Moving Window Partial Least Squares Regression Research Articles

Quantitation of Oxcarbazepine Clinically in Plasma Using Surfaced-Enhanced Raman Spectroscopy (SERS) Coupled with Chemometrics.

Nondestructive, sensitive, near-real-time quantitative analysis approaches are gaining popularity and attention, especially in clinical diagnosis and detection. There is a need to propose an alternative scheme using surface-enhanced Raman spectroscopy (SERS) assisted by chemometrics to improve some defects existing using other analytical instruments to meet clinical demands. In this study, clinical drug oxcarbazepine (OXC) in human blood plasma has been quantified and detected using this method. Partial least squares regression (PLSR) modeling was employed to assess the relationship between full SERS spectral data and OXC concentration. The calibration set's correlation coefficient of the model is > 0.9, the result suggests that this method is favorable and feasible. Furthermore, other multivariate calibration algorithms like Monte Carlo cross-validation (MCCV) sample set partitioning based on joint XY distances (SPXY), adaptive iteratively reweighted penalized least squares (AIR-PLS), moving window partial least squares regression (MWPLS), and leave-one-out cross-validation were used to handle these spectral data to obtain an accurate predictive model. The results achieved in this study provide a possibility and availability for us to apply SERS in combination with chemometrics to diagnosis detection.

Synergetic quantitative composition-activity relationship between the aroma components and aroma quality of 8 famous green teas

This article identifies the aroma quality of eight different types and grades of famous green teas by E-nose combined with partial least squares discriminant analysis (PLSDA). Subsequently, in order to further explain the reasons for the differences in aroma quality, the background drift correction, multi-scale Gaussian smoothing and chromatographic retention time correction methods were employed to preprocess the chromatographic fingerprint of the famous green tea collected from GC-MS technology. Then, the pre-processed fingerprint and the famous green tea aroma score were analyzed to construct spectrum-activity relationship model by moving window partial least squares regression (MWPLSR). As a result, 21 kinds of latent characteristic aroma substances were screened out. Finally, the variable weighted least squares support vector (PSO-VWLS-SVM) was used to correlate the content of 21 kinds of latent characteristic aroma substances with the score for the aroma quality of different green tea. According to the contribution rate of each characteristic aroma substance to the score, a synergistic quantitative composition-activity relationship between aroma substances and the aroma quality of the famous green tea was successfully revealed. This method provided a new strategy for screening and quantitative analysis of characteristic aroma substances , as well their synergistic relationship in aroma quality of green tea.

Determination of water activity, total soluble solids and moisture, sucrose, glucose and fructose contents in osmotically dehydrated papaya using near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is a rapid analysis method that is widely used for quantitative determination of the major constituents in many food products. NIRS was applied in conjunction with a chemometric algorithm, namely the partial least squares regression (PLSR), to develop the optimum model for predicting the qualities of osmotically dehydrated papaya (ODP). Two hundred ODP samples were collected from commercial products and from different laboratory ODP processes with varying sucrose concentrations (35ºBrix, 45ºBirx, 55ºBrix and 65ºBrix) at 40 °C for 6 h and drying times at 60 °C for 2 h, 4 h, 6 h, 8 h, 10 h and 12 h. All samples were divided into a calibration set (n = 140) and a validation set (n = 60) before quality determination and NIRS analysis. Samples were scanned over the NIR spectral range of 800–2400 nm in reflectance mode and their spectra were pretreated using the second derivative method. Suitable predictive models were developed by applying full wavelength PLSR and two wavelength interval selection methods, named the moving window partial least squares regression (MWPLSR) and the searching combination moving window partial least squares regression (SCMWPLSR). The results showed that SCMWPLSR provided better performance than PLSR and MWPLSR. The root mean square error of prediction values of water activity, moisture content, total soluble solids and the sucrose, glucose and fructose contents from SCMWPLSR were 0.014, 0.69% (dry basis), 0.58ºBrix, 14.44 g/100 g of sample, 6.72 g/100 g of sample and 4.89 g/100 g of sample, respectively, with correlation coefficients in the range 0.981–0.994.

Determination of sulfur dioxide content in osmotically dehydrated papaya and its classification by near infrared spectroscopy

Sulfur dioxide (SO2) is used as a preservative in osmotically dehydrated papaya to improve product quality and extend shelf-life. The potential of near infrared spectroscopy, as a rapid method, was investigated to determine sulfur dioxide in osmotically dehydrated papaya. Commercial and laboratory osmotically dehydrated papaya samples were selected to determine the sulfur dioxide content using the Monier–Williams method. From the total of 350 samples, subsets were selected randomly for the calibration set (n=250) and validation set (n = 100). Near infrared spectra in the region 800–2400 nm were measured on the samples of osmotically dehydrated papaya. Quantitative analyses of sulfur dioxide in the osmotically dehydrated papaya and their qualitative analyses were carried out using multivariate analysis. Before developing models, a second derivative spectral pretreatment was applied to the original spectral data. Subsequently, two wavelength interval selection methods, namely moving window partial least squares regression (MWPLSR) and searching combination moving window partial least squares (SCMWPLS), were applied to determine the suitable input wavelength variables. For quantitative analysis, three linear models (partial least squares regression, MWPLSR and SCMWPLS) and a non-linear artificial neural network model were applied to develop predictive models. The results showed that the artificial neural network model produced the best performance, with correlation coefficient (R) and root mean square error of prediction values of 0.937 and 114.53 mg SO2 kg−1, respectively. Qualitative models were developed using partial least squares-discriminant analysis and soft independent modeling of class analogy (SIMCA) for the optimized combination of informative regions of the near infrared spectra to classify osmotically dehydrated papaya into three groups based on sulfur dioxide. The SIMCA in combination with SCMWPLS model had the highest correct classification rate (96%). The study demonstrated that near infrared spectroscopy combined with SCMWPLS is a powerful procedure for both quantitative and qualitative analyses of osmotically dehydrated papaya. Therefore, it was demonstrated that near infrared spectroscopy could be effective tools for food quality and safety evaluation in food industry.

A new strategy of least absolute shrinkage and selection operator coupled with sampling error profile analysis for wavelength selection

A new strategy based on sampling error profile analysis (SEPA) combined with least absolute shrinkage and selection operator (SEPA-LASSO) was proposed. LASSO has been proven to be effective for multivariate calibration with automatic variable selection for high-dimensional data.However, in the previous research, the critical process of multivariate calibration by LASSO was an optimization of 1-norm turning parameter for a fixed sample set without considering the behaviors of variable selection by different subsets of samples. In the present work, Monte Carlo Sampling (MCS), the core of SEPA framework, is used to investigate various sub-models. Least angle regression (LAR) is used to solve LASSO, and various LAR iteration including certain number of variables could be obtained instead of choosing the numerical values of 1-norm turning parameters. SEPA-LASSO algorithm consists of plenty of loops. Under the SEPA framework and LAR algorithm, a number of LASSO sub-models with the same dimensions are built by MCS in each loop, the vote rule is used to determine the importance of variables and select them to build variable subsets. After running the loops, several subsets of variables are obtained and their error profile is used to choose the optimal subset of variables. The performance of SEPA-LASSO was evaluated by three near-infrared (NIR) datasets. The results show that the model built by SEPA-LASSO has excellent predictability and interpretability, compared with some commonly used multivariate calibration methods, such as principal component regression (PCR) and partial least squares (PLS), as well as some wavelength selection methods including LASSO, moving window partial least squares regression (MWPLSR), Monte Carlo uninformative variable elimination (MC-UVE), ordered homogeneity pursuit lasso (OHPL) and stability competitive adaptive reweighted sampling (SCARS).

High-Speed Scanning for the Quantitative Evaluation of Glycogen Concentration in Bioethanol Feedstock Synechocystis sp. PCC6803 Using a Near-Infrared Hyperspectral Imaging System with a New Near-Infrared Spectral Camera.

In the present study, the high-speed quantitative evaluation of glycogen concentration accumulated in bioethanol feedstock Synechocystis sp. PCC6803 was performed using a near-infrared (NIR) imaging system with a hyperspectral NIR spectral camera named Compovision. The NIR imaging system has a feature for high-speed and wide area monitoring and the two-dimensional scanning speed is almost 100 times faster than the general NIR imaging systems for the same pixel size. For the quantitative analysis of glycogen concentration, partial least squares regression (PLSR) and moving window PLSR (MWPLSR) were performed with the information of glycogen concentration measured by high performance liquid chromatography (HPLC) and the calibration curves for the concentration within the Synechocystis sp. PCC6803 cell were constructed. The results had high accuracy for the quantitative estimation of glycogen concentration as the best squared correlation coefficient R2 was bigger than 0.99 and a root mean square error (RMSE) was less than 2.9%. The present results proved not only the potential for the applicability of NIR spectroscopy to the high-speed quantitative evaluation of glycogen concentration in the bioethanol feedstock but also the expansivity of the NIR imaging instrument to in-line or on-line product evaluation on a factory production line of bioethanol in the future.

Baseline Correction of Diffuse Reflection Near-Infrared Spectra Using Searching Region Standard Normal Variate (SRSNV).

An alternative baseline correction method for diffuse reflection near-infrared (NIR) spectra, searching region standard normal variate (SRSNV), was proposed. Standard normal variate (SNV) is an effective pretreatment method for baseline correction of diffuse reflection NIR spectra of powder and granular samples; however, its baseline correction performance depends on the NIR region used for SNV calculation. To search for an optimal NIR region for baseline correction using SNV, SRSNV employs moving window partial least squares regression (MWPLSR), and an optimal NIR region is identified based on the root mean square error (RMSE) of cross-validation of the partial least squares regression (PLSR) models with the first latent variable (LV). The performance of SRSNV was evaluated using diffuse reflection NIR spectra of mixture samples consisting of wheat flour and granular glucose (0-100% glucose at 5% intervals). From the obtained NIR spectra of the mixture in the 10 000-4000 cm(-1) region at 4 cm intervals (1501 spectral channels), a series of spectral windows consisting of 80 spectral channels was constructed, and then SNV spectra were calculated for each spectral window. Using these SNV spectra, a series of PLSR models with the first LV for glucose concentration was built. A plot of RMSE versus the spectral window position obtained using the PLSR models revealed that the 8680–8364 cm(-1) region was optimal for baseline correction using SNV. In the SNV spectra calculated using the 8680–8364 cm(-1) region (SRSNV spectra), a remarkable relative intensity change between a band due to wheat flour at 8500 cm(-1) and that due to glucose at 8364 cm(-1) was observed owing to successful baseline correction using SNV. A PLSR model with the first LV based on the SRSNV spectra yielded a determination coefficient (R2) of 0.999 and an RMSE of 0.70%, while a PLSR model with three LVs based on SNV spectra calculated in the full spectral region gave an R2 of 0.995 and an RMSE of 2.29%. Additional evaluation of SRSNV was carried out using diffuse reflection NIR spectra of marzipan and corn samples, and PLSR models based on SRSNV spectra showed good prediction results. These evaluation results indicate that SRSNV is effective in baseline correction of diffuse reflection NIR spectra and provides regression models with good prediction accuracy.

An Objective Wavelength Selection Method Based on Moving Window Partial Least Squares for Near-Infrared Spectroscopy

An objective wavelength selection method is proposed for near-infrared (NIR) spectroscopy mainly to overcome the possible subjectivity introduced by moving window partial least squares regression (MWPLS). This improved procedure (iMWPLS) introduced an indicator to evaluate importance of each wavelength and then all wavelengths were ranked by these indicators. On the basis of the indicator ranking, a series of PLS models were constructed by starting with one wavelength and incorporating a new one until all wavelengths were involved. Finally, according to root mean square error of cross-validation (RMSECV) obtained by each model, wavelengths that constructed the optimal model were selected as informative ones while the others were discarded. Subsequently, this new objective procedure was applied to two real standard NIR datasets and the prediction performance was compared with full-spectrum PLS and the original MWPLS. Results demonstrated that iMWPLS could achieve an effective wavelength selection and improve predictive accuracy in near-infrared spectroscopy.

Quantitative Analysis of Mixtures Using Terahertz Time-Domain Spectroscopy and Different PLS Algorithms

This paper attempted the feasibility to determine component concentrations in multicomponent mixtures with terahertz time-domain spectroscopy (THz-TDS) combined with different partial least-squares regression (PLS) algorithms. First, THz absorbance spectra for 75 ternary mixtures of anhydrous theophylline, lactose monohydrate and magnesium stearate were investigated using THz-TDS in the frequency range from 0.1 to 3.0 THz, then four different PLS methods, including interval PLS (iPLS), backward interval PLS (biPLS), synergy interval PLS (siPLS) and moving window PLS (mwPLS), were employed to perform quantitative analysis of anhydrous theophylline concentrations in ternary mixtures. The performance of mwPLS model is the best in contrast to other PLS models and full spectrum PLS. The optimal model was achieved with higher correlation coefficient for calibration (RC) of 0.9842, higher correlation coefficient for prediction (RP) of 0.9851, lower root mean square error of cross-validation (RMSECV) of 3.8241, and lower root mean square error of prediction (RMSEP) of 4.1540. Experimental results demonstrate that THz spectroscopy combined with PLS algorithms could be successfully applied as an effective nondestructive tool for the quantitative analysis of component concentrations in multicomponent mixtures, and mwPLS is an ideal method for reducing the complexity and improving the performance of the model.

Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm −1) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice.

Near Infrared Quantitative Analysis of Total Curcuminoids in Rhizomes of Curcuma Longa by Moving Window Partial Least Squares Regression

The present paper reports an application of moving window partial least squares regression (MWPLSR) to determine total curcuminoid content in rhizomes of Curcuma longa by near infrared (NIR) spectroscopy. The MWPLSR method was applied to original and pretreated NIR data of Curcuma longa rhizomes to select informative regions for total curcuminoids. Afterward, partial least squares (PLS) calibration models were developed and compared for each spectral region proposed by MWPLSR and the whole spectral region. The best PLS calibration model for total curcuminoids was obtained from 2nd derivative NIR spectra in the region of 2040–2486 nm. The standard error of prediction was 1.00%w/w and the ratio of prediction to deviation was 4.9 when using seven principle components. NIR spectroscopy combined with MWPLSR can lead to better calibration models with higher performance.

Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration

By employing the simple but effective principle ‘survival of the fittest’ on which Darwin's Evolution Theory is based, a novel strategy for selecting an optimal combination of key wavelengths of multi-component spectral data, named competitive adaptive reweighted sampling (CARS), is developed. Key wavelengths are defined as the wavelengths with large absolute coefficients in a multivariate linear regression model, such as partial least squares (PLS). In the present work, the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each wavelength. Then, based on the importance level of each wavelength, CARS sequentially selects N subsets of wavelengths from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio ( e.g. 80%) of samples is first randomly selected to establish a calibration model. Next, based on the regression coefficients, a two-step procedure including exponentially decreasing function (EDF) based enforced wavelength selection and adaptive reweighted sampling (ARS) based competitive wavelength selection is adopted to select the key wavelengths. Finally, cross validation (CV) is applied to choose the subset with the lowest root mean square error of CV (RMSECV). The performance of the proposed procedure is evaluated using one simulated dataset together with one near infrared dataset of two properties. The results reveal an outstanding characteristic of CARS that it can usually locate an optimal combination of some key wavelengths which are interpretable to the chemical property of interest. Additionally, our study shows that better prediction is obtained by CARS when compared to full spectrum PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE) and moving window partial least squares regression (MWPLSR).

Determination of Glucose in Plasma by Dry Film-based Fourier Transformed-Infrared Spectroscopy Coupled with Boosting Support Vector Regression

In the present study, a dry film-based Fourier transformed-infrared (FT-IR) spectroscopic technique, coupled with boosting support vector regression (BSVR), was employed for a blood glucose assay. Potassium thiocyanate (KSCN) was taken in the dry-film method as an internal standard to compensate for any film thickness variation. This technique circumvents interference from water absorption, and requires only 5 microl of a sample. Moving window partial least-squares regression (MWPLSR) was used for wavenumber interval selection before multivariate modeling. By using the BSVR modeling technique, glucose in plasma could be determined over a 0.4 - 20 mmol/l concentration range with satisfactory accuracy. The performance of the BSVR methodology was compared with that of conventional support vector regression (SVR) as well as partial-least squares (PLS). The results demonstrated that BSVR is an effective multivariate calibration tool, providing better performance than conventional PLS and SVR.

Determination of Ethenzamide and Acetoaminophen in Quaternary Powdered Samples by near Infrared Spectroscopy and Searching Combination Moving Window Partial Least Squares Algorithm: Effects of Spectral Resolution and Signal-to-Noise Ratio

The present study reports an application of the searching combination moving window partial least squares (SCMWPLS) algorithm to the determination of ethenzamide and acetoaminophen in quaternary powdered samples by near infrared (NIR) spectroscopy. Another purpose of the study was to examine the instrumentation effects of spectral resolution and signal-to-noise ratio of the Büchi NIRLab N-200 FT-NIR spectrometer equipped with an InGaAs detector. The informative spectral intervals of NIR spectra of a series of quaternary powdered mixture samples were first located for ethenzamide and acetoaminophen by use of moving window partial least squares regression (MWPLSR). Then, these located spectral intervals were further optimised by SCMWPLS for subsequent partial least squares (PLS) model development. The improved results are attributed to both the less complex PLS models and to higher accuracy of predicted concentrations of ethenzamide and acetoaminophen in the optimised informative spectral intervals that are featured by NIR bands. At the same time, SCMWPLS is also demonstrated as a viable route for wavelength selection. The systematic comparison of different spectral resolutions and signal-to-noise ratios of NIR spectra has shown that the spectral resolution 8 cm−1 and 64 co-added scans can lead to better calibration models and satisfactory modelling performance.

Improvement of partial least squares models for in vitro and in vivo glucose quantifications by using near-infrared spectroscopy and searching combination moving window partial least squares

The aim of this study is to develop optimum partial least squares (PLS) models for glucose quantifications in bovine serum samples and human skin by using near-infrared (NIR) spectroscopy and a chemometrics algorithm named searching combination moving window partial least squares (SCMWPLS). This chemometric method allows one to search for either an optimized spectral region or an optimized combination of the informative spectral regions selected by an early proposed wavelength interval selection method called moving window partial least squares regression (MWPLSR). By using SCMWPLS, we can find the optimum spectral regions for developing efficient PLS models of glucose in the bovine serum samples and the human skin. In the present study, we evaluate the performance of SCMWPLS with the two different NIR data sets, the in vitro NIR spectra of bovine serum samples and the in vivo NIR spectra of human skin.

Optimization of informative spectral regions for the quantification of cholesterol, glucose and urea in control serum solutions using searching combination moving window partial least squares regression method with near infrared spectroscopy

Near infrared (NIR) spectra in the 10 000–4000 cm − 1 region were collected from control serum solutions mixed with cholesterol, glucose and urea solutions with various concentrations. The concentration ranges of cholesterol, glucose and urea were 46.08–395.39, 29.89–318.57 and 5.92–19.66 mg/dl, respectively, which cover the clinically important ranges. Moving window partial least squares regression (MWPLSR) method was applied to the NIR data to select informative regions for cholesterol, glucose and urea. Searching combination moving window partial least squares regression (SCMWPLSR) method was used to search for the optimized combinations of informative regions found by MWPLSR, and partial least squares calibration models were developed and compared for each spectral region proposed by MWPLSR and SCMWPLSR and whole region. The best PLS calibration model for each component was obtained from the spectral region optimized by SCMWPLSR. The best prediction results for cholesterol, glucose and urea have RMSEP of 6.68, 10.35 and 1.28 mg/dl, respectively. Of particular note is that the prediction result for urea is much better than that previously obtained by several research groups under similar conditions. SCMWPLSR can select the optimized combination spectral region for each blood component successfully within the complex blood matrix over the low concentration range.

Determination of total nitrogen content, pH, density, refractive index, and brix in Thai fish sauces and their classification by near-infrared spectroscopy with searching combination moving window partial least squares

Near-infrared (NIR) transflectance spectra in the region of 1100-2500 nm were measured for 100 Thai fish sauces. Quantitative analyses of total nitrogen (TN) content, pH, refractive index, density and brix in the Thai fish sauces and their qualitative analyses were carried out by multivariate analyses with the aid of wavelength interval selection method named searching combination moving window partial least squares (SCMWPLS). The optimized informative region for TN selected by SCMWPLS was the region of 2264-2428 nm. A PLS calibration model, which used this region, yielded the lowest root mean square error of prediction (RMSEP) of 0.100% w/v for the PLS factor of 5. This prediction result is significantly better than those obtained by using the whole spectral region or informative regions selected by moving window partial least squares regression (MWPLSR). As for pH, density, refractive index and brix, the 1698-1722, and 2222-2258 nm regions, the 1358-1438 nm region, the 1774-1846, and 2078-2114 nm regions, and the 1322-1442, and 2000-2076 nm regions were selected by SCMWPLS as the optimized regions. The best prediction results were always obtained by use of the optimized regions selected by SCMWPLS. The lowest RMSEP for pH, density, refractive index and brix were 0.170, 0.007 g cm(-3), 0.0079 and 0.435 degrees Brix, respectively. Qualitative models were developed by using four supervised pattern recognitions, linear discriminant analysis (LDA), factor analysis-linear discriminant analysis (FA-LDA), soft independent modeling of class analog (SIMCA), and K neareat neighbors (KNN) for the optimized combination of informative regions of the NIR spectra of fish sauces to classify fish sauces into three groups based on TN. All the developed models can potentially classify the fish sauces with the correct classification rate of more than 82%, and the KNN classified model has the highest correct classification rate (95%). The present study has demonstrated that NIR spectroscopy combined with SCMWPLS is powerful for both the quantitative and qualitative analyses of Thai fish sauces.

Selective removal of interference signals for near-infrared spectra of biomedical samples by using region orthogonal signal correction

New approach for chemometrics algorithm named region orthogonal signal correction (ROSC) has been introduced to improve the predictive ability of PLS models for biomedical components in blood serum developed from their NIR spectra in the 1280–1849 nm region. Firstly, a moving window partial least squares regression (MWPLSR) method was employed to locate the region due to water as a region of interference signals and to find the informative regions of glucose, albumin, cholesterol and triglyceride from NIR spectra of bovine serum samples. Next, a novel chemometrics method named searching combination moving window partial least squares (SCMWPLS) was used to optimize those informative regions. Then, the specific regions that contained the information of water, glucose, albumin, cholesterol and triglyceride were obtained. When an interested component in the bovine serum solution, such as glucose, albumin, cholesterol or triglyceride is being an analyte, the other three interests and water are considered as the interference factors. Thus, new approach for ROSC has employed for each specific region of interference signal to calculate the orthogonal components to the concentrations of analyte that were removed specifically from the NIR spectra of bovine serum in the region of 1280–1849 nm and the highest interference signal for model of analyte will be revealed. The comparison of PLS results for glucose, albumin, cholesterol and triglyceride built by using the whole region of original spectra and those developed by using the optimized regions suggested by SCMWPLS of original spectra, spectra treated OSC for orthogonal components of 1–3 and spectra treated ROSC using selected removing the highest interference signals from the spectra for orthogonal components of 1–3 are reported. It has been found that new approach of ROSC to remove the highest interference signal located by SCMWPLS improves of the performance of PLS modeling, yielding the lower RMSECV and smaller number of PLS factors.

Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares

Near-infrared (NIR) spectra in the region of 5000–4000 cm −1 with a chemometric method called searching combination moving window partial least squares (SCMWPLS) were employed to determine the concentrations of human serum albumin (HSA), γ-globulin, and glucose contained in the control serum IIB (CS IIB) solutions with various concentrations. SCMWPLS is proposed to search for the optimized combinations of informative regions, which are spectral intervals, considered containing useful information for building partial least squares (PLS) models. The informative regions can easily be found by moving window partial least squares regression (MWPLSR) method. PLS calibration models using the regions obtained by SCMWPLS were developed for HSA, γ-globulin, and glucose. These models showed good prediction with the smallest root mean square error of predictions (RMSEP), the relatively small number of PLS factors, and the highest correlation coefficients among the results achieved by using whole region and MWPLSR methods. The RMSEP values of HSA, γ-globulin, and glucose yielded by SCMWPLS were 0.0303, 0.0327, and 0.0195 g/dl, respectively. These results prove that SCMWPLS can be successfully applied to determine simultaneously the concentrations of HSA, γ-globulin, and glucose in complicated biological fluids such as CS IIB solutions by using NIR spectroscopy.

Simultaneous determination of human serum albumin, gamma-globulin, and glucose in a phosphate buffer solution by near-infrared spectroscopy with moving window partial least-squares regression.

Near-infrared (NIR) spectra in the 12,000-4,000 cm(-1) region were measured for phosphate buffer solutions containing human serum albumin (HSA), gamma-globulin, and glucose with various concentrations at 37 degrees C. Five levels of full factorial design were used to prepare a sample set consisting of 125 samples of three component mixtures. The concentration ranges of HSA, gamma-globulin and glucose were 0.00-6.00 g dl(-1), 0.00-4.00 g dl(-1) and 0.00-2.00 g dl(-1), respectively. The 125 sample data were split into two sets, the calibration set with 95 data and the prediction set with 30 data. The most informative spectral ranges of 4648-4323, 4647-4255 and 4912-4304 cm(-1) were selected by moving window partial least-squares regression (MWPLSR) for HSA, [gamma]-globulin, and glucose in the mixtures, respectively. For HSA, the correlation coefficient (R) of 0.9998 and the root mean square error of prediction (RMSEP) of 0.0289 g dl(-1) were obtained. For [gamma]-globulin, R of 0.9997 and RMSEP of 0.0252 g dl(-1) were obtained. The corresponding statistic values of glucose were 0.9997 and 0.0156 g dl(-1), respectively. These statistical values obtained by MWPLSR are highly significant and better than those calculated by using the regions reported in the literature. The results presented here show that MWPLSR can select the informative regions with a simple procedure and increase the power of NIR spectroscopy for simultaneous determination of the concentrations of HSA, [gamma]-globulin and glucose in the mixture systems.