Performance of a Novel Electronic Nose for the Detection of Volatile Organic Compounds Relating to Starvation or Human Decomposition Post-Mass Disaster.

There has been a recent increase in the frequency of mass disaster events. Following these events, the rapid location of victims is paramount. Currently, the most reliable search method is scent detection dogs, which use their sense of smell to locate victims accurately and efficiently. Despite their efficacy, they have limited working times, can give false positive responses, and involve high costs. Therefore, alternative methods for detecting volatile compounds are needed, such as using electronic noses (e-noses). An e-nose named the 'NOS.E' was developed and has been used successfully to detect VOCs released from human remains in an open-air environment. However, the system's full capabilities are currently unknown, and therefore, this work aimed to evaluate the NOS.E to determine the efficacy of detection and expected sensor response. This was achieved using analytical standards representative of known human ante-mortem and decomposition VOCs. Standards were air diluted in Tedlar gas sampling bags and sampled using the NOS.E. This study concluded that the e-nose could detect and differentiate a range of VOCs prevalent in ante-mortem and decomposition VOC profiles, with an average LOD of 7.9 ppm, across a range of different chemical classes. The NOS.E was then utilized in a simulated mass disaster scenario using donated human cadavers, where the system showed a significant difference between the known human donor and control samples from day 3 post-mortem. Overall, the NOS.E was advantageous: the system had low detection limits while offering portability, shorter sampling times, and lower costs than dogs and benchtop analytical instruments.

Comparison and identification of aroma components in 21 kinds of frankincense with variety and region based on the odor intensity characteristic spectrum constructed by HS-SPME-GC-MS combined with E-nose.

Frankincense is an important seasoning and spice known for its distinctive and intricate flavor profile. Considering the considerable variation in the aromatic quality of frankincense due to geographical origin, species diversity and cultivation conditions, frankincense from major global origins was characterized holistically for the first time. The electronic nose (E-nose) with headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) and sensory evaluation were implemented to characterize the aroma components of 21 commercial varieties of frankincense from around the world. The results showed that a total of 149 volatile organic compounds (VOCs) of 10 categories were identified in frankincense, among which the numbers of alcohols, terpenes and esters compounds accounted for 22.15 %, 18.79 % and 15.44 % of the total VOCs of frankincense, respectively. The PLS-DA model effectively distinguished frankincense from Oman/Somalia and other origins. Furthermore, the study identified two differential VOCs with VIP > 1 in three Asian countries and five in six African countries. The total VOCs content and sensory characteristic score of "Lemon/Citrus" in Oman frankincense is significantly higher than other regions. The OAV results showed that 61 substances (e.g., Diacety, alpha-Pinene, Camphene, Myrcene) as key aroma compounds and OICS model indicated that p-Cymenol was found to contribute significantly to the citrus aroma in frankincense. This study identified the fundamental components of frankincense flavor and revealed different flavor descriptors of frankincense, which are crucial for reconstructing frankincense flavor and improving flavor quality.

Combining with E-nose, GC-MS, GC-IMS and chemometrics to explore volatile characteristics during the different stages of Zanthoxylum bungeanum maxim fruits.

To explore the volatile characteristics of Z. bungeanum fruits during different developmental stages, the dynamical changes of volatile organic compounds (VOCs) were detected by E-nose, GC-MS and GC-IMS, respectively. The results showed that terpenes, alcohols, esters and aldehydes played the important roles in the aroma formation of Z. bungeanum. Meanwhile, these VOCs also exhibited the high abundance levels among five growth stages of Z. bungeanum. According to the analysis of odor activity value (OAV) and relative odor activity value (ROAV), 37 VOCs can be recognized as the important aroma compounds. Thereinto, ß-myrcene and linalool were the most key aroma compounds. Multi-factor analysis exhibited that the combination of GC-MS and GC-IMS was a better strategy to clarify the volatile characteristics comprehensively. Using the above combined VOC datasets, six positively correlated modules and 32 hub VOCs were finally identified by weighted correlation network analysis among five growth stages of Z. bungeanum.

Dynamic changes in volatile profiles and bacterial communities during natural fermentation of Mei yu, traditional Chinese fermented fish pieces.

Microbial metabolism is important for the unique flavor formation of Mei yu, a kind of traditional Chinese fermented fish pieces. However, the interactive relationship between microorganisms and flavor components during fermentation is still unclear. In this study, electronic nose and headspace-solid-phase microextraction-gas chromatography-mass spectrometry analysis were performed to identify flavor components in Mei yu during the fermentation, and the absolute microbial quantification was conducted to identify the diversity and succession of microbial communities. During fermentation, there was an increase in the types of volatile compounds. Alcohols, aldehydes, aromatics and esters were the main flavor compounds and significantly increased in Mei yu, while hydrocarbon and aldehydes significantly decreased. The absolute abundances of Lactobacillus, Lactococcus and Weissella increased significantly after 3 days' fermentation, which were closely associated with the productions of 1-nonanol, 2-methoxy-4-vinylphenol, guaiacol, ethyl palmitate and ethyl caprylate that might though pathways related to fatty acid biosynthesis and amino acid metabolism. However, these genera were negatively correlated with the production of indole. Additionally, the total volatile basic nitrogen (TVB-N) levels of Mei yu fermented during 3 days were within the limits of 25 mg TVB-N/100 g fish, with the contents of free amino acids and lipoxygenase activities were significant lower than that of 4 days' fermentation. In view of food safety and flavor, it suggested that the natural fermented Mei yu at room temperature should be controlled within 3 days. This study highlights the application of absolute quantification to microbiome analysis in traditional fermented Mei yu and provides new insights into the roles of microorganisms in flavor formation during fermentation.

Change in volatile profiles of wheat flour during maturation.

The volatile profiles of wheat flour during maturation were examined through headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) combined with electronic nose (E-nose) and electronic tongue (E-tongue) analyses. The wheat flour underwent maturation under three distinct conditions for predetermined durations. While GC/MS coupled with E-tongue exhibited discernment capability among wheat flour samples subjected to varying maturation conditions, E-nose analysis solely relying on principal component analysis failed to achieve discrimination. 83 volatile compounds were identified in wheat flour, with the highest abundance observed in samples matured for 50 d at 25 °C. Notably, trans-2-Nonenal, decanal, and nonanal were the main contributors to the characteristic flavor profile of wheat flour. Integration of HS-SPME-GC/MS with E-tongue indicated superior flavor development and practical viability in wheat flour matured for 50 d at 25 °C. This study furnishes a theoretical groundwork for enhancing the flavor profiles of wheat flour and its derivative products.

Data set for Gambung green tea aroma using on electronic nose.

OBJECTIVES: In recent years, there has been much discussion and research on electronic nose (e-nose). This topic has developed mainly in the medical and food fields. Typically, e-nose is combined with machine learning algorithms to predict or detect multiple sensory classes in each tea sample. Therefore, in e-nose systems, e-nose signal processing is an important part. In many situations, a comprehensive set of experiments is required to ensure the prediction model can be generalized well. This data set specifically focuses on two main goals such as classification of green tea quality and prediction of organoleptic score. In this experiment, Gambung dry green tea samples were used. The challenge is that dry tea does not emit as strong an aroma as tea infusions, making it more difficult for the e-nose system to detect and identify the aromas. This data set offers a valuable resource for researchers and developers to conduct investigations and experiments by classifying and detecting organoleptic scores that aim to categorize and identify organoleptic ratings. This enables a deeper understanding of the quality of dry green tea and encourages further integration of e-nose technology in the tea industry. DATA DESCRIPTION: This experiment focused on analyzing green tea aroma using six gas sensors. Seventy-eight green tea samples were tested, each observed three times, using a tea chamber connected to a sensor chamber via a hose and an intake micro air pump. Air flowed from the tea chamber to the sensor chamber for 60 s, followed by 60 s of aroma data recording. This data was saved into CSV files and labeled according to the Indonesian National Standard (SNI) 3945:2016, which includes special and general requirements for green tea quality. An organoleptic test by a tea tester further labeled the data set into "good" or "quality defect" for classification and provided organoleptic scores based on dry appearance, brew color, taste, aroma, and dregs of brewing for continuous label.

Machine Learning-Based Nanozyme Sensor Array as an Electronic Tongue for the Discrimination of Endogenous Phenolic Compounds in Food.

The detection of endogenous phenolic compounds (EPs) in food is of great significance in elucidating their bioactivity and health effects. Here, a novel bifunctional vanillic acid-Cu (VA-Cu) nanozyme with peroxidase-like and laccase-like activities was successfully prepared. The peroxidase mimic behavior of VA-Cu nanozyme can catalyze 3,3',5,5'-tetramethylbenzidine (TMB) to generate oxidized TMB (oxTMB). Owing to the high reducing power of EPs, this process can be inhibited, and the degree of inhibition increases with the increase of reaction time. Additionally, owing to the outstanding laccase mimic behavior of the VA-Cu, it can facilitate the oxidation of various EPs, resulting in the formation of colored quinone imines, and the degree of catalysis increases with the increase of reaction time. Based on the interesting experimental phenomena mentioned above, a six-channel nanozyme sensor array (2 enzyme-mimic activities × 3 time points = 6 sensing channels) was constructed, successfully achieving discriminant analysis of nine EPs. In addition, the combination of artificial neural network (ANN) algorithms and sensor arrays has successfully achieved accurate identification and prediction of nine EPs in black tea, honey, and grape juice. Finally, a portable method for identifying EPs in food has been proposed by combining it with a smartphone.

Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers.

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

Electronic Nose Analysis of Exhaled Breath Volatile Organic Compound Profiles during Normoxia, Hypoxia, and Hyperoxia.

This study investigates volatile organic compound (VOC) profiles in the exhaled breath of normal subjects under different oxygenation conditions-normoxia (FiO2 21%), hypoxia (FiO2 11%), and hyperoxia (FiO2 35%)-using an electronic nose (e-nose). We aim to identify significant differences in VOC profiles among the three conditions utilizing principal component analysis (PCA) and canonical discriminant analysis (CDA). Our results indicate distinct VOC patterns corresponding to each oxygenation state, demonstrating the potential of e-nose technology in detecting physiological changes in breath composition (cross-validated accuracy values: FiO2 21% vs. FiO2 11% = 63%, FiO2 11% vs. FiO2 35% = 65%, FiO2 21% vs. FiO2 35% = 71%, and p < 0.05 for all). This research underscores the viability of breathomics in the non-invasive monitoring and diagnostics of various respiratory and systemic conditions.

Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System.

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography-mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.

Portable Sensing Probe for Real-Time Quantification of Ammonia in Blood Samples.

The detection of ammonia levels in blood is critical for diagnosing and monitoring various medical conditions, including liver dysfunction and metabolic disorders. However, traditional diagnostic methods are slow and cumbersome, often involving multiple contact-based steps such as ammonia separation in alkali conditions followed by distillation or microdiffusion, leading to delays in diagnosis and treatment. Herein, we developed a colorimetric assay capable of rapid detection of ammonia in whole blood or plasma samples, utilizing 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO)-oxidized cellulose nanocrystals (TCNC) coupled with gold nanoparticles (AuNPs). The basis of our assay relies on either (i) the interaction between the carboxylate group (-COO) of TEMPO and ammonium ions or (ii) the manipulation of AuNPs surface plasmon resonance (SPR) through the formation of Au(NH3)43+, which displaces a redox mediator, resazurin, resulting in observable multicolor displays at various concentrations of ammonia. The colorimetric assay exhibits a wide linear detection range for dissolved NH4+ (0.1-37 µM) with a low limit of detection (LOD) of 0.1 µM. Additionally, it effectively measures NH3(g) concentrations in the range of 0.5-144 µM. The fabricated electrochemical nose (E-nose) device demonstrates excellent analytical performance for plasma ammonia sensing (0.05-256 µM). Experimental results demonstrate a linear detection range suitable for clinical applications, with excellent correlation to standard laboratory methods, offering a practical solution for point-of-care (PoC) testing. We anticipate that this approach can be applied broadly to improve patient monitoring and treatment by providing immediate and accurate ammonia measurements in a clinical setting.

A comprehensive analysis of aroma quality and perception mechanism in ginger-infused stewed beef using instrumental analysis, sensory evaluation and molecular docking.

The ginger-infused stewed beef exhibited a satisfactory odor in Chinese cooking meat. This study aimed to reveal its aroma quality and perception mechanism through electronic nose, sensory evaluation and gas chromatography-mass spectrometry (GC-MS), gas chromatography-ion mobility spectrometry (GC-IMS) coupled with chemometric methods and molecular docking. Sensory evaluation and electronic nose analysis indicated ginger could greatly modify aroma profile of beef. Most C6-C10 aldehydes significantly decreased and terpenes increased in ginger-infused stewed beef. Orthogonal partial least squares-discriminant analysis (OPLS-DA) found 7 key markers for distinguishing stewed beef with or without ginger. Ginger additions could reduce fatty acids consumption. Moreover, the key contributors of fatty, bloody, meaty, ginger and mint aroma attributes, namely (E)-2-octenal, 1-octen-3-ol, 2-acetylthiazole, zingiberene and γ-elemene, respectively, selected by partial least squares regression (PLSR) analysis were docked with the olfactory receptor. Hydrogen bonds and hydrophobic interactions were the main interaction forces between olfactory receptor and the five compounds.

A machine learning-based electronic nose system using numerous low-cost gas sensors for real-time alcoholic beverage classification.

This study introduces numerous low-cost gas sensors and a real-time alcoholic beverage classification system based on machine learning. Dogs possess a superior sense of smell compared to humans due to having 30 times more olfactory receptors and three times more olfactory receptor types than humans. Thus, in odor classification, the number of olfactory receptors is a more influential factor than the number of receptor types. From this perspective, this study proposes a system that utilizes distinctive data patterns resulting from heterogeneous responses among numerous low-cost homogeneous MOS-based sensors with poor gas selectivity. To evaluate the performance of the proposed system, learning data were gathered using three alcoholic beverage groups including different aged whiskeys, Korean soju with 99% same compositions, and white wines made from the Sauvignon blanc variety, sourced from various countries. The electronic nose system was developed to classify alcoholic samples measured using 30 gas sensors in real time. The samples were injected into a gas chamber for 60 seconds, followed by a 60-second injection of clean air. After preprocessing the time-series data into four distinct datasets, the data were analyzed using a machine learning algorithm, and the classification results were compared. The results showed a high classification accuracy of over 99%, and it was observed that classification performance varied depending on data preprocessing. As the number of gas sensors increased, the prediction accuracy improved, reaching up to 99.83 ± 0.21%. These experimental results indicated that the proposed electronic nose system's classification performance was comparable to that of commercial electronic nose systems. Additionally, the implementation of an alcoholic beverage classification system based on a pretrained LDA model demonstrated the feasibility of real-time classification using the proposed system.

Discriminative analysis of aroma profiles in diverse cigar products varieties through integrated sensory evaluation, GC-IMS and E-nose.

Cigars, treasured for their rich aromatic profiles, occupy a notable segment in the global consumer market. The objective of this study was to characterize the volatile aroma compounds that shape the flavor profiles of six distinct varieties of Great Wall cigars, contributing to the understanding of cigar aroma analysis. Utilizing HS-GC-IMS and sensory evaluation, the study discerned the aroma profiles of GJ No. 6 (GJ), Animal from the Chinese zodiac (SX), Range Rover No. 3 Classic (JD), Miracle 132 (QJ), Sheng Shi No. 5 (SS), and Red 132 (HS) cigars. The analysis uncovered a spectrum of characteristic aromas, including tobacco, creaminess, cocoa, leather, baking, herbaceous, leathery, woodsy, and fruity notes. A total of 88 compounds were identified, categorized into 11 chemical classes, with their quantities varying among the cigars in a descending order of QJ, JD, GJ, SS, HS, and SX. 24 compounds, such as 2-heptanone, n-butanol, 2,6-dimethylpyrazine and 2-furfuryl methyl sulfide were considered as key differential components. The volatile components were effectively differentiated using principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and cluster analysis, revealing correlations between sensory attributes, key components, and electronic nose (E-nose). This research introduces a novel method for analyzing volatile aroma components in cigars, offering insights to enhance cigar quality and to foster the development of new products with unique aroma profiles.

Rapid identification of the aging time of Liupao tea using AI-multimodal fusion sensing technology combined with analysis of tea polysaccharide conjugates.

Identifying the aging time of Liupao Tea (LPT) presents a persistent challenge. We utilized an AI-Multimodal fusion method combining FTIR, E-nose, and E-tongue to discern LPT's aging years. Compared to single-source and two-source fusion methods, the three-source fusion significantly enhanced identifying accuracy across all four machine learning algorithms (Decision tree, Random forest, K-nearest neighbor, and Partial least squares Discriminant Analysis), achieving optimal accuracy of 98-100 %. Physicochemical analysis revealed monotonic variations in tea polysaccharide (TPS) conjugates with aging, observed through SEM imaging as a transition from lamellar to granular TPS conjugate structures. These quality changes were reflected in FTIR spectral characteristics. Two-dimensional correlation spectroscopy (2D-COS) identified sensitive wavelength regions of FTIR from LPT and TPS conjugates, indicating a high similarity in spectral changes between TPS conjugates and LPT with aging years, highlighting the significant role of TPS conjugates variation in LPT quality. Additionally, we established an index for evaluating quality of aging, which is sum of three fingerprint peaks (1029 cm-1, 1635 cm-1, 2920 cm-1) intensities. The index could effectively signify the changes in aging years on macro-scale (R2 = 0.94) and micro-scale (R2 = 0.88). These findings demonstrate FTIR's effectiveness in identifying aging time, providing robust evidence for quality assessment.

Identification of characteristic compounds of sweet orange oil and their sweetening effects on the sucrose solution with sweetness meter, sensory analysis, electronic tongue, and molecular dynamics simulation.

The characteristic aroma compounds of five-fold sweet orange oil were analyzed using gas chromatography-mass spectrometry combined with the odor aroma value (OAV) method. The results indicated that limonene, linalool, dodecanol, (E,E)-2,4-decadienal, (E)-citral, linalool, (E)-2-decenal, and geraniol are important contributors. The sweetening effects of key compounds on sucrose solutions were experimentally investigated. The results showed that the sweetness effects of five compounds (limonene, citronellal, geraniol, ß-sinensal and ß-caryophyllene) were better than those of (E)-citral, linalool and octanal. Molecular dynamics implied that the hydrogen bonding residues of the T1R2/T1R3-sucrose system were converted from LYS65, GLU302, ASP278, and SER144 to ASP278, SER144, ASP142, and ASP213 after the addition of limonene. Meanwhile, the hydrophobic interaction forces of the system are significantly enhanced. The total energy of the T1R2/T1R3-sucrose system decreased from -32.08 kcal/mol to -63.57 kcal/mol. The synergistic sweetening mechanism of characteristic aroma compounds of sweet orange oil on sucrose was revealed.

Chemometrics methods, sensory evaluation and intelligent sensory technologies combined with GAN-based integrated deep-learning framework to discriminate salted goose breeds.

The authenticity of salted goose products is concerning for consumers. This study describes an integrated deep-learning framework based on a generative adversarial network and combines it with data from headspace solid phase microextraction/gas chromatography-mass spectrometry, headspace gas chromatography-ion mobility spectrometry, E-nose, E-tongue, quantitative descriptive analysis, and free amino acid and 5'-nucleotide analyses to achieve reliable discrimination of four salted goose breeds. Volatile and non-volatile compounds and sensory characteristics and intelligent sensory characteristics were analyzed. A preliminary composite dataset was generated in InfoGAN and provided to several base classifiers for training. The prediction results were fused via dynamic weighting to produce an integrated model prediction. An ablation study demonstrated that ensemble learning was indispensable to improving the generalization capability of the model. The framework has an accuracy of 95%, a root mean square error (RMSE) of 0.080, a precision of 0.9450, a recall of 0.9470, and an F1-score of 0.9460.

Characterizing and decoding the key odor compounds of Spirulina platensis at different processing stages by sensomics.

Processing is an indispensable technology in the preparation of Spirulina platensis (S. platensis). The key odorants in liquids, muds, and powders from S. platensis (NM and GZ) were characterized. A total of 90 odorants were identified and 41 odorants were sniffed with the flavor dilution (FD) factors ranging from 1 to 729. Among them, nonanal, decanal, d-limonene, ß-cyclocitral, and ß-ionone with FD factors ≥1 were detected in S. platensis during the whole processing stages. In addition, heptanal, (E, E)-2,4-nonadienal, trans-4,5-epoxy-(E)-2-decenal, 1-hepten-3-one, isophorone, 3-ethyl-2,5-dimethylpyrazine, and α-ionone exhibited higher odor activity values in powders; ß-myrcene, methional, and S-methyl methanethiosulphonate were key odorants in muds; while trans-3-penten-2-ol was key odorant in liquids. Besides, the GZ-mud presented stronger earthy and fishy odor than NM-mud. S. platensis powders have the stronger grassy odor, roasted odor, and marine odor than S. platensis muds. Overall, drying process promotes the formation of aldehydes, heterocyclic compounds, and terpenoids.

Application of electronic nose technology in the diagnosis of gastrointestinal diseases: a review.

Electronic noses (eNoses) are electronic bionic olfactory systems that use sensor arrays to produce response patterns to different odors, thereby enabling the identification of various scents. Gastrointestinal diseases have a high incidence rate and occur in 9 out of 10 people in China. Gastrointestinal diseases are characterized by a long course of symptoms and are associated with treatment difficulties and recurrence. This review offers a comprehensive overview of volatile organic compounds, with a specific emphasis on those detected via the eNose system. Furthermore, this review describes the application of bionic eNose technology in the diagnosis and screening of gastrointestinal diseases based on recent local and international research progress and advancements. Moreover, the prospects of bionic eNose technology in the field of gastrointestinal disease diagnostics are discussed.

Characterization of gelatin-oxidized riclin cryogels and their applications as reusable ice cubes in shrimp preservation.

Traditional ice is usually employed to preserve food freshness and extend shelf life. However, ice cannot bear repeated freeze - thaw cycles during the transportation and retailing process, resulting in microbial cross-contamination and spoilage of foods. Herein, succinoglycan riclin was oxidated (RO) and crosslinked with gelatin (Ge), the Ge-RO cryogels were prepared via Schiff base reaction and three freeze - thaw cycles. The Ge-RO cryogels showed improved storage modulus (G') and thermal stability compared with pure gelatin hydrogel. The polymer framework of Ge-RO gels exhibited stable properties against ice crystals destructions during nine freeze - thaw treatments. During the storage and repeated freeze - thaw treatments of shrimps, Ge-RO cryogels exhibited a remarkable preservation effect on shrimps, and their freshness was evaluated using an electronic nose technique equipped with ten sensors. The results demonstrated that the shrimp muscle preserved in ice generated off-odors and resulted in high sensor responses. The sensor responses were reduced sharply of shrimps preserved in cryogels. Moreover, 1H NMR-based metabolomics analysis revealed that shrimps in Ge-RO cryogels group reversed the metabolic perturbations compared with the traditional ice group, the metabolic pathways were related to energy metabolism, nucleotide metabolism, and amino acid metabolism, which provide new clues to the freshness of shrimps. Furthermore, RO exhibited superior antimicrobial activity against E. coli and S. aureus microorganisms. Thus, the crosslinked cryogels are potentially applicable to food preservation, offering sustainable and reusable solutions against traditional ice.