), Article e72450. https://doi.org/10.1002/fsn3.72450

Image Credits: AI Generated

DOI: 10.1002/fsn3.72450

Keywords: meat freshness, food spoilage, electronic nose, intelligent packaging, hyperspectral imaging, machine learning, Internet of Things, volatile organic compounds, cold chain, food safety, smartphone sensing, shelf-life prediction

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