Recent trends in application of chemometric methods for GC-MS and GC×GC-MS-based metabolomic studies

Simultaneous quantitative analysis of multiple metabolites using label-free surface-enhanced Raman spectroscopy and explainable deep learning

Metabolites serve as vital biomarkers, reflecting physiological and pathological states and offering insights into disease progression and early detection. This study introduces an advanced analytical technique integrating label-free Surface-Enhanced Raman Spectroscopy (SERS) with deep learning, and leverages SHAP (SHapley Additive exPlanations) to provide a visual interpretative analysis of the predictive rationale of the deep learning model, facilitating simultaneous detection and quantitative analysis of multiple metabolites. Monolayer silver nanoparticle SERS substrates were fabricated via a triple-phase interfacial self-assembly method, which captured complex spectral information of target metabolites in mixed solutions. A custom-built deep neural network model with multi-channel feature extraction was employed to predict the concentrations of uric acid (R2 = 0.976), xanthine (R2 = 0.971), hypoxanthine (R2 = 0.977), and creatinine (R2 = 0.940). The method's scalability was validated as the performance remained consistent with an increasing number of simultaneous targets. This approach offers a sensitive, cost-effective, and rapid alternative for metabolite analysis, with significant implications for clinical diagnostics and personalized medicine.

Predicting Disease-Metabolite Associations Based on the Metapath Aggregation of Tripartite Heterogeneous Networks

The exploration of the interactions between diseases and metabolites holds significant implications for the diagnosis and treatment of diseases. However, traditional experimental methods are time-consuming and costly, and current computational methods often overlook the influence of other biological entities on both. In light of these limitations, we proposed a novel deep learning model based on metapath aggregation of tripartite heterogeneous networks (MAHN) to explore disease-related metabolites. Specifically, we introduced microbes to construct a tripartite heterogeneous network and employed graph convolutional network and enhanced GraphSAGE to learn node features with metapath length 3. Additionally, we utilized node-level and semantic-level attention mechanisms, a more granular approach, to aggregate node features with metapath length 2. Finally, the reconstructed association probability is obtained by fusing features from different metapaths into the bilinear decoder. The experiments demonstrate that the proposed MAHN model achieved superior performance in five-fold cross-validation with Acc (91.85%), Pre (90.48%), Recall (93.53%), F1 (91.94%), AUC (97.39%), and AUPR (97.47%), outperforming four state-of-the-art algorithms. Case studies on two complex diseases, irritable bowel syndrome and obesity, further validate the predictive results, and the MAHN model is a trustworthy prediction tool for discovering potential metabolites. Moreover, deep learning models integrating multi-omics data represent the future mainstream direction for predicting disease-related biological entities.

Comprehensive review on the application of omics analysis coupled with Chemometrics in gelatin authentication of food and pharmaceutical products

Gelatin is a protein molecule that can be hydrolyzed from collagen, animal bones, skin and it easily soluble in water. Source animals for gelatin ingredients must be evaluated, as well as their halal status. The omics method towards gelatin authentication in food and pharmaceutical products has several advantages, including high sensitivity and reliable data. Omics investigation employs the process of breaking down substances into small particles, hence enhancing the ability to detect a greater number of compounds. Omics study has the capability to identify substances at the subclass level, which makes it highly suitable for gelatin authentication. Gelatin lipids, metabolites, proteins, and volatile chemicals can be utilized as references to authenticate gelatin. In adopting gelatin authentication, lipidomics, metabolomics, proteomics, and volatilomics must be combined with chemometrics for data interpretation. Chemometrics can convert omics analysis data into easily viewable data. Chemometric approaches capable of presenting omics analysis data for gelatin authentication include PCA, HCA, PLS-DA, PLSR, SIMCA, and FACS. Visually chemometrically explain the differences in gelatin from different animal sources. The combination of omics analysis and chemometrics is a very promising technology for gelatin authentication in food and pharmaceutical products.

Exploration of carbonyl compounds in red-fleshed kiwifruit wine and perceptual interactions among non-volatile organic acids

Carbonyl compounds are vital constituents that contribute to the flavor profile of alcoholic beverages. We examined 3-nitrophenylhydrazine as a derivatizing reagent for the measurement of 34 carbonyl compounds using UPLC-MS/MS. Adding formic acid and sodium acetate to the mobile phase significantly enhanced the detection limit of carbonyl compounds. The technique exhibited a notable extraction efficiency, yielding recovery percentages ranging from 83.6% to 117.1%, coupled with exceptional sensitivity, as evidenced by detection limits spanning from 0.07 μg/L to 4.80 μg/L. The relative standard deviation was <6.9%, indicating the precision and reliability of the analytical methodology. The method was verified by analyzing carbonyl compounds from red-fleshed kiwifruit wine. Furthermore, sensory assessment revealed that the amalgamation of tartaric acid, malic acid, and citric acid contributes to sour taste perception at sub-threshold concentrations through an additive interaction with supra-threshold non-volatile organic acids such as lactic acid and acetic acid.

Ameliorative effect of Nyctanthes arbor-tristis L. by suppression of pentylenetetrazole-induced kindling in mice: An insight from EEG, neurobehavioral and in-silico studies

Epilepsy is an abiding condition associated with recurrent seizure attacks along with associated neurological and psychological emanation owing to disparity of excitatory and inhibitory neurotransmission. The current study encompasses the assessment of the Nyctanthes arbor-tristis L. methanolic extract (Na.Cr) in the management of convulsive state and concomitant conditions owing to epilepsy. The latency of seizure incidence was assessed using pentylenetetrazol (PTZ) kindling models along with EEG in Na.Cr pretreated mice, trailed by behavior assessment (anxiety and memory), biochemical assay, histopathological alterations, chemical profiling through GCMS, and molecular docking. The chronic assessment of PTZ-induced kindled mice depicted salvation in a dose-related pattern and outcomes were noticeable with extract at 400 mg/kg. The extract at 400 mg/kg defends the progress of kindling seizures and associated EEG. Co-morbid conditions in mice emanating owing to epileptic outbreaks were validated by behavioral testing and the outcome depicted a noticeable defense related to anxiety (P<0.001) and cognitive deficit (P<0.001) at 400 mg/kg. The isolated brains were evaluated for oxidative stress and the outcome demonstrated a noticeable effect in a dose-dependent pattern. Treatment with Na.Cr. also preserved the brain from PTZ induced neuronal damage as indicated by histopathological analysis. Furthermore, the GCMS outcome predicted 28 compounds abundantly found in the plant. The results congregated in the current experiments deliver valued evidence about the defensive response apportioned by Na.Cr which might be due to decline in oxidative stress, AChE level, and GABAergic modulation. These activities may contribute to fundamental pharmacology and elucidate some mechanisms behind the activities of Nyctanthes arbor-tristis.

A Comprehensive Analytical Review of Polyphenols: Evaluating Neuroprotection in Alzheimer's Disease

Alzheimer's Disease (AD), a prevalent neurodegenerative disorder, is the primary cause of dementia. Despite significant advancements in neuroscience, a definitive cure or treatment for this debilitating disease remains elusive. A notable characteristic of AD is oxidative stress, which has been identified as a potential therapeutic target. Polyphenols, secondary metabolites of plant origin, have attracted attention due to their potent antioxidant properties. Epidemiological studies suggest a correlation between the consumption of polyphenol-rich foods and the prevention of chronic diseases, including neurodegenerative disorders, which underscores the potential of polyphenols as a therapeutic strategy in AD management. Hence, this comprehensive review focuses on the diverse roles of polyphenols in AD, with a particular emphasis on neuroprotective potential. Scopus, ScienceDirect, and Google Scholar were used as leading databases for study selection, from 2018 to late March 2024. Analytical chemistry serves as a crucial tool for characterizing polyphenols, with a nuanced exploration of their extraction methods from various sources, often employing chemometric techniques for a holistic interpretation of the advances in this field. Moreover, this review examines current in vitro and in vivo research, aiming to enhance the understanding of polyphenols' role in AD, and providing valuable insights for forthcoming approaches in this context.

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

Analysis of dog meat adulteration in beef meatballs using non-targeted UHPLC-Orbitrap HRMS metabolomics and chemometrics for halal authentication study

Due to the different price and high quality, halal meat such as beef can be adulterated with non-halal meat with low price to get an economical price. The objective of this research was to develop an analytical method for halal authentication testing of beef meatballs (BM) from dog meat (DM) using a non-targeted metabolomics approach employing liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and chemometrics. The differentiation of authentic BM from that adulterated with DM was successfully performed using partial least square-discriminant analysis (PLS-DA) with high accuracy (R2X = 0.980, and R2Y = 0.980) and good predictivity (Q2 = 0.517). In addition, partial least square (PLS) and orthogonal PLS (OPLS) were successfully used to predict the DM added (% w/w) in BM with high accuracy (R2 > 0.990). A number of metabolites, potential for biomarker candidates, were identified to differentiate BM and that adulterated with DM. It showed that the combination of a non-targeted LC-HRMS Orbitrap metabolomics and chemometrics could detect up to 0.1% w/w of DM adulteration. The developed method was successfully applied for analysis of commercial meatball samples (n = 28). Moreover, pathway analysis revealed that beta-alanine, histidine, and ether lipid metabolism were significantly affected by dog meat adulteration. In summary, this developed method has great potential to be developed and used as an alternative method for analysis of non-halal meats in halal meat products.

Discovery of bioactive-chemical Q-markers of Acanthopanax sessiliflorus leaves: An integrated strategy of plant metabolomics, fingerprint and spectrum-efficacy relationship research

An integrated bioactive-chemical quality markers (Q-markers) discovery strategy, which was based on the LC-MS plant metabolomics, HPLC fingerprint as well as the chemical spectrum-efficacy relationships, was designed to develop a methodology for accurate and comprehensive evaluation of the quality of Acanthopanax sessiliflorus leaves (ASL). Firstly, a high resolution and sensitivity UHPLC-Q-Orbitrap MS method was used for plant metabolomics analysis to obtain component characterization and screen potential chemical markers that differentiate between different harvesting periods. A total of 53 chemical components were identified, and 8 potential chemical markers were discovered, such as sucrose, maltol and phenylalanine. Secondly, a selective HPLC fingerprint analysis of ASL and its pancreatic lipase activity assay method was successfully investigated in vitro. In the study of chemical spectrum-efficacy relationships, neochlorogenic acid, chlorogenic acid, caffeic acid and hyperoside were screened and showed the inhibited pancreatic lipase activity with IC50 values, 0.16 ± 0.01, 0.13 ± 0.01, 0.31 ± 0.01, and 0.44 ± 0.02 mg/mL, respectively, which indicated the above four constituents were selected as the bioactive-chemical Q-markers of ASL. Finally, an accurate and reliable quantitative HPLC assay was developed and validated for simultaneous determination of four bioactive-chemical Q-markers in ASL, and their content levels in ASL varied widely in different harvesting periods. The systematic and efficient screening strategy for bioactive-chemical Q-markers in this study, based on " LC-MS plant metabolomics, HPLC fingerprint, and spectrum-efficacy relationships," could have effectively improved the quality assessment level of ASL.

Classification of produced water samples using class-oriented chemometrics and comprehensive two-dimensional gas chromatography coupled to mass spectrometry

Produced water (PW) is a type of wastewater that arises during oil and gas production. Due to its potential environmental impact, PW is one of the most closely monitored forms of wastewater in the petroleum industry. The total oil and grease (TOG) content in the water is a crucial parameter for assessing the environmental impact of PW. Traditional methods for analyzing TOG in PW can be time-consuming and may not be compatible with green chemistry principles. In this study, an alternative method for classifying PW samples is proposed using a one-class classifier (OCC) model, which has proven useful for classification problems. To achieve this goal, headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography (GC×GC) were employed to obtain TOG profiles from PW. A series of simulated PW samples containing TOG were generated using a mixture design comprising four petrochemicals at concentrations ranging from 10 mg L-1 to 50 mg L-1. The polydimethylsiloxane (PDMS) fiber showed the most representative extraction of analytes. The optimization of the HS-SPME method was performed using a Doehlert design with two variables, and the final conditions were set at 80 °C and 70 min for extraction temperature and time, respectively. A pixel-based data approach was used to implement data-driven soft independent modeling by class analogy (DD-SIMCA). Although DD-SIMCA is a developing area in GC×GC studies, the proposed model produced outstanding results with a sensitivity of 94.3 %, specificity of 95.0 %, and accuracy of 94.5 %, considering the complex and broad compositional range of the modeled mixtures. These findings demonstrated the effectiveness of the OCC model approach in classifying PW samples according to environmental regulations.

Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice

Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.

Comprehensive two-dimensional gas chromatography- A discussion on recent innovations

Although comprehensive 2-D GC is an established and often applied analytical method, the field is still highly dynamic thanks to a remarkable number of innovations. In this review, we discuss a number of recent developments in comprehensive 2-D GC technology. A variety of modulation methods are still being actively investigated and many exciting improvements are discussed in this review. We also review interesting developments in detection methods, retention modeling, and data analysis.

Unraveling the potential of breath and sweat VOC capture devices for human disease detection: a systematic-like review of canine olfaction and GC-MS analysis

The development of disease screening methods using biomedical detection dogs relies on the collection and analysis of body odors, particularly volatile organic compounds (VOCs) present in body fluids. To capture and analyze odors produced by the human body, numerous protocols and materials are used in forensics or medical studies. This paper provides an overview of sampling devices used to collect VOCs from sweat and exhaled air, for medical diagnostic purposes using canine olfaction and/or Gas Chromatography-Mass spectrometry (GC-MS). Canine olfaction and GC-MS are regarded as complementary tools, holding immense promise for detecting cancers and infectious diseases. However, existing literature lacks guidelines for selecting materials suitable for both canine olfaction and GC-MS. Hence, this review aims to address this gap and pave the way for efficient body odor sampling materials. The first section of the paper describes the materials utilized in training sniffing dogs, while the second section delves into the details of sampling devices and extraction techniques employed for exhaled air and sweat analysis using GC-MS. Finally, the paper proposes the development of an ideal sampling device tailored for detection purposes in the field of odorology. By bridging the knowledge gap, this study seeks to advance disease detection methodologies, harnessing the unique abilities of both dogs and GC-MS analysis in biomedical research.

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.

Exploring the material basis and mechanism of action of clinacanthus nutans in treating renal cell carcinoma based on metabolomics and network pharmacology

BACKGROUND

Clinacanthus nutans (for abbreviation thereafter) is often used as medicine in the form of fresh juice in the folk to treat many kinds of cancers, including renal cell carcinoma (RCC). It is speculated that its active ingredient may have heat sensitivity, but there are currently no reports on this aspect. Therefore, based on the folk application for fresh juice of C nutans, this study used metabonomics and network pharmacology to explore the material basis and mechanism of action of C nutans against RCC.

METHODS

Firstly, untargeted metabolomics profiling was performed by Liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry to screen the metabolites down-regulated by heat in the extract of C nutans. Secondly, we collected the targets of metabolites in the Swiss Target Prediction platform. In addition, the targets of RCC were obtained in the GeneCards database. The "component-target-disease" network was established by Cytoscape3.9.0 software. Then we constructed a protein-protein interaction network in the STRING network platform to screen core targets. The gene ontology and kyoto encyclopedia of genes and genomes enrichment analysis of core targets were carried out to predict the relevant pathway of C nutans in the treatment of RCC. Finally, the molecular docking verification of the core targets were carried out.

RESULTS

In this study, 35 potential active ingredients and 125 potential targets were obtained. And the core targets were Cellular tumor antigen p53, Signal transducer and activator of transcription 3, and so on. Then, 48 biological processes, 30 cell components, and 36 molecular functions were obtained by gene ontology enrichment analysis. Besides, 44 pathways were obtained by Kyoto encyclopedia of genes and genomes enrichment analysis, including Pathway in cancer, PI3K-Akt signal pathway, P53 signal pathway, and so on. The docking model between the core target and its corresponding components was stable.

CONCLUSION

This research is based on the folk application of C nutans, showed its potential active ingredients by metabonomics, and predicted the potential mechanism of C nutans in the treatment of RCC by network pharmacology. It provides new references for follow-up research and new drug development.

Characteristic fingerprints and comparison of volatile flavor compounds in Morchella sextelata under different drying methods

Morchella sextelata is a precious and popular commercial edible fungus that was developed recently in China. This research aimed to characterize the volatile profiles of M. sextelata under three dehydration methods (freeze, hot air, and natural air drying). Comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-ToF-MS) was shown to the best choice to discriminate the volatile profiles of M. sextelata Characteristic flavor substances of M. sextelata were eight-carbon-containing (C8) compounds, hexanal, 2(5 h)-furanone, and benzaldehyde. Drying methods had significant influences on the volatile flavor profiles of M. sextelata, and 104 differential compounds were screened by multivariate statistical analysis. Freeze-dried samples had the most abundant volatile compounds and maintained more alcohols, ketones, aldehydes, and esters described as mushroom, sweet, and green flavor, like 1-octen-3-ol, 1-octen-3-one, nonanal, 2,3-butanedione, and so on. Hot air-drying promoted the production of heterocycles and ketones with roasted flavor due to the thermalreaction, such as 2-cyclohexen-1-one, furan, 3-phenyl-, etc. Natural air-drying resulted in acids releasing an unpleasant flavor, e.g., acetic acid, 2-methylbutanoic acid, etc. Overall, thermal reaction combined with vacuum conditions might be suitable for maintaining and enriching the aroma flavor of dried true morels.

Unveiling differential mechanisms of chuanxiong cortex and pith in the treatment of coronary heart disease using SPME-GC×GC-MS and network pharmacology

Ligusticum chuanxiong Hort (LCH) is a well-known traditional Chinese medicinal herb for treating coronary heart disease (CHD). This study investigated the differential preventive mechanisms of Rhizome Cortex (RC) and Rhizome Pith (RP) of LCH. Solid-phase microextraction combined with comprehensive two-dimensional gas chromatography-tandem mass spectrometry analysis identified 32 differential components, and network pharmacology revealed 11 active ingredients and 191 gene targets in RC, along with 12 active ingredients and 318 gene targets in RP. Primary active ingredients in RC were carotol, epicubenol, fenipentol, and methylisoeugenol acetate, while 3-undecanone, (E)- 5-decen-1-ol acetate, linalyl acetate, and (E)- 2-Methoxy-4-(prop-1-enyl) phenol were dominant in RP. KEGG mapping analysis associated 27 pathways with RC targets and 116 pathways with RP targets. Molecular docking confirmed the efficient activation of corresponding targets by these active ingredients. This study provides valuable insights into the preventive and therapeutic effects of RC and RP in CHD.

Analysis of Pork in Beef Sausages Using LC-Orbitrap HRMS Untargeted Metabolomics Combined with Chemometrics for Halal Authentication Study

Beef sausage (BS) is one of the most favored meat products due to its nutrition and good taste. However, for economic purposes, BS is often adulterated with pork by unethical players. Pork consumption is strictly prohibited for religions including Islam and Judaism. Therefore, advanced detection methods are highly required to warrant the halal authenticity of BS. This research aimed to develop a liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method to determine the halal authenticity of BS using an untargeted metabolomics approach. LC-HRMS was capable of detecting various metabolites in BS and BS containing pork. The presence of pork in BS could be differentiated using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) with high accuracy. PLS-DA perfectly classified authentic BS and BS containing pork in all concentration levels of pork with R2X = (0.821), R2Y(= 0.984), and Q2 = (0.795). The level of pork in BS was successfully predicted through partial least squares (PLS) and orthogonal PLS (OPLS) chemometrics. Both models gave high R2 (>0.99) actual and predicted values as well as few errors, indicating good accuracy and precision. Identification of discriminating metabolites' potential as biomarker candidates through variable importance for projections (VIP) value revealed metabolites of 2-arachidonyl-sn-glycero-3-phosphoethanolamine, 3-hydroxyoctanoylcarnitine, 8Z,11Z,14Z-eicosatrienoic acid, D-(+)-galactose, oleamide, 3-hydroxyhexadecanoylcarnitine, arachidonic acid, and α-eleostearic acid as good indicators to detect pork. It can be concluded that LC-HRMS metabolomics combined with PCA, PLS-DA, PLS, and OPLS was successfully used to detect pork adulteration in beef sausages. The results imply that LC-HRMS untargeted metabolomics in combination with chemometrics is a promising alternative as an analytical technique to detect pork in sausage products. Further analysis of larger samples is required to warrant the reproducibility.

Measurement of chemical penetration in skin using Stimulated Raman scattering microscopy and multivariate curve resolution - alternating least squares

The mechanistic understanding of skin penetration underpins the design, efficacy and risk assessment of many high-value products including functional personal care products, topical and transdermal drugs. Stimulated Raman scattering (SRS) microscopy, a label free chemical imaging tool, combines molecular spectroscopy with submicron spatial information to map the distribution of chemicals as they penetrate the skin. However, the quantification of penetration is hampered by significant interference from Raman signals of skin constituents. This study reports a method for disentangling exogeneous contributions and measuring their permeation profile through human skin combining SRS measurements with chemometrics. We investigated the spectral decomposition capability of multivariate curve resolution - alternating least squares (MCR-ALS) using hyperspectral SRS images of skin dosed with 4-cyanophenol. By performing MCR-ALS on the fingerprint region spectral data, the distribution of 4-cyanophenol in skin was estimated in an attempt to quantify the amount permeated at different depths. The reconstructed distribution was compared with the experimental mapping of CN, a strong vibrational peak in 4-cyanophenol where the skin is spectroscopically silent. The similarity between MCR-ALS resolved and experimental distribution in skin dosed for 4 h was 0.79 which improved to 0.91 for skin dosed for 1 h. The correlation was observed to be lower for deeper layers of skin where SRS signal intensity is low which is an indication of low sensitivity of SRS. This work is the first demonstration, to the best of our knowledge, of combining SRS imaging technique with spectral unmixing methods for direct observation and mapping of the chemical penetration and distribution in biological tissues.

Comparative analysis of flavonoids, polyphenols and volatiles in roots, stems and leaves of five mangroves

Mangrove plants contain a variety of secondary metabolites, including flavonoids, polyphenols, and volatiles, which are important for their survival and adaptation to the coastal environment, as well as for producing bioactive compounds. To reveal differences in these compounds among five mangrove species' leaf, root, and stem, the total contents of flavonoids and polyphenols, types and contents of volatiles were determined, analyzed and compared. The results showed that Avicennia marina leaves contained the highest levels of flavonoids and phenolics. In mangrove parts, flavonoids are usually higher than phenolic compounds. A total of 532 compounds were detected by a gas chromatography-mass spectrometry (GC-MS) method in the leaf, root, and stem parts of five mangrove species. These were grouped into 18 classes, including alcohols, aldehydes, alkaloids, alkanes, etc. The number of volatile compounds in A. ilicifolius (176) and B. gymnorrhiza (172) was lower than in the other three species. The number of volatile compounds and their relative contents differed among all three parts of five mangrove species, where the mangrove species factor had a greater impact than the part factor. A total of 71 common compounds occurring in more than two species or parts were analyzed by a PLS-DA model. One-way ANOVA revealed 18 differential compounds among mangrove species and nine differential compounds among parts. Principal component analysis and hierarchical clustering analysis showed that both unique and common compounds significantly differed in composition and concentration between species and parts. In general, A. ilicifolius and B. gymnorrhiza differed significantly from the other species in terms of compound content, while the leaves differed significantly from the other parts. VIP screening and pathway enrichment analysis were performed on 17 common compounds closely related to mangrove species or parts. These compounds were mainly involved in terpenoid pathways such as C10 isoprenoids and C15 isoprenoids and fatty alcohols. The correlation analysis showed that the content of flavonoids/phenolics, the number of compounds, and the content of some common compounds in mangroves were correlated with their salt and waterlogging tolerance levels. These findings will help in the development of genetic varieties and medicinal utilization of mangrove plants.

Effects of Lead and Cadmium Combined Heavy Metals on Liver Function and Lipid Metabolism in Mice

Although a large number of studies have been conducted on lead (Pb) and cadmium (Cd) exposure individually, information regarding the toxicity of combined Pb and Cd exposure is relatively limited. The present study aims to investigate the toxicity of Pb-Cd combination exposure and the corresponding mechanism. A heavy metal exposure model was established in mice by subcutaneous intragastric administration of Pb-Cd (50:1) for 35 days. Body weight, diet, hair state, mental state, liver index, haematological index, biochemical indicators and pathological section analysis were used to comprehensively evaluate toxicity. Then, classical oxidative stress indexes and lipidomics techniques were used to explore the potential mechanism. The results showed that Pb-Cd caused the mice to have low appetite, poor spirit, significantly reduced activity, slow weight gain and irritated or drying hair. Pb-Cd also caused liver enlargement, significantly increased aspartate aminotransferase (AST) and alanine aminotransferase (ALT) enzyme activities, and resulted in pathological changes to the liver. Prolonged Pb-Cd exposure led to significantly increased thrombocyte haematocrit (PCT), white blood cell (WBC), platelet (PLT) and monocyte (MON) counts and decreased red blood cell (RBC), haemoglobin (HGB), haematocrit (HCT) and lymphocyte (LYM) counts. Pb-Cd increased oxidative stress by increasing the activity of superoxide dismutase (SOD) and lactate dehydrogenase (LDH) and the content of malondialdehyde (MDA). Finally, Pb-Cd triggered lipid metabolism disorders by regulating linoleic acid, sphingolipid and glycerolipid metabolism.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Matrix Effects in GC–MS Profiling of Common Metabolites after Trimethylsilyl Derivatization

Metabolite profiling using gas chromatography coupled to mass spectrometry (GC–MS) is one of the most frequently applied and standardized methods in research projects using metabolomics to analyze complex samples. However, more than 20 years after the introduction of non-targeted approaches using GC–MS, there are still unsolved challenges to accurate quantification in such investigations. One particularly difficult aspect in this respect is the occurrence of sample-dependent matrix effects. In this project, we used model compound mixtures of different compositions to simplify the study of the complex interactions between common constituents of biological samples in more detail and subjected those to a frequently applied derivatization protocol for GC–MS analysis, namely trimethylsilylation. We found matrix effects as signal suppression and enhancement of carbohydrates and organic acids not to exceed a factor of ~2, while amino acids can be more affected. Our results suggest that the main reason for our observations may be an incomplete transfer of carbohydrate and organic acid derivatives during the injection process and compound interaction at the start of the separation process. The observed effects were reduced at higher target compound concentrations and by using a more suitable injection-liner geometry.

Antioxidant Activity and Metabolite Profiling of Xylocarpus granatum Extracts Using Gas Chromatography-Mass Spectrometry

The potential application of Xylocarpus granatum, a mangrove species, as traditional medicine has been widely linked to its high secondary metabolite and antioxidant contents. However, few studies have been reported to identify and classify active metabolites responsible for such excellent biological activities. Therefore, the aim of this work was to determine the antioxidant activity, identify the metabolite profiles, and predict the metabolites acting as antioxidants in X. granatum extract using a gas chromatography-mass spectrometry (GC-MS)-based metabolomics approach. The seeds, stems, fruit peel, pulp, leaves, and twigs of X. granatum were macerated with ethanol. Each extract was analyzed with GC-MS, and the data were processed using mass spectrometry data-independent analysis (MS-DIAL) software to identify the metabolites. The IC₅₀ value of plant parts of X. granatum ranged from 7.73 to 295 ppm. A total of 153 metabolites were identified and confirmed in the X. granatum extracts. Among the identified metabolites, epicatechin and epigallocatechin were the two most abundant in the stem extracts and are expected to have the greatest potential as antioxidants. Principal component analysis (PCA) succeeded in grouping all parts of the plant into three groups based on the composition of the metabolites: group 1 (stems, fruit peel, and twigs), group 2 (seeds and pulp), and group 3 (leaves).

Authentication of fish oil (omega-3) supplements using class-oriented chemometrics and comprehensive two-dimensional gas chromatography coupled to mass spectrometry

Food supplement authentication is an important concern worldwide due to the ascending consumption related to health benefits and its lack of effective regulation in underdeveloped countries, making it a target of fraudulent activities. In this context, this study evaluated fish oil supplements by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) to obtain fingerprints, which were used to build predictive models for automated authentication of the most popular products sold in Brazil. The authentication process relied on a one-class classifier model using data-driven soft independent modeling of class analogy (DD-SIMCA). The output of the model was a binary classifier: certified IFOS fish oils and non-certified ones - regardless of the source of adulteration. The compositional analysis showed a significant variation in the samples, which validated the need for reliable statistical models. The DD-SIMCA algorithm is still incipient in GC×GC studies, but it proved to be an excellent tool for authenticity purposes, achieving a chemometric model with a sensitivity of 100%, specificity of 98.6%, and accuracy of 99.0% for fish oil authentication. Finally, orthogonalized partial least square discriminant analysis (OPLS-DA) was used to identify the features that distinguished the groups, which ascertained the results of the DD-SIMCA model that IFOS-certified oils are positively correlated to omega-3 fatty acids, including eicosapentaenoic acid (EPA, C20:5 n-3) and docosahexaenoic acid (DHA, C22:6 n-3).

The Application of Chemometrics in Metabolomic and Lipidomic Analysis Data Presentation for Halal Authentication of Meat Products

The halal status of meat products is an important factor being considered by many parties, especially Muslims. Analytical methods that have good specificity for the authentication of halal meat products are important as quality assurance to consumers. Metabolomic and lipidomic are two useful strategies in distinguishing halal and non-halal meat. Metabolomic and lipidomic analysis produce a large amount of data, thus chemometrics are needed to interpret and simplify the analytical data to ease understanding. This review explored the published literature indexed in PubMed, Scopus, and Google Scholar on the application of chemometrics as a tool in handling the large amount of data generated from metabolomic and lipidomic studies specifically in the halal authentication of meat products. The type of chemometric methods used is described and the efficiency of time in distinguishing the halal and non-halal meat products using chemometrics methods such as PCA, HCA, PLS-DA, and OPLS-DA is discussed.

A strategy for accurately and sensitively quantifying free and esterified fatty acids using liquid chromatography mass spectrometry

Fatty acid (FA) composition of foods dictates a diversity of aspects regarding food quality, ranging from product shelf life, sensory properties to nutrition. There is a challenge to quantitate FAs using liquid chromatography-mass spectrometry due to poor ionization efficiency and matrix effects. Here, an isotopic-tagged derivatization strategy was established to accurately and sensitively quantify free and esterified FAs. After derivatization reaction, the detection sensitivity of FAs was remarkably improved and the limit of quantitation was lower than 100 ng/L. The quantitative errors caused by matrix effects were diminished benefiting from isotope-derivatized internal standards. The established quantitation strategy was successfully applied to verify both free and esterified FA contents in meat after different post-harvest procedures, finding that free polyunsaturated FAs increased significantly during freezing process.

Advances in Plant Metabolomics and Its Applications in Stress and Single-Cell Biology

In the past two decades, the post-genomic era envisaged high-throughput technologies, resulting in more species with available genome sequences. In-depth multi-omics approaches have evolved to integrate cellular processes at various levels into a systems biology knowledge base. Metabolomics plays a crucial role in molecular networking to bridge the gaps between genotypes and phenotypes. However, the greater complexity of metabolites with diverse chemical and physical properties has limited the advances in plant metabolomics. For several years, applications of liquid/gas chromatography (LC/GC)-mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been constantly developed. Recently, ion mobility spectrometry (IMS)-MS has shown utility in resolving isomeric and isobaric metabolites. Both MS and NMR combined metabolomics significantly increased the identification and quantification of metabolites in an untargeted and targeted manner. Thus, hyphenated metabolomics tools will narrow the gap between the number of metabolite features and the identified metabolites. Metabolites change in response to environmental conditions, including biotic and abiotic stress factors. The spatial distribution of metabolites across different organs, tissues, cells and cellular compartments is a trending research area in metabolomics. Herein, we review recent technological advancements in metabolomics and their applications in understanding plant stress biology and different levels of spatial organization. In addition, we discuss the opportunities and challenges in multiple stress interactions, multi-omics, and single-cell metabolomics.

Metabolic and metabolomics insights into dilated cardiomyopathy (DCM)

BACKGROUND

Dilated cardiomyopathy (DCM) is the most common form of heart muscle disease characterized by progressive dilatation and ventricular dysfunction. Metabolomics is an emerging and powerful discipline that provides a global information on the phenotype of mammalian systems via the study of endogenous and exogenous metabolites in cells, tissues and biofluids. These studies aid in the identification of biomarkers to prevent diseases in later life or help to early detect onset of diseases as well as aiding in the elucidation of disease mechanisms.

SUMMARY

Metabolomics provides a unique opportunity to discover biomarkers for DCM. This review demonstrates evidence of metabolite-based biomarkers useful for predicting, diagnosing and monitoring therapeutic interventions of DCM. Key metabolites identified as potential biomarkers for diagnosing DCM include acyl-carnitines, succinic acid, malate, methylhistidine, aspartate, methionine, phenylalanine. In terms of differentiating DCM from ICM, potential biomarkers including 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol, fatty acid esters of hydroxy fatty acid, and phosphatidylcholine were identified. Acyl-carnitines, isoleucine and linoleic acid and tryptophan were the main biomarkers to monitor treatment response to DCM. Mapping metabolites to metabolic pathways revealed dysregulation of BCAA, glycolysis, tricarboxylic acid cycle and triacylglycerol and pentose phosphate metabolism which have therapeutic potential for DCM. This review shows several limitations including the use of small sample sizes, lack of interpretation of age and sex differences in most studies and the fact that studies have so far been limited to case-control study designs.

KEY MESSAGES

Metabolites have close proximity to disease phenotype. With recent advancements in metabolomics field, potential biomarkers for DCM have been identified based on studies using different biological and metabolomics technologies. However, multi-center studies with larger populations that will lead to validation of these identified biomarkers to enable their clinical translation and utilization are still needed.

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Analysis of the volatile compounds in Fuliji roast chicken during processing and storage based on GC-IMS

To investigate the flavor changes of Fuliji roast chicken during processing and storage, the volatile organic compounds (VOCs) during processing (fresh, fried, stewed and sterilized) and storage (1 month, 2 months and 4 months) were determined by gas chromatography ion mobility spectrometry (GC-IMS). A total of 47 kinds of VOCs were identified across seven sampling stages, including aldehydes, hydrocarbons, alcohols, ketones, esters, ethers and heterocyclic compounds. More diverse range of aldehydes, alcohols, ketones and esters have been detected compared to acids, ethers and heterocyclic substances. Fingerprints directly reflect the pattern of VOCs at different stages of growth and decay, revealing that frying and stewing are key processes in flavor formation, and that sterilization and storage processes lead to flavor loss in Fuliji roast chicken. Hexanal, nonanal, octanal, 2-heptanone, 3-octanol, 1-octene-3-alcohol, 1-pentanol and ethyl acetate were mainly generated during the frying process. Benzaldehyde, nonanal, octanal, methyl-5-hepten-2-one, 2-methyl-3-heptanone, 1,8-Cineole, linalool, butyl acetate, ethyl propionate, ethyl acetate, coumarin, 2-furfuryl methyl disulfide and 2-pentyl furan were mainly generated during the stewing process. After sterilization, the content of octanal-D, 2-heptanone-D, 2-Methyl-3-heptanone, pentan-1-ol-D decreased, resulting in the reduction of aroma, lemon flavor and oil flavor of Fuliji roast chicken. Seven flavor markers, including hexanal-D, nonanal-M, octanal-M, heptanal-D, acetone, 3-octanol and ethyl acetate-D, were identified in the evolution of the aroma profile of Fuliji roast chicken.

•

GC-IMS profiles of flavor components in poultry product processing line.

•

47 kinds of volatile substances were identified by GC-IMS.

•

Frying and stewing were the key processes of flavor formation.

•

Flavor markers in the evolution of aroma characteristics of Fuliji roast chicken were determined.

Human metabolite detection by surface-enhanced Raman spectroscopy

Metabolites are important biomarkers in human body fluids, conveying direct information of cellular activities and physical conditions. Metabolite detection has long been a research hotspot in the field of biology and medicine. Surface-enhanced Raman spectroscopy (SERS), based on the molecular “fingerprint” of Raman spectrum and the enormous signal enhancement (down to a single-molecule level) by plasmonic nanomaterials, has proven to be a novel and powerful tool for metabolite detection. SERS provides favorable properties such as ultra-sensitive, label-free, rapid, specific, and non-destructive detection processes. In this review, we summarized the progress in recent 10 years on SERS-based sensing of endogenous metabolites at the cellular level, in tissues, and in biofluids, as well as drug metabolites in biofluids. We made detailed discussions on the challenges and optimization methods of SERS technique in metabolite detection. The combination of SERS with modern biomedical technology were also anticipated.

Metabolomics: An analytical technique for food processing evaluation

This review aimed to retrieve the most recent research with strong impact concerning the application of metabolomics analysis in food processing. The literature reveals the high capacity of this methodology to evaluate chemical and organoleptic transformations that occur during food production. Current and potential applications of metabolomics analysis will be addressed, focusing on process-composition-function relationships. The use of the metabolomics approach to evaluate transformations in foods submitted to minimal processes, heat or cold treatments, drying, fermentation, chemical and enzymatic treatments and processes using innovative technologies will be discussed. Moreover, the main strategies and advantages of metabolomics-based approaches are reviewed, as well as the most used analytical platforms. Overall, metabolomics can be seen as an important tool to support academia and industry on pursuing knowledge about the transformation of raw animal or plant materials into ready-to-eat products.

The Use of SPME-GC-MS IR and Raman Techniques for Botanical and Geographical Authentication and Detection of Adulteration of Honey

The aim of this review is to describe the chromatographic, spectrometric, and spectroscopic techniques applied to honey for the determination of botanical and geographical origin and detection of adulteration. Based on the volatile profile of honey and using Solid Phase microextraction-Gas chromatography-Mass spectrometry (SPME-GC-MS) analytical technique, botanical and geographical characterization of honey can be successfully determined. In addition, the use of vibrational spectroscopic techniques, in particular, infrared (IR) and Raman spectroscopy, are discussed as a tool for the detection of honey adulteration and verification of its botanical and geographical origin. Manipulation of the obtained data regarding all the above-mentioned techniques was performed using chemometric analysis. This article reviews the literature between 2007 and 2020.

Comprehensive analysis of transcriptomics and metabolomics to understand the flesh quality regulation of crucian carp (Carassius auratus) treated with short term micro-flowing water system

The short term micro-flowing purification system (STMFPS) has been shown to improve the flesh quality of freshwater fish. However, few studies have focused on the involved underlying mechanisms. This study explored the effect of STMFPS on the flesh quality of market-size freshwater fish based on the combination of metabolomics and transcriptomics methods. The UPLC-QTOF/MS based metabolomics method was utilized to screen metabolites and predict the possible major metabolic pathways during different STMFPS treatment periods (0 d, 1 d, 5 d and 9 d). Furthermore, the transcriptomic data demonstrated that the differentially expressed genes detected in crucian carp muscle were 2915, 7852 and 7183 after 1 d, 5 d and 9 d STMFPS treatment. Results showed that the TCA cycle, ornithine cycle, purine metabolism and amino acid catabolism play important roles in improving the flesh quality of crucian carp. This study may help to understand the mechanism of improving the flesh quality of aquatic products using STMFPS.