Volatile organic compounds gas sensor based on quartz crystal microbalance for fruit freshness detection: A review

Exploring flavor patterns in the peel of tangor: A new citrus variety based on electronic sensors and GC-MS/O

This study used electronic sensors and gas chromatography-mass spectrometry-olfactometry to analyze the peels of eight citrus fruits. Five taste patterns were evaluated, with citrus kiyomi having the highest sweetness and bitterness and citrus tsunokaori having the highest sourness. Electronic nose and gas chromatography-mass spectrometry analyses revealed the major volatile compounds to be limonene, myrcene, and α-pinene. Citrus kiyomi had the highest limonene content. Gas chromatography olfactometry analysis identified 12 odor-active compounds, with limonene being the most intense. The analysis of odor contributions expressed as thresholds indicates that limonene accounted for the most significant proportion. Further principal component analysis, hierarchical cluster analysis, partial least squares discriminant, and correlation network analysis were used to identify correlations between flavor compounds and samples, making it easier to assess similarities between samples.

Real-world evaluation of a QCM-based biosensor for exhaled air

The biosensor, named "virusmeter" in this study, integrates quartz crystal microbalance technology with an immune-functionalized chip to distinguish between symptomatic patients with respiratory diseases and healthy individuals by analyzing exhaled air samples. Renowned for its compact design, rapidity, and noninvasive nature, this device yields results within a 5-min timeframe. Evaluated under controlled conditions with 54 hospitalized symptomatic COVID-19 patients and 128 control subjects, the biosensor demonstrated good overall sensitivity (98.15%, 95% CI 90.1-100.0) and specificity (96.87%, 95% CI 92.2-99.1). This proof-of-concept presents an innovative approach with significant potential for leveraging piezoelectric sensors to diagnose respiratory diseases.

ZIF-8-Based Surface Plasmon Resonance and Fabry-Pérot Sensors for Volatile Organic Compounds

This work explores the use of ZIF-8, a metal-organic framework (MOF) material, for its use in the optical detection of volatile organic compounds (VOCs) in Fabry-Pérot and surface plasmon resonance (SPR)-based sensors. The experiments have been carried out with ethanol (EtOH) and show response times as low as 30 s under VOC-saturated atmospheres, and the estimated limit of detection is below 4000 ppm for both sensor types. The selectivity towards other VOCs is relatively poor, although the dynamics of adsorption/desorption differ for each VOC and could be used for selectivity purposes. Furthermore, the hydrophobicity of ZIF-8 has been confirmed and the fabricated sensors are insensitive to this compound, which is a very attractive result for its practical use in gas sensing devices.

Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications

The escalating development and improvement of gas sensing ability in industrial equipment, or "machine olfactory", propels the evolution of gas sensors toward enhanced sensitivity, selectivity, stability, power efficiency, cost-effectiveness, and longevity. Two-dimensional (2D) materials, distinguished by their atomic-thin profile, expansive specific surface area, remarkable mechanical strength, and surface tunability, hold significant potential for addressing the intricate challenges in gas sensing. However, a comprehensive review of 2D materials-based gas sensors for specific industrial applications is absent. This review delves into the recent advances in this field and highlights the potential applications in industrial machine olfaction. The main content encompasses industrial scenario characteristics, fundamental classification, enhancement methods, underlying mechanisms, and diverse gas sensing applications. Additionally, the challenges associated with transitioning 2D material gas sensors from laboratory development to industrialization and commercialization are addressed, and future-looking viewpoints on the evolution of next-generation intelligent gas sensory systems in the industrial sector are prospected.

Engineering of Mesoporous Cube-like In2O3 Products as Ethanol Detection Platform at Low Operating Temperature: Effects of Different Transition Metals as Dopant Ions

Although most semiconductor metal oxides including In2O3 show acceptable sensitivity to volatile organic compounds, it is difficult to detect ethanol effectively at low operating temperatures and detection levels. In this study, pure and Co-, Ni-, and Cu-doped In2O3 products with their doping content maintained at 1 mol % were successfully produced using a hydrothermal approach. Explicit contrast on the structural, microstructural, and textural properties of the synthesized In2O3 products was examined to determine their gas sensing performance. The Cu-doped In2O3 sensor demonstrated improved response of 15.3 to 50 ppm ethanol and has satisfactory selectivity, stability, low detection limit of 0.2, humidity resistance, and decreased working temperature of 80 °C compared to 150 °C of the pure In2O3 sensor. This optimal gas sensing performance is derived from the cube-like morphology assembled with interlinked nanoparticles, which favors trapping more target gas molecules and exposing more active sites, thereby greatly improving its sensing ability. This study showed that the Cu-doped In2O3 sensor with 1 mol % is suitable for monitoring ethanol gas for food safety applications.

Technological tools for the measurement of sensory characteristics in food: A review

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Acetone Detection and Classification as Biomarker of Diabetes Mellitus Using a Quartz Crystal Microbalance Gas Sensor Array

A gas sensor array was developed and evaluated using four high-frequency quartz crystal microbalance devices (with a 30 MHz resonant frequency in fundamental mode). The QCM devices were coated with ethyl cellulose (EC), polymethylmethacrylate (PMMA), Apiezon L (ApL), and Apiezon T (ApT) sensing films, and deposited by the ultrasonic atomization method. The objective of this research was to propose a non-invasive technique for acetone biomarker detection, which is associated with diabetes mellitus disease. The gas sensor array was exposed to methanol, ethanol, isopropanol, and acetone biomarkers in four different concentrations, corresponding to 1, 5, 10, and 15 µL, at temperature of 22 °C and relative humidity of 20%. These samples were used because human breath contains them and they are used for disease detection. Moreover, the gas sensor responses were analyzed using principal component analysis and discriminant analysis, achieving the classification of the acetone biomarker with a 100% membership percentage when its concentration varies from 327 to 4908 ppm, and its identification from methanol, ethanol, and isopropanol.

Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry

Volatile compounds not only contribute to the distinct flavors and aromas found in foods and beverages, but can also serve as indicators for spoilage, contamination, or the presence of potentially harmful substances. As the odor of food raw materials and products carries valuable information about their state, gas sensors play a pivotal role in ensuring food safety and quality at various stages of its production and distribution. Among gas detection devices that are widely used in the food industry, metal oxide semiconductor (MOS) gas sensors are of the greatest importance. Ongoing research and development efforts have led to significant improvements in their performance, rendering them immensely useful tools for monitoring and ensuring food product quality; however, aspects related to their limited selectivity still remain a challenge. This review explores various strategies and technologies that have been employed to enhance the selectivity of MOS gas sensors, encompassing the innovative sensor designs, integration of advanced materials, and improvement of measurement methodology and pattern recognize algorithms. The discussed advances in MOS gas sensors, such as reducing cross-sensitivity to interfering gases, improving detection limits, and providing more accurate assessment of volatile organic compounds (VOCs) could lead to further expansion of their applications in a variety of areas, including food processing and storage, ultimately benefiting both industry and consumers.

Applications of Gas Sensing in Food Quality Detection: A Review

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food

Bionic nose, a technology that mimics the human olfactory system, has been widely used to assess food quality due to their high sensitivity, low cost, portability and simplicity. This review briefly describes that bionic noses with multiple transduction mechanisms are developed based on gas molecules' physical properties: electrical conductivity, visible optical absorption, and mass sensing. To enhance their superior sensing performance and meet the growing demand for applications, a range of strategies have been developed, such as peripheral substitutions, molecular backbones, and ligand metals that can finely tune the properties of sensitive materials. In addition, challenges and prospects coexist are covered. Cross-selective receptors of bionic nose will help and guide the selection of the best array for a particular application scenario. It provides an odour-based monitoring tool for rapid, reliable and online assessment of food safety and quality.

A method of gas sensor drift compensation based on intrinsic characteristics of response curve

Sensor drift, which is an inevitable and challenging problem in gas sensing, seriously affects the detection performance of sensor. In this study, a new sensor drift compensation method, which is based on intrinsic characteristic of sensory response, is proposed. The dataset of gas sensor for two types of gas with a period of 36 months are collected and two features (one is steady-state feature, another is transient feature) are extracted. Their relationship, which is found to be certain for different months and sensors, is explored. Then, drift compensation method is processed based on this relationship, aiming to make the drifted sensor features adjusted to that of month 1, which is considered as having no drift phenomenon. Moreover, small amount of dataset is necessary for model building and it has strong scalability. Finally, SVM is employed for proving the performance of the drift compensation method proposed in this study. The results show the efficacy of 22 month of continuous monitoring, which has been enough for most application scenario, and almost 20% of increasement of correct classification rate of SVM after drift compensation, which indicates the effect of drift compensation method.

Microgravimetric Modeling-A New Method for Extracting Adsorption Parameters of Functionalized MIL-101(Cr)

As a volatile air pollutant, formaldehyde can enter people's living environment through interior decoration, furniture and paint, causing serious harm to human health. Therefore, it is necessary to develop a sensor for the real-time detection of formaldehyde in low concentrations. According to the chemical interaction between amino groups and formaldehyde, a MIL-101(Cr) aminated-material-based formaldehyde cantilever sensor was prepared, of which ethylenediamine- functionalized MIL-101(Cr) named ED-MIL-101(Cr)) showed the best gas sensing performance. Using quasi-in situ infrared spectroscopy, ED-MIL-101(Cr) was found bound to formaldehyde through a Schiff base. The adsorption enthalpy of formaldehyde-bound ED-MIL-101(Cr) was -52.6 kJ/mol, which corresponds to weak chemical adsorption, so the material showed good selectivity. In addition, ED-MIL-101(Cr) has the most active sites, so its response value to formaldehyde is larger and it takes longer to reach saturation adsorption than bare MIL-101(Cr). Through the research on the gas sensing performance of functionalized MIL-101(Cr) material, we found that it has a strong application potential in the field of formaldehyde monitoring, and the material performance can be quantitatively and accurately evaluated through combining calculation and experimentation for understanding the gas sensing mechanism.

Diffusion-modulated colorimetric sensor for continuous gas detection

Due to their high sensitivity and selectivity, low cost, and good compatibility for sensor array integration, colorimetric gas sensors are widely used in hazardous gas detection, food freshness assessment, and gaseous biomarker identification. However, colorimetric gas sensors are usually designed for one-time discrete measurement because the sensing materials are entirely exposed to analytes during the sensing process. The fast consumption of sensing materials limits colorimetric sensors' applications in continuous analytes monitoring, increases the operation complexity and brings challenges for calibration. In this work, we reported a novel sensor design to prolong the lifetime of colorimetric gas sensors by engineering the gas diffusion process to preserve the sensing materials. We compared two geometries for gas diffusion control in a sensing matrix through simulation and experiment on an ammonia sensing platform. We found that the 2-dimensional gas diffusion geometry enabled a better sensor performance, including more stable and higher sensitivity and a more linear response to ammonia concentration compared to 1-dimensional gas diffusion geometry. We also demonstrated the usability of this diffusion-modulated colorimetric sensor for continuous environmental ammonia monitoring.

ZnSnO3 based gas sensors for pyridine volatile marker detection in rice aging during storage

This study reports the development of ZnSnO₃ based gas sensors for pyridine detection in rice aging. Pyridine is one of heterocyclic markers formed via Maillard reaction and lipid oxidation. Herein, graphitic carbon nitride (g-C₃N₄) decorated ZnSnO₃ microstructures were obtained through a template-free approach. And the sensing results reveal that 5 wt%g-C₃N₄ decorated ZnSnO₃ exhibited a high sensitivity (47.9), a short response/recovery time (14/120 s) and a low detection limit (0.45 ppm), which is due to the catalysis of g-C₃N₄ nanosheets, the decorated microstructure and the formation of heterojunctions. Meanwhile, the practical experiment demonstrates that the sensitivity towards volatiles generated from Japonica rice aging is 48.7, which is around 4 and 2.5 times higher than those of Indica rice and Polished Glutinous rice, indicating that the sensor has anticipated application in the development of a high-performance E-nose for the quality inspection of rice and other products.

Application of a Novel One-Side Cell Quartz Crystal Microbalance Immunosensor in the Determination of Alpha-Fetoprotein from Human Serum

The rapid and accurate detection of alpha-fetoprotein (AFP) levels is of great significance for the diagnosis and later treatment evaluation of liver cancer. In this study, a novel integrated quartz crystal microbalance (QCM) immunosensor based on the design to contact liquids on one side only was developed for the label-free detection of AFP. Anti-AFP mouse monoclonal antibodies were immobilized onto the upper electrode surface of the pre-treatment crystals using the staphylococcus aureus protein A. The AFP antigens in human serum were captured by specific surface-coated antibodies, and testing was carried out by monitoring the corresponding changes in frequency. The concentration range of the antigen detected was 13-760 ng/mL. The frequency characteristics of the process of antibody immobilization were investigated in detail, and high reproducibility of AFP antibody immobilization was achieved (standard deviation (SD) = 2.2 Hz). The developed QCM measurement system demonstrated a short test time (only 30 min), good reproducibility (the biological activity can still maintain more than 90% of the first test till it is reused five times), and accuracy as good as the one achieved with the radioimmunoassay (the maximum relative deviation = 4.8%). The designed QCM test system can easily and quickly detect AFP concentrations up to 760 ng/mL, indicating that the developed QCM assay is likely to lead to an alternative approach in large-scale screening for liver cancer in the near future.

Sensing materials for fresh food quality deterioration measurement: a review of research progress and application in supply chain

Fresh food are consumed in large quantities worldwide. During the supply chain, microbial growth in fresh food can lead to the production of a number of metabolites, which make food highly susceptible to spoilage and contamination. The quality of fresh food changes in terms of smell, tenderness, color and texture, which causes a decrease in freshness and consumers acceptance. Therefore, the quality monitoring of fresh food has become an essential part in the supply chain. As traditional analysis methods are highly specialized, expensive and have a small scope of application, which cannot be applied to the supply chain to realize real-time monitoring. Recently, sensing materials have received a lot of attention from researchers due to the low price, high sensitivity and high speed. However, the progress of research on sensing materials has not been critically evaluated. The study examines the progress of research in the application of sensing materials for fresh food quality monitoring. Meanwhile, indicator compounds for spoilage of fresh food are analyzed. Moreover, some suggestions for future research directions are given.

On-Site Detection of Volatile Organic Compounds (VOCs)

Volatile organic compounds (VOCs) are of interest in many different fields. Among them are food and fragrance analysis, environmental and atmospheric research, industrial applications, security or medical and life science. In the past, the characterization of these compounds was mostly performed via sample collection and off-site analysis with gas chromatography coupled to mass spectrometry (GC-MS) as the gold standard. While powerful, this method also has several drawbacks such as being slow, expensive, and demanding on the user. For decades, intense research has been dedicated to find methods for fast VOC analysis on-site with time and spatial resolution. We present the working principles of the most important, utilized, and researched technologies for this purpose and highlight important publications from the last five years. In this overview, non-selective gas sensors, electronic noses, spectroscopic methods, miniaturized gas chromatography, ion mobility spectrometry and direct injection mass spectrometry are covered. The advantages and limitations of the different methods are compared. Finally, we give our outlook into the future progression of this field of research.

Hollow Mesoporous SnO2/Zn2SnO4 Heterojunction and RGO Decoration for High-Performance Detection of Acetone

In this article, the synthesis procedure and sensing properties toward acetone of rGO-HM-SnO₂/Zn₂SnO₄ composites with a hollow mesoporous structure are presented comprehensively. The rGO-HM-SnO₂/Zn₂SnO₄ heterojunction structure is prepared through a self-sacrificial template strategy with a concise acid-assisted etching method. The as-prepared hollow mesoporous architectures are investigated by SEM, TEM, and HRTEM. The phase structure and valence state are also characterized by XRD and XPS, respectively. It is obvious that the hollow mesoporous architecture affords a large specific surface area, which can provide more reaction active sites of sensing materials significantly. Compared to the initial SnO₂/Zn₂SnO₄ composites, the gas sensor fabricated by rGO-HM-SnO₂/Zn₂SnO₄ shows the best gas-sensing properties, and the response value toward 100 ppm acetone is as high as 107 at 200 °C. Moreover, the rGO-HM-SnO₂/Zn₂SnO₄ sensing material reveals excellent properties of shorter response-recovery times and higher long-term stability. This excellent performance can be ascribed to the synergistic effect of the hollow mesoporous n-n heterojunction and abundant-defect rGO. The relevant sensing mechanism of rGO-HM-SnO₂/Zn₂SnO₄ sensing materials is investigated in detail.

Emerging Postharvest Technologies to Enhance the Shelf-Life of Fruit and Vegetables: An Overview

Quality losses in fresh produce throughout the postharvest phase are often due to the inappropriate use of preservation technologies. In the last few decades, besides the traditional approaches, advanced postharvest physical and chemical treatments (active packaging, dipping, vacuum impregnation, conventional heating, pulsed electric field, high hydrostatic pressure, and cold plasma) and biocontrol techniques have been implemented to preserve the nutritional value and safety of fresh produce. The application of these methodologies after harvesting is useful when addressing quality loss due to the long duration when transporting products to distant markets. Among the emerging technologies and contactless and non-destructive techniques for quality monitoring (image analysis, electronic noses, and near-infrared spectroscopy) present numerous advantages over the traditional, destructive methods. The present review paper has grouped original studies within the topic of advanced postharvest technologies, to preserve quality and reduce losses and waste in fresh produce. Moreover, the effectiveness and advantages of some contactless and non-destructive methodologies for monitoring the quality of fruit and vegetables will also be discussed and compared to the traditional methods.

Recent Advances in Graphene-Based Nanocomposites for Ammonia Detection

The increasing demand to mitigate the alarming effects of the emission of ammonia (NH3) on human health and the environment has highlighted the growing attention to the design of reliable and effective sensing technologies using novel materials and unique nanocomposites with tunable functionalities. Among the state-of-the-art ammonia detection materials, graphene-based polymeric nanocomposites have gained significant attention. Despite the ever-increasing number of publications on graphene-based polymeric nanocomposites for ammonia detection, various understandings and information regarding the process, mechanisms, and new material components have not been fully explored. Therefore, this review summarises the recent progress of graphene-based polymeric nanocomposites for ammonia detection. A comprehensive discussion is provided on the various gas sensor designs, including chemiresistive, Quartz Crystal Microbalance (QCM), and Field-Effect Transistor (FET), as well as gas sensors utilising the graphene-based polymer nanocomposites, in addition to highlighting the pros and cons of graphene to enhance the performance of gas sensors. Moreover, the various techniques used to fabricate graphene-based nanocomposites and the numerous polymer electrolytes (e.g., conductive polymeric electrolytes), the ion transport models, and the fabrication and detection mechanisms of ammonia are critically addressed. Finally, a brief outlook on the significant progress, future opportunities, and challenges of graphene-based polymer nanocomposites for the application of ammonia detection are presented.

Monitoring Volatile Organic Compounds in Different Pear Cultivars during Storage Using HS-SPME with GC-MS

Aroma, which plays an essential role in food perception and acceptability, depends on various mixture of volatile organic compounds (VOCs). Meanwhile, as a field of metabolomics, VOC analysis is highly important for aroma improvement and discrimination purposes. In this work, VOCs in pear fruits were determined via headspace solid-phase micro-extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) to study variations among different cultivars and storage stages. In 12 cultivars of pear fruits, a total of 121 VOCs were quantified, including 40 esters, 32 alcohols, 16 aldehydes, 13 alkenes, 11 ketones, 4 acids, and 5 other compounds. The types and amounts of VOCs in different cultivars varied dramatically, which were in the range of 13-71 and 3.63-55.65 mg/kg FW (fresh weight), respectively. The Dr. Guyot cultivar showed the highest level of VOCs, both in type and amount. After 21 days storage at 4 °C, total concentration of VOCs increased from initial levels of 50.76 to 101.33 mg/kg FW. Storage at 20 °C made a larger contribution to production for VOCs than that at 4 °C, resulting in the maximum content of VOCs (117.96 mg/kg FW) in fruit after 14 days storage at 4 °C plus 7 days at 20 °C. During storage, the content of esters showed a gradual increase, while the content of alcohols and aldehydes decreased. Based on the results presented, related alcohols were recognized as the intermediates of conversion from aldehydes to esters.

Molecularly imprinted polymers for sensing gaseous volatile organic compounds: opportunities and challenges

Chemical sensors that can detect volatile organic compounds (VOCs) are the subject of extensive research efforts. Among various sensing technologies, molecularly imprinted polymers (MIPs) are regarded as a highly promising option for their detection with many advantageous properties, e.g., specific binding-site for template molecules, high recognition specificity, ease of preparation, and chemical stability. This review covers recent advances in the sensing application of MIPs toward various types of VOCs (e.g., aliphatic and aromatic compounds). Particular emphasis has been placed on multiple approaches to the synthesis of MIP-based VOC sensors in association with their performance and sensing mechanisms. Current challenges and opportunities for new VOC-sensing applications are also discussed based on MIP technology.

Recent Progress in Amine Gas Sensors for Food Quality Monitoring: Novel Architectures for Sensing Materials and Systems

The increasing demand for food production has necessitated the development of sensitive and reliable methods of analysis, which allow for the optimization of storage and distribution while ensuring food safety. Methods to quantify and monitor volatile and biogenic amines are key to minimizing the waste of high-protein foods and to enable the safe consumption of fresh products. Novel materials and device designs have allowed the development of portable and reliable sensors that make use of different transduction methods for amine detection and food quality monitoring. Herein, we review the past decade's advances in volatile amine sensors for food quality monitoring. First, the role of volatile and biogenic amines as a food-quality index is presented. Moreover, a comprehensive overview of the distinct amine gas sensors is provided according to the transduction method, operation strategies, and distinct materials (e.g., metal oxide semiconductors, conjugated polymers, carbon nanotubes, graphene and its derivatives, transition metal dichalcogenides, metal organic frameworks, MXenes, quantum dots, and dyes, among others) employed in each case. These include chemoresistive, fluorometric, colorimetric, and microgravimetric sensors. Emphasis is also given to sensor arrays that record the food quality fingerprints and wireless devices that operate as radiofrequency identification (RFID) tags. Finally, challenges and future opportunities on the development of new amine sensors are presented aiming to encourage further research and technological development of reliable, integrated, and remotely accessible devices for food-quality monitoring.

One-Step Hydrothermal Synthesis of 3D Interconnected rGO/In2O3 Heterojunction Structures for Enhanced Acetone Detection

Acetone detection is of great significance for environmental monitoring or diagnosis of diabetes. Nevertheless, fast and sensitive detection of acetone at low temperatures remains challenging. Herein, a series of rGO-functionalized three-dimensional (3D) In2O3 flower-like structures were designed and synthesized via a facile hydrothermal method, and their acetone-sensing properties were systematically investigated. Compared to the pure 3D In2O3 flower-like structures, the rGO-functionalized 3D In2O3 flower-like structures demonstrated greatly improved acetone-sensing performance at relatively low temperatures. In particular, the 5-rGO/In2O3 sensor with an optimized decoration exhibited the highest response value (5.6) to 10 ppm acetone at 150 °C, which was about 2.3 times higher than that of the In2O3 sensor (2.4 at 200 °C). Furthermore, the 5-rGO/In2O3 sensor also showed good reproducibility, a sub-ppm-level detection limit (1.3 to 0.5 ppm), fast response and recovery rates (3 s and 18 s, respectively), and good long-term stability. The extraordinary acetone-sensing performance of rGO/In2O3 composites can be attributed to the synergistic effect of the formation of p-n heterojunctions between rGO and In2O3, the large specific surface area, the unique flower-like structures, and the high conductivity of rGO. This work provides a novel sensing material design strategy for effective detection of acetone.

Layer by Layer Optimization of Langmuir–Blodgett Films for Surface Acoustic Wave (SAW) Based Sensors for Volatile Organic Compounds (VOC) Detection

Rayleigh surface acoustic wave (RSAW)-based resonant sensors, functionalized with single and multiple monomolecular layers of Langmuir–Blodgett (LB) films, were thickness and density optimized for the detection of volatile organic compounds (VOC), which could impose a serious threat on the environment and human health. Single layers of a phospholipid (SLP), hexane dissolved arachidic acid (HDAA), and chloroform dissolved arachidic acid (CDAA) were used for the LB film preparation. Several layers of these compounds were deposited on top of each other onto the active surface of high-Q 434 MHz two-port RSAW resonators in a LB trough to prepare a highly sensitive vapor detection quartz surface microbalance (QSM). Frequency shift was measured with a vector network analyzer (VNA). These devices were probed with saturated vapors of hexane, chloroform, methanol, acetone, ethanol, and water after each deposited layer to test the behavior of the QSM’s insertion loss, loaded Q, vapor sensitivity, and to find the optimum trade-off between these parameters for the best real-life sensor performance. With 2200 ppm and 3700 ppm sensitivity to chloroform, HDAA and CDAA coated QSM devices reached the optimum sensor performance at 15 and 11–15 monolayers, respectively. Surface pressure optimized single monolayers of phospholipid LB films were found to provide up to 530 ppm sensitivity to chloroform vapors with a negligible reduction in loss and loaded Q. This vapor sensitivity is higher than the mass of the sensing layer itself, making SLP films an excellent choice for QSM functionalization.

Piezoelectric sensing of glucose oxidase activity of Aspergillus niger spores pretreated by different methods

The development of Aspergillus niger (A. niger) spores as glucose oxidase (GOD) biocatalysts to produce gluconic acid is highly anticipated in the food industry. Herein, a piezoelectric sensor (PIS) method has been developed for the detection of GOD activity and better application of rapid screening of GOD activity in A. niger spores. The GOD activity detection is based on GOD catalyzing β-d-glucose to produce gluconic acid, which results in frequency shift changes recorded by the PIS device in real-time. Using the PIS method, the kinetic parameter 6.5 mg/mL, the correlation equation υ₀=31.92C_GOD+1.04, the recoveries (89.4%-93.9%, and their RSDs were all within 6.1%) and the optimal GOD activity in A. niger spores under different treatment conditions was obtained. Compared with the classical methods, the proposed method is accurate, rapid, convenient and does not require additional reagents. It has a broad range of potential applications for exploring new GOD biocatalysts.

A study of vegetable oil modified QCM sensor to detect β-pinene in Indian cardamom

A quartz crystal microbalance (QCM) sensor was developed in this study with the vegetable oil from olive (OLV-QCM) to detect an important volatile organic compound, β-pinene in Indian cardamom. Hydrophobic vegetable oil from olive, which contains oleic acid and omega-9, a monounsaturated fatty acid was found to be suitable for binding β-pinene through non-covalent bonds. The fabricated QCM sensor coating was examined with the field emission scanning electron microscope (FESEM) and Fourier-transform infrared spectroscopy (FTIR) to determine its surface morphology and chemical compositions. The sensitivity, reproducibility, repeatability, and reusability were studied for the developed sensor. Notably, the sensor was observed to be highly selective towards β-pinene as compared to the other volatile components present in cardamom. The limit of detection (LOD) and limit of quantitation (LOQ) parameters were determined as 5.57 mg L^-1 and 18.57 mg L^-1, respectively. Moreover, the adsorption isotherm models of the sensor were studied to validate the physical adsorption affinity towards β-pinene applying Langmuir, Freundlich, and Langmuir-Freundlich isotherm models. The sensor showed a correlation factor of 0.99 with the peak area percentage of gas chromatography-mass spectrometry (GC-MS) analysis for β-pinene in cardamom samples. The sensor was prepared with natural vegetable oil, unlike health hazard chemicals. In addition to this, the low-cost, easy fabrication process ensured the suitability of the sensor for practical deployment.

Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

One of the biggest global challenges for our societies is to provide natural resources to the rapidly expanding population while maintaining sustainable and ecologically friendly products. The increasing public concern about toxic insecticides has resulted in the rapid development of alternative techniques based on natural infochemicals (ICs). ICs (e.g., pheromones, allelochemicals, volatile organic compounds) are secondary metabolites produced by plants and animals and used as information vectors governing their interactions. Such chemical language is the primary focus of chemical ecology, where behavior-modifying chemicals are used as tools for green pest management. The success of ecological programs highly depends on several factors, including the amount of ICs that enclose the crop, the range of their diffusion, and the uniformity of their application, which makes precise detection and quantification of ICs essential for efficient and profitable pest control. However, the sensing of such molecules remains challenging, and the number of devices able to detect ICs in air is so far limited. In this review, we will present the advances in sensing of ICs including biochemical sensors mimicking the olfactory system, chemical sensors, and sensor arrays (e-noses). We will also present several mathematical models used in integrated pest management to describe how ICs diffuse in the ambient air and how the structure of the odor plume affects the pest dynamics.

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

Recent application of molecular imprinting technique in food safety

Due to the extensive use of chemical substances such as pesticides, antibiotics and food additives, food safety issues have gradually attracted people's attention. The extensive use of these chemicals seriously damages human health. In order to detect trace chemical residues in food, researchers have to find several simple, economical and effective tools for qualitative and quantitative analysis. As a kind of material that specifically and selectively recognize template molecules from real samples, molecular imprinting technique (MIT) has widely applied in food samples analysis. This article mainly reviews the application of molecularly imprinted polymer (MIP) in the detection of chemical residues from food in the past five years. Some recent and novel methods for fabrication of MIP are reviewed. Their application of sample pretreatment, sensors, etc. in food analysis is reviewed. The application of molecular imprinting in chromatographic stationary phase is referred. Additionally, the challenges faced by MIP are discussed.

Research advance in gas detection of volatile organic compounds released in rice quality deterioration process

Rice quality deterioration will cause grievous waste of stored grain and various food safety problems. Gas detection of volatile organic compounds (VOCs) produced by deterioration is a nondestructive detection method to judge rice quality and alleviate rice spoilage. This review discussed the research advance of VOCs detection in terms of nondestructive detection methods of rice quality deterioration, applications of VOCs in grain detection, inspection of characteristic gas produced during rice spoilage, rice deterioration prevention and control, and detection of VOCs released by rice mildew and insect attack. According to the main causes of rice quality deterioration and major sources of VOCs with off-odor generated during rice storage, deterioration can be divided into mold and insect infection. The results of literature manifested that researches mainly focused on the infection of Aspergillus in the mildew process and the attack of certain pests in recent years, thus the research scope was limited. In this paper, the gas detection methods combined with the chemometrics to qualitatively analyze the VOCs, as well as the correlation with the number of colonies and insects were further studied based on the common dominant strains during rice mildew, that is, Aspergillus and Penicillium fungi, and the common pests during storage, that is, Sitophilus oryzae and Rhyzopertha dominica. Furthermore, this paper pointed out that the quantitative determination of characteristic VOCs, the numeration relationship between VOCs and the degree of mildew and insect infestation, the further expansion of detection range, and the application of degraded rice should be the spotlight of future research.

Artificial Intelligence of Things (AIoT) Enabled Virtual Shop Applications Using Self-Powered Sensor Enhanced Soft Robotic Manipulator

Rapid advancements of artificial intelligence of things (AIoT) technology pave the way for developing a digital-twin-based remote interactive system for advanced robotic-enabled industrial automation and virtual shopping. The embedded multifunctional perception system is urged for better interaction and user experience. To realize such a system, a smart soft robotic manipulator is presented that consists of a triboelectric nanogenerator tactile (T-TENG) and length (L-TENG) sensor, as well as a poly(vinylidene fluoride) (PVDF) pyroelectric temperature sensor. With the aid of machine learning (ML) for data processing, the fusion of the T-TENG and L-TENG sensors can realize the automatic recognition of the grasped objects with the accuracy of 97.143% for 28 different shapes of objects, while the temperature distribution can also be obtained through the pyroelectric sensor. By leveraging the IoT and artificial intelligence (AI) analytics, a digital-twin-based virtual shop is successfully implemented to provide the users with real-time feedback about the details of the product. In general, by offering a more immersive experience in human-machine interactions, the proposed remote interactive system shows the great potential of being the advanced human-machine interface for the applications of the unmanned working space.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.