Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy

A new acetolactate synthase (ALS)-inhibiting herbicide, propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273), was applied to oilseed rape (Brassica napus L.) leaves in different leaf positions. Visible/near-infrared (Vis/NIR) spectroscopy was investigated for fast and non-destructive determination of ALS activity and protein content in rapeseed leaves. Partial least squares (PLS) analysis was the calibration method with comparison of different spectral preprocessing by Savitzky-Golay (SG) smoothing, standard normal variate (SNV), first and second derivative. The best PLS models were obtained by first-derivative spectra for ALS, whereas original spectra for soluble, non-soluble and total protein contents. Simultaneously, certain latent variables (LVs) were used as the inputs of back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) models. All LS-SVM models outperformed PLS models and BPNN models. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias in validation set by LS-SVM were 0.998, 0.715 and 0.079 for ALS, 0.999, 33.084 and 1.178 for soluble protein, 0.997, 42.773 and 6.244 for non-soluble protein, 0.999, 59.562 and 7.437 for total protein, respectively. The results indicated that Vis/NIR spectroscopy combined with LS-SVM could be successfully applied for the determination of ALS activity and protein content of rapeseed leaves. The results would be helpful for further on field analysis of using Vis/NIR spectroscopy to monitor the growing status and physiological properties of oilseed rape.     

Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis

Near infrared (NIR) spectroscopy based on effective wavelengths (EWs) and chemometrics was proposed to discriminate the varieties of fruit vinegars including aloe, apple, lemon and peach vinegars. One hundred eighty samples (45 for each variety) were selected randomly for the calibration set, and 60 samples (15 for each variety) for the validation set, whereas 24 samples (6 for each variety) for the independent set. Partial least squares discriminant analysis (PLS-DA) and least squares-support vector machine (LS-SVM) were implemented for calibration models. Different input data matrices of LS-SVM were determined by latent variables (LVs) selected by explained variance, and EWs selected by x-loading weights, regression coefficients, modeling power and independent component analysis (ICA). Then the LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS-DA model, and the optimal LS-SVM model was achieved with EWs (4021, 4058, 4264, 4400, 4853, 5070 and 5273 cm⁻¹) selected by regression coefficients. The determination coefficient (R²), RMSEP and total recognition ratio with cutoff value ±0.1 in validation set were 1.000, 0.025 and 100%, respectively. The overall results indicted that the regression coefficients was an effective way for the selection of effective wavelengths. NIR spectroscopy combined with LS-SVM models had the capability to discriminate the varieties of fruit vinegars with high accuracy.     

Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer

Three effective wavelength (EW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) of beer, including successive projections algorithm (SPA), regression coefficient analysis (RCA) and independent component analysis (ICA). A total of 360 samples were prepared for the calibration (n = 180), validation (n = 90) and prediction (n = 90) sets. The performance of different preprocessing was compared. Three calibrations using EWs selected by SPA, RCA and ICA were developed, including linear regression of partial least squares analysis (PLS) and multiple linear regression (MLR), and nonlinear regression of least squares-support vector machine (LS-SVM). Ten EWs selected by SPA achieved the optimal linear SPA-MLR model compared with SPA-PLS, RCA-MLR, RCA-PLS, ICA-MLR and ICA-PLS. The correlation coefficient (r) and root mean square error of prediction (RMSEP) by SPA-MLR were 0.9762 and 0.1808, respectively. Moreover, the newly proposed SPA-LS-SVM model obtained almost the same excellent performance with RCA-LS-SVM and ICA-LS-SVM models, and the r value and RMSEP were 0.9818 and 0.1628, respectively. The nonlinear model SPA-LS-SVM outperformed SPA-MLR model. The overall results indicated that SPA was a powerful way for the selection of EWs, and Vis/NIR spectroscopy incorporated to SPA-LS-SVM was successful for the accurate determination of SSC of beer.     

Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine

A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis–NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x–y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard–Stone method, as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.     

Polarographic chemometric determination of zinc and nickel in aqueous samples

Polarographic chemometric methods were applied to the determination of zinc and nickel in aqueous solutions previously acidified with 0.1 M acetate buffer (pH 4.2). The studied methods are multivariate methods including classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS); derivative ratio methods (first, ¹D and second, ²D derivative ratio). A comparative study was considered. The studied chemometric methods do not need the presence of any reduction potential shift reagent in spite of the great overlap between the two metals polarograms. A training set consisting of 10 binary mixture solutions in the possible combinations containing 0.13–9.30 μg/ml Zn(II) and 0.20–12.25 μg/ml Ni(II) was used to develop the chemometric calibrations (CLS, PCR and PLS). A validation set containing the synthetic mixtures in the range of 0.29–9.00 μg/ml for Zn(II) and 0.30–11.60 μg/ml for Ni(II) was used to validate the multivariate calibrations. Same mixtures were used to develop the derivative ratio methods. The polarograms were recorded and their current values were measured within the potential range −920 to −1052 mV at 2 mV intervals. The mean percentage recoveries obtained using CLS, PCR and PLS were found to be 99.5 ± 1.5%, 100.0 ± 1.1% and 100.0 ± 1.0% for Zn(II) and 99.4 ± 1.3%, 99.7 ± 1.2% and 99.9 ± 1.0% for Ni(II), respectively. The mean percentage recoveries obtained using ¹D at −950 mV, ¹D at −1010 mV, ¹D at −950 mV–¹D at −1010 mV and ²D at −986 mV for Zn(II) were found to be 99.7 ± 1.2%, 99.2 ± 1.6%, 99.4 ± 1.4% and 99.4 ± 1.4%; and using ¹D at −1030 mV and ²D at −1010 mV for Ni(II) were found to be 100.5 ± 1.3% and 100.4 ± 1.3%, respectively. Interferences due to the presence of Cd, Co, Pb, Fe, Mn, Ca, Mg, Cu and Al were studied. The applicability of the proposed methods was assessed through the determination of both metals in tap drinking-water. Samples were subjected if required up to a 20-fold preconcentration step by microwaving in pyrex vessels. The results were compared with those obtained using the zincon and the heptoxime colorimetric reference methods for the determination of zinc and nickel, respectively.     

Monitoring of technical oils in supercritical CO(2) under continuous flow conditions by NIR spectroscopy and multivariate calibration

Metal parts and residues from machining processes are usually polluted with cutting or grinding oil and have to be cleaned before further use. Supercritical carbon dioxide can be used for extraction processes and precision cleaning of metal parts, as developed at Forschungszentrum Karlsruhe. For optimizing and efficiently conducting the extraction process, in-line analysis of oil concentration is desirable. Therefore, a monitoring method using fiber-optic NIR spectroscopy in combination with PLS calibration has been developed. In an earlier paper we have described the instrumental set-up and a calibration model using the model compound squalane in the spectral range of the CH combination bands from 4900 to 4200 cm⁻¹. With this model only poor prediction results were obtained if applied to technical oil samples in supercritical CO₂. In this paper we describe a new calibration model, which was set up for the squalane/carbon dioxide system covering the 323–353 K temperature and the 16–35.6 MPa pressure range. Here, calibration data in the spectral range from 6100 to 5030 cm⁻¹ have been used. This range includes the 5100 cm⁻¹ CO₂ band of the Fermi triad as well as the hydrocarbon 1st overtone CH stretching bands, where spectral features of oil compounds and squalane are more similar to each other.

The root mean-squared error of prediction obtained with this model is 4 mg cm⁻³ for carbon dioxide and 0.4 mg cm⁻³ for squalane, respectively. The utilizability of the newly developed PLS calibration model for predicting the oil concentration and CO₂ density of solutions of technical oils in supercritical carbon dioxide has been tested. Three types of “real world” cutting and grinding oil formulations were used in these experiments. The calibration proved to be suitable for determining the technical oil concentration with an error of 1.1 mg cm⁻³ and the CO₂ density with an error of 6 mg cm⁻³. Therefore, it seems possible to apply this in-line analytical approach on the basis of a cost-effective and time-saving model compound calibration for the surveillance of real world de-oiling and other extraction process based on supercritical carbon dioxide, and furthermore to establish an automated process termination criterion based on this technique.     

A practical approach for near infrared spectral quantitative analysis of complex samples using partial least squares modeling

The number of latent variables (LVs) or the factor number is a key parameter in PLS modeling to obtain a correct prediction. Although lots of work have been done on this issue, it is still a difficult task to determine a suitable LV number in practical uses. A method named independent factor diagnostics (IFD) is proposed for investigation of the contribution of each LV to the predicted results on the basis of discussion about the determination of LV number in PLS modeling for near infrared (NIR) spectra of complex samples. The NIR spectra of three data sets of complex samples, including a public data set and two tobacco lamina ones, are investigated. It is shown that several high order LVs constitute main contributions to the predicted results, albeit the contribution of the low order LVs should not be neglected in the PLS models. Therefore, in practical uses of PLS for analysis of complex samples, it may be better to use a slightly large LV number for NIR spectral analysis of complex samples. Supported by the National Natural Science Foundation of China (Grant Nos. 20775036 & 20835002)     

近红外光谱法快速测定附子中6个生物碱的含量

应用近红外光谱(NIRS)技术结合偏最小二乘(PLS)和最小二乘支持向量机(LS-SVM)建立了附子中多指标成分的快速无损检测方法。选取38批样品建立了同时测定附子样品中6种成分含量的高效液相色谱(HPLC)方法;通过采集附子样品的NIRS图,分别采用PLS和LS-SVM建立了各个成分HPLC测定值与NIRS图的定量校正模型。所建立的苯甲酰新乌头原碱、苯甲酰乌头原碱、苯甲酰次乌头原碱、新乌头碱、次乌头碱、乌头碱、单酯型生物碱总量和双酯型生物碱总量LS-SVM模型的相对预测偏差(RPD)分别为3.3、3.2、4.1、7.7、8.8、7.6、4.0和8.6;验证集相关系数(rpre)分别为0.9486、0.9475、0.9668、0.9909、0.9946、0.9969、0.9669和0.9927,且LS-SVM模型优于PLS模型,说明NIRS模型验证集与HPLC测定值具有良好的非线性关系,模型预测效果良好。采用NIRS技术结合LS-SVM模型可以快速对附子中的上述6个生物碱含量以及单酯型生物碱总量和双酯型生物碱总量进行检测,方法操作简便,对控制附子中的生物碱含量具有一定的指导作用。     

Simultaneous kinetic-spectrophotometric determination of sulfide and sulfite by partial least squares and genetic algorithm variable selection

Simultaneous multicomponent analysis is usually carried out by multivariate calibration models such as partial least squares (PLS) that utilize the full spectrum. It has been demonstrated by both experimental and theoretical considerations that better results can be obtained by a proper selection of the spectral range to be included in calculations. A genetic algorithm is one of the most popular methods for selecting variables for PLS calibration of mixtures with almost identical spectra without loss of prediction capacity. In this work, a simple and precise method for rapid and accurate simultaneous determination of sulfide and sulfite ions based on the addition reaction of these ions with new fuchsin at pH 8 and 25°C by PLS regression and using a genetic algorithm (GA) for variable selection is proposed. The concentrations of sulfide and sulfite ions varied between 0.05–2.50 and 0.15–2.00 μg/mL, respectively. A series of synthetic solutions containing different concentrations of sulfide and sulfite were used to check the prediction ability of GA-PLS models. The root mean square error of prediction with PLS on the whole data set was 0.19 μg/mL for sulfide and 0.09 μg/mL for sulfite. After the application of GA, these values were reduced to 0.04 and 0.03 μg/mL, respectively. The text was submitted by the authors in English.     

偏最小二乘线性拟合滴定甘蔗糖蜜中多组分有机酸

将滴定体系调节至pH 2.0,用碱标准溶液滴定至特定pH所消耗滴定荆为测量指标,构建了多组分有机酸滴定数据阵,分别以主成分回归法、偏最小二乘法以及人工神经元网络法进行多组分拟合.结果表明,偏最小二乘法的拟合结果最佳,对混合体系中乙酸、乳酸、草酸、琥珀酸、柠檬酸和乌头酸总量的相对预测均方根误差分别为5.80%、8.88%...     

Determination of captopril in pharmaceutical preparation and biological fluids using two- and three-way chemometrics methods

Spectrophotometric method has been developed for the direct quantitative determination of captopril in pharmaceuticalpreparation and biological fluids(human plasma and urine)samples.The method was accomplished based on parallel factoranalysis(PARAFAC)and partial least squares(PLS).The study was carried out in the pH range from 2.0 to 12.8 and with aconcentration from 0.70 to 61.50 μg mL~(-1)of captopril.Multivariate calibration models such as PLS at various pH and PARAFACwere elaborated from ultraviolet spectra deconvolution and captopril determination.The best models for this system were obtainedwith PARAFAC and PLS at pH 2.0.The applications of the method for determination of real samples were evaluated by analysis ofcaptopril in pharmaceutical preparations and biological fluids with satisfactory results.The accuracy of the method,evaluatedthrough the RMSEP,was 0.5801 for captopril with best calibration curve by PARAFAC and 0.6168 for captopril with PLS at pH 2.0model.     

Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.     

氨基酸残基延展态可及表面积的预测

氨基酸残基的延展态可及表面积(ASA)对预测蛋白质结构和功能起着至关重要的作用.采用偏最小二乘法和多元回归法成功构建了具有较高准确率的预测氨基酸延展态ASA的模型.所得模型具有良好的预测性和可重复性,在模型中,训练集的最优Rt2r可以达到0.998 3,而测试集的Rt2e最优可以达到0.999 2.     

Chemometrics: the issues of measurement and modelling

In this paper, two spectral data sets have been used to illustrate the importance of maintaining chemical information whilst generating predictive multivariate calibration models. The first data set is based on 26 duplicate UV/VIS spectra for four meal ions (Fe, Ni, Co, Cu) present at varying concentrations in aqueous solution. Spectra were collected across the range 180–800 nm at a resolution of 3.5 nm generating 211 data points for each sample. Calibration was carried out using multiple linear regression (MLR) and a K-matrix approach to demonstrate the advantages the latter method has in describing real spectral features. In addition, the limitation of MLR in accommodating noise and spectral overlap in the data is also illustrated. The second data set based on NIR spectroscopy, was generated using a four-level 2 factor Factorial design strategy and consisted of two additives present at a range of concentrations in an aqueous caustic system, with the spectra being collected over the range 10,000–3000 cm⁻¹. Whilst a conventional partial least squares (PLS) model was applied to the data, it was through the use of variable selection (VS) prior to PLS and the application of weighted ridge regression (WRR) techniques that the need to develop chemometric methodology which intuitively reflected chemical information has been demonstrated. The results will also illustrate how a poorly designed experimental design protocol and missing data can limit the performance of the calibration models generated. The aims of this paper are not to prescribe ideal calibration methodology but rather to demonstrate the relevance of selecting multivariate calibration methodology that relates more to the chem rather than just the metrics in chemometrics.     

Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares

Changeable size moving window partial least squares (CSMWPLS) and searching combination moving window partial least squares (SCMWPLS) are proposed to search for an optimized spectral interval and an optimized combination of spectral regions from informative regions obtained by a previously proposed spectral interval selection method, moving window partial least squares (MWPLSR) [Anal. Chem. 74 (2002) 3555]. The utilization of informative regions aims to construct better PLS models than those based on the whole spectral points. The purpose of CSMWPLS and SCMWPLS is to optimize the informative regions and their combination to further improve the prediction ability of the PLS models. The results of their application to an open-path (OP)/FT-IR spectra data set show that the proposed methods, especially SCMWPLS can find out an optimized combination, with which one can improve, often significantly, the performance of the corresponding PLS model, in terms of low prediction error, root mean square error of prediction (RMSEP) with the reasonable latent variable (LVs) number, comparing with the results obtained using whole spectra or direct combination of informative regions for a compound. Regions consisting of the combinations obtained can easily be explained by the existence of IR absorption bands in those spectral regions.     

A peroxidase-tetrathiafulvalene biosensor based on self-assembled monolayer modified Au electrodes for the flow-injection determination of hydrogen peroxide

The construction and performance under flow-injection conditions of an integrated amperometric biosensor for hydrogen peroxide is reported. The design of the bioelectrode is based on a mercaptopropionic acid (MPA) self-assembled monolayer (SAM) modified gold disk electrode on which horseradish peroxidase (HRP, 24.3 U) was immobilized by cross-linking with glutaraldehyde together with the mediator tetrathiafulvalene (TTF, 1 μmol), which was entrapped in the three-dimensional aggregate formed.

The amperometric biosensor allows the obtention of reproducible flow injection amperometric responses at an applied potential of 0.00 V in 0.05 mol L⁻¹ phosphate buffer, pH 7.0 (flow rate: 1.40 mL min⁻¹, injection volume: 150 μL), with a range of linearity for hydrogen peroxide within the 2.0 × 10⁻⁷–1.0 × 10⁻⁴ mol L⁻¹ concentration range (slope: (2.33 ± 0.02) × 10⁻² A mol⁻¹ L, r = 0.999). A detection limit of 6.9 × 10⁻⁸ mol L⁻¹ was obtained together with a R.S.D. (n = 50) of 2.7% for a hydrogen peroxide concentration level of 5.0 × 10⁻⁵ mol L⁻¹. The immobilization method showed a good reproducibility with a R.S.D. of 5.3% for five different electrodes. Moreover, the useful lifetime of one single biosensor was estimated in 13 days.

The SAM-based biosensor was applied for the determination of hydrogen peroxide in rainwater and in a hair dye. The results obtained were validated by comparison with those obtained with a spectrophotometric reference method. In addition, the recovery of hydrogen peroxide in sterilised milk was tested.     

Selection of individual variables versus intervals of variables in PLSR

The selection abilities of the two well‐known techniques of variable selection, synergy interval‐partial least‐squares (SiPLS) and genetic algorithm‐partial least‐squares (GA‐PLS), have been examined and compared. By using different simulated and real (corn and metabolite) datasets, keeping in view the spectral overlapping of the components, the influence of the selection of either intervals of variables or individual variables on the prediction performances was examined. In the simulated datasets, with decrease in the overlapping of the spectra of components and cases with components of narrow bands, GA‐PLS results were better. In contrast, the performance of SiPLS was higher for data of intermediate overlapping. For mixtures of high overlapping analytes, GA‐PLS showed slightly better performance. However, significant differences between the results of the two selection methods were not observed in most of the cases. Although SiPLS resulted in slightly better performance of prediction in the case of corn dataset except for the prediction of the moisture content, the improvement obtained by SiPLS compared with that by GA‐PLS was not significant. For real data of less overlapped components (metabolite dataset), GA‐PLS that tends to select far fewer variables did not give significantly better root mean square error of cross‐validation (RMSECV), cross‐validated R² (Q²), and root mean square error of prediction (RMSEP) compared with SiPLS. Irrespective of the type of dataset, GA‐PLS resulted in models with fewer latent variables (LVs). When comparing the computational time of the methods, GA‐PLS is considered superior to SiPLS. Copyright © 2010 John Wiley & Sons, Ltd.     

A new immune resonance scattering spectral assay for trace fibrinogen with gold nanoparticle label

An on-line stacking method based on moving reaction boundary (MRB) was developed for the sensitive determination of barbital and phenobarbital in human urine via capillary electrophoresis (CE). The optimized conditions for the method are: 60 mmol L⁻¹ pH 11.0 Gly–NaOH as the background electrolyte, 10 mmol L⁻¹ pH 5.5 Gly–HCl as sample buffer, secobarbital as the internal standard (IS), 12.5 kV, 1.4 psi 10 s sample injection, 75 μm ID 60.2 cm total length (50 cm effective length) capillary and 214 nm detect wavelength. Under the optimized conditions, the method can well stack and separate barbital and phenobarbital in urine samples and result in 20.5-fold and 22.6-fold improvement in concentration sensitivity for barbital and phenobarbital, respectively. Furthermore, the method holds: (1) good linear calibration functions for the two target compounds (correlation coefficients r > 0.999), (2) low limits of detection (0.27 μg mL⁻¹ for barbital and 0.26 μg mL⁻¹ for phenobarbital), (3) low limits of quantification (0.92 μg mL⁻¹ for barbital and 0.87 μg mL⁻¹ for phenobarbital), (4) good precision (R.S.D. of intra-day and inter-day less than 5.38% for barbital and 1.67% for phenobarbital, respectively) and (5) high recoveries at three concentration levels (90.27–106.36% for barbital and 93.05–113.60% for phenobarbital in urine). The method is simple, sensitive and efficient, and can fit to the need of clinical and forensic toxicology.