On the use of the observation‐wise k‐fold operation in PCA cross‐validation

Cross‐validation (CV) is a common approach for determining the optimal number of components in a principal component analysis model. To guarantee the independence between model testing and calibration, the observation‐wise k‐fold operation is commonly implemented in each cross‐validation step. This operation renders the CV algorithm computationally intensive, and it is the main limitation to apply CV on very large data sets. In this paper, we carry out an empirical and theoretical investigation of the use of this operation in the element‐wise k‐fold (ekf) algorithm, the state‐of‐the‐art CV algorithm. We show that when very large data sets need to be cross‐validated and the computational time is a matter of concern, the observation‐wise k‐fold operation can be skipped. The theoretical properties of the resulting modified algorithm, referred to as column‐wise k‐fold (ckf) algorithm, are derived. Also, its performance is evaluated with several artificial and real data sets. We suggest the ckf algorithm to be a valid alternative to the standard ekf to reduce the computational time needed to cross‐validate a data set. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessment of maximum likelihood PCA missing data imputation

Maximum likelihood principal component analysis (MLPCA) was originally proposed to incorporate measurement error variance information in principal component analysis (PCA) models. MLPCA can be used to fit PCA models in the presence of missing data, simply by assigning very large variances to the non‐measured values. An assessment of maximum likelihood missing data imputation is performed in this paper, analysing the algorithm of MLPCA and adapting several methods for PCA model building with missing data to its maximum likelihood version. In this way, known data regression (KDR), KDR with principal component regression (PCR), KDR with partial least squares regression (PLS) and trimmed scores regression (TSR) methods are implemented within the MLPCA method to work as different imputation steps. Six data sets are analysed using several percentages of missing data, comparing the performance of the original algorithm, and its adapted regression‐based methods, with other state‐of‐the‐art methods. Copyright © 2016 John Wiley & Sons, Ltd.     

Metabolite profiling to discriminate different species and genus from thistles in Korea using liquid chromatography with quadrupole time‐of‐flight mass spectrometry

This study was designed to classify and identify closely related thistle species in the genus Cirsium, as well as Carduus and Cephalonoplos species, which are also thistles. The comprehensive and untargeted metabolite profiles of nine Korean thistles were determined using ultra high performance liquid chromatography combined with hybrid quadrupole time‐of‐flight mass spectrometry. The difference in metabolite profiles among species was explored using principal component analysis and hierarchical clustering analysis. The significantly different metabolites (Bonferroni‐corrected P‐value < 0.001) were used to construct a partial least squares discriminant analysis model to predict the species of thistle. Nine species were successfully classified using a partial least squares discriminant analysis model and confirmed using a cross‐validation method. Species with similar features were grouped based on unique patterns in variable clusters. The present study suggests that liquid chromatography with quadrupole time‐of‐flight mass spectrometry untargeted metabolomic profiling with chemometric analysis is an efficient and powerful tool for discriminating between different species of medicinal herbs.     

The e‐tongue‐based classification and authentication of mineral water samples using cross‐correlation‐based PCA and Sammon's nonlinear mapping

The present paper elaborates on the design of classifiers based on cross‐correlation‐based principal component analysis (PCA) and Sammon's nonlinear mapping (NLM) using current signals obtained from electronic tongue (e‐tongue) with commercial mineral water samples available in the Indian market. The pulse‐voltammetric method is used to capture the electroanalytical/electrochemical characteristics of the sampled mineral waters by considering a real model for the liquid–electrode interface in a given e‐tongue apparatus. Then the cross‐correlation coefficients between the output and input signals are determined. Both PCA and Sammon's NLM create a subspace from high‐dimensional mineral water data by considering the principal eigenvectors and minimising the stress function, respectively. The proposed cross‐correlation‐based PCA and Sammon's classifiers establish the highest separation distance among the investigated water brands and carries out the authentication of more than one unknown sample of the same brand with a certain degree of variability with respect to their sources. Copyright © 2013 John Wiley & Sons, Ltd.     

Rapid and robust confirmation and quantification of 11‐nor‐Δ9‐tetrahydrocannabinol‐9‐carboxylic acid (THC‐COOH) in urine by column switching LC‐MS‐MS analysis

A method for the rapid and robust confirmation of 11‐nor‐?9‐tetrahydrocannabinol‐9‐carboxylic acid (THCA) in urine involving basic hydrolysis with NaOH and direct injection of the hydrolysate in a column‐switching LC‐MS‐MS system was developed and validated. THCA‐d3 was used as internal standard. Detection was performed in negative‐ion mode by monitoring the transitions from the [M‐CO₂]‐ ion m/z 299.2→245.2 and and m/z 299.2→191.1 that were found to provide a better signal‐to‐noise ratio than the transition from the pseudomolecular ion at m/z 343. The high sensitivity of detection enabled the injection of a small volume (10 µl) of the NaOH hydrolysate which, together with the applied column switching system, proved to confer ruggedness to the method and to avoid the deterioration of the instrumental apparatus despite the large amount of inorganic ions in the hydrolysate. The LLOQ was established at 5 ng/ml, and the LLOD was calculated as 0.2 ng/ml (S/N =3). The method was submitted to thorough validation including evaluation of the calibration range (5–500 ng/ml), accuracy and precision, matrix effects, overall process efficiency, autosampler stability, carryover and cross‐talk, and 10‐times reduction of sample volume (0.1 ml). Proof of applicability was obtained by direct comparison with the reference GC‐MS method in use in the lab (the R² between the two methods was 0.9951). Copyright © 2012 John Wiley & Sons, Ltd.     

Screening of Three Transition Metal‐Mediated Reactions Compatible with DNA‐Encoded Chemical Libraries

The construction of DNA‐encoded chemical libraries (DECLs) crucially relies on the availability of chemical reactions, which are DNA‐compatible and which exhibit high conversion rates for a large number of diverse substrates. In this work, we present our optimization and validation procedures for three copper and palladium‐catalyzed reactions (Suzuki cross‐coupling, Sonogashira cross‐coupling, and copper(I)‐catalyzed alkyne‐azide cycloaddition (CuAAC)), which have been successfully used by our group for the construction of large encoded libraries.     

Towards eco‐friendly secondary plant metabolite quantitation: Ultra high performance supercritical fluid chromatography applied to common vervain (Verbena officinalis L.)

This report presents the first ultra high performance supercritical fluid chromatography diode array detector based assay for simultaneous determination of iridoid glucosides, flavonoid glucuronides, and phenylpropanoid glycosides in Verbena officinalis (Verbenaceae) extracts. Separation of the key metabolites was achieved in less than 7 min on an Acquity UPC² Torus Diol column using a mobile phase gradient comprising subcritical carbon dioxide and methanol with 0.15% phosphoric acid. Method validation for seven selected marker compounds (hastatoside, verbenalin, apigenin‐7‐O‐glucuronide, luteolin‐7‐O‐glucuronide, apigenin‐7‐O‐diglucuronide, verbascoside, and luteolin‐7‐O‐diglucuronide) confirmed the assay to be sensitive, linear, precise, and accurate. Head‐to‐head comparison to an ultra high performance liquid chromatography comparator assay did prove the high orthogonality of the methods. Quantitative result equivalence was evaluated by Passing‐Bablok‐correlation and Bland‐Altman‐plot analysis. This cross‐validation revealed, that one of the investigated marker compound peaks was contaminated in the ultra high performance liquid chromatography assay by a structurally related congener. Taken together, it was proven that the ultra high performance supercritical fluid chromatography instrument setup with its orthogonal selectivity is a true alternative to conventional reversed phase liquid chromatography in quantitative secondary metabolite analysis. For regulatory purposes, assay cross‐validation with highly orthogonal methods seems a viable approach to avoid analyte overestimation due to coeluting, analytically indistinguishable contaminants.     

Method for rapidly discovering active components in Yupingfeng granules by UPLC‐ESI‐Q‐TOF‐MS

Yupingfeng granules (YPFG) were isolated from a traditional Chinese medicine (TCM) formulation composed of three herbs (Astragali Radix, Atractylodis Macrocephalae Rhizoma, and Saposhnikoviae Radix). This formulation is used in TCM to tonify qi, and it can help strengthen exterior and reduce sweating. Nevertheless, the active components of YPFG remain unclear. In this study, the chemical constituents of YPFG were systematically characterized by ultra‐performance liquid chromatography coupled with electrospray ionization/ quadrupole time‐of‐flight mass spectrometry (UPLC‐ESI‐Q‐TOF‐MS). Fifty‐eight compounds, namely, 20 flavonoids, 19 saponins, nine organic acids, four volatile coumarins, three lactones, one alkaloid, and two other components, were identified. In addition, the constituents of YPFG with the potential for in vivo bioactivities following oral administration were investigated in Sprague–Dawley rats. Thirteen compounds, namely, 11 flavonoid‐related and 2 saponin‐related components, were detected in rat plasma. After enriching flavonoids and saponins in YPFG by extraction, the extracts and YPFG were administrated to immunosuppressed rats, respectively. Plasma samples were analyzed by UPLC‐ESI‐Q‐TOF‐MS, and principal component analysis (PCA) confirmed that the extracts had similar effects to YPFG. This method could discover active ingredients in YPFG quickly and provide a scientific basis for quality control and mechanism research.     

Molecular modeling studies of human immunodeficiency virus type 1 protease inhibitors using three‐dimensional quantitative structure‐activity relationship,virtual screening,and docking simulations

In this paper, two 3‐dimensional quantitative structure‐activity relationship models for 60 human immunodeficiency virus (HIV)‐1 protease inhibitors were established using random sampling analysis on molecular surface and translocation comparative molecular field vector analysis (Topomer CoMFA). The non–cross‐validation (r²), cross‐validation (q²), correlation coefficient of external validation (Q²_ext), and F of 2 models were 0.94, 0.80, 0.79, and 198.84 and 0.94, 0.72, 0.75, and 208.53, respectively. The results indicated that 2 models were reasonable and had good prediction ability. Topomer Search was used to search R groups in the ZINC database, 20 new compounds were designed, and the Topomer CoMFA model was used to predicate the biological activity. The results showed that 18 new compounds were more active than the template molecule. So the Topomer Search is effective in screening and can guide the design of new HIV/AIDS drugs. The mechanism of action was studied by molecular docking, and it showed that the protease inhibitors and Ile50, Asp25, and Arg8 sites of HIV‐1 protease have interactions. These results have provided an insight for the design of new potent inhibitors of HIV‐1 protease.     

Generalized Dice's coefficient‐based multi‐block principal component analysis with Bayesian inference for plant‐wide process monitoring

Plant‐wide process monitoring is challenging because of the complex relationships among numerous variables in modern industrial processes. The multi‐block process monitoring method is an efficient approach applied to plant‐wide processes. However, dividing the original space into subspaces remains an open issue. The loading matrix generated by principal component analysis (PCA) describes the correlation between original variables and extracted components and reveals the internal relations within the plant‐wide process. Thus, a multi‐block PCA method that constructs principal component (PC) sub‐blocks according to the generalized Dice coefficient of the loading matrix is proposed. The PCs corresponding to similar loading vectors are divided within the same sub‐block. Thus, the PCs in the same sub‐block share similar variational behavior for certain faults. This behavior improves the sensitivity of process monitoring in the sub‐block. A monitoring statistic T² corresponding to each sub‐block is produced and is integrated into the final probability index based on Bayesian inference. A corresponding contribution plot is also developed to identify the root cause. The superiority of the proposed method is demonstrated by two case studies: a numerical example and the Tennessee Eastman benchmark. Comparisons with other PCA‐based methods are also provided. Copyright © 2014 John Wiley & Sons, Ltd.     

Rapidly differentiating grape seeds from different sources based on characteristic fingerprints using direct analysis in real time coupled with time‐of‐flight mass spectrometry combined with chemometrics

The seeds of grapevine (Vitis vinifera) are a byproduct of wine production. To examine the potential value of grape seeds, grape seeds from seven sources were subjected to fingerprinting using direct analysis in real time coupled with time‐of‐flight mass spectrometry combined with chemometrics. Firstly, we listed all reported components (56 components) from grape seeds and calculated the precise m/z values of the deprotonated ions [M–H]^–. Secondly, the experimental conditions were systematically optimized based on the peak areas of total ion chromatograms of the samples. Thirdly, the seven grape seed samples were examined using the optimized method. Information about 20 grape seed components was utilized to represent characteristic fingerprints. Finally, hierarchical clustering analysis and principal component analysis were performed to analyze the data. Grape seeds from seven different sources were classified into two clusters; hierarchical clustering analysis and principal component analysis yielded similar results. The results of this study lay the foundation for appropriate utilization and exploitation of grape seed samples. Due to the absence of complicated sample preparation methods and chromatographic separation, the method developed in this study represents one of the simplest and least time‐consuming methods for grape seed fingerprinting.     

3D－QSAR Study on Diindolylmethane and Its Analogues with Comparative Molecular Field Analysis(CoMFA)

Comparative molecular field analysis (CoMFA),a three dimensional quantitative structure-activity relationship (3D-QSAR) method was applied to a series of diindolylmethane(DIM) analogs to study the relationship between their structure and their induction of CYP 1A1-associated ethoxyresorufin-O-deethylase(EROD) activity.A DISCO model of pharmacophore was derved to guide the superposition of the compounds.The coefficient of cross-validation (q^2) and non cross-validation(r^2) for the model established by the study are 0.827 and 0.988 respectively,the value of variance ratio (F) is 103.53 and standard error estimate (SEE)is 0.044.These values indicate that the CoMFA model derived is significant and might have a good prediction for the catalytic activity of DIM compounds.As a consequence,the predicted activity values of new designed compounds were all higher than that of the reported value.     

Application of genetic algorithm‐support vector regression (GA‐SVR) for quantitative analysis of herbal medicines

In this paper, a genetic algorithm‐support vector regression (GA‐SVR) coupled approach was proposed for investigating the relationship between fingerprints and properties of herbal medicines. GA was used to select variables so as to improve the predictive ability of the models. Two other widely used approaches, Random Forests (RF) and partial least squares regression (PLSR) combined with GA (namely GA‐RF and GA‐PLSR, respectively), were also employed and compared with the GA‐SVR method. The models were evaluated in terms of the correlation coefficient between the measured and predicted values (Rp), root mean square error of prediction, and root mean square error of leave‐one‐out cross‐validation. The performance has been tested on a simulated system, a chromatographic data set, and a near‐infrared spectroscopic data set. The obtained results indicate that the GA‐SVR model provides a more accurate answer, with higher Rp and lower root mean square error. The proposed method is suitable for the quantitative analysis and quality control of herbal medicines. Copyright © 2012 John Wiley & Sons, Ltd.     

Application of a LC–MS/MS method developed for the determination of p‐phenylenediamine,N‐acetyl‐p‐phenylenediamine and N,N‐diacetyl‐p‐phenylenediamine in human urine specimens

Cases of poisoning by p‐phenylenediamine (PPD) are detected sporadically. Recently an article on the development and validation of an LC–MS/MS method for the detection of PPD and its metabolites, N‐acetyl‐p‐phenylenediamine (MAPPD) and N,N‐diacetyl‐p‐phenylenediamine (DAPPD) in blood was published. In the current study this method for detection of these compounds was validated and applied to urine samples. The analytes were extracted from urine samples with methylene chloride and ammonium hydroxide as alkaline medium. Detection was performed by LC–MS/MS using electrospray positive ionization under multiple reaction‐monitoring mode. Calibration curves were linear in the range 5–2000 ng/mL for all analytes. Intra‐ and inter‐assay imprecisions were within 1.58–9.52 and 5.43–9.45%, respectively, for PPD, MAPPD and DAPPD. Inter‐assay accuracies were within ?7.43 and 7.36 for all compounds. The lower limit of quantification was 5 ng/mL for all analytes. The method, which complies with the validation criteria, was successfully applied to the analysis of PPD, MAPPD and DAPPD in human urine samples collected from clinical and postmortem cases.     

Synthesis of All‐Carbon Disubstituted Bicyclo[1.1.1]pentanes by Iron‐Catalyzed Kumada Cross‐Coupling

1,3‐Disubstituted bicyclo[1.1.1]pentanes (BCPs) are important motifs in drug design as surrogates for p‐substituted arenes and alkynes. Access to all‐carbon disubstituted BCPs via cross‐coupling has to date been limited to use of the BCP as the organometallic component, which restricts scope due to the harsh conditions typically required for the synthesis of metallated BCPs. Here we report a general method to access 1,3‐C‐disubstituted BCPs from 1‐iodo‐bicyclo[1.1.1]pentanes (iodo‐BCPs) by direct iron‐catalyzed cross‐coupling with aryl and heteroaryl Grignard reagents. This chemistry represents the first general use of iodo‐BCPs as electrophiles in cross‐coupling, and the first Kumada coupling of tertiary iodides. Benefiting from short reaction times, mild conditions, and broad scope of the coupling partners, it enables the synthesis of a wide range of 1,3‐C‐disubstituted BCPs including various drug analogues.     

Exploiting space‐group symmetry in fragment‐based molecular crystal calculations

Recent developments in fragment‐based methods make it increasingly feasible to use high‐level ab initio electronic structure techniques to molecular crystals. Such studies remain computationally demanding, however. Here, we describe a straightforward algorithm for exploiting space‐group symmetry in fragment‐based methods which often provides computational speed‐ups of several fold or more. This algorithm does not require a priori specification of the space group or symmetry operators. Rather, the symmetrically equivalent fragments are identified automatically by aligning the individual fragments along their principle axes of inertia and testing for equivalence with other fragments. The symmetry operators relating equivalent fragments can then be worked out easily. Implementation of this algorithm for computing energies, nuclear gradients with respect to both atomic coordinates and lattice parameters, and the nuclear hessian is described. © 2014 Wiley Periodicals, Inc.     

Relationship between the UPLC‐Q‐TOF‐MS fingerprinted constituents from Daphne genkwa and their anti‐inflammatory,anti‐oxidant activities

Daphne genkwa Sieb.et Zucc. is a well‐known medicinal plant. This study was designed to apply the ultra‐high performance liquid chromatography system to establish a quality control method for D. genkwa. Data revealed that there were 15 common peaks in 10 batches of D. genkwa Sieb. Et Zucc. (Thymelaeaceae) from different provinces of China. On this basis, the fingerprint chromatogram was established to provide references for quality control. Afterwards, the chemical constitutions of these common peaks were analyzed using the UPLC‐Q‐TOF‐MS system and nine of them were identified. In addition, LPS‐stimulated RAW264.7 murine macrophages and DPPH assay were used to study the anti‐inflammatory and anti‐oxidation effects of D. genkwa . Then the fingerprint–efficacy relationships between UPLC fingerprints and pharmacodynamic data were studied with canonical correlation analysis. Analysis results indicated that the anti‐inflammatory and anti‐oxidation effects differed among the 10 D. genkwa samples owing to their inherent differences of chemical compositions. Taken together, this research established a fingerprint–efficacy relationship model of D. genkwa plant by combining the UPLC analytic technique and pharmacological research, which provided references for the detection of the principal components of traditional Chinese medicine on bioactivity.