The research, published in the Journal of Agriculture and Food Research, was led by Keisuke Soga and colleagues at Japan’s National Institute of Health Sciences and collaborating institutions. The team took soybean samples that had previously been analyzed for herbicide residues and subjected the very same material to a battery of event-specific real-time PCR tests. Their goal was exploratory but ambitious: to see whether the residue profile of glyphosate, glufosinate, and their metabolites could be matched to particular herbicide-tolerance traits in commercially distributed soybeans imported from the United States and Brazil.

The logic behind the approach rests on the biochemistry of herbicide tolerance. Glyphosate kills plants by inhibiting 5-enolpyruvylshikimate-3-phosphate synthase, or EPSPS, an enzyme essential for synthesizing aromatic amino acids through the shikimate pathway. Glyphosate-tolerant soybeans evade this attack by carrying a modified epsps gene, such as the cp4 epsps gene borrowed from the soil bacterium Agrobacterium tumefaciens strain CP4, which produces an enzyme the herbicide cannot bind. Glufosinate works differently: it blocks glutamine synthetase, causing toxic ammonia to accumulate and shutting down photosynthesis. Soybeans tolerate glufosinate by carrying the pat or bar gene, which encodes phosphinothricin N-acetyltransferase, an enzyme that acetylates the herbicide into N-acetyl-glufosinate, an inactive compound that no longer threatens the plant.

These mechanisms create a chemical fingerprint. A soybean carrying a modified epsps gene can survive glyphosate spraying and may retain detectable glyphosate residues, but it cannot convert glufosinate into its acetylated form. Conversely, a soybean carrying pat can metabolize glufosinate into N-acetyl-glufosinate, so the presence of that metabolite hints at a pat-containing event. The Japanese team reasoned that detecting glyphosate or the acetylated glufosinate metabolite in a shipment should statistically correlate with the presence of the corresponding transgenes, and that the strength of the correlation might even reveal which specific events dominate cultivation in the exporting country.

The sample set consisted of fifteen soybean lots: one edible soybean sample grown in Japan, eight feed samples from the United States, three non-GM samples from the United States imported under identity-preserved handling, and three feed samples from Brazil. Identity-preserved systems segregate non-GM soybeans from GM ones through documentation and traceability, though they cannot guarantee the complete absence of unintended commingling. The researchers ground 100 grams of each sample in a cryogenic mill, extracted genomic DNA, and screened for eleven GM events using primers and probes drawn from the European Union’s GMOMETHODS database and Japanese official detection methods, with the soybean lectin gene serving as an endogenous reference.

The qualitative results were striking. In the non-GM samples, GM events were essentially undetectable, with only a faint signal for event DAS-44406 in one sample. In the American feed samples, the team detected RRS, MON89788, FG72, A2704-12, A5547-127, and DAS-44406, while Brazilian feed samples were dominated by RRS, MON89788, and DAS-81419. The edible Japanese sample contained none of the tested events. Crucially, the events found in the herbicide-positive samples matched the residue chemistry: glyphosate-containing lots carried high levels of epsps-harboring events, and lots containing N-acetyl-glufosinate carried high levels of pat-harboring events.

Quantification sharpened the picture considerably. In American feed samples where glyphosate was detected, MON89788 ranged from 8.7 to 50.7 percent commingling, and DAS-44406, a stacked event carrying 2mepsps, pat, and aad-12 transgenes, reached an extraordinary 41.3 to 71.6 percent. In Brazilian feed samples, MON89788 dominated at 62.6 to 102.6 percent, with the value above 100 percent attributed to inherent measurement uncertainty in event-specific quantitative PCR rather than an impossible true percentage. Meanwhile, the American feed sample in which neither glufosinate nor its metabolite was detected showed low commingling for the pat-containing events, at 0.4 percent for DAS-44406 and 1.1 percent for A5547-127, reinforcing the link between the metabolite and the trait.

The authors interpret these patterns through the lens of regional agricultural practice. In the United States, herbicide use on soybeans has diversified markedly as growers battle glyphosate-resistant weeds. Department of Agriculture surveys show that in 2006 more than 50 million soybean acres were treated exclusively with glyphosate, but by 2012 that area had shrunk by roughly 20 million acres as farmers adopted tank mixes. By 2023, glyphosate isopropylamine remained the most widely used herbicide at 46 percent of planted acres, but 2,4-D choline salt followed at 37 percent, with glufosinate-ammonium at 23 percent. This diversification aligns neatly with the prominence of DAS-44406, whose stacked transgenes confer tolerance to glyphosate, glufosinate, and 2,4-D simultaneously.

Brazil tells a different story. No-tillage systems dominate Brazilian soybean production, and without plowing to control weeds, farmers lean heavily on herbicides; glyphosate was applied to roughly 95 percent of the soybean area by 2021. The insect-protected and glyphosate-tolerant stacked variety MON 87701 x MON 89788, introduced in the 2013/14 season, expanded from 1.2 million hectares to more than 30 million hectares, about 80 percent of national acreage by 2020/21. That near-total adoption explains why MON89788 commingling in Brazilian feed samples reached such extreme levels, while glufosinate-tolerant events remained scarce at 0.2 to 3.2 percent. The researchers note that as glyphosate-resistant weeds spread in Brazil, additional herbicide-tolerance traits may follow the American trajectory, making continued monitoring essential.

The study’s implications extend beyond curiosity. If herbicides or their metabolites appear in a product labeled non-GM, that could signal the presence of GM material and justify verification against labeling regulations, as illustrated by the glufosinate found in the identity-preserved American samples, which the authors attribute to cultivation practices, environmental sources, post-harvest handling, or commingling below the detection range of the genetic assays. Conversely, residue data can cross-check genetic findings, boosting overall reliability. The authors also point toward future work on 2,4-D and dicamba, whose tolerance events, DAS-44406 and MON87708 respectively, metabolize these auxin-mimicking herbicides into detectable signature compounds. Because the sample set was small, the team frames the work as exploratory rather than a quantitative correlation analysis, but the concept is clear: two regulatory silos, pesticide monitoring and GM surveillance, each hold half of a story that only makes full sense when the datasets are combined. Extending this integrated approach to maize and other crops could give food safety authorities a powerful, mutually validating tool for traceability and enforcement in global commodity chains.

Subject of Research: Association between herbicide residues and herbicide-tolerance GM soybean events in imported soybean samples

Article Title: Association between herbicide residues and herbicide tolerance-related genetically modified events in imported soybean samples

Article References: Soga, K., Yoshiba, S., Takabatake, R., Taguchi, T., Tsutsumi, T., Ito, R., Iwasaki, Y., Akiyama, H., & Shibata, N. (2026). Association between herbicide residues and herbicide tolerance-related genetically modified events in imported soybean samples. Journal of Agriculture and Food Research, 31, Article 103321. https://doi.org/10.1016/j.jafr.2026.103321

Image Credits: AI Generated

DOI: 10.1016/j.jafr.2026.103321

Keywords: glyphosate, glufosinate, genetically modified soybeans, herbicide tolerance, real-time PCR, food safety monitoring, maximum residue limits, MON89788, DAS-44406, identity preservation, Brazil soybean exports, United States soybean exports

The electronic nose concept was formalised by Gardner and Bartlett in 1988 as an instrument comprising multiple chemical sensors capable of detecting and classifying volatile compounds. Its logic mirrors mammalian olfaction: rather than identifying an odour with a single highly specific receptor, the device derives odour identity from the collective, multi-dimensional response of an array of ten to fifty partially overlapping sensors. Each sensor responds preferentially to a different subset of volatile organic compounds, so the ensemble produces a chemical fingerprint unique to a given aroma. Metal-oxide-semiconductor sensors dominate commercial platforms because they are cheap, broadly sensitive and reasonably stable, while organic field-effect transistors are gaining ground for miniaturised and wearable applications. A sampling system delivers the odour reproducibly, analogue-to-digital converters capture the signals, and a pattern-recognition engine compares the resulting vector against a trained reference database.

The analytical core of any e-nose is its data pipeline, and the review devotes careful attention to it. Raw sensor signals pass through pre-processing stages of normalisation, baseline correction, denoising and transient compression, steps that materially improve reproducibility and reduce variance from sensor drift. Classical statistical methods remain the workhorses: principal component analysis for exploratory visualisation, linear discriminant analysis for supervised class separation, partial least squares for quantifying concentrations, and SIMCA for flagging novel, out-of-class samples. When response surfaces are non-linear, artificial neural networks take over, and recent architectures have pushed performance further. Convolutional neural networks learn hierarchical temporal features directly from raw time-series sensor data without manual feature engineering, while hybrid discrete wavelet transform and long short-term memory models combine wavelet denoising with sequential modelling, outperforming conventional classifiers in beef quality monitoring.

Sensor array design itself has become an optimisation problem. Redundant or uninformative sensors waste computation, raise cost and inject noise into the feature space, so researchers increasingly apply feature selection criteria such as Fisher’s discriminant, t-statistics and minimum redundancy maximum relevance to identify the smallest sensor subset that retains maximum class separability. A tea-aroma detection system was optimised by combining correlation coefficient analysis with hierarchical clustering, and a multi-criteria evaluation framework using the Entropy Weight Method with TOPSIS lifted recognition accuracy for a carbon monoxide and methane gas mixture from 78.3 percent to 96.5 percent. The lesson generalises: a heterogeneous array in which every sensor contributes non-redundant chemical information beats a bigger, blunter one.

The application catalogue is broad and, in places, striking. An organic field-effect transistor e-nose detected spoilage-associated gases at bacterial loads of four times ten to the fourth colony-forming units per gram, far below consumption thresholds, enabling intervention before food becomes hazardous. Miniaturised systems built around MEMS metal-oxide arrays and CMOS circuitry now fit inside domestic refrigerators, monitoring odour changes continuously without sample removal. In meat, fusing e-nose data with Fourier-transform near-infrared spectroscopy improved identification of duck meat and beef combinations beyond the 83.3 percent achieved by the nose alone, and genetic-algorithm feature selection enabled detection of Salmonella typhimurium and Escherichia coli in fresh pork with a coefficient of determination of 0.989. In brewing, a thirteen-sensor prototype tracked alcohol content with a correlation of 0.888, and vertically stacked sensor arrays paired with two-dimensional convolutional networks authenticated beers at 99.3 percent accuracy, a direct answer to product counterfeiting.

Adulteration detection may be the technology’s most commercially compelling use case. A smart e-nose applying sample-slicing, normalisation and pattern recognition achieved a reported classification accuracy of 99.996 percent with a root mean square error of 0.02864 under laboratory conditions. Combined with support vector machines, e-nose systems identified pork adulteration in beef in forty seconds, a third of the time of conventional methods, and have exposed vegetable oil in raw milk, soybean and corn oil in sesame oil, and margarine in cow ghee. In edible oils more broadly, artificial neural network classifiers exceeded 97 percent efficiency, in-situ detection of aldehydes and ketones tracks secondary oxidation, and sensor responses correlated with peroxide values allow degradation phases to be identified without destructive chemical testing.

Seafood, plantation crops and dairy round out the portfolio. A portable seafood e-nose combining four metal-oxide sensors, two electrochemical cells and a photoionisation detector, paired with k-nearest-neighbour classification, achieved perfect accuracy in controlled species and quality grading, while a dedicated Shrimp-Nose device graded Pacific white shrimp with 95.73 percent validation accuracy. Low-cost e-noses have reached the field for fruit crops: a multilayer perceptron predicted kiwifruit soft rot with roughly 90 percent training accuracy before visible symptoms, and a grid-search-optimised support vector machine detected minor mechanical damage in strawberries at 0.84 accuracy. In coffee and tea, systems integrating principal component analysis, radial basis function networks and self-organising maps have classified fermentation and firing conditions almost perfectly, and artificial neural networks distinguish coffee roast levels and geographic origins above 90 percent while catching aroma deviations from mould or poor post-harvest handling.

But the review’s central argument is that headline accuracies are a poor measure of industrial readiness. The highest figures, above 99 percent, come almost exclusively from tightly controlled problems such as laboratory-prepared adulteration models and beer authentication, where classes, matrices and environments are fixed. Performance becomes far less certain when a system meets new production batches, different sensor units, seasonal volatile shifts, fluctuating humidity or heterogeneous supply chains. Sensor drift, the temporal fluctuation of response as sensors age and foul, undermines reproducibility and forces recalibration; metal-oxide sensors are acutely humidity-sensitive; and models trained on narrow datasets overfit, excelling in the lab and failing in the plant. Calibration transfer, adaptive learning, domain adaptation and online model updating are the emerging remedies, but the authors note a shortage of public long-term drift datasets and standardised reporting protocols, making it hard to compare strategies or set realistic expectations. Regulatory acceptance adds another hurdle, since food-quality decisions demand traceability and validated performance across independent batches, sensors and sites, not merely high accuracy from random train-test splits.

The realistic near-term role, the review concludes, is hybrid: e-noses as rapid, non-destructive, low-cost screening tools that triage samples toward confirmatory techniques such as gas chromatography-mass spectrometry, high-performance liquid chromatography or trained sensory panels, which remain the regulatory reference standard. Four converging trajectories could shift that balance. Deep learning, including domain-adversarial networks, active learning and few-shot meta-learning, promises models that adapt to new sensor conditions without full recalibration, though explainable AI and uncertainty estimation will be essential for regulatory trust. Internet-of-things architectures are turning standalone instruments into continuous monitoring nodes, with WiFi-connected brewery units and refrigerator-embedded sensors as early demonstrations and edge-AI chips poised to cut latency. Nanomaterial sensing elements based on graphene oxide, carbon nanotubes and metal-organic frameworks offer detection limits orders of magnitude below conventional metal-oxide sensors. And multi-sensor fusion, combining e-nose smell with e-tongue taste and e-eye vision, exploits physically distinct transduction mechanisms that are unlikely to drift or fail simultaneously, yielding fingerprints that generalise better across batches and sites. If those pieces mature together, the machine nose may finally earn its place as a cornerstone of intelligent, automated food quality assurance, extending eventually into clinical diagnostics and environmental monitoring.

Subject of Research: Electronic nose technology for food and agricultural quality assessment

Article Title: Recent advances and challenges in electronic nose technology for agricultural commodities and food quality assessment

Article References: Maneysha, R. V., Sanjani, V., & Suresh, K. (2026). Recent advances and challenges in electronic nose technology for agricultural commodities and food quality assessment. Discover Agriculture, 4(1), Article 307. https://doi.org/10.1007/s44279-026-00789-9

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00789-9

Keywords: electronic nose, food quality, food safety, volatile organic compounds, machine learning, sensor drift, food adulteration, spoilage detection, metal-oxide semiconductors, deep learning, sensor fusion, IoT

The stakes are enormous. Globally, roughly one-third of all food produced is lost or wasted, according to Fumihiko Tanaka, professor at Kyushu University’s Faculty of Agriculture and a senior author of the study. That waste carries a heavy climate cost as well, because food loss accounts for about 8 percent of global greenhouse gas emissions, a figure approaching the roughly 10 percent attributed to road transport. Any technology that shaves even a fraction off those numbers would have measurable environmental and economic consequences.

Not all food waste happens for the same reason. Some loss is physical: produce gets crushed, torn, or bruised during distribution, and damaged packaging accelerates decay. But a surprisingly large share comes from unnecessary disposal, when food is thrown away before it has actually spoiled. Inventory turnover pressures and conservative printed dates often overrule the real condition of the food itself. The researchers argue that drawing a sharper line between food that is starting to deteriorate and food that is genuinely inedible could prevent a meaningful portion of that premature discard.

The heart of the proposed framework is a closed loop of four stages: recognition, judgment, actuation, and feedback. Recognition begins with the packaging material itself. Sensors embedded in the film act like eyes, detecting the pH shifts, gases, and microbial byproducts that accompany spoilage. Among the strongest candidates for this sensing role are natural pigments such as anthocyanins, the compounds that give purple sweet potatoes their color. These pigments change hue as pH changes, providing a readable signal at every stage of decay. In spoiling meat, for example, alkaline gases accumulate as microbes multiply, and an anthocyanin-based film shifts continuously from purple-red to yellow-green, offering a visual proxy for the food’s declining condition.

Sensing alone, however, is fragile in the rough world of real distribution. Light and heat can cause false readings, and a bump or scratch can interrupt a color-based signal entirely. Xirui Yan, a JSPS researcher at Kyushu University, emphasizes that reliability must be engineered into the material rather than hoped for. One strategy the team has explored is anchoring the pigments within metal-organic frameworks and carbon quantum dots, which stabilize the color-changing compounds against environmental interference. The group has also added self-healing capacity to its films, so that minor damage does not permanently disable the sensing function. A film that keeps working after being scratched is far more valuable in a warehouse or a shipping container than one that fails at the first knock.

Once the material has captured a signal, artificial intelligence takes over the interpretation. In the system the researchers envision, the film converts optical changes and odor-related signals into electrical data, which a connected device then reads and analyzes. Machine learning models trained on these patterns could distinguish harmless early changes from genuine spoilage and decide what should happen next. Possible responses range from releasing antimicrobial agents to slow decay, to sending alerts through a supply chain, to triggering logistical rerouting so that perishable goods reach consumers before quality slips past the point of no return. Yan compares the process to giving produce a full check-up: the film collects the signals, the AI analyzes them, and together they report the food’s condition and recommend the next step.

The team’s ambitions stretch well beyond a single smart package. Different foods spoil in fundamentally different ways, and even closely related products decay at different rates; fruit, meat, and seafood each release their own chemical signatures, and different species of fish break down on different schedules. By tracking the compounds each food emits as it deteriorates, the sensing film captures unique spoilage patterns that AI can learn. Accumulated over many shipments and storage cycles, that data could help material designers and food producers tailor packaging solutions to specific products rather than relying on one-size-fits-all barriers.

That deeper understanding of deterioration could also reshape how food is sold and distributed. Working with local governments and logistics partners, the Kyushu team is exploring ways to grade produce by how well it withstands storage and transport. Items with short shelf lives would be routed to local markets where they can be sold quickly, while hardier varieties are reserved for export, cutting losses by matching each product’s tolerance to the journey it must take. At the consumer end, the same intelligence could be delivered through a simple smartphone scan, giving shoppers instant, readable information about whether the food in front of them is still fresh, rather than forcing them to trust a printed date alone.

Significant hurdles remain before such systems reach supermarket shelves. Long-term safety and stability assessments are essential for any material intended to touch food, and this is particularly true for certain nanomaterials used to stabilize and enhance sensing films. Consistent quality control at industrial scale also poses challenges that laboratory prototypes do not face. The researchers are candid that their review is a directional document rather than a finished product, an attempt to unify three research streams, intelligent sensing, self-healing materials, and AI-driven prediction, that have until now developed largely in isolation.

Fanze Meng, the paper’s first author and a postdoctoral researcher at Kyushu University, frames the effort as an invitation to the wider research community. The goal, the team says, is to set a direction that others can improve upon, because if enough researchers move together, the work becomes a beam of light, then a path, and eventually that path could lead from the laboratory to something real. If they are right, the humble wrapper around a piece of fruit may one day become an active guardian of the food supply, capable of seeing, judging, and acting, and helping the world waste far less of what it grows.

Subject of Research: AI-enabled, self-healing sensing materials for active food packaging to reduce food waste

Article Title: Toward future-ready food packaging where materials meet AI

Article References: Toward future-ready food packaging where materials meet AI. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: food packaging, food waste, artificial intelligence, smart packaging, self-healing materials, anthocyanins, pH sensors, spoilage detection, metal-organic frameworks, supply chain, greenhouse gas emissions, Kyushu University