Search Results (28)

Background/Objectives: In the context of accelerating climate change and growing food insecurity, improving crop resilience to abiotic stresses such as drought, salinity, heat, and cold is a critical agricultural and scientific challenge. Understanding the biochemical mechanisms that underlie plant stress responses is essential for developing resilient crop varieties This review aims to provide an integrative overview of how metabolomics can elucidate biochemical mechanisms underlying stress tolerance and guide the development of stress-resilient crops. Methods: We reviewed the recent literature on metabolomic studies addressing abiotic stress responses in various crop species, focusing on both targeted and untargeted approaches using platforms such as nuclear magnetic resonance (NMR), liquid chromatography–mass spectrometry (LC-MS), and gas chromatography–mass spectrometry (GC-MS). We also included emerging techniques such as capillary electrophoresis–mass spectrometry (CE-MS), ion mobility spectrometry (IMS-MS), Fourier transform infrared spectroscopy (FT-IR), and data-independent acquisition (DIA). Additionally, we discuss the integration of metabolomics with transcriptomics and physiological data to support system-level insights. Results: The reviewed studies identify common stress-responsive metabolites, including osmoprotectants, antioxidants, and signaling compounds, which are consistently linked to enhanced tolerance. Novel metabolic biomarkers and putative regulatory hubs are highlighted as potential targets for molecular breeding and bioengineering. We also address ongoing challenges related to data standardization and reproducibility across analytical platforms. Conclusions: Metabolomics is a valuable tool for advancing our understanding of plant abiotic stress responses. Its integration with other omics approaches and phenotypic analyses offers promising avenues for improving crop resilience and developing climate-adaptive agricultural strategies. Full article

The fermentation of prune juice significantly enhances its nutritional profile, antioxidant capacity, and flavor characteristics. In this study, Non-Saccharomyces cerevisiae and Lactiplantibacillus plantarum were used to co-ferment prune juice to systematically investigate the dynamic changes in physicochemical properties and antioxidant activity during fermentation. The evolution of volatile compounds across fermentation stages was analyzed using gas chromatography–ion mobility spectroscopy (GC-IMS) combined with chemometric methods, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that after fermentation, the total acidity (TA), total phenolic content (TPC), and total flavonoid content (TFC) increased by 37.35%, 20.28%, and 28.95%, respectively. Meanwhile, the pH, total soluble solids (TSS), and reducing sugars (RS) decreased by 16.87%, 23.36%, and 39.94%, respectively. Additionally, the DPPH radical scavenging capacity and ABTS radical scavenging capacity improved by 76.16% and 57.25% during fermentation process. A total of 37 volatile compounds were identified across the four fermentation stages of prune juice (PJ). These compounds included 14 esters, 8 alcohols, 7 aldehydes, 4 terpenoids, 3 ketones, and 1 amine. Considerable quantities of organic acids and free amino acids were detected in samples from all fermentation phases. Among these, lactic acid, citric acid, and D-glucuronic acid exhibited significant increases in their concentration (p < 0.05). In the free amino acid profile of fermented prune juice (FPJ), asparagine was the most abundant component, followed by glutamine and proline. Full article

Twenty-two types of highland barley (HB) raw materials (including 10 common varieties and 5 main planting regions in the Qinghai province) were selected as the experimental materials to investigate their differences in the cooking characteristics, sensory quality, and characteristic flavor of cooked HB. The key volatile flavor components were identified using Gas Chromatography–Ion Mobility Spectroscopy (GC-IMS) combined with relative odor activity value (ROAV) analysis. The results indicated that the highland barley raw materials of Kunlun 15 (M5), Kunlun 14 (M9), Chaiqing 1 (M13) and Kunlun 14 (M14), and Chaiqing 1 (M20) and Kunlun 15 (M21) showed superior cooking quality, texture, and sensory scores. A total of 44 volatile flavor compounds were identified, including 16 aldehydes, 10 alcohols, 9 ketones, 7 esters, 1 acid, and 1 furan. Among these, 13 aldehydes, 4 alcohols, 4 ketones, 7 esters, and 1 furan were found across different cooked HB samples. Notably, ethyl, ethyl 2-methylbutanoate dimer, 2-methylbutanoic acid methyl ester, 2-butanone, 1-octen-3-ol, 1-pentanol dimer, and 2-pentyl furan contributed more significantly to the overall volatile profile. Cluster analysis combining principal component analysis revealed that Kunlun 16 (M16), Kunlun 17 (M17), Kunlun 14 (M18), Kunlun 15 (M19), as well as Chaiqing 1 (M20) and Kunlun 15 (M21), were the most suitable raw materials for cooking due to their better cooking quality, sensory attributes, and flavors, followed by Kunlun 15 (M10) and Kunlun 18 (M12), and Chaiqing 1 (M13) and Kunlun 14 (M14). These findings could help us identify specific HB varieties in corresponding regions with advantages, thus providing a theoretical basis for cooking HB. Full article

In addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp’s origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field remains scarce due to its black-box nature. To address this gap, we attempted to identify the origin of kelp by analyzing its VOCs in conjunction with explainable deep learning. In this work, we identified 115 distinct VOCs in kelp samples using gas chromatography coupled with ion mobility spectroscopy (GC-IMS), of which 68 categories were discernible. Consequently, we developed a comprehensible one-dimensional convolutional neural network (1D-CNN) model that incorporated 107 VOCs exhibiting significant regional disparities (p < 0.05). The model successfully discerns the origin of kelp, achieving perfect metrics across accuracy (100%), precision (100%), recall (100%), F1 score (100%), and AUC (1.0). SHapley Additive exPlanations (SHAP) analysis highlighted the impact of features such as 1-Octen-3-ol-M, (+)-limonene, allyl sulfide-D, 1-hydroxy-2-propanone-D, and (E)-2-hexen-1-al-M on the model output. This research provides deeper insights into how critical product features correlate with specific geographic information, which in turn boosts consumer trust and promotes practical utilization in actual settings. Full article

In order to characterize the volatile chemical components of Schisandra chinensis processed by different Traditional Chinese Medicine Processing methods and establish fingerprint profiles, headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) technology was employed to detect, identify, and analyze Schisandra chinensis processed by five different methods. Fingerprint profiles of volatile chemical components of Schisandra chinensis processed by different methods were established; a total of 85 different volatile organic compounds (VOCs) were detected in the experiment, including esters, alcohols, ketones, aldehydes, terpenes, olefinic compounds, nitrogen compounds, lactones, pyrazines, sulfur compounds, thiophenes, acid, and thiazoles. Principal component analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and Pearson correlation analysis methods were used to cluster and analyze the detected chemical substances and their contents. The analysis results showed significant differences in the volatile chemical components of Schisandra chinensis processed by different methods; the Variable Importance in Projection (VIP) values of the OPLS-DA model and the P values obtained from one-way ANOVA were used to score and screen the detected volatile chemical substances, resulting in the identification of five significant chemical substances with the highest VIP values: Alpha-Farnesene, Methyl acetate,1-octene, Ethyl butanoate, and citral. These substances will serve as marker compounds for the identification of Schisandra chinensis processed by different methods in the future. Full article

Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as chromatographic retention. Significant advancements have been made in predicting these values using machine learning and deep learning. Usually, model predictions do not contain any indication of the possible error (uncertainty) or only one criterion is used for this purpose. The spread of predictions of several models included in the ensemble, and the molecular similarity of the considered molecule and the most “similar” molecule from the training set, are values that allow us to estimate the uncertainty. The Euclidean distance between vectors, calculated based on real-valued molecular descriptors, can be used for the assessment of molecular similarity. Another factor indicating uncertainty is the molecule’s belonging to one of the clusters (data set clustering). Together, all three factors can be used as features for the uncertainty assessment model. Classification models that predict whether a prediction belongs to the worst 15% were obtained. The area under the receiver operating curve value is in the range of 0.73–0.82 for the considered tasks: the prediction of retention indices in gas chromatography, retention times in liquid chromatography, and collision cross-sections in ion mobility spectroscopy. Full article

This study investigates the effect of variable-temperature roasting on the flavor compounds of Xinjiang tannur-roasted mutton. Gas chromatography coupled with ion mobility spectroscopy (GC-IMS) was used to compare and analyze the volatile components and flavor fingerprints of Xinjiang tannur-roasted mutton using variable-temperature electrically heated air roasting (VTR), constant-temperature electrically heated air roasting (EHAR), and constant-burning charcoal roasting (BCR) techniques. The changes in fatty acids and free amino acids in Xinjiang tannur-roasted mutton under different roasting conditions were compared. By using GC-IMS analysis, 11 flavor compounds, including 4-methyl-3-penten-2-one, isoamyl propionate, trans-2-heptenal, trans-2-heptenal, 2-hexanone, n-hexanol, 2-hexenal, 2-ethylfuran, and ethyl 2-methylbutanoate, were identified as characteristic volatile compounds in the temperature-controlled electrothermal roasting of Xinjiang tannur-roasted mutton using the following conditions: 0–4 min, 300 °C; 5–10 min, 220 °C; and 11–17 min, 130 °C (VTR₃). Through principal component analysis, it was found that the substances with the highest positive correlation with PC1 and PC2 were n-hexanol and 3-methylbutanol. The sensory evaluation showed that VTR₃ had high acceptability (p < 0.05) and a fat flavor (p < 0.05). There was no significant difference in the total fatty acid (TFA) content between the VTR₃ and burning charcoal roast for 1–17 min at 300 °C (BCR₃) (p > 0.05), but it was lower than that in the other experimental groups (p < 0.05). The lowest proportion of glutamic acid content in VTR₃ was 22.44%, and the total free amino acid content in the electric thermostatic roasting for the 1–17 min, 300 °C (EHAR₃) group (347.05 mg/100 g) was significantly higher than that in the other experimental groups (p < 0.05). By using Spearman correlation analysis, the roasting loss rate showed a highly significant negative correlation with essential amino acids (EAAs), non-essential amino acids (NEAAs), and total free amino acids (TAAs) (the correlation coefficients (r) were 0.82, 0.87, and 0.87, respectively) with p < 0.01. There was no correlation between changes in the free amino acid content and fatty acid content (p > 0.05). By using Differential scanning calorimetry (DSC) analysis, we also found that there was no significant difference in peak temperature (Tp) between the VTR₃ and EHAR experimental groups (p > 0.05). Variable temperature electric heating can affect the flavor of lamb, and there are significant differences in the content of flavor precursors such as fatty acids and amino acids in Xinjiang tannur-roasted mutton. Full article

Not-from-concentrate (NFC) juice effectively retains the original characteristics of the fruit. Despite the various health benefits of Goji berries, reports on the processing technology and quality changes of NFC goji juice are lacking. We optimised the processing technology of NFC goji juice. Employing fuzzy mathematics evaluation combined with response surface methodology based on single-factor experiments, the physicochemical, nutritional, and microbiological indicators of the juice were determined. Gas chromatography-ion mobility spectroscopy was used to analyse changes in volatile compounds before and after processing. The optimal processing parameters were: times for homogenisation, ultrasonication, and sterilisation of 2 min, 3 min, and 85 s, respectively, and sterilisation temperature of 102 °C. The resulting product had a sensory evaluation score of 85.5 and a rich, pleasant taste. The ratio of total soluble solids to titratable acidity and turbidity were 12.8 and 1420 NTU, respectively. Post-processing, the contents of β-carotene, polysaccharides, and betaine increased by 57.3%, 26.7%, and 31.5%, respectively. Microbiological indicators met Chinese national limits for food pollutants and microorganisms. The total relative content of volatile substances in NFC goji juice decreased by 19.86% after processing. This study provides a theoretical reference for the intensive processing and development of high-value goji berries. Full article

To address the challenge of relying on complex biochemical methods for identifying rice species, a prediction model that combines gas chromatography-ion mobility spectroscopy (GC-IMS) with a convolutional neural network (CNN) was developed. The model utilizes the GC-IMS fingerprint data of each rice variety sample, and an improved CNN structure is employed to increase the recognition accuracy. First, an improved generative adversarial network based on the diffusion model (DGAN) is used for data enhancement to expand the dataset size. Then, on the basis of a residual network called ResNet50, a transfer learning method is introduced to improve the training effect of the model under the condition of a small sample. In addition, a new attention mechanism called Triplet is introduced to further highlight useful features and improve the feature extraction performance of the model. Finally, to reduce the number of model parameters and improve the efficiency of the model, a method called knowledge distillation is used to compress the model. The results of our experiments revealed that the recognition accuracy for identifying the 10 rice varieties was close to 96%; hence, the proposed model significantly outperformed traditional models such as principal component analysis and support vector machine. Furthermore, compared to the traditional CNN, our model reduced the number of parameters and number of computations by 53% and 55%, respectively, without compromising classification accuracy. The study also suggests that the combination of GC-IMS and our proposed deep learning method had better discrimination abilities for rice varieties than traditional chromatography and other spectral analysis methods and that it effectively identified rice varieties. Full article

Chitosan samples were prepared from the shells of marine animals (crab and shrimp) and the cell walls of fungi (agaricus bisporus and aspergillus niger). Fourier-transform infrared spectroscopy (FT-IR) was used to detect their molecular structures, while headspace-gas chromatography–ion mobility spectrometry (HS-GC-IMS) was employed to analyze their odor composition. A total of 220 volatile organic compounds (VOCs), including esters, ketones, aldehydes, etc., were identified as the odor fingerprinting components of chitosan for the first time. A principal component analysis (PCA) revealed that chitosan could be effectively identified and classified based on its characteristic VOCs. The sum of the first three principal components explained 87% of the total variance in original information. An orthogonal partial least squares discrimination analysis (OPLS-DA) model was established for tracing and source identification purposes, demonstrating excellent performance with fitting indices R²X = 0.866, R²Y = 0.996, Q² = 0.989 for independent variable fitting and model prediction accuracy, respectively. By utilizing OPLS-DA modeling along with a heatmap-based tracing path study, it was found that 29 VOCs significantly contributed to marine chitosan at a significance level of VIP > 1.00 (p < 0.05), whereas another set of 20 VOCs specifically associated with fungi chitosan exhibited notable contributions to its odor profile. These findings present a novel method for identifying commercial chitosan sources, which can be applied to ensure biological safety in practical applications. Full article

Chilled and cut chicken is preferred by consumers for its safeness and readiness to cook. To evaluate the quality characteristics of various chilled chicken products, differences in volatile organic components (VOCs) of six different cut parts (breast, back, leg, heart, liver, and gizzard) of Lueyang black chicken were characterized through gas chromatography–ion mobility spectroscopy (GC-IMS) combined with stoichiometry. A total of 54 peaks in the signal of VOCs were detected by GC-IMS, and 43 VOCs were identified by qualitative analysis. There were 22 aldehydes (20.66–54.07%), 8 ketones (25.74–62.87%), 9 alcohols (4.17–14.69%), 1 ether (0.18–2.22%), 2 esters (0.43–1.54%), and 1 furan (0.13–0.52%), in which aldehydes, ketones, and alcohols were the main categories. Among the six cut parts, the relative content of aldehydes (54.07%) was the highest in the gizzard, and the relative content of ketones (62.87%) was the highest in the heart. Meanwhile, the relative content of alcohols (14.69%) was the highest in the liver. Based on a stable and reliable predictive model established by orthogonal partial least squares–discriminant analysis (OPLS-DA), 3-hydroxy-2-butanone (monomer and dimer), acetone, 2-butanone monomer, hexanal (monomer and dimer), isopentyl alcohol monomer, and n-hexanol monomer were picked out as characteristic VOCs based on variable importance in projection (VIP value > 1.0, p < 0.05). Principal component analysis (PCA) and the clustering heatmap indicated that the characteristic VOCs could effectively distinguish the six cut parts of Lueyang black chicken. The specific VOCs responsible for flavor differences among six different cut parts of Lueyang black chicken were hexanal (monomer and dimer) for the gizzard, 2-butanone monomer and hexanal dimer for the breast, hexanal monomer for the back, 3-hydroxy-2-butanone monomer for the leg, 3-hydroxy-2-butanone (monomer and dimer) for the heart, and acetone and isopentyl alcohol monomer for the liver. These findings could reveal references for quality assessment and development of chilled products related to different cut parts of Lueyang black chicken in the future. Full article

Gas chromatography–ion mobility spectroscopy (GC-IMS) was used to analyze the volatile components in dried Hypsizygus marmoreus of different drying methods, including hot air drying (HAD), heat pump drying (HPD), heated freeze-drying (HFD), and unheated freeze-drying (UFD). A total of 116 signal peaks corresponding to 96 volatile compounds were identified, including 25 esters, 24 aldehydes, 23 alcohols, 13 ketones, 10 heterocyclic compounds, 8 carboxylic acids, 7 terpenes, 3 sulfur-containing compounds, 2 nitrogen-containing compounds, and 1 aromatic hydrocarbon. The total content of volatile compounds in H. marmoreus dried by the four methods, from highest to lowest, was as follows: HAD, HPD, HFD, and UFD. The main volatile compounds included carboxylic acids, alcohols, esters, and aldehydes. Comparing the peak intensities of volatile compounds in dried H. marmoreus using different drying methods, it was found that the synthesis of esters, aldehydes, and terpenes increased under hot drying methods such as HAD and HPD, while the synthesis of compounds containing sulfur and nitrogen increased under freeze-drying methods such as HFD and UFD. Nine common key characteristic flavor compounds of dried H. marmoreus were screened using relative odor activity values (ROAV > 1), including ethyl 3-methylbutanoate, acetic acid, 2-methylbutanal, propanal, methyl 2-propenyl sulfate, trimethylamine, 3-octanone, acetaldehide, and thiophene. In the odor description of volatile compounds with ROAV > 0.1, it was found that important flavor components such as trimethylamine, 3-octanone, (E)-2-octenal, and dimethyl disulfide are related to the aroma of seafood. Their ROAV order is HFD > UFD > HPD > HAD, indicating that H. marmoreus using the HFD method have the strongest seafood flavor. The research findings provide theoretical guidance for selecting drying methods and refining the processing of H. marmoreus. Full article

Further assessment of ultraviolet C light-emitting diode (UVC-LED) irradiation for influencing shiitake mushrooms’ (Lentinus edodes) volatile and sensory properties is needed. In this study, a comparison of UVC-LED irradiation treatment on the flavor profiles in various parts of shiitake mushrooms was conducted using gas chromatography–ion mobility spectrometry (GC-IMS) and sensory analysis. Sixty-three volatile compounds were identified in shiitake mushrooms. The fresh shiitake mushrooms were characterized by the highest values of raw mushroom odors. After UVC-LED treatment, the content of C8 alcohols decreased, especially that of 1-octen-3-ol, while the content of aldehydes increased, especially the content of nonanal and decanal. The score of fatty and green odors was enhanced. For fresh samples, the mushroom odors decreased and the mushroom-like odors weakened more sharply when treated in ethanol suspension than when treated with direct irradiation. The fruit odors were enhanced using direct UVC-LED irradiation for fresh mushroom samples and the onion flavor decreased. As for shiitake mushroom powder in ethanol suspension treated with UVC-LED, the sweaty and almond odor scores decreased and the vitamin D₂ content in mushroom caps and stems reached 668.79 μg/g (dw) and 399.45 μg/g (dw), respectively. The results obtained from this study demonstrate that UVC-LED treatment produced rich-flavored, quality mushroom products. Full article

This study aimed to investigate the volatile flavor compounds and tastes of six kinds of sauced pork from the southwest and eastern coastal areas of China using gas chromatography–ion mobility spectroscopy (GC-IMS) combined with an electronic nose (E-nose) and electronic tongue (E-tongue). The results showed that the combined use of the E-nose and E-tongue could effectively identify different kinds of sauced pork. A total of 52 volatile flavor compounds were identified, with aldehydes being the main flavor compounds in sauced pork. The relative odor activity value (ROAV) showed that seven key volatile compounds, including 2-methylbutanal, 2-ethyl-3, 5-dimethylpyrazine, 3-octanone, ethyl 3-methylbutanoate, dimethyl disulfide, 2,3-butanedione, and heptane, contributed the most to the flavor of sauced pork (ROAV ≥1). Multivariate data analysis showed that 13 volatile compounds with the variable importance in projection (VIP) values > 1 could be used as flavor markers to distinguish six kinds of sauced pork. Pearson correlation analysis revealed a significant link between the E-nose sensor and alcohols, aldehydes, terpenes, esters, and hetero-cycle compounds. The results of the current study provide insights into the volatile flavor compounds and tastes of sauced pork. Additionally, intelligent sensory technologies can be a promising tool for discriminating different types of sauced pork. Full article

On-site detection of chemical warfare agents (CWAs) can be performed by various analytical techniques. Devices using well-established techniques such as ion mobility spectrometry, flame photometry, infrared and Raman spectroscopy or mass spectrometry (usually combined with gas chromatography) are quite complex and expensive to purchase and operate. For this reason, other solutions based on analytical techniques well suited to portable devices are still being sought. Analyzers based on simple semiconductor sensors may be a potential alternative to the currently used CWA field detectors. In sensors of this type, the conductivity of the semiconductor layer changes upon interaction with the analyte. Metal oxides (both in the form of polycrystalline powders and various nanostructures), organic semiconductors, carbon nanostructures, silicon and various composites that are a combination of these materials are used as a semiconductor material. The selectivity of a single oxide sensor can be adjusted to specific analytes within certain limits by using the appropriate semiconductor material and sensitizers. This review presents the current state of knowledge and achievements in the field of semiconductor sensors for CWA detection. The article describes the principles of operation of semiconductor sensors, discusses individual solutions used for CWA detection present in the scientific literature and makes a critical comparison of them. The prospects for the development and practical application of this analytical technique in CWA field analysis are also discussed. Full article