A simple approach to the prediction of soil sorption of organophosphorus pesticides.

Organophosphorus pesticides (OP) affect the crops and environments, and the reliable approach to the prediction of soil sorption of pesticides is required. In this respect, we proposed a simple Chemometrics approach, in which the Tchebichef image moment (TM) method was used to extract useful information from the greyscale images of molecular structures and the quantitative model was established by stepwise regression to predict the soil sorption of OPs. Different squared correlation coefficients including the leave-one-out cross-validation (LOO-CV) (Q2) that concerns the training set and the (R2test) which concerns the external independent test set are more than 0.96. This reflects that the established model has considerably high accuracy and reliability. Compared with the literature on the strategies of quantitative structure-property relationship (QSPR), the proposed method is more suitable, in which the established model shows a high predictive ability. Our study provides another effective approach to predict the soil sorption of OPs and also extends the innovative pathway of QSPR modelling.

A simple approach to the determination of three curcuminoids with similar chemical structures in different substrates.

The determination of curcuminoids in mixtures is more difficult due to their similar chemical structures as well as serious interferences, thus the complex pretreatments of samples and the optimization of experimental conditions are often required. Here, owing to the mathematical separation of chemical signals by Tchebichef image moments, a simple and effective approach to the simultaneous quantitative analysis was proposed, and applied to the determination of the three curcuminoids in turmeric and curry based on their raw fluorescence 3D spectra. For the established linear models, the leave-one-out correlation coefficients (R loo-cv) were more than 0.9816 within the linear ranges, and the predictive correlation coefficients (R p) for the external independent samples were more than 0.9897. The intra- and inter-day precision (less than 6.82%, RSD), average spiked recovery (89.9% ~ 100.8%), LOD (less than 0.07 µg/mL) and LOQ (less than 0.23 µg/mL) suggest that the proposed approach is accurate and reliable. Compared with N-PLS and MCR-ALS methods, our method can obtain more satisfactory results. This study provides a convenient pathway for the rapid analysis of multi-target components with similar chemical structures in mixture of different substrates.

A Simple Approach to the Toxicity Prediction of Anilines and Phenols Towards Aquatic Organisms.

Chemicals pollution in the environment has attracted attention all over the world, and the toxicity prediction of chemical pollutants has become quite important. In this paper, we introduce a simple approach to predict the toxicity of some chemical components, in which the Tchebichef image moment (TM) method was employed to extract useful chemical information from the images of molecular structures to establish quantitative structure-activity relationship (QSAR) prediction models. The proposed approach was applied to predict the toxicity of anilines and phenols for the aquatic organisms of P. subcapitata and V. fischeri, in which the obtained TMs were defined as the independent variables, while the biological toxicity (pEC50) was regarded to be the dependent variable. Then, the predictive models were established by stepwise regression, respectively. The obtained squared correlation coefficients of leave-one-out cross-validation (Q2) for training sets and the predictive squared correlation coefficients (Rp2) for test sets of the two groups of data were higher than 0.79 and 0.75, respectively, which indicated that the obtained models possessed satisfactory accuracy and reliability. Compared with several reported methods, the proposed approach was more convenient and has a higher predictive capability. Our study provides another perspective in QSAR research.

Simultaneous quantification of multiple endogenous biothiols in cancer cells based on a multi-signal fluorescent probe.

Although a large number of fluorescent probes have been developed, the simultaneous quantitative analysis of intracellular thiols is still difficult due to the spectral overlap and the complexity of the intracellular environment. In this study, a multi-signal fluorescent probe was employed for the simultaneous quantification of intracellular glutathione (GSH), cysteine (Cys) and homocysteine (Hcy). As the feature variables of the target components, the Tchebichef image moments (TMs) calculated from the grayscale images of the 3D fluorescence spectra were used to establish the quantitative linear models by stepwise regression. The intra-day and inter-day precisions of the proposed method were less than 5.6% and 8.7%, respectively. The recoveries ranged from 97.0% to 105.9%. In addition, the proposed approach was applied to the simultaneous quantitative determination of Cys, GSH and Hcy in the MCF-10A cell (a type of normal cell) and MDA-MB-231 cancer cell. The obtained results indicated that the concentrations of the three thiols in the cancer cell were higher than those in the normal cell. This study not only provides an effective approach for the quantification of multi-target bio-molecules in complicated intracellular environments, but also further extends the applications of multi-signal fluorescent probes, which will promote the design of new multi-signal fluorescent probes.

Applications of Discrete Shmaliy Moments on the Quantitative Analysis of Multitarget Compounds Based on the Chemical Spectra.

To extract the features in first-order or second-order signals, the two kinds of discrete Shmaliy moment (DSM) methods were proposed and applied to the quantitative analysis of multitarget compounds in complexes based on the UV-vis and high-performance liquid chromatography with pulsed amperometric detector (HPLC-PAD) spectra of samples for the first time. All the statistical parameters demonstrated that the obtained models were accurate and the established analytical methods were reliable, even in the presence of a different degree of overlapping signals as well as various interferences. Compared with Tchebichef moment (TM) and other classical methods such as multivariate curve resolution-alternating least-squares (MCR-ALS), partial least-squares (PLS) regression, and N-way partial least-squares (N-PLS), the proposed methods are more convenient and efficient, which not only provides another suitable tool for the quantitative analysis of multitarget components in complex samples but also extends the application of moment invariants in chemical signal analyses.

Chemometrics-assisted simultaneous voltammetric determination of multiple neurotransmitters in human serum.

An electrochemical method combining chemometrics was developed for simultaneous quantification of multiple neurotransmitters including Dopamine (DA), Epinephrine (EP), Norepinephrine (NE) and serotonin (5-hydroxytryptamine, 5-HT) in human blood serum. A reduced graphene oxide modified glassy carbon electrode (RGO/GCE) was prepared via electrodeposition method. Differential pulse voltammetry (DPV) measurement of the four neurotransmitters showed that the voltammetric signals of the four targets overlapped significantly. To facilitate the simultaneous determination of the neurotransmitters, a chemometric tool of Tchebichef curve moment (TcM) method was proposed. The TcMs calculated from the voltammograms were used to establish the quantitative models by stepwise regression. The intra-day and inter-day precisions of the proposed method were less than 3.5% and 8.1%, respectively, and the recoveries were from 87.4% to 124%. The limit of detection (LOD) for DA, EP, NE and 5-HT were 74 nM, 104 nM, 84 nM and 97 nM, respectively. The above results indicated that the proposed approach is simple and reliable for the simultaneous determination of multiple neurotransmitters in human serum.

An effective approach to the early diagnosis of colorectal cancer based on three-dimensional fluorescence spectra of human blood plasma.

Colorectal cancer (CRC) is a common malignancy in the gastrointestinal tract, and its screening rates remain relatively low in the general population due to the lack of specific symptoms and effective methods. It is still in urgent need to develop rapid and reliable approach to the early diagnosis of CRC. Herein, based on the three-dimensional (3D) fluorescence spectra of human blood plasma, a combination strategy of Tchebichef image moments coupled with partial least squares-discriminate analysis (TM-PLS-DA) was proposed for the detection of CRC from three classes (CRC samples, adenomas samples and non-malignant findings). The established TM-PLS-DA classification model provided an 84 % correct classification for CRC prediction. Venetian blinds 10-fold cross validation was carried out. The error rates both in cross validation and test sets were less than 0.16. Sensitivity and specificity for CRC prediction were 0.95 and 0.88, respectively. At the same time, the diagnostic capacity of the proposed method was tested by receiver operating characteristics (ROC) analysis with area under the curve (AUC) of 0.94 for CRC diagnosis. These results demonstrate that the proposed TM-PLS-DA method based on the 3D fluorescence spectra of blood plasma has great advantage for the accurate CRC detection, which will provide a potential alternative approach for cancer diagnostics.

One-pot facile synthesis of CuNCs/RGO nanocomposite for the sensitive detection of heparin in human serum samples.

A simple and facile one-pot approach for the synthesis of copper nanoclusters decorated reduced graphene oxide (CuNCs/RGO) nanocomposite was proposed, in which the CuNCs attached to the surface of the reduced glutathione (GSH) functionalized RGO through ligand exchange via their thiol functionalities. The synthesized nanocomposite was verified by structural characterizations, and the further investigation of density functional theory (DFT) indicated that Cu3R2 cluster (R = C10H16O6N3S) with the lowest energy was the most stable structure in GSH-capped CuNCs. Although the CuNCs/RGO nanocomposite exhibited rather weak fluorescence, with the addition of heparin (Hep), the significant enhancement of fluorescence at 595 nm was achieved, which was developed to detect Hep in human serum samples with high selectivity and sensitivity. The mechanisms of fluorescence quenching of CuNCs/RGO nanocomposite and the sensing of Hep were discussed. The linear range was 0.1-10 µM with the detection limit of 26 nM in buffer solution containing 2% human serum sample, and satisfactory recovery in the range of 96.6%-104% was obtained, suggesting that the proposed method could applied to the detection of Hep in human serum samples.

The activity prediction of indole inhibitors against HCV NS5B polymerase.

Non-structural viral protein 5B (NS5B) is a viral protein in hepatitis C virus. Although various inhibitors against NS5B have been found, the activity prediction of similar untested inhibitors is still highly desirable. In this respect, the Tchebichef moments (TMs) calculated from the images of molecular structures were regarded as the independent variables while the inhibitory activity (pIC50 ) was the dependent variable, and the predictive model was established by means of stepwise regression. The R-squared of leave-one-out cross-validation (Q2 ) for the training set and the R-squared of prediction ( Rp2 ) for external independent test set were 0.919 and 0.927, respectively. The obtained model was also evaluated strictly. Compared with the multivariate curve resolution with alternating least squares (MCR-ALS) and the QSAR approaches derived from the literature, the proposed method is more accurate and reliable. This study not only provides an effective approach to predict the biological activity of RNA replication's inhibitors, but also extends the QSAR modeling technique.

The rapid quantitative analysis of three pesticides in cherry tomatoes and red grape samples with Tchebichef image moments.

Diverse interferences often affect the determination of pesticide residues in various kinds of food, and complex pretreatments are necessary. An effective approach for the simultaneous quantitative analysis of multi-target components in different substrates was proposed, and applied to the determination of ternary pesticides in cherry tomatoes and red grape samples. By means of HPLC-DAD, the spectra were obtained under isocratic elution. Utilizing the Tchebichef image moments, unified quantitative models were established for the three target components in the samples with two different substrates, respectively. The correlation coefficients of leave-one-out cross-validation (Rloo-cv2) of the obtained models were more than 0.9975. The predictive correlation coefficients (Rp2) were more than 0.9831. Compared with the N-PLS and MCR-ALS methods, the proposed approach is not only more accurate and reliable, but also can extract essential information and expected to be a potential tool to rapidly quantify multi-component in different substrates without complex pretreatments.