Determination of organic acids for predicting sourness intensity of tea beverage by liquid chromatography-tandem mass spectrometry and chemometrics methods.

The contents of organic acids (OAs) in tea beverage and their relationship with taste intensity have not been fully understood. In this work, a rapid (10 min for a single run) and sensitive (limits of quantification: 0.0044-0.4486 µg/mL) method was developed and validated for the simultaneous determination of 17 OAs in four types of tea, based on liquid chromatography-tandem mass spectrometry with multiple reaction monitoring mode. The contents of 17 OAs in 96 tea samples were measured at levels between 0.01 and 11.80 g/kg (dried weight). Quinic acid, citric acid, and malic acid were determined as the major OAs in green, black, and raw pu-erh teas, while oxalic acid and tartaric acid exhibited the highest contents in ripe pu-erh tea. Taking the OAs composition as input features, a partial least squares regression model was proposed to predict the sourness intensity of tea beverages. The model achieved a root-mean-square error of 0.58 and a coefficient of determination of 0.84 for the testing set. The proposed model provides a theoretical way to evaluate the sensory quality of tea infusion based on its chemical composition.

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities.

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

Fluorescence sensor based on optimized quantum yield manganese-carbon polymer dots and smartphone-integrated sensing platform for tetracycline detection.

The one-step synthesis of Mn-doped carbon quantum dots (Mn-CPDs) with a high quantum yield (QY = 45%) is reported using the microwave-assisted method. Subsequently, Mn-CPDs were successfully combined with Eu3+ ions to construct an Eu3+@Mn-CPDs fluorescence sensor. The presence of tetracycline (TC) induced a transition of fluorescence emission from blue (434 nm) to red (618 nm), and a robust linear relationship was observed between the ratio of F618 nm / F434 nm and the TC concentration (5 - 50 nmol/L), with a limit of detection (LOD) of 5.76 nmol/L. The underlying mechanism of Eu3+@Mn-CPDs and TC sensing is unveiled as a synergistic effect involving inner filter effect (IFE) and concurrent interactions. Notably, the smartphone-integrated sensing platform based on Eu3+@Mn-CPDs enables rapid and quantitative TC detection within a short time (< 30 s) by monitoring fluorescence color changes, achieving high-detection sensitivities (with a LOD of 6.18 nmol/L). This versatile and efficient sensing platform demonstrates its potential for the determination of TC concentrations in milk, honey, and tap water samples.

Chemical antifouling strategies in sensors for food analysis: A review.

Surface biofouling induced by the undesired nonspecific adsorption of foulants (e.g., coexisting proteins and cells) in food matrices is a major issue of sensors for food analysis, hindering their reliability and accuracy of sensing. This issue can be addressed by developing antifouling strategies to prevent or alleviate nonspecific binding. Chemical antifouling strategies involve the use of chemical modifiers (i.e., antifouling materials) to strongly hydrate the surface and reduce surface biofouling. Through appropriate immobilization approaches, antifouling materials can be tethered onto sensors to form antifouling surfaces with well-ordered structures, balanced surface charges, and appropriate surface density and thickness. A rational antifouling surface can reduce the matrix effect, simplify sample pretreatment, and improve analytical performance. This review summarizes recent developments in chemical antifouling strategies in sensing. Surface antifouling mechanisms and common antifouling materials are described, and factors that may influence the antifouling effects of antifouling surfaces and approaches incorporating antifouling materials onto sensing surfaces are highlighted. Moreover, the specific applications of antifouling sensors in food analysis are introduced. Finally, we provide an outlook on future developments in antifouling sensors for food analysis.

Electrochemiluminescence sensor based on cyclic peptides-recognition and Au nanoparticles assisted graphitic carbon nitride for glucose determination.

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.

Metabolic Profiling by UPLC-Orbitrap-MS/MS of Liver from C57BL/6 Mice with DSS-Induced Inflammatory Bowel Disease.

Liver disorder often occurs in patients with inflammatory bowel disease (IBD); however, the changes in IBD-induced liver disorder at the intrinsic molecular level (chiefly metabolites) and therapeutic targets are still poorly characterized. First, a refined and translationally relevant model of DSS chronic colitis in C57BL/6 mice was established, and cecropin A and antibiotics were used as interventions. We found that the levels of tumor necrosis factor (TNF)-α, interleukin (IL)-1ß, and IL-6 in the liver tissues of mice were highly increased in the context of DSS treatment but were lowered by cecropin A and antibiotics. Subsequently, an untargeted metabolomics analysis was performed by UPLC-Orbitrap-MS/MS to reveal the metabolic profile and attempt to find the potential therapeutic targets of the liver disorders that occur in IBD. Notably, 133 metabolites were identified by an integrated database. Metabolism network and pathway analyses demonstrated that the metabolic disturbance of the liver in IBD mice was mainly enriched in bile acid metabolism, arachidonic acid metabolism, amino acid metabolism, and steroid hormone biosynthesis, while those disturbances were regulated or reversed through cecropin A and antibiotic treatment. Furthermore, the top 20 metabolites, such as glutathione, maltose, arachidonic acid, and thiamine, were screened as biomarkers via one-way analysis of variance (one-way ANOVA, p < 0.05) coupled with variable importance for project values (VIP >1) of orthogonal partial least-squares discriminant analysis (OPLS-DA), which could be upregulated or downregulated with the cecropin A and antibiotics treatment. Spearman correlation analysis showed that the majority of the biomarkers have a significant correlation with cytokines (TNF-α, IL-1ß, IL-6, and IL-10), indicating that those biomarkers may act as potential targets to interact directly or indirectly with cecropin A and antibiotics to affect liver inflammation. Collectively, our results extend the understanding of the molecular alteration of liver disorders occurring in IBD and offer an opportunity for discovering potential therapeutic targets in the IBD process.

In situ ionic liquid dispersive liquid-liquid microextraction combined with ultra high performance liquid chromatography for determination of neonicotinoid insecticides in honey samples.

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.

Tuning model parameters in class-imbalanced learning with precision-recall curve.

An issue for class-imbalanced learning is what assessment metric should be employed. So far, precision-recall curve (PRC) as a metric is rarely used in practice as compared with its alternative of receiver operating characteristic (ROC). This study investigates the performance of PRC as the evaluating criterion to address the class-imbalanced data and focuses on the comparison of PRC with ROC. The advantages of PRC over ROC on assessing class-imbalanced data are also investigated and tested on our proposed algorithm by tuning the whole model parameters in simulation studies and real data examples. The result shows that PRC is competitive with ROC as performance measurement for handling class-imbalanced data in tuning the model parameters. PRC can be considered as an alternative but effective assessment for preprocessing (such as variable selection) skewed data and building a classifier in class-imbalanced learning.

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis.

Due to their beneficial effects on human health, antioxidant peptides have attracted much attention from researchers. However, the structure-activity relationships of antioxidant peptides have not been fully understood. In this paper, quantitative structure-activity relationships (QSAR) models were built on two datasets, i.e., the ferric thiocyanate (FTC) dataset and ferric-reducing antioxidant power (FRAP) dataset, containing 214 and 172 unique antioxidant tripeptides, respectively. Sixteen amino acid descriptors were used and model population analysis (MPA) was then applied to improve the QSAR models for better prediction performance. The results showed that, by applying MPA, the cross-validated coefficient of determination (Q²) was increased from 0.6170 to 0.7471 for the FTC dataset and from 0.4878 to 0.6088 for the FRAP dataset, respectively. These findings indicate that the integration of different amino acid descriptors provide additional information for model building and MPA can efficiently extract the information for better prediction performance.

Combined application of chromatographic techniques for the separation of phenolic compounds from Stenoloma chusanum Ching.

High-speed countercurrent chromatography combined with preparative high-performance liquid chromatography was successfully used to separate seven phenolic compounds from Stenoloma chusanum Ching. A biphasic solvent system composed of hexane/ethyl acetate/methanol/water (1:2:1:2, v/v) was used for the first step high-speed countercurrent chromatography separation in elution-extrusion mode. A mobile phase composed of acetonitrile (18%) and pure water (82%) was used for further preparative high-performance liquid chromatography purification. In total, the combined separation yielded seven compounds, including 3,4-dihydroxy benzoic acid, 3,4-dihydroxy benzaldehyde, esculetin, caffeic acid, syringic acid, luteolin, and apigenin, at a purity of over 90%. Esculetin was separated from Stenoloma chusanum Ching for the first time. The results suggest that the proposed combination method is a useful strategy for separating compounds from complex samples.

GC-MS Fingerprinting Combined with Chemometric Methods Reveals Key Bioactive Components in Acori Tatarinowii Rhizoma.

This present study aims to identify the key bioactive components in acorus tatarinowii rhizoma (ATR), a traditional Chinese medicine (TCM) with various bioactivities. Partial least squares regression (PLSR) was employed to describe the relationship between the radical scavenging activity and the volatile components. The PLSR model was improved by outlier elimination and variable selection and was evaluated by 10-fold cross-validation and external validation in this study. Based on the PLSR model, eleven chemical components were identified as the key bioactive components by variable importance in projection. The final PLS regression model with these components has good predictive ability. The Q² was 0.8284, and the root mean square error for prediction was 2.9641. The results indicated that the eleven components could be a pattern to predict the radical scavenging activity of ATR. In addition, we did not find any specific relationship between the radical scavenging ability and the habitat of the ATRs. This study proposed an efficient strategy to predict bioactive components using the combination of quantitative chromatography fingerprints and PLS regression, and has potential perspective for screening bioactive components in complex analytical systems, such as TCM.

Discrimination of Acori Tatarinowii Rhizoma and Acori Calami Rhizoma based on quantitative gas chromatographic fingerprints and chemometric methods.

This study was conducted to investigate the chemical differences between Acori Tatarinowii Rhizoma and Acori Calami Rhizoma using gas chromatography with mass spectrometry and chemometric methods. Quantitative fingerprints were established. A total of 90 volatile compounds were identified and quantified using heuristic evolving latent projection and retention index. An efficient model based on partial least squares-discriminant analysis coupled with variable iterative space shrinkage approach was developed to distinguish Acori Tatarinowii Rhizoma from Acori Calami Rhizoma. The correct rate was 95.83%, and the area under the receiver operating characteristic curve was 100%. Finally, three volatiles, namely, camphor, longicyclene, and δ-cadinene, were selected as key discrimination factors between Acori Tatarinowii Rhizoma and Acori Calami Rhizoma. The proposed protocol can serve as a valid strategy for quality control and screening of potential bioactive components of herbal medicines.

An ultra high performance liquid chromatography with tandem mass spectrometry method for plasma and cerebrospinal fluid pharmacokinetics of rhein in patients with traumatic brain injury after administration of rhubarb decoction.

Damage of blood-brain barrier is a common result of traumatic brain injury. This damage can open the blood-brain barrier and allow drug passage. An ultraperformance liquid chromatography with tandem mass spectrometry method was established to determine the concentration of rhein in the biofluids (plasma and cerebrospinal fluid) of patients with a compromised blood-brain barrier following traumatic brain injury after rhubarb administration. Furthermore, the pharmacokinetic profiles were analyzed. A triple-quadruple tandem mass spectrometer with electrospray ionization was used for rhein detection. The mass transition followed was m/z 283.06→239.0. The calibration curve was linear in the concentration range of 10-8000 ng/mL for the biofluids. The intra- and interday precisions were less than 10%. The relative standard deviation of recovery was less than 15% in biological matrices. The pharmacokinetic data showed that rhein was rapidly transported into biofluids, and exhibited a peak concentration 1 h after rhubarb administration. The elimination rate of rhein was slow. The AUCcerebrospinal fluid /AUCplasma (AUC is area under curve) of rhein was approximately 17%, indicating that portions of rhein could pass the impaired blood-brain barrier. The method was successfully applied to quantify rhein in the biofluids of all patients. The data presented can help to guide clinical applications of rhubarb for treating traumatic brain injury.

Prediction of peptide fragment ion mass spectra by data mining techniques.

Accurate prediction of peptide fragment ion mass spectra is one of the critical factors to guarantee confident peptide identification by protein sequence database search in bottom-up proteomics. In an attempt to accurately and comprehensively predict this type of mass spectra, a framework named MS(2)PBPI is proposed. MS(2)PBPI first extracts fragment ions from large-scale MS/MS spectra data sets according to the peptide fragmentation pathways and uses binary trees to divide the obtained bulky data into tens to more than 1000 regions. For each adequate region, stochastic gradient boosting tree regression model is constructed. By constructing hundreds of these models, MS(2)PBPI is able to predict MS/MS spectra for unmodified and modified peptides with reasonable accuracy. Moreover, high consistency between predicted and experimental MS/MS spectra derived from different ion trap instruments with low and high resolving power is achieved. MS(2)PBPI outperforms existing algorithms MassAnalyzer and PeptideART.

Multistimuli-responsive supramolecular gels: design rationale, recent advances, and perspectives.

This manuscript presents a brief overview of recent advances in multistimuli-responsive supramolecular gels (MRSGs). The synthesis of MRSGs with faster and smarter responsive abilities to a variety of external stimuli, such as redox reagents, pH changes, ligands, and coupling reagents, is one key issue for the upgrade of current molecular motors, signal sensors, shape memory devices, drug delivery systems, display devices, and other devices. However, the design rules of MRSGs are still not well understood. The lack of information about the relationship between the spatial structure and gelation behavior of existing gelators means that the knowledge required to design new gelators by the addition of functional moieties to well-known gelators is lacking. Insights into the gelation pathway of known gelators may bring inspiration to researchers who want to exploit elegant designs and specific building blocks to obtain their own MRSGs with predictable stimuli-responsive abilities.

A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.

Joint MS-based platforms for comprehensive comparison of rat plasma and serum metabolic profiling.

Metabolomics is a rapidly growing field in the comprehensive understanding of cellular and organism-specific responses associated with perturbations induced by medicines, chemicals and environment. Blood matrices are frequently used in clinical and biological studies. In this study, we compared metabolic profiling between rat plasma and serum using complementary platforms of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-quadruple time-of-flight-mass spectrometry (LC-QTOF-MS). The sample types that were tested included plasma prepared with K2 EDTA and serum collected using venous blood collection protocols. The results of peak area variation for each detected metabolite/feature in the quality control samples showed a good reproducibility in LC-QTOF-MS and better reproducibility in GC-MS. In GC-MS analysis: (a) 25.8% of the defined metabolites differed serum from plasma profiling (t-test, p < 0.05); and (b) serum possessed higher sensitivity than plasma for its generally higher peak intensity in the metabolic profiling. In LC-QTOF-MS analysis, 13 (in positive ion mode) and seven (in negative ion mode) important metabolites were identified as mainly contributing to the separation between serum and plasma.

An antifouling electrochemical aptasensor based on a polydopamine-polyzwitterion copolymer for tetracycline analysis.

Matrix interference resulting from the nonspecific adsorption of non-target components, particularly proteins (fouling), onto sensor surfaces poses a persistent challenge in electrochemical detection of food hazards. The development of antifouling sensor surfaces presents a viable approach to mitigate nonspecific adsorption. In this study, a novel antifouling electrochemical aptasensor, utilizing a zwitterionic polymer, was developed for the sensitive, accurate, and selective detection of tetracycline (TC) in milk. This sensor employs a poly (dopamine)-poly (sulfobetaine methacrylate) (PDA-PSBMA) antifouling copolymer, which is synthesized through an in-situ initiated copolymerization of dopamine on the sensor's surface. Subsequently, the thiol-containing aptamers were immobilized onto the PDA-PSBMA coating through a Michael addition reaction with the poly(dopamine). The resulting antifouling electrochemical aptasensor exhibited robust antifouling performance in various single protein solutions and diluted milk samples, coupled with sensitive and selective recognition of TC. The sensor demonstrated a broad linear response range of 0.1-1000.0 ng mL-1 and a low limit of detection (LOD) of 68.0 pg mL-1. The antifouling electrochemical aptasensor proved effective in assaying TC in diluted milk, with recoveries ranging from 100.0 % to 104.4 %, eliminating the need for additional pretreatments due to its exceptional resistance to nonspecific adhesion.

Mass filtering combined with photochemical derivatization enables high throughput mass spectrometric analysis of unsaturated phosphatidylcholine isomers.

Phosphatidylcholines (PCs) are closely related to coronary heart disease, such as myocardial infarction. The analysis of the deep structure of PCs is of great significance for exploring the effects of exercise rehabilitation and lipid metabolism. Here, we present a mass filtering combined with photochemical derivatization method for rapid screening and accurate identification of the CC position and sn-location isomer of PCs. This method is simple to execute and easily implementable for routine analysis. The accurate qualitative and quantitative analysis of PCs and isomers facilitates the discovery of biomarkers for exercise rehabilitation of patients with myocardial infarction.

Aroma and taste analysis of pickled tea from spontaneous and yeast-enhanced fermentation by mass spectrometry and sensory evaluation.

Anaerobically fermented pickled tea (PT) can be produced by spontaneous fermentation (SF) or yeast-enhanced fermentation (YF). Aroma and taste characteristics of PT during YF and SF were investigated using sensory evaluation, odour activity, aroma character impact values, HS-SPME-GC-MS, UPLC-QQQ-MS/MS, and spectrophotometry, annotating 198 volatile and 115 non-volatile components. The main contributing volatile components were ß-ionone, and 1-octanol, promoted by YF and SF, and yielding floral and fruity aromas respectively. Additionally, compared with SF, YF promoted the formation of citronellol yielding a floral aroma, inhibited the stale aroma of methoxybenzenes, and reduced bitter, astringent, and sour tastes. Furthermore, partial least-squares regression analysis identified the main components related to the 'acides aroma' of PT as linalool oxide, n-decanoic acid, hexanoic acid, 3,7-dimethyl-2,6-octadienoic acid, 3-methyl-1-dodecyn-3-ol, and nerolidol. This application could be used as methodology for the comprehensive analysis of tea aroma and taste and these results can act as guidelines for PT production and quality control.