Orthogonal Signal Correction-partial Least Squares Research Articles

Application of MIA-QSAR in Designing New Protein P38 MAP Kinase Compounds Using a Genetic Algorithm

Multivariate image analysis quantitative structure-activity relationship (MIA-QSAR) study aims to obtain information from a descriptor set, which are image pixels of two-dimensional molecule structures. In the QSAR study of protein P38 mitogen-activated protein (MAP) kinase compounds, the genetic algorithm application for pixel selection and image processing is investigated. There is a quantitative relationship between the structure and the pIC50 based on the information obtained. (The pIC50 is the negative logarithm of the half-maximal inhibitory concentration ( IC50 ), so pIC50 = −log IC50 .) Protein P38 MAP kinase inhibitors are used in the treatment of malignant tumors. The development of a model to predict the pIC50 of these compounds was performed in this study. To accomplish this, the molecules were first plotted and fixed in the same coordinates in ChemSketch. Then, the images were processed in the MATLAB program. Partial least squares (PLS) model, orthogonal signal correction partial least squares (OSC-PLS) model, and genetic algorithm partial least squares (GA-PLS) model methods are used to generate quantitative models, and pIC50 prediction is performed. The GA-PLS model has the highest predictive power for a series of statistical parameters such as root mean square error of prediction (RMSEP) and relative standard errors of prediction (RSEP). Finally, the molecular junction (docking) was done for predicted molecules in quantitative structure activity relationship (QSAR) with an appropriate receptor and acceptable results were obtained. These results are good and proper for the prediction of compounds with better properties.

Estimation of early postmortem interval in rats by GC-MS-based metabolomics.

Accurately predicting the early postmortem interval (PMI) is of great significance in forensic practice. This study aimed to establish a novel method for estimating the early PMI by analyzing endogenous substances in the cardiac blood of male and female rats and compare different model for estimating early PMI using these data. Adult Sprague-Dawley (SD) rats (50% male) were sacrificed by suffocation. Then, cardiac blood was collected at various time intervals (0, 3, 6, 12, 24, 48, and 72 h) after death, and the collected samples were analyzed by gas chromatography-tandem mass spectrometry (GC-MS). The data were analyzed by multivariate statistical analysis. An orthogonal signal correction-partial least squares (OSC-PLS) regression model was constructed with whole endogenous metabolites to validate the PMI. The OSC-PLS regression model successfully predicted the PMI of the forecast set and no significant differences was observed between male and female rats. This is the first study to establish an OSC-PLS regression model for predicting PMI with the metabolome, which provides a new technical method and platform for estimating PMI through metabolomics.

Development of multi-cultivar models for predicting the soluble solid content and firmness of European pear (Pyrus communis L.) using portable vis–NIR spectroscopy

The feasibility of a multi-cultivar model’s development for the on-site determination of the internal qualities of European pear (Pyrus communis L.) cv. ‘Abate’, ‘Cascade’, ‘Conference’, ‘Red Comice’ and ‘Wujiuxiang’ was investigated. In total, 240 pear samples, 48 for each cultivar, were collected during fruit post-ripening period under retail stage conditions to explore the performance of a technique for developing soluble solid content and firmness models with partial least squares (PLS) and multiple linear regression (MLR) methods A commercially available portable visible and near-infrared spectrometer was used to collect the absorbance spectra (500–1010nm) of pears. A hybrid method, orthogonal signal correction (OSC) combined with other spectral pre-process methods, such as standardization, second-order derivation and Savitzkye–Golay smoothing, was employed to filter out the variations in signals caused by different pear cultivars. Compared with the routine PLS and MLR models, the performances of the OSC–PLS and OSC–MLR models resulted in higher ratios of prediction to deviation. A new external sample set obtained at the retail stage was used to assess the performances of the best multi-cultivar models of soluble solid content and firmness, which resulted in greater coefficients of determination for prediction of 0.87 and 0.66, respectively, and lower root mean square errors of prediction of 0.45% and 8.11N, respectively. The results highlighted the potential of portable Vis–NIR instruments for the on-site testing of internal quality indices of European pears. The development of a multi-cultivar model for fruit is necessary and significant, in particular for improving the performance of the firmness model.

Prediction of the acid value, peroxide value and the percentage of some fatty acids in edible oils during long heating time by chemometrics analysis of FTIR-ATR spectra

Edible oils are used in the preparation of foods as a part of their recipe or for frying. So to ensure of food safety, checking the quality of the oils before and after usage is an important subject in food control laboratories. In this study, edible oils from four different sources (canola, corn, sunflower and frying) were heated for 36 h at 170 °C and sampling was done every 6 h. The free fatty acid, peroxide value and the content of some fatty acids (C16:0, C18:0, C18:1, C18:2, C18:3) of the oil samples were determined by standard methods. Then, the ATR-FTIR spectra of the samples were collected. The partial least squares (PLS) regression combined with genetic algorithm was performed on the spectroscopic data to obtain the appropriate predictive models for the simultaneous estimation of acid value, peroxide value and the percentage of five kinds of fatty acids. The effect of some preprocessing methods on these models was also investigated. Preprocessing of data by orthogonal signal correction (OSC) resulted in the best predictive models for all oil properties. The correlation coefficients of calibration set (>0.99) and validation set (>0.86 and in most case >0.94) of the OSC–PLS model suggested suitable predictive modeling for all studied parameters in the oil samples. This method could be suggested as a rapid, economical and environmental friendly technique for simultaneous determination of seven noted parameters in the edible oils.

Plasma fatty acid metabolic profiles for traditional Chinese medicine syndrome differentiation in diabetic patients using uncorrelated linear discriminant analysis

Diabetes is a common metabolic syndrome which presents a serious threat to human health. Traditional Chinese medicine (TCM) has been widely paid attention to its advantages and characteristics in the diagnosis and the treatment of diabetes. A strategy of classifying five TCM syndromes in diabetes (Qi-deficiency, Yin-deficiency, Qi- and Yin-deficiency, Damp heat and Blood stasis) was employed based on plasma fatty acid metabolic profiles, lipid metabolism indicators and chemometrics methods. Using orthogonal signal correction-partial least squares (OSC-PLS) method, the five syndromes were obviously distinguished from those of the health control, which confirmed there existed metabolite differences in different traditional Chinese medicine syndromes. Furthermore, a new method, uncorrelated linear discriminant analysis (ULDA), was applied in the discrimination of health control, TCM deficiency syndromes (Qi-deficiency, Yin-deficiency, Qi- and Yin-deficiency) and TCM empirical syndromes (Damp heat, Blood stasis), which demonstrated better clustering results, the correct rate reached 95.7%. The four potential biomarkers, C20: 2, C20: 5, triglycerides (TG) and high density lipoprotein (HDL), performed large contributions to the classification which can provide important information assisting TCM clinical diagnosis.

Product quality modelling and prediction based on wavelet relevance vector machines

In order to predict product quality and optimize production process, the product quality models need to be built. However, there are complex nonlinear relationship among the product quality parameters and the production process variables. The common methods cannot model the production process with high accuracy and the prediction intervals cannot be given by those methods. In contrast, the kernel methods can transform the original input data into a feature space via kernel function, and then the linear methods can be used to resolve the nonlinear problem accurately. Moreover, the relevance vector machine as a kernel method can give the prediction intervals, and wavelet kernel can inherit the ability of local analysis and feature extraction from the wavelet function. The product quality models based on wavelet relevance vector machine are proposed in this paper. A simulation data set, two chemistry data sets and a real field data set of zinc coating weights from strip hot-dip galvanizing are used to validate the model. The results demonstrate that the model based on wavelet relevance vector machines has a higher prediction precision than the common methods such as partial least squares(PLS), orthogonal signal correction-partial least squares(OSC-PLS), Quadratic-PLS, kernel partial least squares(KPLS), orthogonal signal correction-kernel partial least squares(OSC-PLS), least squares-support vector machines (LS-SVM) and ordinary relevance vector machines(RVM). The prediction intervals are also given by the presented model. Mexican, Morlet and Difference of Gaussian (DOG) wavelet relevance vector machines (WRVMs) for multi-group data show superior prediction performance compared to other methods mentioned above.

Discrimination of type 2 diabetes mellitus corresponding to different traditional Chinese medicine syndromes based on plasma fatty acid profiles and chemometric methods

Aims of the studyTraditional Chinese medicine (TCM) has a long history and particular advantages in the diagnosis and treatment of type 2 diabetes mellitus (T2DM). Syndrome differentiation is the foundation and essence of TCM theories. The aims of the study are to discriminate T2DM corresponding to different syndromes (Qi-deficiency, Qi and Yin-deficiency and Damp heat) and discover syndrome-related biomarkers using metabolomics technology. Materials and methodsPlasma fatty acid profiles of 85 clinical samples were established by high performance liquid chromatography (HPLC). Moreover, some of the lipid parameters, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL), were obtained through clinical testing methods. Orthogonal signal correction-partial least squares (OSC-PLS) and uncorrelated linear discriminant analysis (ULDA) were employed to establish two-class models for three different syndromes. ResultsCompared with the plasma fatty acid profiles of healthy controls, the characteristic fatty acids of three TCM syndromes were discovered (p<0.01). Totally, the plasma fatty acids of T2DM were up-regulated, while significant differences existed in different syndromes. Results of ULDA indicate that the three TCM syndromes could be effectively separated by the plasma fatty acid profiles and the syndrome-related biomarkers were also screened. ConclusionsThe fact that three TCM syndromes can be separated indicates certain metabolic differences in different TCM syndromes of T2DM really exist and such differences can be manifested by fatty acids and lipid parameters. The results benefit modern biological interpretation of the three TCM syndromes and in a sense the diagnosis and treatment of diabetes.

Spectrophotometric simultaneous determination of orotic acid, creatinine and uric acid by orthogonal signal correction‐partial least squares in spiked real samples

An orthogonal signal correction-partial least squares (OSC-PLS) method was developed for the simultaneous spectrophotometric determination of orotic acid (OA), creatinine (CRE), and uric acid (UA) in spiked real samples. By multivariate calibration methods, such as PLS regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. The effect of OSC used to remove the information unrelated to the target variables is studied. In this study, the calibration model is based on absorption spectra in the 220-320 nm rang for 36 different mixtures of OA, CRE and UA. Calibration matrices contained 1.74-47.00 of OA, 1.13-33.95 of CRE, and 1.68-28.58 of UA in µg/ml. The number of principal component for OA, CRE, and UA with OSC were 3, 4, and 4, and 4, 6, and 5, without OSC, respectively. The evaluation of the prediction errors for the prediction set reveals that the OSC-treated data give substantially lower root mean square error of prediction (RMSEP) values than the original data. The RMSEP for OA, CRE, and UA with OSC were 0.69, 0.20, and 0.53 and 0.80, 0.69, and 0.73 without OSC, respectively. The proposed method was applied for the simultaneous determination of OA, CRE, and UA in spiked biological fluids with satisfactory results.

The Prediction Model of COREX Cold Gas Content of Carbon Dioxide Based on MOSC-PLS

In COREX processes, the cold gas is produced in melter gasifier, after being cooled and dust controlled, blown into the blast furnace and used in the reduction reaction. The cold gas content plays a key role in the reaction of lump ore and pellets reduction. A prediction model of COREX cold gas content of carbon dioxide is proposed based on modified orthogonal signal correction partial least squares algorithm (MOSC-PLS). Firstly, the input and output variables of the model are selected according to the COREX processes principle. Secondly, MOSC algorithm is used to preprocess the data, in order to remove the irrelevant information between the input and output variables of the model. Finally, prediction model is built based on PLS. The real field data of cold gas content of carbon dioxide from Baosteel COREX are used for verification. The results show that MOSC-PLS has an advantage over the orthogonal signal correction partial least squares (OSC-PLS) in prediction accuracy. Thus the necessary decision supports and analysis tools for the cold gas content control are provided.

Silent myocardial ischemia is associated with altered plasma phospholipids

Timely and accurate confirmation of the occurrence of silent myocardial ischemia (SMI) is critical both for prevention and therapy management. Metabolomics assay may offer an alternative for SMI differentiation and altered biomolecule discovery in addition to traditional measures. In this study, plasma samples were obtained from 14 diagnosed SMI subjects and 25 healthy controls and analyzed by liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry in view of metabolomics. Obtained data were subjected to orthogonal signal correction partial least-squares discriminate analysis. Multi-variate statistic analysis indicated a clear separation between the two studied groups. Plasma concentration fluctuation of four kinds of phospholipids showed tight relationship with the occurrence of SMI, among which 1-linoleoylglycerophosphocholine (C18:2) was decreased statistically in SMI population (P=0.01). The plasma phospholipids' changes were before enzymatic alteration in SMI, which might be a useful complementary reference to facilitate SMI diagnosis.

Simultaneous Voltammetric Determination of Lead and Tin by Adsorptive Differential Pulse Stripping Method and Orthogonal Signal Correction‐Partial Least Squares in Water Samples

AbstractAn adsorptive differential pulse stripping method for the simultaneous determination of lead and tin is proposed. The procedure involves an adsorptive accumulation of lead and tin on a hanging mercury drop electrode (HMDE), followed by oxidation of adsorbed lead and tin by voltammetric scan using differential pulse modulation. The optimum experimental conditions are: 0.2 mol L−1 HNO3, accumulation potential of −900 mV versus Ag/AgCl, accumulation time of 200 s, scan rate of 20 mV s−1 and pulse height of 80 mV. Lead and tin peak currents were observed in the same potential region at about −400 mV. The simultaneous determination of lead and tin by using voltammetry is a difficult problem in analytical chemistry, due to voltammogram interferences. The resolution of a mixture of lead and tin by the application of orthogonal signal correction‐partial least squares (OSC‐PLS) was performed. The linear dynamic ranges were 0.003‐0.35 and 0.008‐0.50 μg mL−1 and detection limits were land 3 ng mL−1 for lead and tin, respectively. The RMSEP for lead and tin with OSC and without OSC were 2.8737, 6.0557 and 8.0941, 9.5151, respectively. The capability of the method for the analysis of real samples was evaluated by the determination of lead and tin in water samples with satisfactory results.

Kinetic spectrophotometric determination of trace amounts of palladium by whole kinetic curve and a fixed time method using resazurine sulfide reaction

The univariate and multivariate calibration methods were applied for the determination of trace amounts of palladium based on the catalytic effect on the reaction between resazurine and sulfide. The decrease in absorbance of resazurine at 602 nm over a fixed time is proportional to the concentration of palladium over the range of 10.0–160.0 ng mL −1. The calibration matrix for partial least squares (PLS) regression was designed with 14 samples. 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 without loss of prediction ability using spectrophotometric method. The root mean square error of prediction (RMSEP) for palladium determination with fixed-time, PLS and OSC–PLS were 3.71, 2.84 and 0.68, respectively. This procedure allows the determination of palladium in synthetic and real samples with good reliability of the determination.

Simultaneous determination of vitamin B12 and its derivatives using some of multivariate calibration 1 (MVC1) techniques

Resolution of binary mixtures of vitamin B12, methylcobalamin and B12 coenzyme with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable (PLS1), orthogonal signal correction/partial least squares (OSC/PLS), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV–vis spectra. The UV–vis spectra of the vitamin B12, methylcobalamin and B12 coenzyme were recorded in the same spectral conditions. The method of central composite design was used in the ranges of 10–80 mg L −1 for vitamin B12 and methylcobalamin and 20–130 mg L −1 for B12 coenzyme. The models refinement procedure and validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 2.26 mg L −1 for vitamin B12 with PLS1, 1.33 mg L −1 for methylcobalamin with OSC/PLS and 3.24 mg L −1 for B12 coenzyme with HLA techniques. Figures of merit such as selectivity, sensitivity, analytical sensitivity and LOD were determined for three compounds. The procedure was successfully applied to simultaneous determination of three compounds in synthetic mixtures and in a pharmaceutical formulation.

Simultaneous determination of dopamine and ascorbic acid by linear sweep voltammetry along with chemometrics using a glassy carbon electrode

A linear sweep voltammetric method has been used for simultaneous determination of mixtures of dopamine and ascorbic acid. It relies on oxidative reaction of dopamine and ascorbic acid at a glassy carbon electrode in a phosphate buffer (pH = 5). The voltammograms of these two compounds overlap strongly, and show non-linear character. Thus, it is difficult to analyze the compounds in their mixtures. In this work, chemometrics methods such as classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and orthogonal signal correction-partial least squares (OSC-PLS) were applied for the simultaneous determination of these compounds. The linear ranges for dopamine and ascorbic are 3–350 and 5–350 ppm, respectively. The root mean squares error of prediction (RMSEP) are 7.675, 5.938, 5.900, 5.750 for dopamine and 7.533, 6.857, 6.110, 6.031 for ascorbic acid and relative errors of prediction (REP) are 6.695, 5.180, 5.147, 5.016 for dopamine and 5.717, 5.204, 4.637, 4.577 for ascorbic acid by CLS, PCR, PLS and OSC-PLS, respectively. The effects of the different variables on the sensitivity and selectivity of the determination are discussed.