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Among the methods of detecting food adulteration, the PCR amplification techniques of the endogenous reference gene are increasingly significant. In this article, the improved SDS-CTAB is chosen for the DNA extraction of Flammulina velutipes, the tre6P Phosphatase gene (Tps2) is selected an endogenous reference gene suitable to identify agricultural important fungus F. velutipes and by-products via PCR methods. The Tps2 gene was assayed in 2 varieties of F. velutipes and 9 non-F. velutipes species by both qualitative and quantitative PCR, no amplicon was captured in the non-F. velutipes species. By SYBR Green quantitative PCR, the melting curve showed that the target Tps2 sequence displayed high homogeneity in different F. velutipes varieties. In Taqman probe quantitative PCR analysis, the detection limit was as low as 3 pg/uL of DNA template. From what is said in the foregoing, we can conclude that the Tps2 gene is a reliable, convenient, and accurate endogenous reference gene for detecting F. velutipes component, even in the processed food products, such as sauce and biscuits.

Citrus flavonoids are attracting great interest due to their well-known beneficial effects, but many of them have not been characterized. In this work, ultra-high liquid chromatography coupled to high resolution mass spectrometry (UHPLCHRMS) was used for profiling flavonoids in citrus fruit. We proposed a strategy combining mass defect filtering (MDF) and MS/MS-based molecular networking (MMN) to handle complex UHPLC-HRMS data. The proposed strategy was explained and validated in the fruit of Citrus sinensis (L.) Osbeck, and when specific mass and mass defect windows were pre-defined, MDF enable removal of considerable un-related and/or interference MS

Myricetin, with its pyrogallol B‐ring, is evidently instable in DMEM (Dulbecco's modified Eagle's) medium at 37°C. However, the underlying mechanism of this instability is not clear yet. Herein, the reaction products of myricetin in DMEM were investigated via an in situ UPLC–MS–MS analysis. Myricetin was mixed with prewarmed DMEM in an autosampler screw top vial for UPLC. Immediately, a 50‐µl sample was automatically injected every 50 min. Myricetin was highly stable in dimethyl sulfoxide (DMSO) when kept at 37°C for 2 weeks. Myricetin was very unstable in DMEM at 37°C and largely converted to new products A (m/z = 635.07) and B (m/z = 633.05) within 1 min. Another new product, C (m/z = 469.04), was detected after the fourth injection (150 min). Myricetin was much more stable in DMEM at 4°C as compared to 37°C. There was a minor conversion of myricetin to the new product A (m/z = 635.07) within 1.0 min, but it subsequently increased until the sixth injection (250 min), and disappeared after the fifteenth injection (700 min). In summary, myricetin first formed a dimer A (m/z = 635.07) and then was oxidized to B (m/z = 633.05), which was finally degraded to C (m/z = 469.04) in DMEM. This process was significantly affected by the temperature, and low temperature is helpful to detect the unstable products.

Tea flowers and fresh tea leaves are biological products of tea, but tea flower is often regarded as waste during tea production, resulting in notable waste of tea flower resources. At present, analysis of the chemical components in tea flowers focuses on single types of chemical components such as amino acids and tea polyphenols, and there are only a few reports on the simultaneous analysis of numerous chemical components in tea flowers. Researchers are not completely clear about the types and amounts of the chemical components in tea flowers; this has hindered the in-depth development and effective utilization of tea flowers. In this study, ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) was used to detect the chemical constituents of tea flowers. This technique was combined with the integrated filtering strategy (IFS) of nitrogen rule filtering (NRF), mass defect filtering (MDF), and diagnostic fragment ion filtering (DFIF) for screening the characteristic mass spectra of the target chemical components. Furthermore, the chemical constituents of tea flowers were annotated with information about the retention time, MS fragmentation, and MS/MS fragmentation. All the qualitative chemical components were divided into six categories with a total of 137 chemical constituents, including 3 alkaloids, 38 flavonoids, 31 phenolic acids and their derivatives, 37 catechins and their derivatives, 18 amino acids, and 10 other components. The internal standard method was used to quantify all the qualitative chemical components. The quantitative results showed that the amounts of the six kinds of chemical components in tea flowers were as follows: amino acids, 9371.42 μg/g; catechins and their derivatives, 9068.43 μg/g; phenolic acids and their derivatives, 8696.92 μg/g; alkaloids, 4392.52 μg/g; flavonoids, 1192.88 μg/g; and others, 139.94 μg/g. Quality control samples were used to evaluate the stability of the instrument and the repeatability of the tested data. Nine representative chemical components with high, medium, and low contents in tea were selected, and the relative standard deviation (RSD) of the results was used to evaluate the repeatability of the data. These nine chemical constituents are selected from amino acids, alkaloids, flavonoids, phenolic acids and their derivatives, catechins and their derivatives, and other components, and the response intensities were different. The relative standard deviations of the nine chemical components were in the range of 2.11% to 12.17%. The above results demonstrated the good stability of the instrument and excellent repeatability of the test data. Chlorogenic acid components (CGAs) and glycosylated quercetin components (GQs) were used as two representative components to explain the entire process of extracting the target compounds by IFS. CGAs comprise a class of special esters formed by the esterification of cinnamic acid derivatives with quinic acid as the parent structure. The most common cinnamic acid derivatives are p-coumaric acid, caffeic acid, and ferulic acid. On the one hand, according to the above information and the different positions and degree of quinic acid esterification, the CGAs were structurally classified as monosubstituted CGAs (Mono-CGAs), disubstituted CGAs (Di-CGAs), and trisubstituted CGAs (Tri-CGAs), and three different mass defect filtering windows were set. Therefore, 751 possible target components were selected from 3537 mass spectra in accordance with the nitrogen rule. On the other hand, 22 target components in accordance with the nitrogen rule were obtained by further screening the m/z 191.0551 ion as the diagnostic fragment ion of the CGAs. Combining the overall analytical data with the above mass defect filtering and diagnostic fragment ion filtering screening results, nine target CGAs were selected and characterized based on the MS information. This study reveals the types and amounts of the chemical components accumulated in tea flowers, thus providing valuable information and serving as data reference for the in-depth development and effective utilization of tea flowers.

Food may be contaminated by various hazardous substances in all stages of the food supply chain, which may pose a wide variety of human health risks. The ability to construct sensors capable of highly selective analysis in complex food matrix could offer strong support for guaranteeing food safety. The design, preparation, and introduction of biomimetic functional materials as antifouling materials or recognition receptors provide new ideas for further improvement of the anti-interference and specificity of sensory system. Herein, biomimetic functional materials commonly used in sensor preparation, including biomimetic antifouling materials [poly(ethylene glycol), zwitterionic polymers, and synthetic antifouling peptides] and biomimetic recognition receptors (molecularly imprinted polymers, aptamers, and mimetic recognition peptides) are reviewed. The mechanisms, advantages, limitations of these biomimetic functional materials and the applications of biomimetic functional material-based sensors in food safety analysis are summarized. Finally, the challenges and prospects of sensors based on biomimetic functional materials are analyzed.

Edible fungi have high nutritional value and great potential. Confusion among edible fungi species, and foodborne diseases due to toadstool poisoning or death induced by inadvertent consumption exist across the world. Therefore, edible fungi must be accurately identified. Based on different substances in edible fungi, there are different detection methods, and the same method can use different identification technology. Sensory identification methods include morphological and odor methods. Instrumental analysis methods based on chemical composition include chromatographic, mass spectrometry and spectral technology. Molecular biology identification methods based on nucleic acids include molecular marker technology, sequencing technology, isothermal amplification technology and endogenous reference gene method. Method is channel, and technology is the means. The principles, advantages, disadvantages and applications of various identification techniques and detection methods were discussed in this work to provide reference for the identification research of edible fungi and technical support for preventing food safety incidents caused by toadstools.

Background: Black tea is famous for its unique aroma. The analysis of aroma components has attracted considerable attention worldwide because of its complex chemical composition and low concentration. Objective: Steeping temperature is one of the most important factors affecting the aroma of black tea. This study aims to evaluate the effects of four steeping temperatures [60, 70, 80, and 95°C (boiling water)]. Methods: Two major factors affecting extraction performance, including the type of extraction method [direct headspace injection (HS) and solid-phase microextraction (SPME)] and extraction time (50, 60, and 70 min), were optimized to enrich and analyze the aroma components of Congou black tea by GC–MS. In addition, heuristic evolving latent projection (HELP), an effective chemometric resolution method, was employed to resolve the overlapped peaks. Results: A total of 83 aroma components were tentatively identified by GC–MS, such as alcohol (42.06–50.52%), aldehyde (12.09–15.97%), and hydrocarbon (4.79–15.32%). Linalool and its oxides (25.49–36.24%) were the most abundant aroma components, followed by geraniol (2.55–8.54%), methyl salicylate (1.84–9.50%), and nerol (1.93–4.41%). Conclusions: The black tea steeped at 95°C smelled more pleasant with mild green, roast, and fruity aroma. Moreover, at 80°C, the tea had sweeter fragrance with floral aroma, while steeping at 60 and 70°C resulted in more reinforced woody and fatty aroma. Highlights: A total of 83 aroma components of black tea were tentatively identified by SPME–GC–MS. The overlapped peaks were resolved by the HELP method. Aroma characteristics of different steeping temperatures were revealed.

Grape seeds are rich in proanthocyanidins, which are well-known natural antioxidants. In this work, an aqueous two-phase system (ATPS) based on ethanol/(NH4)2SO4 was proposed to extract and recover proanthocyanidins from grape seeds. Ionic liquids (IL) were used as adjuvants of ATPS to improve the extraction performance. The extraction efficiencies (EE) of proanthocyanidins were dramatically raised from 47.89 to 52.42% to 97.23 – 97.79% by adding 5 wt% [C6mim]BF4 into ATPS. Extraction conditions were optimized as follows: 29 wt% ethanol, 19 wt% (NH4)2SO4, 4 wt% [C6mim]BF4, solid–liquid ratio = 1:20 g/mL, temperature (T) = 40 °C, pH = 5.0, and extraction time (t) = 20 min. Under optimum conditions, the yields of catechin, epicatechin, and procyanidin B2 reached 1.21, 1.22, and 0.14 mg/g dry weight, respectively, with EE of over 99.01%. Compared with conventional organic solvents such as methanol, ethanol, and isopropanol, the ATPS using ILs as adjuvants provided higher extraction yields. Results suggested that ethanol-based ATPS can extract value-added compounds from waste residues in the fruit-processing industry. In addition, low amount of IL addition can significantly enhance the extraction performance of ATPS.

The sluggish nature of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) coupled with the high cost of related noble-metal catalysts stimulate the research of active non-noble met...

Lotus plumule, a traditional Chinese medicine, has significant pharmaceutical value to treat various health problems. Flavonoids are important bioactive ingredients in lotus plumule. In this study, we proposed an efficient "fishing" strategy to profile and identify non-targeted flavonoids from lotus plumule aqueous extract based on a self-built mass data "pool" with an untargeted mass data acquisition method in order to acquire comprehensive structural information of flavonoids in lotus plumule. This mass data acquisition method was applied by ultra-high performance liquid chromatography coupled with hybrid quadrupole orbitrap high-resolution mass spectrometry (UHPLC/Q-orbitrap HRMS) executed in full MS/dd-MS2 mode with different collision energies in positive ionization mode. The self-built targeted data "pool" was constructed by the basic structure of flavonoids in lotus plumule, such as odd quasi-molecular ions, C6-C3-C6 skeleton, molecular weight limit. The "molecule lures" of fishing are characterized by molecular features, fragmental patterns, characteristic fragments ions, relative abundance and retention times. More importantly, the special "lures" of PredRet (the tool of predicting the retention times) and ClogP (the calculated lipophilicity parameter) were adopted to distinguish some isomers and some compounds whose mass spectra are difficult to determine with no reference substances. The "fished" flavonoids were further confirmed by their molecular features and fragmental patterns. This developed analysis method was successfully applied to profile 29 flavonoids compounds in the aqueous extract of lotus plumule, and 3 of them were recognized for the first time in lotus plumule. To our knowledge, this is the first analytical screening methods without standard samples to systematically identify flavonoids in lotus plumule.

A Chinese herbal formula of Anti-Renal Interstitial Fibrosis I Decoction (ARIF I) is composed of Eucommia ulmoides Oliv., Scutellaria baicalensis Georgi, and Rheum. officinale Baill., and it has been used for the treatment of chronic kidney disease for many years. However, the comprehensive chemical analysis of the formulated ARIF I has yet to be clarified. In this study, an ultra-high performance liquid chromatography coupled with hybrid quadrupole-orbitrap high resolution mass spectrometry (UHPLC/Q-orbitrap HRMS) method executed in full scan/data-dependent MS2 (full MS/dd-MS2) mode has been established in negative ion mode to analyze the major chemical constituents in ARIF I as well as in its three constituent herbs. To rapidly and efficient identify these compounds, we employed a combination of statistical analysis of the MS2 data and fragmentation pathway method to screen for elementary characteristic ions and sub-characteristic ions. Then, we applied multifold characteristic ion filtering to comprehensively profile ARIF I extract chemical constituents with diverse structures. A total of 360 compounds, including anthraquinones, sennosides, galloyl glucoside derivatives, catechin and its derivatives, flavonoids, iridoids, phenylpropanoids, lignan, organic acid, and other compounds were rapidly identified or tentatively characterized by multifold characteristic ion filtering based on their accurate mass spectra (within 5 ppm error) and structure types or by comparison with reference standards. All the ARIF I compounds were also detected in the individual constituent herbs. This study provides a basis for further pharmacokinetic studies of ARIF I and may help in the discovery of new chemical drugs to treat chronic kidney disease. This study also provides a more efficient approach for the screening of appropriate characteristic ions, and a practical strategy for the detection of the types of compounds identified in this study in other herbal medicines.

Elastic net (Enet) and sparse partial least squares (SPLS) are frequently employed for wavelength selection and model calibration in analysis of near infrared spectroscopy data. Enet and SPLS can perform variable selection and model calibration simultaneously. And they also tend to select wavelength intervals rather than individual wavelengths when the predictors are multicollinear. In this paper, we focus on comparison of Enet and SPLS in interval wavelength selection and model calibration for near infrared spectroscopy data. The results from both simulation and real spectroscopy data show that Enet method tends to select less predictors as key variables than SPLS; thus it gets more parsimony model and brings advantages for model interpretation. SPLS can obtain much lower mean square of prediction error (MSE) than Enet. So SPLS is more suitable when the attention is to get better model fitting accuracy. The above conclusion is still held when coming to performing the strongly correlated NIR spectroscopy data whose predictors present group structures, Enet exhibits more sparse property than SPLS, and the selected predictors (wavelengths) are segmentally successive.

In vitro mimics recognized that the propensity of a negatively charged phospholipid, DPPS, monolayers to self-aggregate to three-dimensional (3D) giant folds under overcompression at an air–water interface. Time elapsing microscopical observations confirmed that such giant folds were able to float stably on the air–water interface for weeks or even longer. Ex situ atomic force microscopy (AFM) and transmission electronic microscopy (TEM) characterizations pointed out that such giant folds were composed of compactly stacked lipid layers. Phospholipase A2 (PLA2), a principal bactericide in human and animal tear secretion, was chosen to drive the in situ lipid giant folds disassembly on water and supported substrate surfaces, respectively. Our experimental results confirmed the layer-by-layer structures of the giant folds. It is noteworthy that the defect-rich areas of the giant lipid folds were eliminated quickly by PLA2 while defect-free lipid zones were left untouched, suggesting that PLA2 may serve as a ...

An efficient in situ ionic liquid dispersive liquid-liquid microextraction followed by ultra-performance liquid chromatography was developed to determine four neonicotinoid insecticides in wild and commercial honey samples. In this method, a hydrophobic ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate, formed by in situ reaction between potassium hexafluorophosphate and 1-butyl-3-methylimidazolium bromide in sample solution, was used as the extraction solvent. In comparison with the traditional dispersive liquid-liquid microextraction method, the developed method required no dispersive solvent. To achieve high extraction efficiency and enrichment factor, the effects of various experimental parameters were studied in detail. Under the optimized conditions, the limits of detection and quantification were in the ranges of 0.30-0.62 and 1.20-2.50 μg/L, respectively. The method showed high enrichment factors (74-115) with the recoveries between 81.0 and 103.4%. The proposed method was finally applied to different wild and commercial honey samples.

Joint publication by University of Macau (Macau), Kunming University of Science and Technology (China), Universität Würzburg (Germany), De Montfort University (UK), Dongguk University-Seoul (South Korea), University of Vigo (Spain), Guangdong Ocean University (China). The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Though the instability of polyphenols in cell culture experiment has been investigated previously, the underlying mechanism is not completely clear yet. Therefore, in this study, the stability of epigallocatechin gallate (EGCG) in cell culture medium DMEM was investigated at 4 ◦C and 37 ◦C via UPLC-MS-MS analysis followed by determination of the antioxidant capacity of EGCG. EGCG was instable in DMEM and formed various degradation products derived from its dimer with increasing incubation time with many isomers being formed at both temperatures. The dimer products were more stable at 4 ◦C than at 37 ◦C. The structure and formation mechanism of five products were analyzed with four unidentified. Ascorbic acid significantly improved the stability of EGCG by protecting EGCG from auto-oxidation in DMEM, particularly at 4 ◦C. The antioxidative activity of EGCG in DMEM was determined by DPPH, ABTS and FRAP assay. The antioxidative properties of EGCG continuously decreased over 8 h in DMEM, which was consistent with its course of degradation.

Feature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by various filtering techniques are generally different, and this inconsistency among different variable ranking methods is usually ignored in practice. To address this problem, we propose a simple strategy called rank aggregation with re-balance (RAR) for finding key variables from class-imbalanced data. RAR fuses each rank to generate a synthetic rank that takes every ranking into account. The class-imbalanced data are modified via different re-sampling procedures, and RAR is performed in this balanced situation. Five class-imbalanced real datasets and their re-balanced ones are employed to test the RAR’s performance, and RAR is compared with several popular feature screening methods. The result shows that RAR is highly competitive and almost better than single filtering screening in terms of several assessing metrics. Performing re-balanced pretreatment is hugely effective in rank aggregation when the data are class-imbalanced.

A glucose (Glu) sensor was designed by introducing synthetic cyclic peptides (CPs) as recognition receptors and Au nanoparticles assisted graphitic carbon nitride (AuNPs/g-C3N4) for electrochemiluminescence (ECL) enhancement. The synthetic CP receptor (cyclo-[−CNDNHCRDNDC−]) with natural active center of Glu binding protein can mimic the interactions between Glu and Glu binding protein to specifically capture Glu. The AuNPs were reduced on g-C3N4 and formed a new nanohybrid that can be applied as an ECL emitter. The AuNPs/g-C3N4 effectively ameliorated the ECL response of bare g-C3N4. The ECL enhancement mechanism was theoretically speculated through computer simulation. Glu quantification was conducted by recording ECL shifts induced by the binding of Glu to CPs. The linear detection range of the fabricated CPs-based ECL sensor was 1 to 100 mmol L−1, and the detection limit (LOD) was 0.57 nmol L−1 (S / N = 3). The CP-based ECL sensor also showed good specificity, repeatability, stability, and favorable recoveries in sample analysis. This work offer a promising analytical method for Glu assay in clinical diagnostics and bioprocess monitoring.

Glycosylated flavones (GFs) are important components of green tea and have various structures and isomers. The annotation of GFs' chemical structures is challenging. Ultrahigh-performance liquid chromatography-high resolution mass spectrometry can provide informative mass ions for GF annotation. However, distinguishing the mass features of GFs from those of thousands of ions is difficult. In this study, integrated ion-filtering strategies for O- and C-glycosyl flavones were constructed, and the mass-fragmentation characteristics were summarized from GF standards. Ultimately, 29 GFs with different types of aglycones and glycosides, connection modes, and locations were annotated. According to principal component analysis and t-test results, significant differences were observed in the contents of 16 components in the two kinds of tea. Among them, the contents of 11 GFs in autumn teas were significantly higher than those in spring teas. This study provided an efficient strategy for isomer annotation in food analysis.

Coronary heart disease (CHD) has a high mortality worldwide. This study aimed to screen lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry. Extraction and reconstitution solvents, liquid chromatographic and mass spectrometry conditions were optimized to detect more plasma lipid metabolites. In this study, the chromatographic and mass spectra characteristics of lipid metabolites were summarized. A total of 316 lipid metabolites were annotated via diagnostic fragment ion filtration, nitrogen rule filtration, and neutral loss filtration. Glycerophospholipid metabolism and sphingolipid metabolism were revealed as the main lipid disorders of CHD. This study provides a novel insight for high-throughput detection of lipid metabolites in plasma and provides a further understanding of the occurrence of CHD, which can provide valuable suggestions for the prevention of CHD.

UPLC-Orbitrap-MS/MS and ICP-MS were applied to analyze and characterize the chemical composition of Acori Tatarinowii Rhizoma (ATR) and its adulterants including Acori Calami Rhizoma (ACR) and Anemones Altaicae Rhizoma (AAR). Fifty-five, fifty-five and thirty-six organic components were identified in ATR, ACR and AAR by UPLC-Orbitrap-MS/MS, respectively. Forty elements were detected by ICP-MS. AAR is different from ATR and ACR. 19 organic components existed in ATR and ACR were not detected in AAR. And, concentration of β-asarone in AAR was 1000 times lower than ATR and ACR. Chemical components in ATR and ACR are very similar. Partial least squares-discriminant analysis (PLS-DA) was used to distinguish between ATR and ACR. Variable important projection (VIP) was employed to improve the classification ability of PLS-DA. Twenty-one chemical components were identified, and were used to establish the characteristic quantitative fingerprints of ATR and ACR. Our results indicated that the combination of two analytical platforms and chemometrics will provide more information to characterize and distinguish the complex analytical objects, such as similar Chinese herbs.

Metabolomics is an emerging tool to uncover the complex pathogenesis of disease, as well as the multitargets of traditional Chinese medicines, with chemometric analysis being a key step. However, conventional algorithms are not suitable for directly analyzing data at all times. The variable iterative space shrinkage approach-partial least squares-discriminant analysis, a novel algorithm for data mining, was first explored to screen metabolic varieties to reveal the multitargets of Xuefu Zhuyu decoction (XFZY) against traumatic brain injury (TBI) by the 7th day. Rat plasma from Sham, Vehicle, and XFZY groups was used for gas chromatography/mass spectrometry-based metabolomics. This method showed an improved discrimination ability (area under the curve = 93.64%). Threonine, trans -4-hydroxyproline, and creatinine were identified as the direct metabolic targets of XFZY against TBI. Five metabolic pathways affected by XFZY in TBI rats, were enriched using Metabolic Pathway Analysis web tool (i.e., phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; galactose metabolism; alanine, aspartate, and glutamate metabolism; and tryptophan metabolism). In conclusion, metabolomics coupled with variable iterative space shrinkage approach-partial least squares-discriminant analysis model may be a valuable tool for identifying the holistic molecular mechanisms involved in the effects of traditional Chinese medicine, such as XFZY.

Hydrophilic constituents are significant for the taste and nutrition of tea, but their simultaneous quantification remains challenging due to the lack of efficient methods. Based on the hydrophilic interaction chromatography coupled with triple quadrupole-tandem mass spectrometry, this work developed and validated an efficient (8.5 min per run), sensitive (LOQ: 0.002–0.493 μg/mL) and accurate method. This method was successfully used to determine the contents of 45 hydrophilic constituents in Yunnan large-leaf tea. Umami amino acids and umami-enhanced nucleotides generally exhibited higher content in green tea and Pu-erh raw tea. By contrast, a few number of amino acids (e.g., proline and γ-aminobutyric acid) and most alkaloids and nucleosides showed significantly higher contents in black tea or Pu-erh ripen tea. By performing the orthogonal partial least squares discriminant analysis, classification models for distinguishing four types of tea, and green tea from Pu-erh raw tea were established.

This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma (ATR) samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry (GC-MS) was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index (RI). Furthermore, least absolute shrinkage and selection operator (LASSO), a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis (PLS-DA). After variable selection by LASSO, three chemical markers, β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.

Ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) has been used as a powerful tool to profile chemicals in traditional Chinese medicines. However, identification of potentially bioactive compounds is still a challenging work because of the large amount of information contained in the raw UPLC-HRMS data. Especially the ubiquitous matrix interference makes it more difficult to characterize the minor components. Therefore, rapid recognition and efficient extraction of the corresponding parent ions is critically important for identifying the attractive compounds in complex samples. Herein, we propose an integrated filtering strategy to remove un-related or interference MS1 ions from the raw UPLC-HRMS data, which helps to retain the MS features of the target components and expose the compounds of interest as effective as possible. The proposed strategy is based on the use of a combination of different filtering methods, including nitrogen rule, mass defect, and neutral loss/diagnostic fragment ions filtering. The strategy was validated by rapid screening and identification of 16 methoxylated flavonoids and 55 chlorogenic acids analogues from the raw UPLC-HRMS dataset of Folium Artemisiae Argyi. Particularly, successful detection of several minor components indicated that the integrated strategy has obvious advantages over individual filtering methods, and it can be used as a promising method for screening and identifying compounds from complex samples, such as herbal medicines.

Screening important variables for class-imbalanced data is still a challenging task. In this study, we propose an algorithm for stably selecting key variables on class-imbalanced data based on the precision-recall curve (PRC), where the PRC is utilized as the assessment criterion in the model building stage, and sparse regularized logistic regression combined with subsampling (SRLRS) is designed to perform stable variable selection. Considering the characteristic of class-imbalanced data, we also proposed classification-based partition for cross validation, as well as leaving half of majority observations out and leaving one minority observation out (LHO-LOO) for subsampling. Simulation results and real data showed that our algorithm is highly suitable for handling class-imbalanced data, and that the PRC can be an alternative evaluation criterion for model selection when handling class-imbalanced data.

Antioxidants from natural sources, such as vegetables and fruits, are attracting more and more interest. In this work, we evaluated the antioxidant potential of Folium Artemisia Argyi, a traditional Chinese herb medicine and food supplement. The total phenolic content, total flavonoid content, and antioxidant ability of the crude extracts and fractions obtained from consecutively partition of n-hexane, ethyl acetate, and n-butanol were measured and compared. Ethyl acetate fraction shows the highest total phenolic and flavonoid contents and highest antioxidant capability with regard to DPPH, ABTS, superoxide anion free radical scavenging ability, and ferric-reducing antioxidant power. In addition, the potential antioxidant components were screened by DPPH-UHPLC-MS experiments and subsequently characterized by using high-resolution tandem mass spectrometry. This work finally identified 45 antioxidants, including organic acids, phenolic compounds, flavonoids, and methoxylated flavonoids. The results suggested that Folium Artemisiae Argyi is a potential inexpensive resource of natural antioxidants.

A facile one-step strategy for anchoring defective CoNx single clusters on partly reduced graphene oxide (RGO) is constructed to significantly improve the catalytic performance of non-noble metal complexes toward oxygen reduction reaction (ORR). Sequent loading with trace amounts of metal-free porphyrin and Co2+ in RGO can dramatically enhance both the half-wave potential and the peak current density. Intriguingly, the RGO/P/2Co single cluster exhibits the best ORR catalytic performance with the half-wave potential of 0.834 V, extremely approaching that of commercial Pt/C (0.836 V). This half-wave potential surpasses most of the reported half-wave potentials of RGO supported non-noble metal ORR catalysts through low-temperature synthesis. Furthermore, the as-prepared RGO/P/2Co delivers a peak current density of 1.3 times higher than that of Pt/C at the same loading, together with a high mass activity of 2.76 A mgCo–1. During the durability test, a cathodic current loss less than 10% is recorded after 8000...

Alzheimer's disease (AD) is the most common cause of dementia in elderly people and is among the greatest healthcare challenges of the 21st century. However, the etiology and pathogenesis of AD remain poorly understood, and no curative treatments are available to slow down or stop the degenerative effects of AD. As a high-throughput approach, metabolomics is gaining significant attention in AD research, because it has a powerful potential to discover novel biomarkers, unravel new therapeutic targets for AD, and identify perturbed metabolic pathways involved in AD progression. Here, we systematically review metabolomics with regard to its recent advances and applications in the identification of potential biomarkers for early AD diagnosis and pathogenesis research. In addition, we illustrate the developments in metabolomics as an effective tool for understanding the anti-AD mechanisms of natural products. We believe that the insights from these advances can narrow the gap between metabolomics research and clinical applications of laboratory findings. Moreover, we discuss some limitations and perspectives of biomarker identification in metabolomics.