An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partia...An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.展开更多
Near infrared reflectance spectroscopy (NIRS), a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis (PLS-DA) to discriminate the transgenic (TCTP and mi...Near infrared reflectance spectroscopy (NIRS), a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis (PLS-DA) to discriminate the transgenic (TCTP and mi166) and wild type (Zhonghua 11) rice. Furthermore, rice lines transformed with protein gene (OsTCTP) and regulation gene (Osmi166) were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.展开更多
Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperatur...Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC (grain protein content) estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.展开更多
Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares (PLS) is proposed in this article, where PLS, the method for predicting a set of dependent var...Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares (PLS) is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.展开更多
A quantitative structure-activity relationships (QSAR) study is suggested for the prediction of solubility of some thiazolidine-4- carboxylic acid derivatives in aqueous solution. Ab initio theory was used to calcul...A quantitative structure-activity relationships (QSAR) study is suggested for the prediction of solubility of some thiazolidine-4- carboxylic acid derivatives in aqueous solution. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the solubility of thiazolidine- 4-carboxylic acid derivatives as a function of molecular structures was established by means of the partial least squares (PLS). The subset of descriptors, which resulted in the low prediction error, was selected by genetic algorithm. This model was applied for the prediction of the solubility of some thiazolidine-4-carboxylic acid derivatives, which were not in the modeling procedure. The relative errors of prediction lower that -4% was obtained by using GA-PLS method. The resulted model showed high prediction ability with RMSEP of 3.8836 and 2.9500 for PLS and GA-PLS models, respectively.展开更多
The computer auxiliary partial least squares is introduced to simultaneously determine the contents of Deoxyschizandin, Schisandrin, r-Schisandrin in the extracted solution of wuweizi. Regression analysis of the exper...The computer auxiliary partial least squares is introduced to simultaneously determine the contents of Deoxyschizandin, Schisandrin, r-Schisandrin in the extracted solution of wuweizi. Regression analysis of the experimental results shows that the average recovery of each component is all in the range from 98.9% to 110.3% , which means the partial least squares regression spectrophotometry can circumvent the overlappirtg of absorption spectrums of mlulti-components, so that sctisfactory results can be obtained without any scrapple pre-separation.展开更多
Near-infrared (NIR) spectroscopy was applied to reagent-free quantitative analysis of polysaccharide of a brand product of proprietary Chinese medicine (PCM) oral solution samples. A novel method, called absorbance up...Near-infrared (NIR) spectroscopy was applied to reagent-free quantitative analysis of polysaccharide of a brand product of proprietary Chinese medicine (PCM) oral solution samples. A novel method, called absorbance upper optimization partial least squares (AUO-PLS), was proposed and successfully applied to the wavelength selection. Based on varied partitioning of the calibration and prediction sample sets, the parameter optimization was performed to achieve stability. On the basis of the AUO-PLS method, the selected upper bound of appropriate absorbance was 1.53 and the corresponding wavebands combination was 400 - 1880 & 2088 - 2346 nm. With the use of random validation samples excluded from the modeling process, the root-mean-square error and correlation coefficient of prediction for polysaccharide were 27.09 mg·L<sup>-</sup><sup>1</sup> and 0.888, respectively. The results indicate that the NIR prediction values are close to those of the measured values. NIR spectroscopy combined with AUO-PLS method provided a promising tool for quantification of the polysaccharide for PCM oral solution and this technique is rapid and simple when compared with conventional methods.展开更多
Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this pap...Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.展开更多
Near-infrared spectroscopy(NIR),which is generally used for online monitoring of the food analysis and production process, was applied to determine the internal quality of toothpaste samples.It is acknowledged that ...Near-infrared spectroscopy(NIR),which is generally used for online monitoring of the food analysis and production process, was applied to determine the internal quality of toothpaste samples.It is acknowledged that the spectra can be significantly influenced by non-linearities introduced by light scatter,therefore,four data preprocessing methods,including off-set correction, 1st-derivative,standard normal variate(SNV) and multiplicative scatter correction(MSC),were employed before the date analysis. The multivariate calibration model of partial least squares(PLS) was established and then was used to predict the pH values of the toothpaste samples of different brand.The results showed that the spectral date processed by MSC was the best one for predicting the pH value of the toothpaste samples.展开更多
In this paper,we report a method through the combination of ab-initio calculations and partial least squares(PLS)analysis to develop the Quantitative Structure eActivity Relationship(QSAR)formulations of cathode volum...In this paper,we report a method through the combination of ab-initio calculations and partial least squares(PLS)analysis to develop the Quantitative Structure eActivity Relationship(QSAR)formulations of cathode volume changes in lithium ion batteries.The PLS analysis is based on ab-initio calculation data of 14 oxide cathodes with spinel structure LiX2O4 and 14 oxide cathodes with layered-structure LiXO_(2)(X=Ti,V,Cr,Mn,Fe,Co,Ni,Nb,Mo,Ru,Rh,Pd,Ta,Ir).Five types of descriptors,describing the characteristics of each compound from crystal structure,element,composition,local distortion and electronic level,with 34 factors in total,are adopted to obtain the QSAR formulation.According to the variable importance in projection analysis,the radius of X4t ion,and the X octahedron descriptors make major contributions to the volume change of cathode during delithiation.The analysis is hopefully applied to the virtual screening and combinatorial design of low-strain cathode materials for lithium ion batteries.展开更多
Partial least squares (PLS) regression was applied to the Lunar Soft Characterization Consortium (LSCC) dataset for spectral estimation of TiO2. The LSCC dataset was split into a number of subsets including the lo...Partial least squares (PLS) regression was applied to the Lunar Soft Characterization Consortium (LSCC) dataset for spectral estimation of TiO2. The LSCC dataset was split into a number of subsets including the low-Ti, high-Ti, total mare soils, total highland, Apollo 16, and Apollo 14 soils to investigate the effects of interfering minerals and nonlinearity on the PLS performance. The PLS weight loading vectors were analyzed through stepwise multiple regression analysis (SMRA) to identify mineral species driving and interfering the PLS performance. PLS exhibits high performance for estimating TiO2 for the LSCC low-Ti and high-Ti mare samples and both groups analyzed together. The results suggest that while the dominant TiO2-bearing minerals are few, additional PLS factors are required to compensate the effects on the important PLS factors of minerals that are not highly corrected to TiO2, to accommodate nonlinear relationships between reflectance and TiO2, and to correct inconsistent mineral-TiO2 correlations between the high-Ti and iow-Ti mare samples. Analysis of the LSCC highland soil samples indicates that the Apollo 16 soils are responsible for the large errors of TiO2 estimates when the soils are modeled with other subgroups. For the LSCC Apollo 16 samples, the dominant spectral effects of plagioclase over other dark minerals are primarily responsible for large errors of estimated TiO2. For the Apollo 14 soils, more accurate estimation for TiO2 is attributed to the posi- tive correlation between a major TiOe-bearing component and TiO2, explaining why the Apollo 14 soils follow the regression trend when analyzed with other soils groups.展开更多
Pseudomonas spp.and Enterobacteriaceae are dominant spoilage bacteria in chicken during cold storage(0°C-4°C).In this study,high resolution spectra in the range of 900-1700 nm were acquired and preprocessed ...Pseudomonas spp.and Enterobacteriaceae are dominant spoilage bacteria in chicken during cold storage(0°C-4°C).In this study,high resolution spectra in the range of 900-1700 nm were acquired and preprocessed using Savitzky-Golay convolution smoothing(SGCS),standard normal variate(SNV)and multiplicative scatter correction(MSC),respectively,and then mined using partial least squares(PLS)algorithm to relate to the total counts of Pseudomonas spp.and Enterobacteriaceae(PEC)of fresh chicken breasts to predict PEC rapidly.The results showed that with full 900-1700 nm range wavelength,MSC-PLS model built with MSC spectra performed better than PLS models with other spectra(RAW-PLS,SGCS-PLS,SNV-PLS),with correlation coefficient(RP)of 0.954,root mean square error of prediction(RMSEP)of 0.396 log10 CFU/g and residual predictive deviation(RPD)of 3.33 in prediction set.Based on the 12 optimal wavelengths(902.2 nm,905.5 nm,923.6 nm,938.4 nm,946.7 nm,1025.7 nm,1124.4 nm,1211.6 nm,1269.2 nm,1653.7 nm,1691.8 nm and 1693.4 nm)selected from MSC spectra by successive projections algorithm(SPA),SPA-MSC-PLS model had RP of 0.954,RMSEP of 0.397 log10 CFU/g and RPD of 3.32,similar to MSC-PLS model.The overall study indicated that NIR spectra combined with PLS algorithm could be used to detect the PEC of chicken flesh in a rapid and non-destructive way.展开更多
A multi-loop adaptive internal model control (IMC) strategy based on a dynamic partial least squares (PLS) frame-work is proposed to account for plant model errors caused by slow aging,drift in operational conditions,...A multi-loop adaptive internal model control (IMC) strategy based on a dynamic partial least squares (PLS) frame-work is proposed to account for plant model errors caused by slow aging,drift in operational conditions,or environmental changes.Since PLS decomposition structure enables multi-loop controller design within latent spaces,a multivariable adaptive control scheme can be converted easily into several independent univariable control loops in the PLS space.In each latent subspace,once the model error exceeds a specific threshold,online adaptation rules are implemented separately to correct the plant model mismatch via a recursive least squares (RLS) algorithm.Because the IMC extracts the inverse of the minimum part of the internal model as its structure,the IMC controller is self-tuned by explicitly updating the parameters,which are parts of the internal model.Both parameter convergence and system stability are briefly analyzed,and proved to be effective.Finally,the proposed control scheme is tested and evaluated using a widely-used benchmark of a multi-input multi-output (MIMO) system with pure delay.展开更多
Simultaneous determination of heavy metal cations and accurate quantitative prediction of them are of great interest in analytical chemistry.This work has focused on a comprehensive comparison of partial least squares...Simultaneous determination of heavy metal cations and accurate quantitative prediction of them are of great interest in analytical chemistry.This work has focused on a comprehensive comparison of partial least squares(PLS-1)and artificial neural networks(ANN)as two types of chemometric methods.For this purpose,aluminum,iron and copper were studied as three analytes whose UV-Vis absorption spectra highly overlap each other.Accordance with determined parameters(ligand concentration,pH,waiting times,the relationship between absorbance and concentration of metal ion effect and foreign ions)are provided and the optimum conditions.After establishing the optimum conditions for Fe^(3+),Al^(3+) and Cu^(2+) containing mixtures spectrophotometric determinations and the data calibration method of least squares(PLS-1)regression,and artificial neural network(ANN)methods were used.Chemometric methods are applied in a fast,simple,and the results are applicable.展开更多
The application of near-infrared(NIR)spectroscopy combined with multivariate calibration methods can achieve the rapid analysis of methanol gasoline.However,instrumental or environmental differences found for spectra ...The application of near-infrared(NIR)spectroscopy combined with multivariate calibration methods can achieve the rapid analysis of methanol gasoline.However,instrumental or environmental differences found for spectra make it impossible to continuously apply the previously developed calibration model.Therefore,the calibration transfer technique would be required to solve the time-consuming and laborious problem of reestablishing a new model.In this work,a calibration transfer method named kernel domain adaptive partial least squares(kda-PLS)was applied to the calibration transfer from the primary instrument to the secondary ones.Firstly,wavelet transform(WT)and variable importance in projection(VIP)were employed to enhance the predictive performance of the kda-PLS transfer model.Then,the results found for the calibration transfer by piecewise direct standardization(PDS)and domain adaptive partial least squares(da-PLS)were compared to verify the calibration transfer(CT)effect of kda-PLS.The results point that the kda-PLS method can transfer the PLS model developed on the primary instrument to the secondary ones,and achieve results comparable to the those of reestablishing a new PLS model on the secondary instrument,with R_(P)^(2)=0.9979(R_(P)^(2):coefficients of determination of the prediction set),RMSEP=0.0040(RMSEP:root mean square error of the prediction set),and MREP=3.03%(MREP:mean relative error of the prediction set).Therefore,kda-PLS will provide a new method for quantitative analysis of methanol content in methanol gasoline.展开更多
Large-scale parallel gene expression analysis has provided a greater ease for investigating the underlying mechanisms of Duchenne muscular dystrophy(DMD).Previous studies typically implemented variance/regression anal...Large-scale parallel gene expression analysis has provided a greater ease for investigating the underlying mechanisms of Duchenne muscular dystrophy(DMD).Previous studies typically implemented variance/regression analysis,which would be fundamentally flawed when unaccounted sources of variability in the arrays existed.Here we aim to identify genes that contribute to the pathology of DMD using partial least squares(PLS)based analysis.We carried out PLS-based analysis with two datasets downloaded from the Gene Expression Omnibus(GEO)database to identify genes contributing to the pathology of DMD.Except for the genes related to inflammation,muscle regeneration and extracellular matrix(ECM)modeling,we found some genes with high fold change,which have not been identified by previous studies,such as SRPX,GPNMB,SAT1,and LYZ.In addition,downregulation of the fatty acid metabolism pathway was found,which may be related to the progressive muscle wasting process.Our results provide a better understanding for the downstream mechanisms of DMD.展开更多
Environmental problems have attracted much attention in recent years,especially for papermak-ing wastewater discharge.To reduce the loss of effluence discharge violation,quality-related multivariate statistical method...Environmental problems have attracted much attention in recent years,especially for papermak-ing wastewater discharge.To reduce the loss of effluence discharge violation,quality-related multivariate statistical methods have been successfully applied to achieve a robust wastewater treatment system.In this work,a new dynamic multiblock partial least squares(DMBPLS)is pro-posed to extract the time-varying information in a large-scale papermaking wastewater treatment process.By introducing augmented matrices to input and output data,the proposed method not only handles the dynamic characteristic of data and reduces the time delay of fault detection,but enhances the interpretability of model.In addition,the DMBPLS provides a capability of fault location,which has certain guiding significance for fault recovery.In comparison with other mod-els,the DMBPLS has a superior fault detection result.Specifically,the maximum fault detection rate of the DMBPLS is improved by 35.93%and 12.5%for bias and drifting faults,respectively,in comparison with partial least squares(PLS).展开更多
During the course of calculating the rice evapotranspiration using weather factors,we often find that some independent variables have multiple correlation.The phenomena can lead to the traditional multivariate regress...During the course of calculating the rice evapotranspiration using weather factors,we often find that some independent variables have multiple correlation.The phenomena can lead to the traditional multivariate regression model which based on least square method distortion.And the stability of the model will be lost.The model will be built based on partial least square regression in the paper,through applying the idea of main component analyze and typical correlation analyze,the writer picks up some component from original material.Thus,the writer builds up the model of rice evapotranspiration to solve the multiple correlation among the independent variables (some weather factors).At last,the writer analyses the model in some parts,and gains the satisfied result.展开更多
In this work a new method is presented for simultaneous colorimetric determination of morphine (MOR) and ibuprofen (IBU) based on the aggregation of citrate-capped gold nanoparticles (AuNPs). Citrate-capped AuNP...In this work a new method is presented for simultaneous colorimetric determination of morphine (MOR) and ibuprofen (IBU) based on the aggregation of citrate-capped gold nanoparticles (AuNPs). Citrate-capped AuNPs were aggregated in the presence of MOR and IBU. The difference in kinetics of AuNPs aggregation in the presence of MOR/IBU was used for simultaneous analysis of MOR and IBU. The formation and size of synthesized AuNPs and the aggregated forms were monitored by infra-red (IR) spectroscopy and transmission electron microscopy (TEM), respectively. By adding MOR or IBU the absorbance was decreased at 520 nm and increased at 620 nm. The difference in kinetic profiles of aggregation was applied for simultaneous analysis of MOR and IBU using partial least square (PLS) regression as an efficient multivariate calibration method. The number of PLS latent variables was optimized by leave-one-out cross-validation method using predicted residual error sum of square. The proposed model exhibited a high capability in simultaneous prediction of MOR and IBU concentrations in real samples. The results showed linear ranges of 1.33-33.29 μg/mL (R2=0.9904) and 0.28-6.9 μg/mL (R2=0.9902) for MOR and IBU respectively with low detection limits of 0.15 and 0.03 μg/mL(S/N=5).展开更多
Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-ind...Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.展开更多
基金National Natural Science Foundation of China (No. 61074079)Shanghai Leading Academic Discipline Project,China (No.B504)
文摘An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.
基金supported by the projects under the Innovation Team of the Safety Standards and Testing Technology for Agricultural Products of Zhejiang Province, China (Grant No.2010R50028)the National Key Technologies R&D Program of China during the 11th Five-Year Plan Period (Grant No.2006BAK02A18)
文摘Near infrared reflectance spectroscopy (NIRS), a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis (PLS-DA) to discriminate the transgenic (TCTP and mi166) and wild type (Zhonghua 11) rice. Furthermore, rice lines transformed with protein gene (OsTCTP) and regulation gene (Osmi166) were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.
基金the National Natural Science Foundation of China (41171281, 40701120)the Beijing Nova Program, China (2008B33)
文摘Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC (grain protein content) estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.
文摘Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares (PLS) is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.
文摘A quantitative structure-activity relationships (QSAR) study is suggested for the prediction of solubility of some thiazolidine-4- carboxylic acid derivatives in aqueous solution. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the solubility of thiazolidine- 4-carboxylic acid derivatives as a function of molecular structures was established by means of the partial least squares (PLS). The subset of descriptors, which resulted in the low prediction error, was selected by genetic algorithm. This model was applied for the prediction of the solubility of some thiazolidine-4-carboxylic acid derivatives, which were not in the modeling procedure. The relative errors of prediction lower that -4% was obtained by using GA-PLS method. The resulted model showed high prediction ability with RMSEP of 3.8836 and 2.9500 for PLS and GA-PLS models, respectively.
文摘The computer auxiliary partial least squares is introduced to simultaneously determine the contents of Deoxyschizandin, Schisandrin, r-Schisandrin in the extracted solution of wuweizi. Regression analysis of the experimental results shows that the average recovery of each component is all in the range from 98.9% to 110.3% , which means the partial least squares regression spectrophotometry can circumvent the overlappirtg of absorption spectrums of mlulti-components, so that sctisfactory results can be obtained without any scrapple pre-separation.
文摘Near-infrared (NIR) spectroscopy was applied to reagent-free quantitative analysis of polysaccharide of a brand product of proprietary Chinese medicine (PCM) oral solution samples. A novel method, called absorbance upper optimization partial least squares (AUO-PLS), was proposed and successfully applied to the wavelength selection. Based on varied partitioning of the calibration and prediction sample sets, the parameter optimization was performed to achieve stability. On the basis of the AUO-PLS method, the selected upper bound of appropriate absorbance was 1.53 and the corresponding wavebands combination was 400 - 1880 & 2088 - 2346 nm. With the use of random validation samples excluded from the modeling process, the root-mean-square error and correlation coefficient of prediction for polysaccharide were 27.09 mg·L<sup>-</sup><sup>1</sup> and 0.888, respectively. The results indicate that the NIR prediction values are close to those of the measured values. NIR spectroscopy combined with AUO-PLS method provided a promising tool for quantification of the polysaccharide for PCM oral solution and this technique is rapid and simple when compared with conventional methods.
基金Project supported by the Fundamental Research Funds for the Central Universities, China (Grant No. 2019XD-A02)the National Natural Science Foundation of China (Grant Nos. U1636106, 61671087, 61170272, and 92046001)+2 种基金Natural Science Foundation of Beijing Municipality, China (Grant No. 4182006)Technological Special Project of Guizhou Province, China (Grant No. 20183001)the Foundation of Guizhou Provincial Key Laboratory of Public Big Data (Grant Nos. 2018BDKFJJ016 and 2018BDKFJJ018)。
文摘Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.
基金the financial support by the National Natural Science Foundation of China (No.21065007)the State Key Laboratory of Food Science and Technology of Nanchang University(Nos.MB-201002 and TS-200919)
文摘Near-infrared spectroscopy(NIR),which is generally used for online monitoring of the food analysis and production process, was applied to determine the internal quality of toothpaste samples.It is acknowledged that the spectra can be significantly influenced by non-linearities introduced by light scatter,therefore,four data preprocessing methods,including off-set correction, 1st-derivative,standard normal variate(SNV) and multiplicative scatter correction(MSC),were employed before the date analysis. The multivariate calibration model of partial least squares(PLS) was established and then was used to predict the pH values of the toothpaste samples of different brand.The results showed that the spectral date processed by MSC was the best one for predicting the pH value of the toothpaste samples.
基金We acknowledge the National Natural Science Foundation of China(Grant Nos.11234013)“863”Project(Grant No.2015AA034201)Beijing S&T Project(Grant No.D161100002416003)for financial support and the Shanghai Supercomputer Center for providing computing resources.
文摘In this paper,we report a method through the combination of ab-initio calculations and partial least squares(PLS)analysis to develop the Quantitative Structure eActivity Relationship(QSAR)formulations of cathode volume changes in lithium ion batteries.The PLS analysis is based on ab-initio calculation data of 14 oxide cathodes with spinel structure LiX2O4 and 14 oxide cathodes with layered-structure LiXO_(2)(X=Ti,V,Cr,Mn,Fe,Co,Ni,Nb,Mo,Ru,Rh,Pd,Ta,Ir).Five types of descriptors,describing the characteristics of each compound from crystal structure,element,composition,local distortion and electronic level,with 34 factors in total,are adopted to obtain the QSAR formulation.According to the variable importance in projection analysis,the radius of X4t ion,and the X octahedron descriptors make major contributions to the volume change of cathode during delithiation.The analysis is hopefully applied to the virtual screening and combinatorial design of low-strain cathode materials for lithium ion batteries.
基金supported by the Research Support Funds Grant (RSFG) program of Indiana University-Purdue University at Indianapolis
文摘Partial least squares (PLS) regression was applied to the Lunar Soft Characterization Consortium (LSCC) dataset for spectral estimation of TiO2. The LSCC dataset was split into a number of subsets including the low-Ti, high-Ti, total mare soils, total highland, Apollo 16, and Apollo 14 soils to investigate the effects of interfering minerals and nonlinearity on the PLS performance. The PLS weight loading vectors were analyzed through stepwise multiple regression analysis (SMRA) to identify mineral species driving and interfering the PLS performance. PLS exhibits high performance for estimating TiO2 for the LSCC low-Ti and high-Ti mare samples and both groups analyzed together. The results suggest that while the dominant TiO2-bearing minerals are few, additional PLS factors are required to compensate the effects on the important PLS factors of minerals that are not highly corrected to TiO2, to accommodate nonlinear relationships between reflectance and TiO2, and to correct inconsistent mineral-TiO2 correlations between the high-Ti and iow-Ti mare samples. Analysis of the LSCC highland soil samples indicates that the Apollo 16 soils are responsible for the large errors of TiO2 estimates when the soils are modeled with other subgroups. For the LSCC Apollo 16 samples, the dominant spectral effects of plagioclase over other dark minerals are primarily responsible for large errors of estimated TiO2. For the Apollo 14 soils, more accurate estimation for TiO2 is attributed to the posi- tive correlation between a major TiOe-bearing component and TiO2, explaining why the Apollo 14 soils follow the regression trend when analyzed with other soils groups.
基金The authors acknowledged that this work was financially supported by Major Scientific and Technological Project of Henan Province(Grant No.161100110600)Key Scientific and Technological Project of Henan Province(No.212102310491,No.182102310060)+3 种基金China Postdoctoral Science Foundation(No.2018M632767)Henan Postdoctoral Science Foundation(No.001801021)Youth Talents Support Project of Henan Province(No.2018HYTP008)and Bainong Outstanding Talents Project of Henan Institute of Science and Technology(No.BNYC2018-2-27).
文摘Pseudomonas spp.and Enterobacteriaceae are dominant spoilage bacteria in chicken during cold storage(0°C-4°C).In this study,high resolution spectra in the range of 900-1700 nm were acquired and preprocessed using Savitzky-Golay convolution smoothing(SGCS),standard normal variate(SNV)and multiplicative scatter correction(MSC),respectively,and then mined using partial least squares(PLS)algorithm to relate to the total counts of Pseudomonas spp.and Enterobacteriaceae(PEC)of fresh chicken breasts to predict PEC rapidly.The results showed that with full 900-1700 nm range wavelength,MSC-PLS model built with MSC spectra performed better than PLS models with other spectra(RAW-PLS,SGCS-PLS,SNV-PLS),with correlation coefficient(RP)of 0.954,root mean square error of prediction(RMSEP)of 0.396 log10 CFU/g and residual predictive deviation(RPD)of 3.33 in prediction set.Based on the 12 optimal wavelengths(902.2 nm,905.5 nm,923.6 nm,938.4 nm,946.7 nm,1025.7 nm,1124.4 nm,1211.6 nm,1269.2 nm,1653.7 nm,1691.8 nm and 1693.4 nm)selected from MSC spectra by successive projections algorithm(SPA),SPA-MSC-PLS model had RP of 0.954,RMSEP of 0.397 log10 CFU/g and RPD of 3.32,similar to MSC-PLS model.The overall study indicated that NIR spectra combined with PLS algorithm could be used to detect the PEC of chicken flesh in a rapid and non-destructive way.
基金Project supported by the National Natural Science Foundation of China (No.60574047)the National High-Tech R & D Program (863)of China (No.2007AA04Z168)the Research Fund for the Doctoral Program of Higher Education of China (No.20050335018)
文摘A multi-loop adaptive internal model control (IMC) strategy based on a dynamic partial least squares (PLS) frame-work is proposed to account for plant model errors caused by slow aging,drift in operational conditions,or environmental changes.Since PLS decomposition structure enables multi-loop controller design within latent spaces,a multivariable adaptive control scheme can be converted easily into several independent univariable control loops in the PLS space.In each latent subspace,once the model error exceeds a specific threshold,online adaptation rules are implemented separately to correct the plant model mismatch via a recursive least squares (RLS) algorithm.Because the IMC extracts the inverse of the minimum part of the internal model as its structure,the IMC controller is self-tuned by explicitly updating the parameters,which are parts of the internal model.Both parameter convergence and system stability are briefly analyzed,and proved to be effective.Finally,the proposed control scheme is tested and evaluated using a widely-used benchmark of a multi-input multi-output (MIMO) system with pure delay.
文摘Simultaneous determination of heavy metal cations and accurate quantitative prediction of them are of great interest in analytical chemistry.This work has focused on a comprehensive comparison of partial least squares(PLS-1)and artificial neural networks(ANN)as two types of chemometric methods.For this purpose,aluminum,iron and copper were studied as three analytes whose UV-Vis absorption spectra highly overlap each other.Accordance with determined parameters(ligand concentration,pH,waiting times,the relationship between absorbance and concentration of metal ion effect and foreign ions)are provided and the optimum conditions.After establishing the optimum conditions for Fe^(3+),Al^(3+) and Cu^(2+) containing mixtures spectrophotometric determinations and the data calibration method of least squares(PLS-1)regression,and artificial neural network(ANN)methods were used.Chemometric methods are applied in a fast,simple,and the results are applicable.
基金supported by the National Natural Science Foundation of China(Nos.22173701,22073074,21873076,21775118)the Youth Innovative Team Project of Higher Education of Shaanxi Province,China(No.2019.21).
文摘The application of near-infrared(NIR)spectroscopy combined with multivariate calibration methods can achieve the rapid analysis of methanol gasoline.However,instrumental or environmental differences found for spectra make it impossible to continuously apply the previously developed calibration model.Therefore,the calibration transfer technique would be required to solve the time-consuming and laborious problem of reestablishing a new model.In this work,a calibration transfer method named kernel domain adaptive partial least squares(kda-PLS)was applied to the calibration transfer from the primary instrument to the secondary ones.Firstly,wavelet transform(WT)and variable importance in projection(VIP)were employed to enhance the predictive performance of the kda-PLS transfer model.Then,the results found for the calibration transfer by piecewise direct standardization(PDS)and domain adaptive partial least squares(da-PLS)were compared to verify the calibration transfer(CT)effect of kda-PLS.The results point that the kda-PLS method can transfer the PLS model developed on the primary instrument to the secondary ones,and achieve results comparable to the those of reestablishing a new PLS model on the secondary instrument,with R_(P)^(2)=0.9979(R_(P)^(2):coefficients of determination of the prediction set),RMSEP=0.0040(RMSEP:root mean square error of the prediction set),and MREP=3.03%(MREP:mean relative error of the prediction set).Therefore,kda-PLS will provide a new method for quantitative analysis of methanol content in methanol gasoline.
文摘Large-scale parallel gene expression analysis has provided a greater ease for investigating the underlying mechanisms of Duchenne muscular dystrophy(DMD).Previous studies typically implemented variance/regression analysis,which would be fundamentally flawed when unaccounted sources of variability in the arrays existed.Here we aim to identify genes that contribute to the pathology of DMD using partial least squares(PLS)based analysis.We carried out PLS-based analysis with two datasets downloaded from the Gene Expression Omnibus(GEO)database to identify genes contributing to the pathology of DMD.Except for the genes related to inflammation,muscle regeneration and extracellular matrix(ECM)modeling,we found some genes with high fold change,which have not been identified by previous studies,such as SRPX,GPNMB,SAT1,and LYZ.In addition,downregulation of the fatty acid metabolism pathway was found,which may be related to the progressive muscle wasting process.Our results provide a better understanding for the downstream mechanisms of DMD.
基金supported by Student’s Platform for Innovation and Entrepreneurship Training Program in Jiangsu Province(no.202010298029Z)Guangdong Provincial Natural Science Foundation(no.2016A030306033).
文摘Environmental problems have attracted much attention in recent years,especially for papermak-ing wastewater discharge.To reduce the loss of effluence discharge violation,quality-related multivariate statistical methods have been successfully applied to achieve a robust wastewater treatment system.In this work,a new dynamic multiblock partial least squares(DMBPLS)is pro-posed to extract the time-varying information in a large-scale papermaking wastewater treatment process.By introducing augmented matrices to input and output data,the proposed method not only handles the dynamic characteristic of data and reduces the time delay of fault detection,but enhances the interpretability of model.In addition,the DMBPLS provides a capability of fault location,which has certain guiding significance for fault recovery.In comparison with other mod-els,the DMBPLS has a superior fault detection result.Specifically,the maximum fault detection rate of the DMBPLS is improved by 35.93%and 12.5%for bias and drifting faults,respectively,in comparison with partial least squares(PLS).
文摘During the course of calculating the rice evapotranspiration using weather factors,we often find that some independent variables have multiple correlation.The phenomena can lead to the traditional multivariate regression model which based on least square method distortion.And the stability of the model will be lost.The model will be built based on partial least square regression in the paper,through applying the idea of main component analyze and typical correlation analyze,the writer picks up some component from original material.Thus,the writer builds up the model of rice evapotranspiration to solve the multiple correlation among the independent variables (some weather factors).At last,the writer analyses the model in some parts,and gains the satisfied result.
文摘In this work a new method is presented for simultaneous colorimetric determination of morphine (MOR) and ibuprofen (IBU) based on the aggregation of citrate-capped gold nanoparticles (AuNPs). Citrate-capped AuNPs were aggregated in the presence of MOR and IBU. The difference in kinetics of AuNPs aggregation in the presence of MOR/IBU was used for simultaneous analysis of MOR and IBU. The formation and size of synthesized AuNPs and the aggregated forms were monitored by infra-red (IR) spectroscopy and transmission electron microscopy (TEM), respectively. By adding MOR or IBU the absorbance was decreased at 520 nm and increased at 620 nm. The difference in kinetic profiles of aggregation was applied for simultaneous analysis of MOR and IBU using partial least square (PLS) regression as an efficient multivariate calibration method. The number of PLS latent variables was optimized by leave-one-out cross-validation method using predicted residual error sum of square. The proposed model exhibited a high capability in simultaneous prediction of MOR and IBU concentrations in real samples. The results showed linear ranges of 1.33-33.29 μg/mL (R2=0.9904) and 0.28-6.9 μg/mL (R2=0.9902) for MOR and IBU respectively with low detection limits of 0.15 and 0.03 μg/mL(S/N=5).
基金financially supported by the National Key R&D Program Projects of China (No.2021YFB3202402)National Natural Science Foundation of China (No.62173321)。
文摘Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.