On multivariate calibration with unlabeled data

The multivariate methods, principal component regression (PCR) and partial least squares (PLS) were tested as a calibration procedure for simultaneous ultraviolet spectrophotometric determination of acetaminophen (AC) and ascorbic acid (AA). Determination of these compounds is important because of their pharmacotherapeutic advantages. Due to spectral overlapping of AC and AA, PCR and PLS were used for construction of the calibration sets. The concentration linear range of AC and AA were 1.5-24.2 and 1.8-21.1 microg mL(-1) respectively. The absorption spectra were recorded from 215-310 nm. The minimum root mean square error of prediction (RMSEP) was 1.3507 and 0.4088 for AC and AA, by PLS, 0.7525 and 0.4015 by PCR in original data and 0.9454 and 0.2875, by PLS and 1.0386 and 0.4000 by PCR in derivative data. The procedure allows the simultaneous determination of AC and AA in synthetic mixtures and real sample solutions made up from pharmaceutical products, human serum and urine.

This study aims to estimate simultaneously metformin hydrochloride (MET) and glyburide (GLY) in a multicomponent tablets dosage form by spectrophotometric method using chemometric approaches such as principal component regression (PCR) and partial least-squares regression (PLS). Because of highly overlapped in UV spectra and difference proportions of two active ingredients, the conventional univariate calibration methods was not allowed without previous separation. The linearity ranges used to construct the calibration matrix were selected in the ranges from 40.00 to 200.00 mg L-1for MET and from 1.00 to 10.00 mg L-1for GLY. The absorbances were measured in the wavelength range of 200-400 nm, using ethanol as solvent. The resulting UV spectra were subjected to PCR and PLS algorithms and the optimum numbers of principal components (PCs) were selected according to prediction residual error sum of squares (PRESS) values of leave-one out cross-validation. The number of PCs for MET and GLY were found to be 5, 3 by PCR and 5, 3 by PLS, respectively. A set of synthetic mixtures was employed to verify the models and the performance of the models were shown in the values of the root mean square error in prediction (RMSEP). RMSEP values of MET and GLY were 1.806, 0.256 for PCR and 1.802, 0.185 for PLS, respectively. The suitable calibration models were applied to the analysis of these compounds in pharmaceutical formulation.

The objective of this study was to compare the predictions of the protein contents and hardness values by partial least squares regression (PLSR) and principal components regression (PCR) models for bulk samples of Canadian wheat, which were obtained from different locations and crop years. Wheat samples of Canada Western Red Spring (CWRS), Canada Western Hard White Spring (CWHWS), Canada Western Soft White Spring (CWSWS), and Canada Prairie Spring Red (CPSR) classes were obtained from nearby agricultural farms in the main wheat growing locations in the Provinces of Alberta, Saskatchewan, and Manitoba from 2007, 2008, and 2009 crop years. Wheat samples were conditioned to moisture levels of 13, 16, and 19 % (wet basis) and pooled together for developing the regression models. A database of the near-infrared (NIR) hyperspectral image cubes of bulk samples of wheat classes was created in the wavelength region of 960–1,700 nm with 10 nm intervals. Reference protein contents and hardness values were determined using the Dumatherm method and single kernel characterization system (SKCS), respectively. A tenfold cross-validation was used for the ten-factor partial least squares regression (PLSR) and principal components regression (PCR) models for prediction purposes. Prediction performances of regression models were assessed by calculating the estimated mean square errors of prediction (MSEP), standard error of cross-validation (SECV), and correlation coefficient (r). Using the full data set in the protein prediction study, the ten-component PLSR model gave 1.76, 1.33, and 0.68 for the estimated MSEP, SECV, and r, respectively, which were better than the results for the ten-component PCR model (2.02, 1.42, and 0.62, respectively). For the hardness prediction, the estimated MSEP, SECV, and r values were 147.7, 12.15, and 0.82, respectively, for the ten-component PLSR model using the full data set. The PLSR models prediction performances outperformed the PCR models for predicting protein contents and hardness of wheat.

The utilization of selection methods such as genetic algorithm (GA) aims to construct better partial least squares (PLS) and principal component regression (PCR) models than those established from the full-spectrum range. Determination of paracetamol (PAR), orphenadrine citrate (Or.cit), and caffeine (CAF) in the presence of PAR nephrotoxic impurity [p-aminophenol (PAP)]. GA was applied to select the optimum wavelengths used. A calibration set was prepared in which the three drugs, together with PAP, were modeled by multilevel multifactor design. This calibration set was used to build the PLS and PCR models, either with or without preprocessing the data using GA. Results were compared with and without preprocessing, and this revealed that GA can find an optimized combination of spectral wavelengths, yielding a lower root mean square error of prediction as well as a lower number of latent variables used. The results of the two models show that simultaneous determination of the aforementioned drugs can be performed in the concentration ranges of 20-60, 3-11, and 1-9 μg/mL for PAR, Or.cit, and CAF, respectively. The proposed models were applied for the determination of the three drugs in their pharmaceutical formulations, and the results were verified by the standard addition technique. GA can be useful as a wavelength selection tool before applying multivariate PLS and PCR methods. GA gives an improvement in the predictive ability of the models with lower RMSEP and less number of latent variables (LVs). The proposed PLS, PCR, GA-PLS, and GA-PCR spectrophotometric methods were able to determine paracetamol, orphenadrine citrate, and caffeine in the presence of p-aminophenol and severe spectral overlapping.

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

Objective: The aims of this research were to analyse the fatty acids contained in Patin (Pangasius micronemus) and Gabus (Channa striata) fish oils also its authentication using FTIR spectroscopy combined with chemometrics.
 Methods: Patin fish oil (PFO) was extracted from patin flesh using the maceration method with petroleum benzene as the solvent, while gabus fish oil (GFO) was purchased from the market in Yogyakarta. The analysis of fatty acid was done using gas chromatography–flame ionization detector (GC-FID). The authentication was performed using FTIR spectrophotometer and chemometrics methods. Principal component analysis (PCA) was used to determine the proximity of oils based on the characteristic similarity. The quantification of adulterated PFO was performed using multivariate calibrations, partial least square (PLS) and principal component regression (PCR). The classification between authentic oils and those adulterated used discriminant analysis (DA).
 Results: The level of saturated and polyunsaturated fatty acids in PFO is higher than in GFO. The PLS and PCR methods using the second derivative spectra at wavenumbers of 666–3050 cm-1 offered the highest values of coefficient of determination (R2) and lowest root means the square error of calibration (RMSEC) and root mean square error of prediction (RMSEP).
 Conclusion: The PCA method was successfully used to determine the proximity of oils. Among oils studied, PFO has a similarity fatty acid composition with GFO. The DA method was able to screen pure PFO from adulterated PFO without any misclassification reported. FTIR spectroscopy in combined with chemometrics can be used for authentication and quantification.

Recycled polypropylene (rPP) often contains a small amount of polyethylene (PE). Since polypropylene (PP) and PE are incompatible, the presence of PE compromises the performance of rPP materials and needs to be closely monitored. In our previous work, Raman and near-infrared (NIR) spectrometries were evaluated to monitor PE content in rPP with partial least square regression (PLSR) modeling. The NIR spectrometry exhibited a wider application range, but the accuracy of the prediction models might be further improved. In the current work, a different modeling method, principal component regression (PCR) was employed to analyze PE content in rPP with NIR spectrometry. Spectrum pretreatment methods, including multivariate scatter correction (MSC), standard normal variate transformation (SNV), smoothing, and first derivative, were investigated to improve the NIR spectrum quality. Forward and backward interval methods were used to optimize spectral range selection. The outcomes were compared with our previous PLSR modeling results. The highest accuracy in independent validation was achieved by a PCR model with an R 2 of 0.9991 and a root-mean-square error of prediction (RMSEP) of 0.1596 PE%. On the other hand, a PLSR model achieved the lowest RMSEP of 0.9712 PE% for a non-colored post-consumer rPP sample. The PCR models might be sensitive to interference and more suitable for post-industrial materials, which have a simpler chemical composition. The PLSR models might have better stability and be more suitable for complicated post-consumer samples. Both the PCR and PLSR models were successfully applied to a gray commercial rPP sample.

Development of novel univariate and multivariate validated chemometric methods for the analysis of dasatinib, sorafenib, and vandetanib in pure form, dosage forms and biological fluids

BackgroundMultivariate calibration by Partial Least Squares (PLS) on near-infrared data has been applied successfully in several industrial sectors, including pulp and paper. The creation of multivariate calibration models relies on a set of well-characterised samples that cover the range of the intended application. However, sample sets that originate from an industrial process often show an uneven distribution of reference values. This can be addressed by curation of the reference data and the methodology for multivariate calibration. It needs to be better understood, how these approaches affect the quality and scope of the final model. ResultsWe describe the effect of log10 transformation of the reference values, regular PLS, robust PLS, the newly introduced bin PLS, and their combinations to select more evenly distributed reference values for the quantification of five pulp characteristics (kappa number, R18, R10, cuen viscosity, and brightness; 200 samples) by near-infrared spectroscopy. The quality of the models was assessed by root mean squared error of prediction, calibration range, and coverage of sample types. The best models yielded uncertainty levels equivalent to that of the reference measurement. The optimal approach depended on the investigated reference value. SignificanceRobust PLS commonly gives the model with the lowest error, but this usually comes at the cost of a notably reduced calibration range. The other approaches rarely impacted the calibration range. None of them stood out as superior; their performance depended on the calibrated parameter. It is therefore worthwhile to investigate various calibration options to obtain a model that matches the requirements of the application without compromising calibration range and sample coverage.

In this study, a model for prediction of lignocellulose components of agricultural residues has been developed with Fourier Transformed Near Infrared (FT-NIR) spectroscopy data. Two calibration techniques (Principal Component Regression (PCR) and Partial Least Square Regression (PLSR)) were assessed for prediction of lignin, holocellulose, α-cellulose, pentosan and ash, and found the PLSR better for lignin, holocellulose and α-cellulose. The PCR also produced better results for quantification of pentosan and ash. Spectral range (7000–5000 cm–1) showed more informative than other parts of the spectral data. The PLSR showed maximum value of R2 (R2 = 0.91%) for prediction of holocellulose. For the prediction of pentosan, the PCR was better (R2 = 0.68%). The PCR also showed better results (R2 = 86%) for quantification of ash. To determine amount of lignin, the PLSR was the best (R2 = 0.83%) when the spectral data were de-trained and smoothed with Savitzky-Golay (S-G) filtering simultaneously. For prediction of α-cellulose, the PLSR was the best model (R2 = 0.94%) when the data were pretreated with mean normalization. Considering the best alternatives inNear Infrared (NIR) data preprocessing and calibration techniques, methods for quantification of lignocellulose components of agricultural residues have been developed which is rapid, cost effective, and less chemical intensive and easily usable in pulp and paper industries and pulp testing laboratories.

A comparison of two multivariate calibration methods, partial least squares (PLS) and principal component regression (PCR), applied to high-performance liquid chromatography (HPLC) data, is presented for the resolution of a pesticide mixture. The data set showed both coeluted peaks and overlapped absorption spectra. Besides, there is an additional overlapping between the signal of the mobile phase and that of some pesticide. Multivariate calibration models were evaluated using different criteria to choose the optimum number of latent variables. It is shown that PLS yields the best predictive models. Furthermore, two methods for selecting regions were applied with the goal to achieve an improved prediction ability in the present multicomponent determination by HPLC-DAD (diode array detector) with PLS. The selection of regions associated with a large correlation to the concentration and with large values in loading-weighs (from PLS) were considered. It is concluded that feature selection can also improve the multivariate calibration results using chromatographic data.

In order to determine the proper multivariate calibration model, it is necessary to select the number of respective basis vectors (latent vectors, factors, etc.) when using principal component regression (PCR) or partial least squares (PLS). These values are commonly referred to as the prediction rank of the model. Comparisons between PCR and PLS models for a given data set are often made with the prediction rank to determine the more parsimonious model, ignoring the fact that the values have been obtained using different basis sets. Additionally, it is not possible to use this approach for determining the prediction rank of models generated by other modeling methods such as ridge regression (RR). This paper presents measures of effective rank for a given model that can be applied to all modeling methods, thereby providing inter‐model comparisons. A definition based on the regression vector norm and is compared with two alternative forms from the literature. With a proper definition of effective rank, a better assessment of degrees of freedom for statistical computations is possible. Additionally, the true nature of variable selection for improved parsimony can be properly assessed. Spectroscopic data sets are used as examples with PCR, PLS and RR. Copyright © 2004 John Wiley & Sons, Ltd.

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.

Weather index insurance is a relatively new alternative to traditional agricultural insurance such as individual yield-based crop insurance. It is still mostly at the experimental stage, rather than in widespread use like traditional crop insurance. A major challenge for weather insurance is basis risk, where the loss estimated by the index differs from the actual loss, and this is generally believed to be the main limitation in the use of weather index insurance for crops. Variable basis risk is an important type of basis risk that occurs when there are incorrect variables or missing variables for the design of the weather index. In agriculture, there is a relatively small sample size of yields, and therefore, as the number of considered weather variables increases, the problems of limited degrees of freedom for predictive models must be overcome. The objective of this article is to demonstrate two possible approaches that could be used to construct a multivariable weather index to reduce variable basis risk. Forage insurance is used as an example, and a main focus of the research is on reducing the dimensionality of the predictive model and resolving the problem of multicollinearity among weather variables. The research uses daily weather information and county-level forage yield data from Ontario, Canada. Two multivariable indices are developed based on principal component regression (PCR) and partial least squares regression (PLSR) methods, and they are evaluated against a single-variable benchmark index based on cumulative precipitation (SVCP) using several basis risk metrics. The results show that both the PCR and PLSR models are superior compared to the single-variable weather index based on cumulative rainfall (SVCR) index and can be used to achieve the objective of reducing the dimensionality of the weather variable matrix and addressing the issue of multicollinearity. Though the PLSR indices perform better than the PCR and SVCR indices in terms of average value of basis risk (),(rom) the PCR method produces a smaller percentage of mismatch, suggesting that the PCR method may be superior in correctly detecting when the insurance payment should be triggered. The methods demonstrated in this article will assist in the development of weather index–based crop insurance.

This paper investigated the feasibility of Fourier transform infrared transmission (FT-IR) spectroscopy to detect talcum powder illegally added in tea based on chemometric methods. Firstly, 210 samples of tea powder with 13 dose levels of talcum powder were prepared for FT-IR spectra acquirement. In order to highlight the slight variations in FT-IR spectra, smoothing, normalize and standard normal variate (SNV) were employed to preprocess the raw spectra. Among them, SNV preprocessing had the best performance with high correlation of prediction (RP = 0.948) and low root mean square error of prediction (RMSEP = 0.108) of partial least squares (PLS) model. Then 18 characteristic wavenumbers were selected based on a hybrid of backward interval partial least squares (biPLS) regression, competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA). These characteristic wavenumbers only accounted for 0.64% of the full wavenumbers. Following that, 18 characteristic wavenumbers were used to build linear and nonlinear determination models by PLS regression and extreme learning machine (ELM), respectively. The optimal model with RP = 0.963 and RMSEP = 0.137 was achieved by ELM algorithm. These results demonstrated that FT-IR spectroscopy with chemometrics could be used successfully to detect talcum powder in tea.