Geographical origin identification of winter jujube(Ziziphus jujuba Dongzao')by using multi-element fingerprinting with chemometrics

Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.

Simultaneous spectrophotometric determination of cobalt,copper and nickel using 1-(2-thiazolylazo)-2-naphthol by chemometrics methods

The simultaneous determination of cobalt, copper and nickel using 1-（2-thiazolylazo）-2-naphthol （first figure of this article） by spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares （PLS） regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction （OSC） is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 550-750-nm range for 21 different mixtures of cobalt, copper and nickel. Calibration matrices were formed from samples containing 0.05-1.05, 0.05-1.30 and 0.05-0.80 μg·mL^-1 for cobalt, copper and nickel, respectively. The root mean square error of prediction （RMSEP） for cobalt, copper and nickel with OSC and without OSC were 0.007, 0.008, 0.011 and 0.031,0.037, 0.032 μg· mL^-1, respectively. This procedure allows the simultaneous determination of cobalt, copper and nickel in synthetic and real samples and good reliability of the determination was proved.

Authentication of Cassia seeds on the basis of two-wavelength HPLC fingerprinting with the use of chemometrics

High performance liquid chromatographic(HPLC) fingerprints of Cassia seed,a traditional Chinese medicine(TCM),were developed by means of the chromatograms at two wavelengths of 238 and 282 nm.Then,the two data sets were combined into one matrix.The application of principal component analysis(PCA) for this data matrix showed that the samples were clustered into four groups in accordance with the plant sources and preparation procedures.Furthermore,partial least squares(PLS),back propagation artificial neural...

Investigation of the interaction of carbofuran and DNA by voltammetry and fluorescence with the aid of chemometrics

The interactions of carbofuran and DNA were studied using voltammetry and fluorescence spectroscopy.The formation of carbofuran-DNA makes the current peak of DNA decreased by voltammetry method.The binding number（n） and constant（Ka） for complex carbofuran-DNA were calculated to be 1.06±0.04 and 0.11±0.03mol^-1 L,respectively by fluorescence measurement.Chemometrics approach,such as singular value decomposition（SVD） was used to evaluate the number of spectral species in the drug-DNA binding process.And the pure spectra and concentration profiles in the kinetic system were clearly deduced by multivariate curve resolution alternating least squares（MCR-ALS） with the initial estimates by evolving factor analysis（EFA）.

Simultaneous Voltammetric Determination of Three Herbicides in Food and Water Samples with the Aid of Chemometrics

Differential pulse stripping voltammetry method（DPSV） was applied to the determination of three herbicides, ametryn, cyanatryn, and dimethametryn. It was found that their voltammograms overlapped strongly, and it is difficult to determine these compounds individually from their mixtures. With the aid of chemometrics, classical least squares（CLS）, principal component regression（PCR） and partial least squares（PLS）, voltammogram resolution and quantitative analysis of the synthetic mixtures of the three compounds were successfully performed. The proposed method was also applied to the analysis of some real samples with satisfactory results.

Simultaneous kinetic spectrophotometric determination of cephalexin and trimethoprim in pharmaceutical preparation and human urine with the aid of chemometrics

A procedure for the simultaneous kinetic spectrophotometric determination of cephalexin and trimethoprim was described. It was based on the different reaction rate of oxidation of these compounds with yellow ammonium cerous （Ⅳ） sulfate in acidic medium and colorless cerous （Ⅲ） sulfate was produced. The overlapped kinetic data was quantitatively resolved by the use of chemometric methods, partial least squares （PLS）, principal component regression （PCR） and radial basis function-artificial neural network （RBF-ANN）. The proposed method was also applied to the simultaneous determination of cephalexin and trimethoprim in pharmaceutical preparation and human urine with satisfied results, which compared well with those obtained by HPLC.

Rapid qualitative and quantitative analysis of strong aroma base liquor based on SPME-MS combined with chemometrics

To objectively classify and evaluate the strong aroma base liquors(SABLs)of different grades,solid-phase microextraction-mass spectrometry(SPME-MS)combined with chemometrics were used.Results showed that SPME-MS combined with a back-propagation artificial neural network(BPANN)method yielded almost the same recognition performance compared to linear discriminant analysis(LDA)in distinguishing different grades of SABL,with 84%recognition rate for the test set.Partial least squares(PLS),successive projection algorithm partial least squares(SPA-PLS)model,and competitive adaptive reweighed samplingpartial least squares(CARS-PLS)were established for the prediction of the four esters in the SABL.CARS-PLS model showed a greater advantage in the quantitative analysis of ethyl acetate,ethyl butyrate,ethyl caproate,and ethyl lactate.These results corroborated the hypothesis that SPME-MS combined with chemometrics can effectively achieve an accurate determination of different grades of SABL and prediction performance of esters.

Qualitative and quantitative evaluation of Flos Puerariae by using chemical fingerprint in combination with chemometrics method

In order to better control the quality of Flos Puerariae(FP),qualitative and quantitative analyses were initially performed by using chemical fingerprint and chemometrics methods in this study.First,the fingerprint of FP was developed by HPLC and the chemical markers were screened out by similarity analysis(SA),hierarchical clustering analysis(HCA),principal components analysis(PCA),and orthogonal partial least squares discriminant analysis(OPLS-DA).Next,the chemical constituents in FP were profiled and identified by HPLC coupled to Fourier transform ion cyclotron resonance mass spectrometry(HPLCFT-ICR MS).Then,the characteristic constituents in FP were quantitatively analyzed by HPLC.As a result,31 common peaks were assigned in the fingerprint and 6 of them were considered as qualitative markers.A total of 35 chemical constituents were detected by HPLC-FT-ICR MS and 16 of them were unambiguously identified by comparing retention time,UV absorption wavelength,accurate mass,and MS/MS data with those of reference standards.Subsequently,the contents of glycitin,genistin,tectoridin,glycitein,genistein,and tectorigenin in 13 batches of FP were detected,ranging from 0.4438 to 11.06 mg/g,0.955 to 1.726 mg/g,9.81 to 57.22 mg/g,3.349 to 41.60 mg/g,0.3576 to 0.989 mg/g,and 2.126 to 9.99 mg/g,respectively.In conclusion,fingerprint analysis in combination with chemometrics methods could discover chemical markers for improving the quality control standard of FP.It is expected that the strategy applied in this study will be valuable for further quality control of other traditional Chinese medicines.

TGA/Chemometrics addressing innovative preparation strategies for functionalized carbon nanotubes

In this work,functionalized carbon nanotubes(CNTs)using two polyamine polymers,polyethyleneimine(PEI)and polyamidoamine dendrimer(PAMAM),were investigated by thermal analysis in order to address preparation strategies to obtain low cytotoxic compounds with the ability to conjugate micro-RNAs and,at the same time,to transfect efficiently endothelial cells.Thermogravimetric analysis(TGA)was coupled to chemometrics as a novel analytical strategy to characterize functionalized CNTs from different preparation conditions.In particular,two starting materials were considered:very small CNTs and carboxylated CNTs(CNT-COOH)in order to examine the affinity with polymers.Chemometrics permitted to compare results from TGA and to investigate the effect of a number of factors affecting the synthesis of coated nanotubes including a different amount of involved polymer and the time required for the suspension for a satisfactory and reproducible preparation procedure.The results demonstrated the effectiveness of TGA as a tool able to address synthesis of coated CNTs to be employed as efficient drug delivery vectors in biomedical applications.

pH value monitoring during human albumin purification with near infrared spectroscopy and chemometrics

Human albumin(HA)is a very important blood product which requires strict quality controlstrategy.Acid precipitation is a key step which has a great effect on the quality of final product.Therefore,a new method based on quality by design(QbD)was proposed to investigate thefeasibility of realizing online quality control with the help of near infrared spectroscopy(NIRS)and chemometrics.The pH value is the critical process parameter(CPP)in acid precipitationprocess,which is used as the end-point indicator.Six batches,a total of 74 samples of acidprecipitation process,were simulated in our lab.Four batches were selected randomly as cali-bration set and remaining two batches as validation set.Then,the analysis based on materialinformation and three dfferent variable selection methods,including interval partial least squaresregression(iPLS),competitive adaptive reweighted sampling(CARS)and correlation coeficient(CC)were compared for eliminating irrelevant variables,Fimally,iPLS was used for variablesselection.The quantitative model was built up by partial least squares regression(PLSR).Thevalues of determination coeficients(R^(2)_(C) and R^(2)_(P)),root mean squares error of prediction(RMSEP),root mean squares error of calibration(RMSEC)and root mean squared error of crossvalidation(RMSECV)were 0.969,0.953,0.0496,0.0695 and 0.0826,respectively.The paired t test and repeatability test showed that the model had good prediction ability and stability.The results indicated that PLSR model could give accurate measurement of the pH value.

Chemometrics:Research on Basic Algorithms and Their Applications

Some aspects of the fundamental problems of chemometrics are reviewed based on the research work undertaken in this laboratory. The topics touched upon Include analytical information theory, experimental design and optimization, sampling, analytical detection theory, calibration, signal processing, chemical pattern recognition, quantitative structure-activity relationships, digital simulation, and teaching chemometrics as a chemical discipline.

Carob Origin Classification by FTIR Spectroscopy and Chemometrics

The goal of this study was to use Fourier transform mid-infrared （FTIR） spectroscopy for discrimination of samples of pods and seeds of carob from three Moroccan regions. The origin of samples Pods and seeds of carob could be distinguished from their IR spectra and this measurement was used for discriminate analysis. A multivariate analysis procedure based on the combined use of Hierarchical Cluster Aanalysis （HCA） and Partial Least Squares-Discriminant Analysis （PLS-DA） was tested and provided good classification results. Three distinctive clusters were recognised, related to the three Moroccan regions. Afterwards, PLS-DA was used for the discrimination and classification of the origin of the various Pods and seeds of carob samples. The results demonstrated that the combined use of FTIR and chemometric analysis （cluster analysis and discrimination by PLS- DA） can be used to rapidly and simply determine the origin of carob pulpe samples.

Characterization and Chemometrics Based-Approach to Classify Some Algerian Blossom Honeys

Physicochemical characterization of 82 Algerian honeys, collected between 2005 and 2010, from different botanical and geographical origins were analyzed. The studied parameters were： water content, pH, free acidity （FA）, electrical conductivity （EC）, ash content, hydroxymethylfurfuraldehyde （HMF）, proline content, specific rotatory power and color. Most of the measured parameters had showed values in the range of the international standards, with a particular richness in proline and ash content. Chemometrics-based approach reveals that the discriminated groups were Citrus, Ziziphus and forest even with over represented groups like Eucalyptus. Principle component analysis （PCA） enabled to extract three principal components explaining nearly 65% of total variance, PCj and PC2 were related to botanical origin whereas PC3 to honey age. Analysis of variance showed that the studied variables were almost different depending on botanical, geographical origin and season. The current study also shows the presence of diverse honey varieties in Algeria. The collected data will contribute to the creation of products with protected geographical or/and botanical origins.

Discrimination of Cheddar and Kefalotyri Cheese Samples:Analysis by Chemometrics of Proton-NMR and FTIR Spectra

Cheddar and Kefalotyri cheese belong to the category of hard cheeses.Cheddar has an English origin,while Kefalotyri is a traditional cheese in Greece and a well-consumed dairy product in Cyprus.Discrimination of dairy products can be determined through several chemical methods.The aim of this study was to discriminate the samples of Cheddar and Kefalotyri cheese by analyzing various samples,from different brands.Two spectroscopic techniques namely proton nuclear magnetic resonance(1H-NMR)and Fourier-transformed infrared(FTIR)spectroscopy were chosen in order to chemically characterise the samples.The first step of the methodology was the freeze-drying process for lyophilisation of the samples.The number of samples reached 28,including 14 Cheddar samples and 14 samples of Kefalotyri cheese.After that,measurements for each sample have been obtained by FTIR(%transmittance-wavenumber in cm^-1)and ^1H-NMR(signal intensity-chemical shift in ppm)techniques.The data were analysed using SIMCA software.The proposed techniques along with chemometrics allow the discrimination of those two types of cheese.Both techniques employed are of significant importance,since they provide information about good classification of the samples when they are combined together.Interpretation of results and classification by using chemometric methods confirmed the different recipe of the two types of cheese.This study is the initial step of the future work.Future research will focus on discrimination based on the species’origin of milk of these and other cheese samples.

Analysis of ATR-FTIR Absorption-Reflection Data from 13 Polymeric Fabric Materials Using Chemometrics

We used both correlation and covariance-principal component analysis (PCA) to classify the same absorption-reflectance data collected from 13 different polymeric fabric materials that was obtained using Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR). The application of the two techniques, though similar, yielded results that represent different chemical properties of the polymeric substances. On one hand, correlation-PCA enabled the classification of the fabric materials according to the organic functional groups of their repeating monomer units. On the other hand, covariance-PCA was used to classify the fabric materials primarily according to their origins;natural (animal or plant) or synthetic. Hence besides major chemical functional groups of the repeat units, it appears covariance-PCA is also sensitive to other characteristic chemical (inorganic and/or organic) or biochemical material inclusions that are found in different samples. We therefore recommend the application of both covariance-PCA and correlation-PCA on datasets, whenever applicable, to enable a broader classification of spectroscopic information through data mining and exploration.

Geographical Traceability of Clinacanthus nutans with Near-Infrared Pectroscopy and Chemometrics

In this study, a seed origin discrimination model for Clinacanthus nutans was developed. First, 81 C. nutans samples from three seed origin locations were collected, and their Near-Infrared (NIR) spectra were obtained. Next, Principal Component Analysis (PCA) was performed on the NIR spectra of the 81 C. nutans samples. Then, MSC (multiplicative scatter correction), SNV (standard normal variate), first derivative, and second derivative pre-treatments of the C. nutans spectra were performed and combined with the Support Vector Machine (SVM) algorithm for modelling and analysis. Among these methods, first-order derivative pre-treatment achieved the best SVM model effectiveness, with a training set accuracy of 93.44% (57/61) and a test set accuracy of 85.00% (17/20). In order to further improve the discrimination accuracy of the model, three optimization algorithms Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed to identify the best c and g parameters for the SVM model. The results demonstrated that the PSO optimization algorithm yielded the best parameters of c = 0.8343, g = 57.8741, with corresponding model training set the accuracy of 96.36% (60/61) and test set the accuracy of 95.00% (20/21). Therefore, developing a seed origin classification model for C. nutans based on NIR spectroscopy combined with chemometrics is feasible and has the advantages of being simple, rapid, and green.

Chemometrics-aided Metabolic Fingerprint Method Applied in Bladder Cancer Stages Differentiating

Objective To establish early detection and diagnosis for bladder cancer.Methods In the current study,a metabolomics strategy was used to profile bladder cancer urine metabolites in mice and to further characterize the disease status at different stages.In addition,some chemometrics algorithms were adopted to analyze the metabolites fingerprints,including baseline removal and retention time shift,to overcome variations in the experimental process.After processing,metabolites were qualitatively and quantitatively analyzed in each sample at different stages.Finally,a random forest algorithm was used to discriminate the differences among different groups.Results Four potential biomarkers,including glyceric acid,(R*,R*)-2,3-Dihydroxybutanoic acid,N-(1-oxohexyl)-glycine and D-Turanose,were discovered by exploring the characteristics of different groups.Conclusion These results suggest that combining chemometrics with the metabolites profile is an effective approach to aid in clinical diagnosis.

A systematic quantitative method for quality evaluation of Bupleurum chinense DC. based on multi-wavelength fusion fingerprint combined with multivariate chemometrics

A quantitative evaluation method for the quality consistency of Bupleurum chinense DC. was developed by combining multiwavelength fusion fingerprint with chemometric methods. The fingerprint information of Bupleurum at five wavelengths (210, 254, 265, 280 and 320 nm) collected by HPLC-DAD instrument was fused to overall control its quality. The fusion fingerprints and four components (saikosaponin a, saikosaponin d, rutin, quercitrin) were studied by systematic quantitative fingerprint method and principal component analysis, respectively. Besides, fingerprint-efficacy relationship was constructed in vitro using partial least-squares model. In conclusion, all work can serve as a valid reference for the quality control of Bupleurum .

HPLC combined with chemometrics for quality control of Huamoyan Granules or Capsules

Objective:Huamaoyan Granules(HMYG)and Huamaoyan Capsules(HMYC)are Chinese patent medicines with different dosage forms of the same prescription.Due to the different preparation process,the chemical composition of these Chinese patent medicines varies greatly among different forms,but there were few studies on the difference comparison and quality control of them.In order to improve the effectiveness and safety in its clinical application,an idea combining high performance liquid chromatography(HPLC)and chemometrics was put forward to study the quality control of Chinese patent medicines in different dosage forms of the same prescription.Methods:The differential markers of HMYG and HMYC were explored based on HPLC fingerprint and chemometrics including orthogonal projections to latent structures-discriminant analysis(OPLS-DA),principal component analysis(PCA),and hierarchical cluster analysis(HCA).Finally,the quantitative analysis method of related components was established by HPLC.Results:A quality control method for HMYG and HMYC was established.Firstly,the chemical components of HMYG and HMYC were systematically analyzed by HPLC fingerprinting.Further exploration showed that there were 20 characteristic peaks and 57 common peaks.Then,the potential differential markers between HMYG and HMYC were explored by chemometrics,and the differential markers were screened after intersection with the 20 characteristic peaks.Finally,HPLC quantitative analysis methods for nine components were established,including seven differential markers(neochlorogenic acid,protocatechualdehyde,chlorogenic acid,cryptochlorogenic acid,caffeic acid,rosmarinic acid and salvianolic acid A).The results of HPLC quantitative analysis showed that the contents of eight components in HMYG and HMYC samples were significantly different.According to the above results,the differential markers between HMYG and HMYC screened based on HPLC fingerprint and chemometrics can effectively characterize the differences between the two dosage forms.Conclusion:The present work provides a rapid and effective method for routine quality evaluation and control of HMYG and HMYC.This work also provides feasible methods for the quality evaluation and control of Chinese patent medicines with different dosage forms of the same prescription.