Discrimination and prediction of Qingzhuan tea storage year using quantitative chemical profile combined with multivariate analysis: Advantages of MRMHR based targeted quantification metabolomics.

The duration of storage significantly influences the quality and market value of Qingzhuan tea (QZT). Herein, a high-resolution multiple reaction monitoring (MRMHR) quantitative method for markers of QZT storage year was developed. Quantitative data alongside multivariate analysis were employed to discriminate and predict the storage year of QZT. Furthermore, the content of the main biochemical ingredients, catechins and alkaloids, and free amino acids (FAA) were assessed for this purpose. The results show that targeted marker-based models exhibited superior discrimination and prediction performance among four datasets. The R2Xcum, R2Ycum and Q2cum of orthogonal projection to latent structure-discriminant analysis discrimination model were close to 1. The correlation coefficient (R2) and the root mean square error of prediction of the QZT storage year prediction model were 0.9906 and 0.63, respectively. This study provides valuable insights into tea storage quality and highlights the potential application of targeted markers in food quality evaluation.

UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties.

Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines.

Third-order calibration applied to process surfactant-modulated excitation-emission matrix four-way fluorescence data for the direct determination of four polycyclic aromatic hydrocarbons in oilfield produced water.

Fluorescence spectroscopy is a powerful tool to determine polycyclic aromatic hydrocarbons (PAHs) owing to the strong endogenous fluorescence of these compounds. However, the presence of unknown interferences and overlapped spectra hinders the accurate determination of PAHs in oilfield produced water. Moreover, surfactants frequently coexist in oilfield produced water and will seriously affect the fluorescence signals of PAHs. Herein, a new methodology applying third-order calibration to process four-way (4D) fluorescence data was proposed to solve these problems and achieve accurate determination of pyrene, fluorene, phenanthrene, and fluoranthene as an example in oilfield produced water. The methodology is based on excitation-emission matrix fluorescence modulated by different concentrations of sodium dodecyl benzene sulfonate (SDBS) in the analyzed samples. The 4D fluorescence data were processed by third-order calibration methods including four-way parallel factor analysis (4-PARAFAC) and alternating weighted residue constraint quadrilinear decomposition (AWRCQLD), and the results were compared with those of second-order calibration methods. It was proved that third-order calibration was capable of accurately identifying and quantifying PAHs together with SDBS in oilfield produced water, which has better quantitative results and figures of merit compared to second-order calibration. This study provided a new approach to generating 4D fluorescence data and opened up an avenue for the accurate determination of PAHs in complex oilfield produced water with surfactants.

Revealing the chemical differences and their application in the storage year prediction of Qingzhuan tea by SWATH-MS based metabolomics analysis.

It's generally believed that the longer the storage, the better the quality of dark tea, but the chemical differences of Qingzhuan tea (QZT) with different storage years is still unclear. Herein, in this work, an untargeted metabolomic approach based on SWATH-MS was established to investigate the differential compounds of QZT with 0-9 years' storage time. These QZT samples were roughly divided into two categories by principal component analysis (PCA). After orthogonal projections to latent structures discriminant analysis (OPLS-DA), 18 differential compounds were putatively identified as chemical markers for the storage year variation of QZT. Heatmap visualization showed that the contents of catechins, fatty acids, and some phenolic acids significantly reduced, flavonoid glycosides, triterpenoids, and 8-C N-ethyl-2-pyrrolidinone-substituted flavan-3-ols (EPSFs) increased with the increase of storage time. Furthermore, these chemical markers were verified by the peak areas corresponding to MS2 ions from SWATH-MS. Based on the extraction chromatographic peak areas of MS and MS2 ions, a duration time prediction model was built for QZT with correlation coefficient R2 of 0.9080 and 0.9701, and RMSEP value of 0.85 and 1.24, respectively. This study reveals the chemical differences of QZT with different storage years and provides a theoretical basis for the quality evaluation of stored dark tea.

A comprehensive review of the current trends and recent advancements on the authenticity of honey.

The authenticity of honey currently poses challenges to food quality control, thus requiring continuous modernization and improvement of related analytical methodologies. This review provides a comprehensively overview of honey authenticity challenges and related analytical methods. Firstly, direct and indirect methods of honey adulteration were described in detail, commenting the existing challenges in current detection methods and market supervision approaches. As an important part, the integrated metabolomic workflow involving sample processing procedures, instrumental analysis techniques, and chemometric tools in honey authenticity studies were discussed, with a focus on their advantages, disadvantages, and scopes. Among them, various improved microscale extraction methods, combined with hyphenated instrumental analysis techniques and chemometric data processing tools, have broad application potential in honey authenticity research. The future of honey authenticity determination will involve the use of simplified and portable methods, which will enable on-site rapid detection and transfer detection technologies from the laboratory to the industry.

A Comprehensive Review on the Excitation-Emission Matrix Fluorescence Spectroscopic Characterization of Petroleum-Containing Substances: Principles, Methods, and Applications.

Petroleum-containing substance (PCS) is a general term used for petroleum and its derivatives. A comprehensive characterization of PCSs is crucial for resource exploitation, economic development and environmental protection. Fluorescence spectroscopy, especially excitation-emission matrix fluorescence (EEMF) spectroscopy, has been proved to be a powerful tool to characterize PCSs since its remarkable sensitivity, selectivity, simplicity and high efficiency. However, there is a lack of systematic review focusing on this field in the literature. This paper reviews the fundamental principles and measurements of EEMF for characterizing PCSs, and makes a systematic introduction to various information mining methods including basic peak information extraction, spectral parameterization and some commonly used chemometric methods. In addition, recent advances in applying EEMF to characterize PCSs during the whole life-cycle process of petroleum are also revisited. Furthermore, the current limitations of EEMF in the measurement and characterization of PCSs are discussed and corresponding solutions are provided. For promoting the future development of this field, the urgent need to build a relatively complete EEMF fingerprint library to trace PCSs, not only pollutants but also crude oil and petroleum products, is proposed. Finally, the extensions of EEMF to high-dimensional chemometrics and deep learning are prospected, with the expectation of solving more complex systems and problems.

UHPLC-QTOF-MS-based untargeted metabolomics and mineral element analysis insight into the geographical differences of Chrysanthemum morifolium Ramat cv. "Hangbaiju" from different origins.

Chrysanthemum morifolium Ramat cv. "Hangbaiju" (HBJ), known as one of the "eight flavors of Zhejiang", is commonly used as a classical tea material for both food and medicine over three thousand years in China. The quality of HBJ is closely related to its geographical origins. However, the mechanism underlying the geographical differences of HBJ remains to be elucidated. In this study, an untargeted metabolomic strategy based on UHPLC-QTOF-MS was established to discover the differential metabolites in HBJ samples from four different origins and explore the possible relationship with mineral elements in planting soils by chemometric analysis. Eight compounds were screened and identified as the key differential metabolites in HBJ samples from different origins. Among them, four important pharmacodynamic compounds including L-arginine, rutin, chlorogenic acid and apigenin-7-O-glucoside are the most abundant in HBJ samples from Tongxiang region, which suggests that HBJ planted in Tongxiang has higher medicinal values. Pearson correlation analysis revealed that the contents of soil mineral elements are positively correlated with those of chlorogenic acid, rutin, apigenin-7-O-glucoside in HBJ samples. Furthermore, an interrelationship model based on random forest algorithm was established to successfully predict the contents of differential metabolites in HBJ samples by soil mineral elements. All these results indicated that the contents of differential metabolites in HBJ samples seemed to be affected by soil mineral elements and therefore resulted in the geographical differences of HBJ.

Tracing sources of oilfield wastewater based on excitation-emission matrix fluorescence spectroscopy coupled with chemical pattern recognition techniques.

In order to prevent the illegal discharge of oilfield wastewater, this work proposed excitation-emission matrix fluorescence (EEMF) spectroscopy coupled with two kinds of chemical pattern recognition methods for tracing the sources of oilfield wastewater. The first pattern recognition method was built from the relative concentrations extracted by alternating trilinear decomposition (ATLD) based on partial least squares-discriminant analysis (PLS-DA) algorithm, and the other one was modeled based on strictly multi-way partial least squares-discriminant analysis (N-PLS-DA). Both methods showed good discrimination abilities for oilfield wastewater samples from three different sources. The total recognition rates of the training and prediction sets are 100%, the values of sensitivity and selectivity are 1. This study showed that EEMF spectroscopy combined with chemical pattern recognition techniques could be used as a potential tool for tracing the sources of oilfield wastewater.

Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics.

Identifying geographical origins of red wines made in specific regions is of significance since the false claim of geographical origins has been frequently exposed in China's wine industry. In this work, an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines. The principal component analysis (PCA) showed significant differences between wine samples from three famous geographical origins in China. The metabolites contributing to the differentiation were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) with pairwise modeling. 40 and 46 differential metabolites in positive and negative ionization modes were putatively identified as chemical markers. Furthermore, heatmap visualization and OPLS-DA models were constructed based on these identified markers and external verification wine samples from different regions were successfully discriminated, with recognition rate up to 96.7%. This study indicated that UPLC-QTOF-MS-based untargeted metabolomics has great potential for the geographical origin traceability of Chinese red wines.

Highly fluorescent N doped C-dots as sensor for selective detection of Hg2+ in beverages.

In this work, nitrogen doped carbon dots (NCDs) were synthesized through one step hydrothermal reaction using citric acid and ethylenediamine as precursors. The prepared NCDs exhibit high quantum yield of 67.4%, good stability, excellent selectivity and sensitivity. It was found that the NCDs have potential as a fluorescence sensor for the detection of Hg2+. Under optimal conditions, good linearity between the change in NCDs fluorescence intensity and Hg2+ concentration was obtained in the range of 0.3 to 2.0 µM with a detection limit at 0.24 µM. The possible detection mechanism was static quenching of NCDs by Hg2+. The established method was successfully applied to the determination of Hg2+ in beverage samples. The results indicated that the NCDs-based sensor has potential for detection of Hg2+ in real beverage sample.

Target-induced activation of DNAzyme for sensitive detection of bleomycin by using a simple MOF-modified electrode.

In this work, a sensitive electrochemical method for bleomycin (BLM) determination was reported on the basis of BLM-mediated activation of Zn2+-dependent DNAzyme and the adsorption of signal probes by a metal-organic framework (MOF) modified electrode. Two hairpin DNAs were employed in this protocol, one (HP1) for BLM recognition and one (HP2) for amplified signal output. The presence of BLM and Fe2+ caused the formation of BLM-Fe (II) complex to cleave HP1, releasing DNAzyme fragments, which could further hybridize with substrate HP2 to form a partial double-stranded DNA duplex and enable the activation of Zn2+-dependent DNAzyme with the coexistence of Zn2+. The Zn2+-dependent DNAzyme catalyzed the cyclic cleavage of magnetic beads (MB)-immobilized HP2 to release massive DNA fragments with a Fc-labeled- terminal, which could be used for BLM quantification through electrochemical measurement after their adsorption on a MOF modified electrode. Attributing to the high catalytic efficiency of DNAzyme and excellent electrochemical performance of MOF modified electrode, our method revealed an impressive limit of detection as low as 4 pM BLM with a linear range of 5-2000 pM. Besides, the easy synthesis of MOF without further modification and the easy way of adsorption for signal achievement facilitated the operation process. In virtue of the high sensitivity, selectivity and the simple-to-implement features, this method is believed to hold a great promising application for BLM determination in biomedical and clinical study.

HPLC-DAD fingerprints combined with chemometric techniques for the authentication of plucking seasons of Laoshan green tea.

Laoshan green teas plucked in summer and autumn were measured by high performance liquid chromatography-diode array detector (HPLC-DAD). After baseline correction, the fingerprints data were resolved by multivariate curve resolution-alternating least squares (MCR-ALS) and a total of 57 components were acquired. Relative concentrations of these components were afterwards applied to distinguish plucking seasons using principal component analysis (PCA), support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA). For both SVM and PLS-DA models, the total recognition rates of training set, cross-validation and testing set were 100%, 91.3% and 100%, respectively. Besides, three variable selection methods were employed to determine characteristic components for the authentication of summer and autumn teas. Results showed that PLS-DA model based on three characteristic components selected by VIP possesses identical predictive ability as the original model. This study demonstrated that our proposed strategy is competent for the authentication of plucking seasons of Laoshan green tea.

Impact of diverse background interferences on the alternating trilinear decomposition modeling of excitation-emission matrix fluorescence data acquired from different sample sources.

Alternating trilinear decomposition (ATLD) method enables the qualitative and quantitative analysis of excitation-emission matrix fluorescence (EEMF) data acquired from complex samples. However, the impact of diverse background interferences from different sample sources on the performances of ATLD method has never been lucubrated. In this work, simulated and real EEMF data sets from different sample sources with diverse background interferences were collected and subjected to ATLD analysis. The performances of ATLD modeling individual and global EEMF data sets were comprehensively compared in terms of the resolved spectral profiles and quantitative results. It was found that ATLD method can use the same set of calibration samples to resolve and quantify multiple components of interest in multiple complex systems with diverse background interferences, regardless of individual or global modeling. The results revealed that the qualitative and quantitative results provided by ATLD method were affected neither by diversity of background interferences nor by data merging as long as the acquired EEMF data sets conform to the trilinear component model. This property of ATLD method can enrich the "second-order advantage", i.e. the term "unknown interferences" in the concept of "second-order advantage" refers to not only constant background interferences but also diverse background interferences, which will be certain to further expand the practicality of ATLD method in complex sample analysis, especially in the field of fluorescence spectroscopy.

A dual-response biosensor for electrochemical and glucometer detection of DNA methyltransferase activity based on functionalized metal-organic framework amplification.

DNA methylation is catalyzed by DNA methyltransferase (MTase) and concerned with many biological processes including pathogenesis of various human diseases. The monitoring of MTase activity is thus of great significance in disease diagnosis and drug screening. Herein, we developed a facile way to synthesize biocompatible invertase enzyme modified metal-organic framework (Invertase/MOF) materials, and explored its application in constructing a dual-response Dam MTase sensor for the first time. By using them as signal probes, in which high density of metal sites could be electrochemically detected and invertase could hydrolyze sucrose into glucose for generation of glucometer signal output, dual-response for accurate detection of Dam MTase was realized. In the presence of Dam MTase, the methylation of hairpin probe 1 (HP1) occurred and thus caused the cleavage of HP1 assisted by a restriction endonuclease (DpnI) to produce the binding sequences. The binding sequences then hybridized with the electrode-assembled HP2 to expose their sticky termini which sequentially hybridized with the Invertase/MOFs-tethered capture probes. Finally, the electrodes were incubated with a sucrose solution, followed by the separate electrochemical and glucometer detection. The present assay brought good performance which could detect Dam MTase activity as low as 0.001 U mL-1 with wide linear range and good selectivity against other cytosine MTase (M.SssI MTase). Moreover, it also owns ability to be potentially applied for the inhibitors screening by utilization of 5-fluorouracil as an inhibitor model. The results imply that our proposed method provides a convenient platform for early cancer diagnosis and therapeutic applications.

MATLAB in electrochemistry: A review.

MATLAB (MATrix LABoratory) is a multi-paradigm numerical computing environment and fourth-generation programming language. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Electrochemistry is a branch of chemistry that studies the relationship between electricity, as a measurable and quantitative phenomenon, and identifiable chemical change, with either electricity considered an outcome of a particular chemical change or vice versa. MATLAB has obtained a wide range of applications in different fields of science and electrochemists are also using it for solving their problems which can help them to obtain more quantitative and qualitative information about systems under their studies. In this review, we are going to cast a look on different applications of MATLAB in electrochemistry and for each section, a number of selected articles published in the literature will be discussed and finally, the results will be summarized and concluded.

Angle Distribution of Loading Subspace (ADLS) for estimating chemical rank in multivariate analysis: Applications in spectroscopy and chromatography.

Multivariate analyses are increasingly popular to explore the underlying structure of multivariate datasets, which are more and more prevalent in analytical chemistry. However, difficulties can be associated with estimating the number of components for the data with considerable coherence and noise. The method of Angle Distribution of Loading Subspace (ADLS) has been proposed to estimate the number of components for Principal Component Analysis (PCA) and PARAllel FACtor analysis (PARAFAC), which showed some advantages, in particular in the case of datasets with high coherence, over the commonly used methods (scree plot and cross-validation in PCA, and core consistency diagnostics (CORCONDIA) in PARAFAC). In this paper, we systematically improved and applied ADLS to estimate the number of components in different multivariate methods including, Multivariate Curve Resolution (MCR), PARAFAC and four-way PARAFAC. Firstly, we showed that ADLS performed better when estimating the chemical rank for MCR analysis, compared with scree plots. As well as this, we improved ADLS in multi-way analysis (three- and four-way PARAFAC) by calculating the loading subspace in advance using the Khatri-Rao product. The improved ADLS in multi-way analysis provided the correct result for the simulated three-way fluorescence datasets with unevenly distributed coherence at different dimensions, while the previous version of ADLS showed biased results and CORCONDIA / split-half analysis provided relatively unstable results. Moreover, ADLS was used to estimate the chemical rank for a four-way real-life fluorescence dataset analyzed by four-way PARAFAC. In this case the result of chemical rank results from ADLS was more precise and informative compared with CORCONDIA /split-half analysis in four-way analysis.

Simultaneous co-immobilization of three enzymes onto a modified glassy carbon electrode to fabricate a high-performance amperometric biosensor for determination of total cholesterol.

In this work, we have fabricated a novel amperometric cholesterol (CHO) biosensor because of the importance of determination of CHO levels in blood which is an important parameter for diagnosis and prevention of disease. To achieve this goal, cholesterol oxidase, cholesterol esterase and horseradish peroxidase were simultaneously co-immobilized onto a glassy carbon electrode (GCE) modified with gold nanoparticles/chitin-ionic liquid/poly(3,4-ethylenedioxypyrrole)/graphene-multiwalled carbon nanotubes-1,1'-ferrocenedicarboxylic acid-ionic liquid. Modifications applied to the bare GCE were characterized by cyclic voltammetry, electrochemical impedance spectroscopy and scanning electron microscopy. The biosensor detected CHO in linear ranges of 0.1-25 µM and 25-950 µM with a detection limit of 0.07 µM. The sensitivity of the biosensor was estimated to be 6.6 µA µM-1 cm-2, its response time was <5 s and Michaelis-Menten constant was calculated to be 0.12 µM. Results obtained in this study revealed that the biosensor was selective, sensitive, stable, repeatable and reproducible. Finally, the biosensor was successfully applied to the determination of CHO levels in rats plasma.

Dealing with overlapped and unaligned chromatographic peaks by second-order multivariate calibration for complex sample analysis: Fast and green quantification of eight selected preservatives in facial masks.

The quantification of preservatives in cosmetics has attracted great attentions for their controversial and widespread use. HPLC is a prevailing method for preservatives determination among various analytical methods. However, it takes long time to fully separate these compounds because of the complexity of cosmetic matrices. In this study, a fast and green HPLC-DAD strategy assisted with second-order multivariate calibration methods based on alternating trilinear decomposition (ATLD) and multivariate curve resolution-alternating least squares (MCR-ALS) was developed for the simultaneous determination of eight selected preservatives in complex facial mask samples. This appealing strategy proved to be a useful tool for eliminating unknown interferences in complex matrices without complete separation, which benefited from the "second-order advantages" and thus made the determination of the eight analytes in facial mask samples shorten to 8.2 min under a fast elution program. In particular, for the first time, we focused on the applicability of ATLD method for modeling of HPLC-DAD data with severe signal overlapping and slight time shifts. The spiked recovery values were in the range of 71.4-124.6%, and the RMSEP and REP values ranged from 0.07 to 2.4 µg mL-1 and 1.3-14.5%, respectively, indicating that the ATLD method could provide satisfactory prediction. The resolved spectral profiles and concentration values were compared with those obtained by the MCR-ALS method, an excellent tool for modeling of data deviating from trilinearity. Both qualitative and quantitative results from the two methods were consistent with each other, which evidenced the competence of ATLD method in handling HPLC-DAD data with severe signal overlapping and slight time shifts.

Second-order calibration serves as a remedial measure for the simultaneous determination of andrographolide and dehydroandrographolide in Andrographis paniculata and its preparations by HPLC without complete baseline separation.

In this work, high-performance liquid chromatography with diode array detection was applied for the simultaneous determination of andrographolide and dehydroandrographolide in Andrographis paniculata and its preparations. As a result of the incomplete baseline separation caused by complex backgrounds, the classical univariate calibration method failed to determine accurate contents of the analytes. On this occasion, chemometric second-order calibration based on the well-known alternating trilinear decomposition algorithm was then explored to serve as a post-experimental remedial tool to solve this problem. By using the intelligent "mathematical separation" of alternating trilinear decomposition, the peak areas of the analytes do not need to be directly measured and the predictive results become accurate. The contents of andrographolide and dehydroandrographolide were determined to be (7.95 ± 0.15) and (1.85 ± 0.02) µg/mL for Andrographis paniculata, (1.34 ± 0.01) and (5.53 ± 0.04) µg/mL for its preparations, which was in agreement with those obtained by a reference liquid chromatography with mass spectrometry method. This study showed the superiority of second-order calibration method over classical univariate calibration method for simultaneous determination of multi-analytes in complex samples. It also proved that second-order calibration may be a good choice for remedying incomplete baseline separation problem, with the accompanied reduction of experimental burden and toxic organic solvents as well as analysis time and cost.

Investigation of interactions of Comtan with human serum albumin by mathematically modeled voltammetric data: A study from bio-interaction to biosensing.

In this work, voltammetric data recorded at a glassy carbon electrode (GCE) were separately used to investigate the interactions of entacapone (Comtan, CAT) with human serum albumin (HSA). Then, an augmented data matrix was constructed by the combination of voltammetric and spectroscopic data and simultaneously analysed by multivariate curve resolution-alternating least squares (MCR-ALS) to obtain more information about CAT-HSA interactions. The absence of rotational ambiguities in results obtained by MCR-ALS was verified with the help of MCR-BANDS and we confirmed that the results were unambiguous and reliable. Binding of CAT to HSA was also modeled by molecular docking and the results were compatible with those of obtained by recording experimental data. Hard-modeling of combined voltammetric and spectroscopic data by EQUISPEC as an efficient chemometric algorithm helped us to compute binding constant of CAT-HSA complex specie which was in a good agreement with the binding constant value obtained by direct analysis of experimental data. For electrochemical sensing of serum albumin two amperometric measurements were performed to determine HSA in 2-27 nM and 27-70 nM with a limit of detection of 0.51 nM and a sensitivity of 1.84 µA nM-1.