Combined OPLS-DA and decision tree as a strategy to identify antimicrobial biomarkers of volatile oils analyzed by gas chromatography–mass spectrometry

Comprehensive analysis of volatile compounds in hybrid sweetgum population and other Liquidambar species by HS-SPME-GC-MS

BACKGROUND

Liquidambar species are rich sources of essential oils, with applications in cosmetics, perfumes, and pharmaceuticals. Traditionally, the industrial utilization of Liquidambar essential oil has focused on extraction from the resin, while the full potential of their leaf-derived essential oils remains underexplored. This study systematically compares the essential oil composition, sensory characteristics, and aroma profiles of leaves from various Liquidambar species, including hybrid sweetgum, using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS). A precise internal standard method was conducted to identify progenies exhibiting heterosis in volatile content from a hybrid sweetgum population of 110 full-sib progenies.

RESULTS

A total of 44 volatile compounds were identified, with monoterpenes (α-pinene, β-pinene, d-limonene) dominating Liquidambar styraciflua, Liquidambar formosana and hybrid sweetgum, while Liquidambar orientalis exhibited a unique d-Camphene-rich profile. Hybrid sweetgum progenies exhibiting heterosis in essential oil content were found, with several progenies significantly exceeding the average level. Sensory analysis, combined with odor activity value (OAV) calculations, revealed species-specific aroma profiles: L. styraciflua and hybrid sweetgum were characterized by citrus/herbaceous notes (high d-limonene OAV = 11.07), whereas L. orientalis exhibited pungent camphoraceous tones (d-camphene OAV = 15.9). Multivariate analyses identified eight key volatile compounds with high variable influence on projection (VIP > 1).

CONCLUSIONS

This study highlights the hybrid sweetgum population as a promising genetic resource for high essential oil yield, with seven elite progenies showing industrial potential. The distinct volatile profiles of Liquidambar species, particularly the d-camphene dominance in L. orientalis, underscore genus-wide metabolic diversity.

Identification of allelochemicals under continuous cropping of Morchella mushrooms

Continuous cropping obstacle has been becoming the bottleneck for the stable development of morel cultivation. The allelopathic effect of soil allelochemicals may play an instrumental role in the morel soil sickness. In this study, the allelochemicals were identified by gas chromatography-mass spectrometry (GC-MS) combined with in vitro bioassay. A total of 61 chemical substances were identified through the GC-MS analysis of 12 replanting and control soil samples, comprising 10 phenolic acids, 36 acids, 3 aldehydes, etc. Among which, 15 compounds with values of variable importance for the projection (VIP) greater than 1 in the orthogonal partial least squares-discriminant analysis (OPLS-DA) were selected as the differential metabolites between soil samples of continuous cropping and control. The bioassay showed that 4-coumaric acid and vanillic acid exhibited inhibitory effect on mycelial growth of three cultivated Morchella mushrooms (M. sextelata, M. eximia and M. importuna) under soil native concentrations. Analysis of potential biosynthetic pathways of phenolic acids found that the 3 cultivable Morchella mushrooms are unable to synthesize phenolic acid allelochemicals. Therefore, although they were detected in trace amounts in static culture broth of M. sextelata, the two phenolic acids can only be defined as morel allelochemicals rather than autotoxins. Taken together, we found two morel allelochemicals that may derive from morel related microbes and plants, which will be helpful for further fundamental study and application in morel artificial cultivation.

Study on Enzyme Activity and Metabolomics during Culture of Liquid Spawn of Floccularia luteovirens

To comprehensively investigate the physiological characteristics and metabolic processes of the mycelium of Floccularia luteovirens (F. luteovirens), a wild edible fungus unique to the plateau region, we conducted an in-depth analysis of the mycelium enzyme activity and metabolites during different culture periods. The activity of seven enzymes all followed a trend of initially increasing and then decreasing. The intra- and extracellular activity peaks of three hydrolases-amylase, protease, and cellulase-all occurred on the 20th day, except for the extracellular amylase, which peaked on the 15th day. In contrast, the peak activity of laccase occurred on the 10th day. Moreover, three types of oxidoreductases in the mycelium (catalase (CAT), superoxide dismutase (SOD), and 2,3,5-triphenyltetrazolium chloride (TTC)-dehydrogenase (TTC-DH)) also exhibited significant changes in activity. CAT and SOD activity reached their maximum on the 20th day, whereas TTC-DH showed high activity on both the 10th and 20th days. Through a comprehensive assessment of the evolving trends of these physiological parameters, we determined that the optimal cultivation cycle for F. luteovirens liquid spawn is 20 days. An untargeted metabolomic analysis revealed that 3569 metabolites were detected in the F. luteovirens mycelium, including a variety of secondary metabolites and functional components, with terpenoids being particularly abundant, accounting for 148 types. By comparing three different culture stages (10 days, 20 days, and 30 days), 299, 291, and 381 metabolites, respectively, showed different accumulation patterns in the comparison groups of 10d vs. 20d, 20d vs. 30d, and 10d vs. 30d. These differential metabolites were primarily concentrated in carboxylic acids and their derivatives, fatty acyl groups, organic oxygen compounds, and lipid compounds. In addition, there were several amino acids whose abundance continued to grow during culturing. The metabolism of amino acids greatly affects mycelium growth and development. This research delineates the interplay between mycelium growth and metabolism, offering empirical support for a cultivation strategy for liquid F. luteovirens, and an exploration of its metabolites for potential applications.

Characterization of the flavor profile of Hulatang using GC-IMS coupled with sensory analysis

Background

Hulatang is a traditional specialty snack in Henan, China, and is well known for its unique flavor.

Methods

In this study, the volatile organic compounds (VOCs) in four kinds of Hulatang from two representative regions in Henan Province (Xiaoyaozhen and Beiwudu) were evaluated using headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS).

Results

The results showed that Xiaoyaozhen Hulatang exhibited more ethers, fewer terpenes and ketones than Beiwudu Hulatang. Additionally, Hulatang from different regions were classified using the orthogonal partial least squares-discriminant analysis (OPLS-DA) based on GC-IMS data. Twenty aroma substances were selected as the potential markers using the variable importance in the projection (VIP) variable selection method. Additionally, fifteen aroma components significantly contributing to the aroma of Hulatang were screened using the relative odor activity value (ROAV) (ROAV > 1). Combined with the sensory score results, twelve key substances with significant correlation with odor perception were selected. The flavor characteristics of the key substances revealed that the flavor of Hulatang was mainly composed of volatile components with camphor, green, almond, fatty, spicy, herbal, vegetable, fruity, floral, musty, and solvent aromas.

Conclusion

Overall, the experimental results provide a theoretical basis for evaluating the flavor characteristics of Hulatang from different regions using GC-IMS.

Characteristics and Discrimination of the Commercial Chinese Four Famous Vinegars Based on Flavor Compositions

Shanxi aged vinegar (SAV), Zhenjiang aromatic vinegar (ZAV), Sichuan bran vinegar (SBV), and Fujian monascus vinegar (FMV) are the representative Chinese traditional vinegars. However, the basic differential compositions between the four vinegars are unknown. In this study, compositions of commercial vinegar were investigated to evaluate the influence of diverse technologies on their distinct flavor. Unlike amino acids and organic acids which were mostly shared, only five volatiles were detected in all vinegars, whereas a dozen volatiles were common to each type of vinegar. The four vinegars could only be classified well with all compositions, and difference analysis suggested the most significant difference between FMV and SBV. However, SAV, ZAV, and SBV possessed similar volatile characteristics due to their common heating treatments. Further, the correlation of identification markers with vinegars stressed the contributions of the smoking process, raw materials, and Monascus inoculum to SAV, SBV, and FMV clustering, respectively. Therefore, regardless of the technology modification, this basic process supported the uniqueness of the vinegars. This study contributes to improving the standards of defining the characteristics of types of vinegar.

Analysis of the Volatile Flavor Compounds of Pomegranate Seeds at Different Processing Temperatures by GC-IMS

This study sought to reveal the mechanism of flavor generation when pomegranate seeds are processed, as well as the contribution of volatile organic components (VOCs) to flavor formation. Gas chromatography–ion mobility spectrometry (GC-IMS), combined with relative odor activity (ROAV) and statistical methods, was used for the analysis. The results showed that 54 compounds were identified from 70 peaks that appeared in the GC-IMS spectrum. Then, the ROAV results showed 17 key volatile components in processing pomegranate seeds, and 7 flavor components with large differential contributions were screened out using statistical methods. These included γ-butyrolactone, (E)-3-penten-2-one (dimer), pentanal, 1-propanethiol, octanal, and ethyl valerate (monomer). It is suggested that lipid oxidation and the Maillard reaction may be the main mechanisms of flavor formation during the processing of pomegranate seeds. Furthermore, this study lays the experimental and theoretical foundations for further research on the development of flavor products from pomegranate seeds.

Establishment of a NIR-based methodology for tracking the blend homogeneity of HTPB propellant slurry in the mixing process

The hydroxyl-terminated polybutadiene (HTPB) propellants with high level of solid loadings from 80 wt% to 90 wt% consist of aluminum (Al) powder, ammonium perchlorate (AP) and HTPB. The Al/AP/HTPB adhesive system full of solid grains appears high viscosity against flow. Therefore, the mixing is a crucial procedure in the production as it directly affects the structural integrity of the finished product. This work focused on the feasibility of tracking the blend homogeneity of Al/AP/HTPB adhesive system in the mixing process through using the near-infrared (NIR) spectroscopy and orthogonal partial least squares discrimination analysis (OPLS-DA). The OPLS-DA classification models were created by variable selection, spectral pretreatment and latent variables (LVs) optimization. It had been demonstrated that the developed models presented an excellent predictability with the root mean square error of cross-validation (RMSECV) for slurries in Ⅰ, Ⅱ groups of 0.1261 and 0.0789, respectively. Meanwhile, the well-fitted models for slurries in Ⅰ, Ⅱ groups with the squared correlation coefficient (R²) of 0.806 and 0.980, exhibited separately an acceptable predictive capability with the predictive squared correlation coefficient (Q²) > 0.5. Furthermore, Euclidean distance and move block standard deviation (MBSD) as reference methods were used to validate the predictive performance of the developed models with respect to the blend homogeneity of HTPB propellant slurry. The experimental results showed that the terminal time for each batch of slurry reaching to ideal uniformity predicted by Euclidean distance/MBSD and OPLS-DA were both at 26-30 min. Therefore, it had been proved that the method we proposed was a potential tool to monitor the variation of the uniform state of HTPB propellant slurry in the mixing process.

Natural Products Drug Discovery: On Silica or In-Silico?

Natural products have been the most important source for drug development throughout the human history. Over time, the formulation of drugs has evolved from crude drugs to refined chemicals. In modern drug discovery, conventional natural products lead-finding usually uses a top-down approach, namely bio-guided fractionation. In this approach, the crude extracts are separated by chromatography and resulting fractions are tested for activity. Subsequently, active fractions are further refined until a single active compound is obtained. However, this is a painstakingly slow and expensive process. Among the alternatives that have been developed to improve this situation, metabolomics has proved to yield interesting results having been applied successfully to drug discovery in the last two decades. The metabolomics-based approach in lead-finding comprises two steps: (1) in-depth chemical profiling of target samples, e.g. plant extracts, and bioactivity assessment, (2) correlation of the chemical and biological data by chemometrics. In the first step of this approach, the target samples are chemically profiled in an untargeted manner to detect as many compounds as possible. So far, NMR spectroscopy, LC-MS, GC-MS, and MS/MS spectrometry are the most common profiling tools. The profile data are correlated with the biological activity with the help of various chemometric methods such as multivariate data analysis. This in-silico analysis has a high potential to replace or complement conventional on-silica bioassay-guided fractionation as it will greatly reduce the number of bioassays, and thus time and costs. Moreover, it may reveal synergistic mechanisms, when present, something for which the classical top-down approach is clearly not suited. This chapter aims to give an overview of successful approaches based on the application of chemical profiling with chemometrics in natural products drug discovery.

Characterization of Differences in the Composition and Content of Volatile Compounds in Cucumber Fruit

The cucumber is characterized by the presence of a wide range of volatile organic compounds (VOCs), which are recognized as the main responsible for its unique flavor. However, research on the types and contents of VOCs in different cucumber cultivars remains fragmentary. Here, using an automatic headspace solid-phase microextraction coupled with the gas chromatography-mass spectrometry method, the VOCs were analyzed in three representative cucumber cultivars, including YX, KX, and GX, with the best, middle, and worst flavor quality, respectively, which were selected from 30 cultivars after flavor quality evaluation. Principal component analysis revealed that the six biological replicates were grouped, indicating high reliability of the data. A total of 163 VOCs were detected. There were 28 differential VOCs in YX compared to GX, 33 differential VOCs in YX compared to KX, and 10 differential VOCs in KX compared to GX. Furthermore, K-means clustering analysis showed that 38 of the 43 no-overlapping differential VOCs were represented by the most abundant compounds detected in YX. The prevailing VOCs in YX included: hydrocarbons, aldehydes, and ketones. The data obtained in the present study extend our understanding the impact of cultivars on VOCs in cucumber and will help facilitate targeted breeding.

Differences between Kazak Cheeses Fermented by Single and Mixed Strains Using Untargeted Metabolomics

Mixed fermentation improves the flavor quality of food. Untargeted metabolomics were used to evaluate the impact of mixed fermentation and single-strain fermentation on the volatile and non-volatile compound profiles of Kazak cheese. Lacticaseibacillus paracasei SMN-LBK and Kluyveromyces marxianus SMN-S7-LBK were used to make mixed-fermentation cheese (M), while L. paracasei SMN-LBK was applied in single-strain-fermentation cheese (S). A higher abundances of acids, alcohols, and esters were produced via mixed fermentation. Furthermore, 397 differentially expressed non-volatile metabolites were identified between S and M during ripening. The flavor compounds in mixed-fermentation cheese mainly resulted from ester production (ethyl butanoate, ethyl acetate, ethyl octanoate, and ethyl hexanoate) and amino acid biosynthesis (Asp, Glu, Gln, and Phe). The metabolites were differentially expressed in nitrogen metabolism, D-glutamine and D-glutamate metabolism, phenylalanine metabolism, D-alanine metabolism, and other metabolic pathways. The amount of flavor compounds was increased in M, indicating that L. paracasei SMN- LBK and K. marxianus SMN-S7-LBK had synergistic effects in the formation of flavor compounds. This study comprehensively demonstrated the difference in metabolites between mixed-fermentation and single-strain-fermentation cheese and provided a basis for the production of Kazak cheese with diverse flavor characteristics.

Anti-obesity effect of Auricularia heimuer fruiting body alcohol extraction on obese mice and crucial metabolite pathway analysis by liquid chromatography-tandem mass spectrometry

In recent years, the increasing obese and overweight population has become a worldwide public health problem, as there is no effective medication to control obesity. Auricularia heimuer is rich in active substances that have potential biologically active functions. The anti-obesity effect and mechanism of Auricularia heimuer fruiting body alcohol extraction (AHA, 150-600 mg/kg·bw) was investigated in obese mice by assessing changes in endogenous liver metabolites using a liquid chromatography-tandem mass spectrometry approach. The aim of this study was to identify an effective food to control human obesity. AHA of 600 mg/kg·bw (HC) significantly decreased body weight and improved serum biochemistry indices. Sixty-eight liver metabolites were identified and significantly separated among the normal, high-fat diet (HFD), and HC groups. Moreover, the metabolic analysis revealed that HC significantly regulated specific metabolites in mice including amino acids, lipids, and carbohydrate compounds. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that HC was significantly involved in different metabolite pathways including arachidonic acid metabolism, galactose metabolism, carbohydrate digestion and absorption, linoleic acid metabolism, and starch and sucrose metabolism. Eight weeks after supplementing with HC, major metabolites in related pathways that were disrupted by an HFD were restored to normal levels, suggesting that HC had anti-obesity activity.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

Intelligence Algorithms for Protein Classification by Mass Spectrometry

Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work.