Freshness Evaluation of Three Kinds of Meats Based on the Electronic Nose

Insight into flavor changes in bighead carp (Aristichthys nobilis) fillets during storage based on enzymatic, microbial, and metabolism analysis

The flavor alterations in bighead carp subjected to varying storage temperatures and the underlying metabolic mechanism were elucidated. Analysis of volatile flavor compounds, electronic nose, free amino acids, ATP-related compounds, and sensory evaluations uncovered a progressive flavor deterioration during storage, especially at 25 °C. Metabolomics-based flavor relating component profiling analysis showed that free fatty acids formed various fatty aldehydes including (E, E)-2,4-heptadienal and nonanal under lipoxygenase catalysis. Alcohol dehydrogenase and alcohol acyltransferases were intimately involved in alcohol and ester generation, while alkaline phosphatase, 5'-nucleotidase, and acid phosphatase were closely associated with IMP, Hx, and HxR conversion, respectively. Aeromonas, Serratia, Lactococcus, Pseudomonas, and Peptostreptococcus notably influenced flavor metabolism and enzyme activities. The metabolism disparities of valine, leucine, isoleucine, lysine, and α-linolenic acid could be the primary factors contributing to flavor metabolism distinctions. This study offers novel insights into the flavor change mechanisms and potential regulation strategies of bighead carp during storage.

A Review of Advanced Sensor Technologies for Aquatic Products Freshness Assessment in Cold Chain Logistics

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

Graphene Thermal Infrared Emitters Integrated into Silicon Photonic Waveguides

Cost-efficient and easily integrable broadband mid-infrared (mid-IR) sources would significantly enhance the application space of photonic integrated circuits (PICs). Thermal incandescent sources are superior to other common mid-IR emitters based on semiconductor materials in terms of PIC compatibility, manufacturing costs, and bandwidth. Ideal thermal emitters would radiate directly into the desired modes of the PIC waveguides via near-field coupling and would be stable at very high temperatures. Graphene is a semimetallic two-dimensional material with comparable emissivity to thin metallic thermal emitters. It allows maximum coupling into waveguides by placing it directly into their evanescent fields. Here, we demonstrate graphene mid-IR emitters integrated with photonic waveguides that couple directly into the fundamental mode of silicon waveguides designed to work in the so-called "fingerprint region" relevant for gas sensing. High broadband emission intensity is observed at the waveguide-integrated graphene emitter. The emission at the output grating couplers confirms successful coupling into the waveguide mode. Thermal simulations predict emitter temperatures up to 1000 °C, where the blackbody radiation covers the mid-IR region. A coupling efficiency η, defined as the light emitted into the waveguide divided by the total emission, of up to 68% is estimated, superior to data published for other waveguide-integrated emitters.

System Design Based on Biological Olfaction for Meat Analysis Using E-Nose Sensors

The deterioration of food, especially in meat products, can lead to serious health problems. Even with modern preservation technologies, a significant amount of food is lost due to microbial deterioration. As the very first step of the preservation process, the microflora that grows during the storage time and in spoiling foods should be well-known to identify critical levels. Electronic nose and gas chromatography analysis systems can provide sensitive and promising results. Similarly, bacterial analysis is an important process for determining bacterial groups that result in the emergence of such gases in gas chromatography-mass spectrometry (GC-MS) analysis during the degradation time. This study aims to determine the degradation levels for some meat types under different environmental conditions, such as temperature and duration, to compare with other measurement techniques for evaluating the verification of data. E-nose device, developed in this study, can detect carbon monoxide (CO), methane (CH4), ethanol (C2H5OH), and ammonia (NH3) using metal oxide semiconductor (MOS) sensors. In order to test sensory measurements during this period, GC-MS and microbial measurements were used. E-nose measurements show that the results are in accord with each other. This system can easily be made portable, occupying very little space.

Unraveling the Chicken Meat Volatilome with Nanostructured Sensors: Impact of Live and Dehydrated Insect Larvae Feeding

Incorporating insect meals into poultry diets has emerged as a sustainable alternative to conventional feed sources, offering nutritional, welfare benefits, and environmental advantages. This study aims to monitor and compare volatile compounds emitted from raw poultry carcasses and subsequently from cooked chicken pieces from animals fed with different diets, including the utilization of insect-based feed ingredients. Alongside the use of traditional analytical techniques, like solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS), to explore the changes in VOC emissions, we investigate the potential of S3+ technology. This small device, which uses an array of six metal oxide semiconductor gas sensors (MOXs), can differentiate poultry products based on their volatile profiles. By testing MOX sensors in this context, we can develop a portable, cheap, rapid, non-invasive, and non-destructive method for assessing food quality and safety. Indeed, understanding changes in volatile compounds is crucial to assessing control measures in poultry production along the entire supply chain, from the field to the fork. Linear discriminant analysis (LDA) was applied using MOX sensor readings as predictor variables and different gas classes as target variables, successfully discriminating the various samples based on their total volatile profiles. By optimizing feed composition and monitoring volatile compounds, poultry producers can enhance both the sustainability and safety of poultry production systems, contributing to a more efficient and environmentally friendly poultry industry.

Fabrication and characterization of sodium alginate/blueberry anthocyanins/hinokitiol loaded ZIF-8 nanoparticles composite films with antibacterial activity for monitoring pork freshness

A smart film was developed to detect the freshness of pork by incorporating blueberry anthocyanins (BAs) and hinokitiol (HIN) loaded zeolite-imidazolium framework (HIN@ZIF-8) with into a sodium alginate matrix, and its microstructure and physicochemical properties were studied. The SA matrix was doped with BAs and HIN@ZIF-8 nanoparticles (SA-BAs/HIN@ZIF-8) to increase its tensile strength and reduce its water vapor permeability. HIN@ZIF-8 has low cytotoxicity, and SA-BAs/HIN@ZIF-8 membranes have long-lasting antimicrobial and highly sensitive color development properties against Escherichia coli and Staphylococcus aureus. The results of pork preservation experiments showed that SA-BA/HIN@ZIF-8 could extend the shelf life of pork to 6 days at 4 ℃. E-nose evaluation experiments showed that SA-BAs/HIN@ZIF-8 could inhibit compounds that cause unpleasant and irritating odours. Therefore, SA-BAs/HIN@ZIF-8 was considered to be an effective method to improve the freshness of pork, and the results showed that it has a promising application in food preservation.

Inclusion of Beef Heart in Ground Beef Patties Alters Quality Characteristics and Consumer Acceptability as Assessed by the Application of Electronic Nose and Tongue Technology

Consumer purchasing of beef is often driven by the trinity of flavor, palatability, and convenience. Currently, beef patties in the United States are manufactured with fat and lean trimmings derived from skeletal muscles. A reduction in total beef supply may require the use of animal by-product utilization such as variety meats to achieve patty formulations. The current study aimed to assess textural, color, and flavor characteristics in addition to volatile compounds through electronic technology, e-nose and e-tongue, of ground beef patties formulated with beef heart. Ground beef patties were manufactured with 0%, 6%, 12%, or 18% beef heart, with the remainder of the meat block being shoulder clod-derived ground beef. Patties (n = 65/batch/treatment) within each batch (n = 3) with each treatment were randomly allocated to cooked color (n = 17/batch/treatment), Allo-Kramer shear force (AKSF; n = 17/batch/treatment), texture profile analysis (TPA; n = 6/batch/treatment), cooking loss (n = 17/batch/treatment), consumer panel (n = 3/batch/treatment), e-nose (n = 1/batch/treatment), and e-tongue (n = 1/batch/treatment) analysis groups. Patties containing beef heart did not require additional cooking time (p = 0.1325) nor exhibit greater cooking loss (p = 0.0803). Additionally, inclusion rates of beef heart increased hardness (p = 0.0030) and chewiness values (p = 0.0316) in TPA, were internally redder (p = 0.0001), and reduced overall liking by consumer panelists (p = 0.0367). Lastly, patties containing beef heart exhibited greater red-to-brown (p = 0.0003) and hue angle (p = 0.0001) values than control patties. The results suggest that beef heart inclusion does alter ground beef quality characteristics and consumer acceptability.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

In-fiber Mach-Zehnder interferometer as a promising tool for optical nose and odor prediction during the fermentation process

In this paper, we present an in-fiber Mach-Zehnder interferometer (MZI) applied to coffee bean fermentation monitoring. Two MZIs, based on a combination of a fiber taper cascaded by a micro-tapered long-period fiber grating, were installed in a fermentation barrel to monitor the liquids and gases released during the fermentation process. During this process, a variety of odors arise due to the yeast activity and their classification is important to decide when to stop the fermentation process. In this work, we show that the in-fiber MZIs are good candidates for optical noses in this scenario.

Research on Automatic Classification and Detection of Mutton Multi-Parts Based on Swin-Transformer

In order to realize the real-time classification and detection of mutton multi-part, this paper proposes a mutton multi-part classification and detection method based on the Swin-Transformer. First, image augmentation techniques are adopted to increase the sample size of the sheep thoracic vertebrae and scapulae to overcome the problems of long-tailed distribution and non-equilibrium of the dataset. Then, the performances of three structural variants of the Swin-Transformer (Swin-T, Swin-B, and Swin-S) are compared through transfer learning, and the optimal model is obtained. On this basis, the robustness, generalization, and anti-occlusion abilities of the model are tested and analyzed using the significant multiscale features of the lumbar vertebrae and thoracic vertebrae, by simulating different lighting environments and occlusion scenarios, respectively. Furthermore, the model is compared with five methods commonly used in object detection tasks, namely Sparser-CNN, YoloV5, RetinaNet, CenterNet, and HRNet, and its real-time performance is tested under the following pixel resolutions: 576 × 576, 672 × 672, and 768 × 768. The results show that the proposed method achieves a mean average precision (mAP) of 0.943, while the mAP for the robustness, generalization, and anti-occlusion tests are 0.913, 0.857, and 0.845, respectively. Moreover, the model outperforms the five aforementioned methods, with mAP values that are higher by 0.009, 0.027, 0.041, 0.050, and 0.113, respectively. The average processing time of a single image with this model is 0.25 s, which meets the production line requirements. In summary, this study presents an efficient and intelligent mutton multi-part classification and detection method, which can provide technical support for the automatic sorting of mutton as well as for the processing of other livestock meat.

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.

Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review

The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.

Recent Applications of Potentiometric Electronic Tongue and Electronic Nose in Sensory Evaluation

Electronic tongue (e-tongue) and electronic nose (e-nose) have been widely used to determine food products' taste, aroma, and flavor profiles. Several researchers and industries have recently attempted to find relationships between these e-senses and human sensory panels to ultimately replace sensory panels or use them as a viable alternative to timeconsuming and expensive traditional sensory evaluation (e.g., consumer acceptance testing or descriptive sensory analysis). This study investigated the recent applications of e-tongue and e-nose in the food and beverages sectors and their relationships with human sensory panels, including a trained sensory panel and naïve consumers. According to several studies, the e-tongue, e-nose, or a combination of e-tongue and e-nose can be an effective and powerful tool for rapid assessment of sensory profiles and quality detection with significant correlations with human sensory data. These instruments are also often reported to be more sensitive to detect subtle changes/differences that the human panel cannot detect. Future trends and projections of the e-tongue and e-nose with limitations are also discussed.

Olfactory visualization sensor system based on colorimetric sensor array and chemometric methods for high precision assessing beef freshness

Beef is easily spoiled, resulting in foodborne illness and high societal costs. This study proposed a novel olfactory visualization system based on colorimetric sensor array and chemometric methods to detect beef freshness. First, twelve color-sensitive materials were immobilized on a hydrophobic platform to acquire scent information of beef samples according to solvatochromic effects. Second, machine vision algorithms were used to extract the scent fingerprints, and principal component analysis (PCA) was employed to compress the feature dimensions of the fingerprints. Finally, four qualitative models, k-nearest neighbor, extreme learning machine, support vector machine (SVM), and random forest, were constructed to evaluate the beef freshness according to the value of total volatile basic nitrogen (TVB-N) and total viable counts (TVC). Results demonstrated that SVM had a preferable prediction ability, with 95.83% and 95.00% precision in the training and prediction sets, respectively. The results revealed that the simple constructed olfactory visualization sensor system could rapidly, robustly, and accurately assess beef freshness.

A Virtual Electronic Nose for the Efficient Classification and Quantification of Volatile Organic Compounds

Although many chemical gas sensors report high sensitivity towards volatile organic compounds (VOCs), finding selective gas sensing technologies that can classify different VOCs is an ongoing and highly important challenge. By exploiting the synergy between virtual electronic noses and machine learning techniques, we demonstrate the possibility of efficiently discriminating, classifying, and quantifying short-chain oxygenated VOCs in the parts-per-billion concentration range. Several experimental results show a reproducible correlation between the predicted and measured values. A 10-fold cross-validated quadratic support vector machine classifier reports a validation accuracy of 91% for the different gases and concentrations studied. Additionally, a 10-fold cross-validated partial least square regression quantifier can predict their concentrations with coefficients of determination, R², up to 0.99. Our methodology and analysis provide an alternative approach to overcoming the issue of gas sensors' selectivity, and have the potential to be applied across various areas of science and engineering where it is important to measure gases with high accuracy.

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.

The Use of Electronic Nose in the Quality Evaluation and Adulteration Identification of Beijing-You Chicken

The objective of this study was to reveal the secrets of the unique meat characteristics of Beijing-you chicken (BJY) and to compare the difference of quality and flavor with Luhua chicken (LH) and Arbor Acres broiler (AA) at their typical market ages. The results showed the meat of BJY was richer in essential amino acids, arachidonic acid contents, inosine monophosphate (IMP), and guanosine monophosphate (GMP). The total fatty acid and unsaturated fatty acid contents of BJY chicken and LH chicken were lower than that of AA broilers, whereas the ratios of unsaturated fatty acids/saturated fatty acids (2.31) and polyunsaturated fatty acids/monounsaturated fatty acids (1.52) of BJY chicken were the highest. The electronic nose and SPME-GC/MS analysis confirmed the significant differences among these three chickens, and the variety and relative content of aldehydes might contribute to a richer flavor of BJY chicken. The meat characteristics of BJY were fully investigated and showed that BJY chicken might be favored among these three chicken breeds with the best flavor properties and the highest nutritional value. This study also provides an alternative way to identify BJY chicken from other chickens.

Preparation of zein-lecithin-EGCG complex nanoparticles stabilized peppermint oil emulsions: Physicochemical properties, stability and intelligent sensory analysis

Peppermint oil emulsions were prepared by using zein-lecithin-EGCG (Z-L/E) complex nanoparticles as emulsifiers. The preparation conditions of emulsions were optimized via measuring the particle size, surface tension and stability of emulsions, and peppermint oil of 3% (particle size = 375 nm, polydispersity index (PDI) = 0.45), the zein:lecithin ratio of 4:1 (w/w) (particle size = 396 nm), and the zein:EGCG ratio of 10:1 (w/w) (surface tension = 47.32 N/m) was the optimal condition. The rapid stability analysis showed that the instability mechanism of emulsions was ascribed to creaming and stratification, and the stability mechanism of emulsions was explored, indicating that the complex nanoparticles adsorbed on the surface of oil droplets to give Pickering emulsions. Electronic tongue experiments showed that the Z-E/L_4:1 stabilized emulsion was distinguished from the other three samples due to its good stability. The electronic nose experiment could distinguish the emulsions with different droplet sizes.

Analysis of the relationship between microorganisms and flavour development in dry-cured grass carp by high-throughput sequencing, volatile flavour analysis and metabolomics

Complex microbial community plays an important role for flavor formation in traditional dry-cured grass carp. To investigate the correlation between microorganisms and flavour development, the bacterial diversity and flavour quality of dry-cured fish at different stages of fermentation were analysed using high-throughput sequencing, volatile flavour analysis and metabolomics. Cobetia, Staphylococcus and Ralstonia were the dominant genera in dry-cured fish, with relative abundances of 37.78%, 34.46% and 3.2%, respectively. The flavour of dry-cured fish samples varied as the abundance of aldehydes, alcohols, small peptides, FAAs and carboxylic acids showed a great increase during fermentation. Moreover, there were significant correlations (P < 0.05) between specific microorganisms and volatile indicators, as well as flavour metabolites. Staphylococcus, as the dominant bacterial genus, is involved in the mechanism of flavour formation in dry-cured fish during fermentation. This information is useful for elucidating the mechanism of flavour formation in dry-cured fish.

Online Inertial Machine Learning for Sensor Array Long-Term Drift Compensation

The sensor drift problem is objective and inevitable, and drift compensation has essential research significance. For long-term drift, we propose a data preprocessing method, which is different from conventional research methods, and a machine learning framework that supports online self-training and data analysis without additional sensor production costs. The data preprocessing method proposed can effectively solve the problems of sign error, decimal point error, and outliers in data samples. The framework, which we call inertial machine learning, takes advantage of the recent inertia of high classification accuracy to extend the reliability of sensors. We establish a reasonable memory and forgetting mechanism for the framework, and the choice of base classifier is not limited. In this paper, we use a support vector machine as the base classifier and use the gas sensor array drift dataset in the UCI machine learning repository for experiments. By analyzing the experimental results, the classification accuracy is greatly improved, the effective time of the sensor array is extended by 4–10 months, and the time of single response and model adjustment is less than 300 ms, which is well in line with the actual application scenarios. The research ideas and results in this paper have a certain reference value for the research in related fields.

Effect of Freeze-Thaw Cycles on Juice Properties, Volatile Compounds and Hot-Air Drying Kinetics of Blueberry

This paper studied the effects of freeze-thaw (FT) cycles on the juice properties and aroma profiles, and the hot-air drying kinetics of frozen blueberry. After FT treatment, the juice yield increased while pH and total soluble solids of the juice keep unchanged. The total anthocyanins contents and DPPH antioxidant activities of the juice decreased by FT treatments. The electronic nose shows that FT treatments significantly change the aroma profiles of the juice. The four main volatile substances in the fresh juice are (E)-2-hexenal, α-terpineol, hexanal and linalyl formate, which account for 48.5 ± 0.1%, 17.6 ± 0.2%, 14.0 ± 1.5% and 7.8 ± 2.7% of relative proportions based on total ion chromatogram (TIC) peak areas. In the FT-treated samples, the amount of (E)-2-hexenal and hexanal decreased significantly while α-terpineol and linalyl formate remained almost unchanged. Repeated FT cycles increased the ethanol content and destroyed the original green leafy flavor. Finally, the drying kinetics of FT-treated blueberries was tested. One FT treatment can shorten the drying time by about 30% to achieve the same water content. The D_eff values of the FT-treated sample are similar, which are about twice as large as the value of the fresh sample. The results will be beneficial for the processing of frozen blueberry into juice or dried fruits.

An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy

Effective and rapid assessment of pork freshness is significant for monitoring pork quality. However, a traditional sensory evaluation method is subjective and physicochemical analysis is time-consuming. In this study, the near-infrared spectroscopy (NIRS) technique, a fast and non-destructive analysis method, is employed to determine pork freshness. Considering that commonly used statistical modeling methods require preprocessing data for satisfactory performance, this paper presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIRS. The developed model enhances the one-dimensional residual network (1D-ResNet) with squeeze-and-excitation (SE) blocks. As a deep learning model, the proposed method is capable of extracting features from the input spectra automatically and can be used as an end-to-end model to simplify the modeling process. A comparison between the proposed method and five popular classification models indicates that the 1D-SE-ResNet achieves the best performance, with a classification accuracy of 93.72%. The research demonstrates that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection and therefore is helpful for ensuring food safety.

Development of a Novel Gas-Sensing Platform Based on a Network of Metal Oxide Nanowire Junctions Formed on a Suspended Carbon Nanomesh Backbone

Junction networks made of longitudinally connected metal oxide nanowires (MOx NWs) have been widely utilized in resistive-type gas sensors because the potential barrier at the NW junctions leads to improved gas sensing performances. However, conventional MOx–NW-based gas sensors exhibit limited gas access to the sensing sites and reduced utilization of the entire NW surfaces because the NW networks are grown on the substrate. This study presents a novel gas sensor platform facilitating the formation of ZnO NW junction networks in a suspended architecture by growing ZnO NWs radially on a suspended carbon mesh backbone consisting of sub-micrometer-sized wires. NW networks were densely formed in the lateral and longitudinal directions of the ZnO NWs, forming additional longitudinally connected junctions in the voids of the carbon mesh. Therefore, target gases could efficiently access the sensing sites, including the junctions and the entire surface of the ZnO NWs. Thus, the present sensor, based on a suspended network of longitudinally connected NW junctions, exhibited enhanced gas response, sensitivity, and lower limit of detection compared to sensors consisting of only laterally connected NWs. In addition, complete sensor structures consisting of a suspended carbon mesh backbone and ZnO NWs could be prepared using only batch fabrication processes such as carbon microelectromechanical systems and hydrothermal synthesis, allowing cost-effective sensor fabrication.

Antimicrobial effect and mechanism of non-antibiotic alkyl gallates against Pseudomonas fluorescens on the surface of Russian sturgeon (Acipenser gueldenstaedti)

Since Pseudomonas fluorescens is the main microorganism causing severe spoilage in refrigerated aquatic products, the searching for non-antibiotic antibacterial agents effective against it continues to receive increasing interest. This study aimed to investigate the antibacterial effects and mechanisms of alkyl gallic esters against P. fluorescens isolated from the Russian sturgeon (Acipenser gueldenstaedti), as well as the effectiveness in combination with chitosan films on the preservation of sturgeon meats at 4 °C. Our data shows that the alkyl chain length plays a significant role in eliciting their antibacterial activities and octyl gallate (GAC8) exhibited an outstanding inhibitory efficacy. GAC8 can rapidly enter into the membrane lipid bilayer portion to disorder the membrane, and further inhibit the growth of the P. fluorescens through interfering both tricarboxylic acid cycle related to energy supply and amino acid metabolism associated with cell membranes, suppressing oxygen consumption and disturbing the respiration chain. Moreover, the alteration in membrane fatty acids indicated that GAC8 could disrupt the composition of cell membrane fatty acids, rendering the bacteria more sensitive to the antibacterial. The SEM results also substantiate the damage of the structure of the bacterial membrane caused by GAC8. Additionally, the edible chitosan-based films incorporated with GAC8 showed the enhanced antibacterial efficacy to remarkably extend the shelf life of Russian sturgeon. Overall, our findings not only provide new insight into the mode of action of GAC8 against P. fluorescens but also demonstrate composite films containing GAC8, as a kind of safe and antibacterial material, have a great promise for application in food preservations.

Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness

This study presents a system for assessing the freshness of meat with electrical impedance spectroscopy (EIS) in the frequency range of 125 Hz to 128 kHz combined with an image classifier for non-destructive and low-cost applications. The freshness standard is established by measuring the aerobic plate count (APC), 2-thiobarbituric acid reactive substances (TBARS), and composition analysis (crude fat, crude protein, and moisture) values of the microbiological detection to represent the correlation between EIS and meat freshness. The EIS and images of meat are combined to predict the freshness with the Adaboost classification and gradient boosting regression algorithms. As a result, when the elapsed time of beef storage for 48 h is classified into three classes, the time prediction accuracy is up to 85% compared to prediction accuracy of 56.7% when only images are used without EIS information. Significantly, the relative standard deviation (RSD) of APC and TBARS value predictions with EIS and images datum achieves 0.890 and 0.678, respectively.

Sensing of Hydrogen Sulfide Gas in the Raman-Silent Region Based on Gold Nano-Bipyramids (Au NBPs) Encapsulated by Zeolitic Imidazolate Framework-8

Mostly, surface-enhanced Raman scattering (SERS) sensors used the Raman characteristic bands concentrated in the Raman "fingerprint" region (500-1800 cm^-1), which may result in spectral overlapping interference. The study of the response in the Raman-silent region (10-500 and 1800-2800 cm^-1) can help overcome this problem. Hydrogen sulfide (H₂S) gas causes a great threat to human's health, but its low concentration in the airborne species is a challenge for sensitive and selective detection. Herein, a novel low-wavenumber (10-500 cm^-1) SERS sensor for H₂S gas detection has been developed based on gold nano-bipyramids (Au NBPs) encapsulated by zeolitic imidazolate framework-8 (ZIF-8) (Au NBPs@ZIF-8). The sensor takes advantage of the high adsorption capacity of ZIF-8 toward H₂S gas and the H₂S-triggered SERS spectral changes in the low-wavenumber Raman-silent region. A clear SERS peak of Au-Br at ∼175 cm^-1 generated from Au NBPs@ZIF-8 showed a decrease in the presence of H₂S because of the competition of adsorption sites between Au-S and Au-Br bonds. Furthermore, Au NBPs@ZIF-8 can enrich and monitor the level of H₂S gas with high efficiency and low interference. The developed sensor has a detection range of 0.2 nM to 20 mM with a limit of detection (LOD) of 0.17 nM. The developed sensor had been applied to detect the H₂S gas released from the spoiled fish meat with high selectivity.

Ionic-liquid doped polymeric composite as passive colorimetric sensor for meat freshness as a use case

A composite membrane containing 1,2-naphthoquinone-4-sulfonic acid sodium salt (NQS) embedded in an ionic liquid (IL)- polydimethylsiloxane (PDMS)- tetraethyl orthosilicate (TEOS)- SiO₂ nanoparticles (NPs) polymeric matrix is proposed. The selected IL was 1-methyl-3-octylimidazolium hexafluorophosphate (OMIM PF6). It is demonstrated that ILs chemical additives of PDMS influenced the sol-gel porosity. The sensor analytical performance for ammonia atmospheres has been tested as a function of sampling time (between 0.5 and 312 h), temperature (25 °C and 4 °C) and sampling volume (between 2L and 22 mL) by means of diffuse reflectance measurements and sensor photos, which can be registered and saved as images by a smartphone, which permit RGB measurements too. Flexible calibration was possible, adapting it to the sampling time, temperature and sampling volume needed for its application. Calibration linear slopes (mA vs ppmv) between 1.7 and 467 ppmv^-1 were obtained for ammonia in function of the several studied conditions. Those slopes were between 48 and 91% higher than those achieved with sensors without ILs. The practical application of this sensing device was demonstrated for the analysis of meat packaging environments, being a potential cost-effective candidate for in situ meat freshness analysis. NQS provided selectivity in reference to other family compounds emitted from meat products, such as sulphides. After 10 days at 4 °C ammonia liberated by the assayed meat was 20 ± 4 μg/kg and 18 ± 3 μg/kg, quantified by using diffuse reflectance and %R measurements, respectively. Homogeneity of the ammonia atmosphere was tested by using two sensors placed in two different positions inside the packages.

Traditional Sensory Evaluation and Bionic Electronic Nose as Innovative Tools for the Packaging Performance Evaluation of Chitosan Film

Inspired by the natural epidermis of animals and plants with antioxidant and antibacterial properties, the aim of this research was to characterize and analyze the effects of the chitosan concentrations on properties of glycerol plasticized chitosan (GPC) film and to investigate the suitability of sensory evaluation and bionic electronic nose (b-electronic nose) detection to assess the freshness of ground beef packaged in the GPC film. The increase in chitosan concentration resulted in an increase in solubility value, total color differences and color intensity of chitosan films. The water vapor permeability (WVP) of the GPC films decreased with the increasing of the chitosan concentration and then increased at higher chitosan concentrations. Longer storage time led to poorer freshness of the ground beef and the GPC film could keep beef samples fresher and delay the deterioration of the beef. Both the traditional sensory evaluation and b-electronic nose technology were fit for evaluating the quality and shelf-life of ground beef, which could advantageously be applied in the future for analyzing other bionic food packaging materials.

Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model

For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general.

Five Typical Stenches Detection Using an Electronic Nose

This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, γ-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.

Rapid and Non-Destructive Detection of Compression Damage of Yellow Peach Using an Electronic Nose and Chemometrics

The rapid and non-destructive detection of mechanical damage to fruit during postharvest supply chains is important for monitoring fruit deterioration in time and optimizing freshness preservation and packaging strategies. As fruit is usually packed during supply chain operations, it is difficult to detect whether it has suffered mechanical damage by visual observation and spectral imaging technologies. In this study, based on the volatile substances (VOCs) in yellow peaches, the electronic nose (e-nose) technology was applied to non-destructively predict the levels of compression damage in yellow peaches, discriminate the damaged fruit and predict the time after the damage. A comparison of the models, established based on the samples at different times after damage, was also carried out. The results show that, at 24 h after damage, the correct answer rate for identifying the damaged fruit was 93.33%, and the residual predictive deviation in predicting the levels of compression damage and the time after the damage, was 2.139 and 2.114, respectively. The results of e-nose and gas chromatography-mass spectrophotometry (GC–MS) showed that the VOCs changed after being compressed—this was the basis of the e-nose detection. Therefore, the e-nose is a promising candidate for the detection of compression damage in yellow peach.

Development of Electronic Nose for Qualitative and Quantitative Monitoring of Volatile Flammable Liquids

A real-time electric nose (E-nose) with a metal oxide sensor (MOS) array was developed to monitor 5 highly flammable liquids (ethanol, tetrahydrofuran, turpentine, lacquer thinner, and gasoline) in this work. We found that temperature had a significant impact on the test results and temperature control could efficiently improve the performance of our E-nose. The results of our qualitative analysis showed that principal component analysis (PCA) could not efficiently distinguish these samples compared to a back-propagation artificial neural network (BP-ANN) which had a 100% accuracy rate on the test samples. Quantitative analysis was performed by regression analysis and the average errors were 9.1%–18.4%. In addition, through anti-interference training, the E-nose could filter out the potential false alarm caused by mosquito repellent, perfume and hair jelly.

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R² ≥ 0.9998).

Review on Smart Gas Sensing Technology

With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology

In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.