Intelligent Gas Sensors for Food Safety and Quality Monitoring: Advances, Applications, and Future Directions
Abstract
1. Introduction
2. Gas Sensor Principles and Types
2.1. Metal-Oxide Semiconductor (MOS) Sensors
2.2. Electrochemical Sensors
2.3. Optical Sensors
2.3.1. Colorimetric
2.3.2. Fluorescence Sensor
2.4. Conducting Polymer Sensors
2.5. Sensor Array
3. Applications in Food Quality and Safety
3.1. Spoilage and Freshness Monitoring
3.2. Authenticity and Adulteration Detection
3.3. Profiling and Process Optimization
Sensor Type | Sample | Application | Analyte | Study Duration | Result | Reference |
Colorimetric Film | Pork | Quality evaluation | Amine | 60 h | Showed distinct color changes at different spoilage stages | [33] |
Colorimetric solid-state sensor | Veal, chicken, fish | Spoilage detection | Ammonia | 5 days | Developed sensor with LOD of 0.02 ppm | [34] |
Colorimetric sensor array | Wheat | Spoilage detection | VOCs | Mold infection in wheat | [68] | |
Colorimetric sensor array | Chilled beef | Freshness monitoring | Trimethylamine | 18 days | Developed sensor with LOD of 8.02 ppb | [35] |
Colorimetric sensor array | Rice | Freshness evaluation | Alcohols, aldehydes, alkenes, alkanes, ketones, organic acid, heterocyclic compounds | - | Discrimination of aged and fresh rice (100%) | [69] |
Colorimetric Film | Milk, fish | Spoilage detection | VOCs | 9 days | Biomaterial based edible and pH-sensitive film | [70] |
Colorimetric Film | Pork, chicken, salmon, and shrimp | Spoilage detection | Ammonia, dimethylamine, and trimethylamine | 7 days | LODs were determined to be 0.26 μM for NH3, 0.24 μM for DMA, and 0.38 μM for TMA | [71] |
Colorimetric Film | Beef | Spoilage detection | Ammonia | 8 days | Developed a photothermally stable and ammonia-responsive film | [72] |
(001)TiO2/MXene sensor | Fish, pork and shrimp | Quality monitoring | Ammonia | 36 h | Developed sensor with LOD of 156 ppt | [36] |
NiCo2O4-ZnO sensor | - | Quality evaluation | Trimethylamine | - | Improved response value | [37] |
Electrochemical, infrared (IR), MOS | Lamp | Quality evaluation | O2, CO2, and NH3 | Gas composition analysis along with impedance | [38] | |
Electrochemical sensor | Pork | Freshness monitoring | Trimethylamine | Range: 3.33 μg/L–1200 μg/L | [73] | |
E-nose, fluorescence hyperspectral imaging | Pork | Freshness monitoring | Alcohols, aldehydes, ketones, alkanes, and sulfides | 7 days | End-to-end data fusion approach for freshness | [74] |
E-nose | Spinach | Quality evaluation during storage | Hydrogen sulfide, methane, alcohol, ammonia, and carbon monoxide | 12 days | Optimized sensor array for odors classification | [40] |
Color-sensitive gas sensor array | Wheat flour | Quality evaluation during storage | VOCs | 6 months | Odor information of flour samples of different storage periods | [75] |
E-nose | Edible oil | Quality evaluation during storage | Hydrogen, carbon monoxide, methane, ethanol, toluene, acetone, and formaldehyde | 5 days | Change in quality during storage | [76] |
E-nose | Green tea | Quality evaluation | Alcohols, aldehydes, and esters | - | Improved classification accuracy | [43] |
E-nose | Apple | Spoilage monitoring | Nitrogen oxide, ammonia, hydrogen, alkanes, sulfides, alcohol, aromatic compounds, inorganic sulfur, organic compounds, methane, and aliphatic organic compounds | 7 days | Integrated terminal and remote platform enabled real-time monitoring | [77] |
E-nose | Egg | Sensory quality traits evaluation | Ammonia | 42 days | Maturity level recognition at 95% accuracy | [44] |
E-nose | Chicken drumstick | Quality evaluation | Alcohols, aldehydes, phenols, ketones, and cis-anethol | - | Optimum sugar smoking effects on flavors | [39] |
Colorimetric film | Apple | Spoilage moni-toring | CO2 | 5 days | High recognition rate for spoilage | [78] |
E-nose | Chilled Chicken | Quality evaluation | Sulfides, organic sulfides, and hydrides | 3 days | Difference in VOC produced by different species of bacteria | [79] |
Gas sensor array | Apple | Quality of pathogen-contaminated apples | VOCs | 7 days | Prototype for early warning of apple spoilage | [80] |
E-nose, Colorimetric sensor array | Fermented bean curd | Flavor quality analysis | VOCs | - | Determine ripeness and predict hardness | [81] |
BME688 sensor | Olive oil | Adulteration detection | VOCs, carbon monoxide, and hydrogen | - | High sensitive detection of sunflower oil adulteration | [48] |
E-nose | Powdered milk | Adulteration detection | 2-propanone, 5-methyl-2(3H) furanone | - | Whey adulteration in powdered milk | [47] |
E-nose | Minced chicken meat | Adulteration detection | Alcohols, aldehydes, ketones, aromatics, and other organic vapors | - | Detection of soybean protein isolate adulteration | [49] |
E-nose, GC-MS | Sesame oil | Adulteration detection | Alcohol, organic solvents, SO2, CO, alkenes, ammonia, benzene, sulfides, hydrogen, methane | - | Adulteration with soybean and corn oils | [46] |
E-nose | Beef | Adulteration detection | VOCs | - | Adulteration with pork | [82] |
E-nose | Zanthoxylum bungeanum Maxim | Discrimination based on geographical origin | VOCs | - | Discrimination based on geographical origin | [51] |
E-nose and e-tongue | Red wine | Discrimination based on geographical origin | Alcohols, esters, aldehydes, and ketones | - | Discrimination based on geographical origins, brands, and grape varieties | [52] |
Smartphone-based colorimetric sensor array | Rice | Discrimination based on geographical origin | VOCs | - | Discrimination based on geographical origin | [53] |
Colorimetric sensor array | Edible bird’s nests | Discrimination based on geographical origin | Octadecanoic acid, propanetriol, and 4-terpenol | - | Discrimination based on geographical origin | [83] |
E-nose | Meat floss | Classification | Hydrocarbons and alcohols | - | Differentiate beef, chicken, and pork meat floss | [50] |
E-nose | Lemon juice | Quality evaluation | VOCs | 120 days | Freshness during storage | [84] |
Proton-Transfer-Reaction Mass Spectrometry (PTR-MS) | Occidental pears | Fruit ripeness monitoring | VOCs (esters and terpenes) | - | Identification of three ripening stages of occidental pears | [85] |
E-nose, e-tongue | Fermented soybean paste | Flavor quality analysis | VOCs | - | Evaluation of sensory properties and overall flavor quality | [55] |
E-nose, e-tongue, GC-MS | Soybean paste | Flavor quality analysis | Nitrogen oxides, ammonia, hydrogen, methane, H2S, terpenes, alcohol, alkenes, and aromatic organic compounds | - | Evaluation of sensory properties and overall flavor quality | [56] |
E-nose | White tea | Authentication | Alcohols, esters, aldehydes, ketones, alkenes, hydrocarbons, ammonia, and alkyl aromatic compounds | - | Vintage authentication by integrating appearance, taste and aroma assessments. | [86] |
E-nose | Tea | Monitor fermentation | Isobutane, propane, methane, hydrogen, smoke, benzene, hydrogen, and alcohol | - | Detection of fermentation stages and detects aroma changes | [58] |
MOS sensor | Oolong tea | Monitor fermentation | Air contaminants, odorous gases, hydrocarbons, solvents, and sulfur compounds | - | Control flavor quality during manufacturing | [60] |
Generic resistive gas sensor | Black tea | Monitor fermentation | VOCs | - | Optimizes fermentation time | [59] |
MOS sensor | Oolong tea | Monitor oxidation | Ammonia, hydrogen, ethanol, sulfides, benzene, methane, propane, butane, alkenes, toluene, acetone, ethanol, and formaldehyde | - | Monitor oxidation process | [87] |
E-nose, e-tongue | Tremella aurantialba | Monitor and detect the fermentation process | Methane, ethane, dimethyl methane, hydrogen sulfide, and alcohol | - | Predict key chemical indicators | [88] |
Gas sensor array | Sourdough | Monitor fermentation | Oxygen, carbon dioxide, and hydrogen sulfides | - | Online gas measurements predict pH and acidity | [61] |
Headspace-gas chromatography-ion mobility spectrometry | Shrimp paste samples | Monitor fermentation | VOCs | - | Alcohols and amines dominated volatile compounds | [62] |
E-nose | Bread | Monitor fermentation | Alcohols, aldehydes, esters, ketones, terpenoids, pyridines, hydrocarbons, and amides | - | Pyridines are characteristic for emissions during baking | [63] |
E-nose | Rice and wheat crop residues | Understand bioethanol production dynamics | Ammonia, NO2, i-butane, propane, methane, alcohol, hydrogen, CO, toluene, and xylene | - | Artificial intelligence optimized sensor responses for classification and prediction | [64] |
E-nose | Mulberry wine | Monitor fermentation | Alcohol, H2S, terpenes, organic sulfur, nitrogen, oxygen, ammonia, alkenes, and methane | - | Effect of selenium-enriched yeast fermentation on flavor profiles | [89] |
E-nose, GC-MS | Steam bread | Aroma assessment | Alcohols, esters, aldehydes, and furan | - | Effects of multi-strain co-fermentation flavor profiles | [90] |
Colorimetric sensor array | Tencha | Aroma assessment | VOCs | - | Developed an olfactory visualization system to optimize drying | [65] |
Gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography-mass spectrometry-olfactometry (GC-MS-O) techniques. | Sturgeon meat | Flavor stability analysis | VOCs | - | Identify optimal steaming conditions and formic acid as crucial volatile compounds contribute flavor | [91] |
Colorimetric sensor array | Fermented bean curd | Flavor quality analysis | VOCs | - | Discrimination based on different brand | [66] |
MQ-3 gas sensor | Glucose | Quantitatively assesses fermentation | Alcohol | - | Identified correlation between gas bubble formation and alcohol production | [92] |
Colorimetric sensor array | Baijiu | Quality control | Ethyl caproate, ethyl lactate, n-propanol, n-butanol, isobutanol, isoamyl alcohol, acetic acid, butyric acid, and capric acid | - | Discrimination brands and authenticity | [67] |
Colorimetric sensor array | Baijiu | Quality evaluation | VOCs | - | Discrimination of different grades | [93] |
E-nose | Coffee leaves | Quality control | Nitrogen oxides, short-chain alkanes, sulfur-inorganic compounds, alcohols, aldehydes, ketones, and sulfur-containing organic compounds | - | Difference in aroma profiles of freeze-dried and hot-air dried leaves | [94] |
4. Data Acquisition and Pattern Recognition
5. Challenges and Future Trends
- Gas sensors exhibit low selectivity, making it challenging to accurately identify specific components for detection. Selectivity is a crucial factor in the effectiveness of gas sensors, as different food products exhibit distinct characteristic aroma profiles. The presence of VOCs with similar molecular structures may hinder the ability of basic gas sensors to accurately detect specific target compounds, resulting in false results. Therefore, it is crucial to focus on the development of gas sensors with high specificity in order to improve detection accuracy. Utilizing novel sensing materials, specifically metal-oxide nanoparticles, has been found to be an effective method for addressing the selectivity issue in gas sensors. The incorporation of these materials into mesoporous structures has been shown to enhance gas absorption and consequently increase sensor sensitivity, ultimately improving sensor selectivity.
- The integration of gas sensors into food packaging or processing lines is a complex task due to the rigid nature of most sensors which are not suitable for packaging systems or processing lines. It is challenging to incorporate sensors into various packaging materials and food containers without compromising the integrity of the system. Therefore, the development of flexible gas sensors is crucial for the successful integration of sensors into complete packaging systems.
- Certain sensors, such as MOS and surface acoustic wave sensors, have limitations in their application for the real-time analysis of food products, particularly perishable foods, due to their long recovery time. It is imperative to obtain information quickly in order to implement measures to prevent and address issues such as spoilage, adulteration, and changes in quality. The utilization of nanostructures as a sensitive material, as well as doping and composite material sensors, has been shown to be highly effective in reducing operation time and improving absorption and desorption kinetics. Incorporating a MEMS platform or layer onto an existing gas sensor is a crucial method for achieving faster cycling between the response and recovery phases.
- A portable detection device utilizing a wireless communication protocol is essential for the continuous monitoring of food freshness within the supply chain management system. This real-time monitoring will decrease the need for manual sampling and laboratory testing, while also aiding in the prediction of the maintenance required to ensure food quality and safety. To optimize supply chain management for perishable food over an extended period, low-power sensors are utilized in conjunction with IoT nodes to integrate data on temperature, humidity, and CO2 levels. This comprehensive dataset provides insight into the environmental conditions affecting food products, enabling informed decision-making based on the entirety of available information.
- Gas sensor readings are significantly influenced by environmental factors, particularly temperature and humidity. Unpredictable fluctuations in these factors can lead to unreliable readings. This attribute is of great importance for a gas sensor, particularly when utilized in monitoring food products in fluctuating storage environments. In the future, it is recommended to implement a system that incorporates humidity and temperature compensations to enhance the robustness of the sensor and enable its operation in diverse environmental conditions.
- The absence of a universal database for evaluating the VOCs associated with food spoilage hinders the utilization of gas sensors. In the future, there is a need for the establishment of a centralized Open Access database for referencing compounds detected using gases in order to facilitate reliable decision-making.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ANN | Artificial neural networks |
CP | Conducting polymers |
CSA | Colorimetric sensor array |
GCMS | Gas chromatography-mass spectrometry |
GPR | Gaussian process regression |
IoT | Internet of Things |
LSTM | Long short-term memory |
MEMS | Micro-electro-mechanical systems |
MLP | Multi-layer perceptron |
MOF | Metal–organic framework |
MOS | Metal-oxide semiconductors |
NFC | Near-field communication |
PCA | Principal component analysis |
QCM | Quartz crystal microbalance |
SAW | Surface acoustic wave |
SVM | Support vector machine |
TMA | Trimethylamine |
TVBN | Total volatile basic nitrogen |
VOCs | Volatile organic compounds |
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Technique | Key Characteristics | Fabrication Method | Advantages | Limitations |
---|---|---|---|---|
MOS Sensor | Detect gases by measuring changes in electrical resistance due to reversible interactions between gases and the metal-oxide surface | Wafer fabrication, oxidation, mask generation, photolithography, diffusion, and deposition | High gas response, reversible reactions, cost-effective, sensitive to freshness marker gases, operable across a range of temperatures, and compatible with sensor array integration | Limited selectivity, slow response and recovery times, and limited mass transfer in the gas phase |
Electrochemical sensor | Convert chemical concentrations into electrical signals via redox reactions, enabling selective and accurate gas detection | Electrochemical deposition, electroless deposition, microspotting, dip-pen lithography, and self-assembly | Highly sensitive, selective, rapid response, portable, and adaptable to various conditions, with compatibility for sensor array integration | Cross-sensitivity to various gases, limited lifespan, and sensitivity to temperature and humidity |
Optical sensor | Detect gases through chemical reactions between the gas and a chromogenic dye, which results in absorbance or fluorescence shifts. | Dip-coating technique, electrospun nanofibers, electrochemical writing, inkjet printing, and sol–gel techniques | Simple, cost-effective, and provides visual results for gas detection | Environmental interference, poor long-term stability, single-use design, rapid consumption of sensing materials, and challenges in calibration |
Conducting Polymer Sensor | Detect gases through chemiresistive behavior, where electrical resistance changes upon gas exposure. | Sol–gel, in situ oxidative polymerization, template-based methods, solid-state synthesis, and oxidative chemical vapor deposition | Large-scale production, tunable electrical properties, flexibility, biocompatibility, ease of fabrication, and high sensitivity to gases like ammonia and hydrogen sulfide | Poor long-term stability, environmental interference, potential irreversible changes upon gas exposure, and high production costs |
Sensor Array | Detect multiple VOCs simultaneously, mimicking the human olfactory system through a combination of diverse gas sensors. | Fabrication of patterned devices using engineered nanomaterials and integration of sensors into array systems via micro-electro-mechanical systems (MEMS) | Rapid detection, stability, portability, compactness, ability to identify complex gas mixtures, and adaptable for multiple applications | Response time issues, partial sensor specificity, interference from overlapping compounds, and need for advanced pattern recognition for accurate odor classification |
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Jayan, H.; Zhou, R.; Sun, C.; Wang, C.; Yin, L.; Zou, X.; Guo, Z. Intelligent Gas Sensors for Food Safety and Quality Monitoring: Advances, Applications, and Future Directions. Foods 2025, 14, 2706. https://doi.org/10.3390/foods14152706
Jayan H, Zhou R, Sun C, Wang C, Yin L, Zou X, Guo Z. Intelligent Gas Sensors for Food Safety and Quality Monitoring: Advances, Applications, and Future Directions. Foods. 2025; 14(15):2706. https://doi.org/10.3390/foods14152706
Chicago/Turabian StyleJayan, Heera, Ruiyun Zhou, Chanjun Sun, Chen Wang, Limei Yin, Xiaobo Zou, and Zhiming Guo. 2025. "Intelligent Gas Sensors for Food Safety and Quality Monitoring: Advances, Applications, and Future Directions" Foods 14, no. 15: 2706. https://doi.org/10.3390/foods14152706
APA StyleJayan, H., Zhou, R., Sun, C., Wang, C., Yin, L., Zou, X., & Guo, Z. (2025). Intelligent Gas Sensors for Food Safety and Quality Monitoring: Advances, Applications, and Future Directions. Foods, 14(15), 2706. https://doi.org/10.3390/foods14152706