Gas sensor technologies and mathematical modelling for quality sensing in fruit and vegetable cold chains: A review

Adsorption and sensor performance of transition metal-decorated zirconium-doped silicon carbide nanotubes for NO2 gas application: a computational insight

Owing to the fact that the detection limit of already existing sensor-devices is below 100% efficiency, the use of 3D nanomaterials as detectors and sensors for various pollutants has attracted interest from researchers in this field. Therefore, the sensing potentials of bare and the impact of Cu-group transition metal (Cu, Ag, Au)-functionalized silicon carbide nanotube (SiCNT) nanostructured surfaces were examined towards the efficient detection of NO2 gas in the atmosphere. All computational calculations were carried out using the density functional theory (DFT) electronic structure method at the B3LYP-D3(BJ)/def2svp level of theory. The mechanistic results showed that the Cu-functionalized silicon carbide nanotube surface possesses the greatest adsorption energies of -3.780 and -2.925 eV, corresponding to the adsorption at the o-site and n-site, respectively. Furthermore, the lowest energy gap of 2.095 eV for the Cu-functionalized surface indicates that adsorption at the o-site is the most stable. The stability of both adsorption sites on the Cu-functionalized surface was attributed to the small ellipticity (ε) values obtained. Sensor mechanisms confirmed that among the surfaces, the Cu-functionalized surface exhibited the best sensing properties, including sensitivity, conductivity, and enhanced adsorption capacity. Hence, the Cu-functionalized SiCNT can be considered a promising choice as a gas sensor material.

Exploring the power of nitric oxide and nanotechnology for prolonging postharvest shelf-life and enhancing fruit quality

Nitric oxide (NO) is a versatile signaling molecule that plays a crucial role in regulating postharvest fruit quality. The utilization of NO donors to elevate endogenous NO levels and induce NO-mediated responses represents a promising strategy for extending fruit shelf-life after harvest. However, the effectiveness of NO treatment is influenced by various factors, including formulation and application methods. In this review, we investigate the impact of NO supply on different fruits, aiming to prolong postharvest shelf-life and enhance fruit quality. Furthermore, we delve into the underlying mechanisms of NO action, particularly its interactions with ethylene and reactive oxygen species (ROS). Excitingly, we also highlight the emerging field of nanotechnology in postharvest applications, discussing the use of nanoparticles as a novel approach for achieving sustained release of NO and enhancing its effects. By harnessing the potential of nanotechnology, our review is a starting point to help identify gaps and future directions in this important, emerging field.

The variations in gene expression of GAPDH in Ocimum basilicum cultivars under drought-induced stress conditions

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) holds a pivotal role within the glycolytic pathway of higher plants. It has garnered attention as a significant target protein in instances of oxidative stress, where it can engage in thiolation reactions within its active site. Numerous genes encoding cytosolic iterations of GAPDH have been identified and analyzed in specific plant species. This investigation was conducted to gain insights into GAPDH's function amidst drought-induced stress. Within this framework, the basil plant (Ocimum basilicum) was chosen for focused exploration, encompassing the cloning of the comprehensive cDNA of basil GAPDH (ObGAPDH) and scrutinizing its patterns of expression. The complete sequence of Ob-GAPDH spanned 1315 base pairs. The resultant protein derived from this sequence comprised 399 amino acids, projecting a molecular weight of approximately 42.54 kDa and an isoelectric point (pI) of 6.01. An examination of the evolutionary connections among various GAPDH proteins unveiled ObGAPDH's shared lineage with GAPDH proteins sourced from other plants, such as Salvia splendens and Sesamum indicum. Furthermore, computational methodologies were harnessed to predict the potential oxidative role of ObGAPDH in response to external signals. Molecular docking simulations illuminated the interaction between ObGAPDH and hydrogen peroxide (H2O2) as a ligand. Scrutinizing the expression patterns of the ObGAPDH gene under conditions of water scarcity stress brought to light diverse levels of transcriptional activity. Collectively, these findings underscore the notion that the regulation of ObGAPDH expression is contingent upon both the specific plant cultivar and the presence of stress stemming from drought conditions.

A synergistic approach to enhance sensitivity and selectivity of room temperature operable ammonia gas sensor with humidity assistance using RGO/WO3nanocomposite

In this study, the fabrication of an ultrahigh selective NH3gas sensor based on RGO/WO3nanocomposite has been proposed. The hydrothermal method was employed to synthesize the RGO/WO3nanocomposite. The formation of RGO/WO3nanocomposite and the elemental composition, structure and morphology of the as-synthesized materials were confirmed through an array of analytical techniques, including XRD, Raman, FT-IR, XPS and TEM. For gas sensing applications, pure RGO and RGO/WO3have effectively spin-coated onto the interdigitated electrodes (IDE's) based on fluorine doped tin oxide (FTO) respectively, and their sensitivity towards NH3was tested. Gas sensing characteristics of prepared materials were analyzed at room temperature (25 °C) under different relative humidity (RH) levels. The developed RGO/WO3sensor was subjected to different NH3concentrations, demonstrating a high sensing response of 89% towards 500 ppm NH3under 11%-97%-11% RH conditions. Notably, the sensor exhibited rapid response and recovery times with an average response time of 92 s and recovery time of 26 s when exposed to 500 ppm NH3under the specified RH conditions. To gauge the material selectivity, the prepared nanocomposite was exposed to a range of volatile organic compounds and the results showcased the sensor's remarkable selectivity and sensitivity specifically toward NH3vapor. This superior performance can be attributed to the abundant active sites and the excellent electron transport properties inherent to the RGO component. Importantly, the RGO/WO3sensor displayed high reproducibility and consistent responses, with minimal degradation (1.98% degradation) over 30 d at 11%-97%-11% RH. Furthermore, we examined the sensor's response with varying levels of relative humidity to assess its potential for real-world applications. The sensor exhibited extremely low power consumption, outperforming a commercially available metal oxide sensor while operating at ambient temperature. The robust performance of RGO/WO3coupled with low power requirements and ambient temperature operation, positions it as a promising candidate for next-generation gas sensing technologies.

Nano Hotplate Fabrication for Metal Oxide-Based Gas Sensors by Combining Electron Beam and Focused Ion Beam Lithography

Metal oxide semiconductor (MOS) gas sensors are widely used for gas detection. Typically, the hotplate element is the key component in MOS gas sensors which provide a proper and tunable operation temperature. However, the low power efficiency of the standard hotplates greatly limits the portable application of MOS gas sensors. The miniaturization of the hotplate geometry is one of the most effective methods used to reduce its power consumption. In this work, a new method is presented, combining electron beam lithography (EBL) and focused ion beam (FIB) technologies to obtain low power consumption. EBL is used to define the low-resolution section of the electrode, and FIB technology is utilized to pattern the high-resolution part. Different Au++ ion fluences in FIBs are tested in different milling strategies. The resulting devices are characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), and secondary ion mass spectrometry (SIMS). Furthermore, the electrical resistance of the hotplate is measured at different voltages, and the operational temperature is calculated based on the Pt temperature coefficient of resistance value. In addition, the thermal heater and electrical stability is studied at different temperatures for 110 h. Finally, the implementation of the fabricated hotplate in ZnO gas sensors is investigated using ethanol at 250 °C.

Preparation of degradable chemically cross-linked polylactic acid films and its application on disposable straws

The semi-rigidity of the polylactic acid (PLA) molecular chain makes it brittle, poor impact resistance and barrier properties, which severely limits its practical applications. In this paper, a bio-based reactive plasticizer epoxy soybean oil (ESO) was used to improve the mechanical and barrier properties of maleic anhydride grafted polylactic acid (MAPLA) by the chemical reaction between the epoxy and anhydride group. Firstly, the optimum curing conditions were 93.5 °C, 100 °C, and 110.8 °C for 2 h. The effects of different mass fractions of ESO on the properties of MAPLA-ESO (ME) films were systematically investigated. It was found that when the content of ESO was 10 wt%, the tensile properties of the resulting ME films were the best, with a tensile strength of 35.2 MPa. And it had an elongation at break of 20.0 % and toughness of 5.4 MJ/m3, which increased to 690 % and 675 %, respectively, compared with pure MAPLA films. The chemically crosslinked ME films also displayed excellent water resistance, well degradation, low migration properties, and better performance than that of commercial paper straws and PLA straws, exhibiting great application potential as degradable disposable straws. Therefore, this work provides an effective way to develop high-performance, green, and degradable PLA films and products.

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.

Genetic basis of genome size variation of wheat

Research on various species has revealed a connection between genome size variation and the physiological and ecological characteristics of the species, suggesting that it could be a crucial factor influencing a species' adaptability to different environments. Wheat, being one of the world's three primary grains, holds significance in this regard. Investigating the genome size of wheat and analyzing the genetic factors contributing to its variation could offer valuable insights for enhancing wheat agronomic traits. This project has developed a conservative site ratio calculation approach to determine the size of the wheat genome. Additionally, it employs flow cytometry and k-mer distribution analysis to validate this method. Furthermore, the researchers use re-sequencing data to investigate the impact of environmental selection pressure and transposon dynamics on the variation in the size of the wheat genome. The findings from this study demonstrate a strong relationship between the size of the wheat genome and several environmental factors. These results serve as a valuable reference for understanding the development of variation in the size of the hetero-hexaploid wheat genome. Moreover, they contribute to advancing fundamental research on the genetic mechanisms underlying wheat characteristics. Additionally, the study paves the way for exploring new research directions in wheat breeding, which holds promise for future advancements in this field.

Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust

This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git.

Fabrication of polypyrrole gas sensor for detection of NH3 using an oxidizing agent and pyrrole combinations: Studies and characterizations

The organic polymer known as Polypyrrole (Ppy) is synthesized when pyrrole monomers are polymerized. Excellent thermal stability, superior electrical conductivity, and environmental stability are all characteristics of Polypyrrole. Chemical oxidative polymerization was used to synthesize Ppy using Ferric chloride (FeCl₃) as an oxidizing agent and surfactant CTAB in aqueous solution. Oxidant (FeCl₃) to pyrrole varied in different molar ratios (2, 3, 4 and 5). It was found that increasing this ratio up to 4 increases PPy's conductivity. XRD, FTIR, and SEM were used to characterize Ppy. The conductive nature of Ppy was studied by I-V characteristics. The best conductive polymer is studied for the NH₃ gas response.

Response Characteristics Study of Ethylene Sensor for Fruit Ripening under Temperature Control

Post-ripening fruits need to be ripened to reach edible conditions, as they are not yet mature enough when picked. Ripening technology is based mainly on temperature control and gas regulation, with the proportion of ethylene being one of the key gas regulation parameters. A sensor's time domain response characteristic curve was obtained through the ethylene monitoring system. The first experiment showed that the sensor has good response speed (maximum of first derivative: 2.01714; minimum of first derivative: -2.01714), stability (xg: 2.42%; trec: 2.05%; Dres: 3.28%), and repeatability (xg: 20.6; trec: 52.4; Dres: 2.31). The second experiment showed that optimal ripening parameters include color, hardness (Change Ⅰ: 88.53%, Change Ⅱ: 75.28%), adhesiveness (Change Ⅰ: 95.29%, Change Ⅱ: 74.72%), and chewiness (Change Ⅰ: 95.18%, Change Ⅱ: 74.25%), verifying the response characteristics of the sensor. This paper proves that the sensor was able to accurately monitor changes in concentration which reflect changes in fruit ripeness, and that the optimal parameters were the ethylene response parameter (Change Ⅰ: 27.78%, Change Ⅱ: 32.53%) and the first derivative parameter (Change Ⅰ: 202.38%, Change Ⅱ: -293.28%). Developing a gas-sensing technology suitable for fruit ripening is of great significance.

Portable FBAR based E-nose for cold chain real-time bananas shelf time detection

Being cheap, nondestructive, and easy to use, gas sensors play important roles in the food industry. However, most gas sensors are suitable more for laboratory-quality fast testing rather than for cold-chain continuous and cumulative testing. Also, an ideal electronic nose (E-nose) in a cold chain should be stable to its surroundings and remain highly accurate and portable. In this work, a portable film bulk acoustic resonator (FBAR)-based E-nose was built for real-time measurement of banana shelf time. The sensor chamber to contain the portable circuit of the E-nose is as small as a smartphone, and by introducing an air-tight FBAR as a reference, the E-nose can avoid most of the drift caused by surroundings. With the help of porous layer by layer (LBL) coating of the FBAR, the sensitivity of the E-nose is 5 ppm to ethylene and 0.5 ppm to isoamyl acetate and isoamyl butyrate, while the detection range is large enough to cover a relative humidity of 0.8. In this regard, the E-nose can easily discriminate between yellow bananas with green necks and entirely yellow bananas while allowing the bananas to maintain their biological activities in their normal storage state, thereby showing the possibility of real-time shelf time detection. This portable FBAR-based E-nose has a large testing scale, high sensitivity, good humidity tolerance, and low frequency drift to its surroundings, thereby meeting the needs of cold-chain usage.

Application of the IoT in the Food Supply Chain─From the Perspective of Carbon Mitigation

With the rising demands on supply chain transparency and food security, the rapid outspread of the Internet of Things (IoT) to improve logistical efficiency, and the rising penetration of sensor technology into daily life, the extensive integration of the IoT in the food sector is well anticipated. A perspective on potential life cycle trade-offs in regard to the type of integration is necessary. We conduct life cycle assessment (LCA) integrated with shelf life-food loss (SL-FL) models, showing an overall 5-fold leverage on carbon reduction, which is diet dependent and a function of income. Meat presents the highest leverage, 35 ± 11-times, owing to its high carbon footprint. Two-thirds (65%) of global sensors (1 billion) engaged in monitoring fruits and vegetables can mitigate less than 7% of the total reduced carbon emissions. Despite the expected carbon emission reductions, widespread adoption of the IoT faces multiple challenges such as high costs, difficulties in social acceptance, and regional variability in technological development. Furthermore, changes in the distribution of transportation resources and dealer service models, requirements regarding the accuracy of sensor data analysis, efficient and persistent operation of devices, development of agricultural infrastructure, and farmer education and training have all increased uncertainty. Nonetheless, the research trend in smart sensors toward smaller chips and the potential integration of machine learning or blockchain as further steps make it possible to leverage these advantages to facilitate market penetration. These insights facilitate the future optimization of the application of IoT sensors for sustainability.

Microheater Integrated Nanotube Array Gas Sensor for Parts-Per-Trillion Level Gas Detection and Single Sensor-Based Gas Discrimination

Real-time monitoring of health threatening gases for chemical safety and human health protection requires detection and discrimination of trace gases with proper gas sensors. In many applications, costly, bulky, and power-hungry devices, normally employing optical gas sensors and electrochemical gas sensors, are used for this purpose. Using a single miniature low-power semiconductor gas sensor to achieve this goal is hardly possible, mostly due to its selectivity issue. Herein, we report a dual-mode microheater integrated nanotube array gas sensor (MINA sensor). The MINA sensor can detect hydrogen, acetone, toluene, and formaldehyde with the lowest measured limits of detection (LODs) as 40 parts-per-trillion (ppt) and the theoretical LODs of ∼7 ppt, under the continuous heating (CH) mode, owing to the nanotubular architecture with large sensing area and excellent surface catalytic activity. Intriguingly, unlike the conventional electronic noses that use arrays of gas sensors for gas discrimination, we discovered that when driven by the pulse heating (PH) mode, a single MINA sensor possesses discrimination capability of multiple gases through a transient feature extraction method. These above features of our MINA sensors make them highly attractive for distributed low-power sensor networks and battery-powered mobile sensing systems for chemical/environmental safety and healthcare applications.

Consensual Regression of Soluble Solids Content in Peach by Near Infrared Spectrocopy

In order to reduce the uncertainty of the genetic algorithm (GA) in optimizing the near-infrared spectral calibration model and avoid the loss of spectral information of the unselected variables, a strategy of fusing consensus models is proposed to measure the soluble solids content (SSC) in peaches. A total of 266 peach samples were collected at four arrivals, and their interactance spectra were scanned by an integrated analyzer prototype, and then an internal index of SSC was destructively measured by the standard refractometry method. The near-infrared spectra were pre-processed with mean centering and were selected successively with a genetic algorithm (GA) to construct the consensus model, which was integrated with two member models with optimized weightings. One was the conventional partial least square (PLS) optimized with GA selected variables (PLS_GA), and the other one was the derived PLS developed with residual variables after GA selections (PLS_RV). The performance of PLS_RV models showed some useful spectral information related to peaches’ SSC and someone performed close to the full-spectral-based PLS model. Among these 10 runs, consensus models obtained a lower root mean squared errors of prediction (RMSEP), with an average of 1.106% and standard deviation (SD) of 0.0068, and performed better than that of the optimized PLS_GA models, which achieved a RMSEP of average 1.116% with SD of 0.0097. It can be concluded that the application of fusion strategy can reduce the fluctuation uncertainty of a model optimized by genetic algorithm, fulfill the utilization of the spectral information amount, and realize the rapid detection of the internal quality of the peach.

A Data-Driven Packaging Efficiency Optimization Method for a Low Carbon System in Agri-Products Cold Chain

The of monitoring the Internet of Things (IoT) in the cold chain allows process data, including packaging data, to be more easily accessible. Proper optimization modelling is the core driving force towards the green and low-carbon operation of cold chain logistics, laying the necessary foundation for the development of a data-driven modelling system. Since efficient packaging is necessary for loss control in the cold chain, its final efficiency during circulation is important for realizing continuous loss prevention and efficient supply. Thus, it is urgent to determine how to utilize these continuously acquired data and how to formulate a more accurate packaging efficiency control methodology in the agri-products cold chain. Through continuous monitoring, we examined the feasibility of this topic by focusing on the concept of data-driven evaluation modelling and the dynamic formation mechanism of comprehensive packaging efficiency in cold chain logistics. The packaging efficiency in the table grape cold chain was used as an example to evaluate the comprehensive efficiency evaluation index system and data-driven evaluation framework proposed in this paper. Our results indicate that the established methodology can adapt to the continuity of comprehensive packaging efficiency, also reflecting the comprehensive efficiency evaluation of the packaging for different times and distances. Through the evaluation of our results, the differences and the dynamic processes between different final packaging efficiencies at different moments are effectively displayed. Thus, the continuous improvement of a low-carbon system in cold chain logistics could be realized.

Model Supporting Development Decisions by Considering Qualitative–Environmental Aspects

Dynamic changes in customers’ expectations and unfavorable climate changes have generated the need to consider such aspects in the process of creating new products and the modernization of existing products. Simultaneously including customers’ expectations and environmental impact is a key element of the sustainable development of products. Enterprises attempt, within their awareness and possibilities, to apply the idea of sustainability; they do this more or less methodically. As such, an instrument to support decision-making in the area of product development is still needed because it would both be desirable for customers and have less of a negative effect on the natural environment. The purpose of this study was to develop a model that supports decision-making in the development of products while considering sustainability. The model determines the key criteria of the product, criteria states (current and future), and their positive correlations (e.g., achieving high levels of product quality and no (or a reduction in) destructive impact on the environment). To reduce the fuzzy decision-making environment, multiplicative decision methods with the fuzzy Saaty scale were implemented. These methods were the fuzzy analytic hierarchy process (FAHP) and the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The model is able to support qualitative–environment decisions in the development of any product.