Multivariate Calibration Methods Research Articles

Application assessment of UV–vis and NIR spectroscopy for the quantification of fuel dilution problems on used engine oils

Fuel dilution in engine oil is a frequent problem in internal combustion engines (ICE); its main consequence is the reduction of the oil viscosity, decreasing lubrication film strength, and causing a negative impact on friction and wear. The standard and more precise methods for assessing fuel content in oil are based on chromatographic analysis (e.g., ASTM D3524, ASTM D7593), requiring high-cost equipment and highly qualified personnel. This work performed a study to propose an alternative method for quantifying diesel fuel dilution in used engine oil by UV–vis and NIR spectroscopy. The samples for the study were prepared from used oil from six different vehicles with various mileages. According to the results obtained in this study, NIR spectroscopy proved to be the most suitable method for the quantification of diesel fuel in used engine oils. Furthermore, the use of NIR spectroscopy combined with multivariate calibration methods could predict the fuel concentration of the samples used for validating the model. The best predictive model for the quantification was obtained by Partial Least Squares Regression, which achieved a Root Mean Squared Error of prediction of 0.436% and a coefficient of determination of 0.9435. In comparison, the parameters for Principal Component Regression were 1.049% and 0.8441, respectively.

Effect of variable selection and rapid determination of total tea polyphenols contents in Fuzhuan tea by near-infrared spectroscopy

ABSTRACT This study attempted to measure the total polyphenols contents in Fuzhuan tea by near-infrared (NIR) spectroscopy coupled with an appropriate multivariate calibration method. Partial least squares (PLS), synergy interval PLS (si-PLS), and genetic algorithm-based PLS (ga-PLS) were carried out comparatively to calibrate regression models. The root mean square error of prediction (RMSEP), determination coefficient (Rp2), and P-value between the true and predicted values of prediction set were used to evaluate the performance of the final model. The ga-PLS model showed the best performance compared with the PLS and si-PLS models. The optimal model obtained Rp2 = 0.9996 and RMSEP = 0.0488 for the prediction set using only 37 spectral data points. No significant difference was observed between the true and predicted tea polyphenol contents in the prediction set (P > 0.05). NIR spectroscopy together with the ga-PLS algorithm can be used to rapidly predict the total polyphenol contents in Fuzhuan tea.

A novel amperometric biosensor for multi-enzymatic biosensing of triglycerides

This work has been focused on developing a novel biosensor assisted by multivariate calibration methods to determine triglycerides (TGs, triacetin, tributyrin, tricaproin, tricaprylin, and tricaprin) in lyophilized serum samples. To achieve this goal, a bare glassy carbon electrode (GCE) was modified and used as the biosensing platform. To increase the sensitivity of the developed method, hydrodynamic methods were used to calibrate the biosensor response. To increase the selectivity of the developed biosensor, it was assisted by partial least squares-1 (PLS-1), radial basis function-PLS (RBF-PLS), and RBF-artificial neural network (RBF-ANN) for exploiting first-order advantage. After characterization of the modifications, the first-order advantage was exploited to increase the selectivity of the method by building a multivariate calibration set in a pre-analyzed lyophilized serum with different TGs concentrations, which were chosen according to the individual calibration curve. Calibration models were then built in the same pre-analyzed lyophilized serum and analyzed by PLS-1, RBF-PLS, and RBF-ANN. Therefore, their performances were examined to predict concentrations of a validation set. The results confirmed the successfulness of the calibration model developed by RBF-ANN. Finally, it was used to analyze two serum samples, and the results demonstrated that the method was successful because its results were compared with a reference method.

Application of net analyte signal and principal component regression for rapid simultaneous determination of Levodopa and carbidopa in commercial pharmaceutical formulation and breast (human) milk sample using spectrophotometric method

In this study, a UV–vis spectrophotometric method coupled with net analyte signal (NAS) and principal component regression (PCR) as multivariate calibration methods were used for the simultaneous determination of levodopa (LEV) and carbidopa (CBD) in prepared mixtures, pharmaceutical formulation, and breast milk sample. The mean recovery of the NAS model was 98.10% and 99.60% for LEV and CBD, respectively. Also, the relative standard deviation (RSD%) values were found to be lower than 5.5% and 4% for LEV and CBD, respectively. On the other hand, the mean recovery of LEV and CBD related to the PCR method was obtained at 96.86% and 92.43%, respectively. K-Fold cross-validation was used to estimate the number of components, which was 7 and 3 with a mean square error prediction (MSEP) of 1.50 and 7.14 for LEV and CBD, respectively. The results revealed that the NAS model was better than the PCR model. Additionally, the proposed NAS-based calibration method was successfully developed for the simultaneous analyses of LEV and CBD in a commercial tablet and breast milk.

Simultaneous estimation of rates of DNA damage induced by three important chemotherapy drugs by a novel electrochemical biosensor assisted by chemometric multivariate calibration methods

In this work, a novel electrochemical biosensor assisted by multivariate calibration methods was developed for simultaneous estimation of rates of DNA damage induced by doxorubicin (DX), daunorubicin (DR) and idarubicin (ID), and also to simultaneous determination of the drugs. A glassy carbon electrode was efficiently modified and used as the biosensing platform. Binding and interactions of DX, DR and ID with DNA were modeled by molecular docking methods, and theoretical information was completed by experimental results. The methylene blue was able to intercalate within the DNA structure and by incubation of the biosensor with DX or DR or ID, the methylene blue was replaced by drug and therefore, the voltammetric signal of the biosensor was changed due to the exposed DNA and repelling the electrochemical probe molecules carrying negative charge. The DNA damage induced by each drug was individually monitored by differential pulse voltammetry and then, rates of DNA damage were calibrated and validated by mixture design and multivariate calibration methods. The developed multivariate calibration model constructed based on vectorization of the data was able to simultaneous detection of the rates of DNA damage induced by all the three drugs. The change in the biosensor response in the presence of the drugs was also modeled by multivariate calibration methods to simultaneous determination of the drugs.

Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network

pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectroscopy. Two groups of Vis-NIR spectral analysis experiments of water pH detection were employed to evaluate the performance of 1D-CNN. Two conventional multivariate regression calibration methods, including partial least squares (PLS) and least squares support vector machine (LS-SVM), were introduced for comparative analysis with 1D-CNN. The successive projections algorithm (SPA) was adopted to select the feature variables. In addition, the learning mechanism of 1D-CNN was interpreted through visual feature maps by convolutional layers. The results showed that the 1D-CNN models obtained the highest prediction accuracy based on full spectra for the two experiments. For the spectrophotometer experiment, the root mean square error of prediction (RMSEP) was 0.7925, and the determination coefficient of prediction () was 0.8515. For the grating spectrograph experiment, the RMSEP was 0.5128 and the was 0.9273. The convolutional layers could automatically preprocess the spectra and effectively extract the spectra features. Compared with the traditional regression methods, 1D-CNN does not need complex spectra pretreatment and variable selection. Therefore, 1D-CNN is a promising regression approach, with higher prediction accuracy and better modeling convenience for rapid water pH detection using Vis-NIR spectroscopy.

Chemometric determination of common cold infection drugs in human urine

Abstract In this work, spectrophotometric identification of acetylsalicylic acid (ASA), paracetamol (PCM), and caffeine (CAF) (common cold infection drugs) in human urine samples was investigated. For ASA, PCM, and CAF, chemometric analysis of human urine samples has proved successful. Spectrophotometric analysis of common cold infection drugs was performed using multivariate calibration methods (principal component regression [PCR] and partial least-squares regression). For the simultaneous prediction of common cold infection drugs in prepared mixes and human urine samples without prior separation, two spectrophotometric-chemometric approaches were proposed. The synthetic mixes were made with common cold infection drugs in the first stage, and the absorbance values were obtained using spectrophotometry. The quantities of common cold infection drugs in the human urine sample were calculated in the second stage. The calibration curves for each medication are linear in the concentration range of the synthetic mixes. The two methods were tested for accuracy and repeatability, and high recoveries and low standard deviations were calculated. sum of prediction residual errors, observation limit, and detection limit, and % recovery values, which are the analytical properties of the proposed methods, were 0.00029, 0.096, and 0.290, respectively; 0.0069, 0.086, and 0.260; 0.0077, 0.094, and 0.285; 0.0049, 0.066, and 0.199 for PCM, ASA, and CAF for the principal component regression method, respectively; 0.0059, 0.066, and 0.199; 0.0065, 0.069, and 0.210. The results produced using the employed chemometric methods are quick, easy, and consistent. The proposed methods are extremely sensitive and precise and have thus been effectively employed to detect active chemicals (ASA, PCM, and CAF) in human urine samples.

Chemometric Tools in the Analysis of Pharmaceutics Samples: a Comparison Among Several Multivariate Calibration Methods

Bivariate calibration algorithm is compared with the results obtained by the usage of high-dimensional calibration methods such as partial least squares (PLS) and multi-way partial least-squares (N-PLS) by using UV-Vis spectrophotometric data of first and second-order. The algorithms were applied to the determination of a mixture of an analgesic and a stimulant compound and their actual concentrations of them were calculated by using spectroscopic data. The direct reading of absorbance values at 227 nm and 271 nm were employed for quantification of the compounds in the case of the bivariate method. The approaches of first-order and multi-way methods were applied with a previous optimization of the calibration matrix by constructing sets of calibration and validation with 20 and 10 samples (mixtures) respectively according to a central composite design and their UV absorption spectra were recorded at 200-350 nm. All algorithms were satisfactorily applied to the simultaneous determination of these compounds in pharmaceutical formulations with mean percentage recovery of 100.5 ± 3.67, 98.7 ± 3.42, and 100.5 ± 3.74 for bivariate, PLS-1, and N-PLS, respectively. The statistical evaluation of the bivariate method showed that this procedure is comparable with those algorithms that employ high-dimensional structured information. The aim of the work is to compare the methods under study and it can be seen that there are no significant differences, so a simple spectrophotometer can be used up to a very specialized one. However, the advantage of bivariate calibration is its simplicity, due to the minimal experimental manipulation.

Simultaneous Determination of Adulterants in Dietary Food Supplements Using Multivariate Data Analysis after Preconcentration with Novel Nanosorbents and Chromatographic Measurement.

The increasing popularity of dietary supplements and, consequently, related adulteration emphasizes the rising need to examine the association of food supplements with fraud. Intentional or unintentional fraud in food supplements by hazardous chemicals compounds is a problem that many countries are struggling with. Much effort have been made to effectively and reliably control the quality of food supplements. Due to the importance of the subject, an analytical method for the simultaneous and reliable detection and quantitative determination of three key adulterants in dietary food supplements was developed. The proposed method benefits from analytical methods and multivariate calibration methods to progress the determination of adulterants in a complex matrix. HPLC assisted by multivariate curve resolution-alternating least square (MCR-ALS) analysis was used to detect adulterants in real samples after separation and preconcentration using novel mesoporous carbon nanoparticles. Solid-phase extraction (SPE) optimization was accomplished by central composite design (CCD). In order to obtain the best results, the MCR-ALS model was compared with the parallel factor analysis 2 (PARAFAC2) model and validated by estimation of linearity, detection limits, and recovery. The detection limits and linear dynamics were calculated as 1.5, 4.27, and 4.77 µg/mL, and 1-50, 5-20, and 5-20 µg/mL for caffeine, ephedrine, and fluoxetine, respectively. Mean recovery for determination of caffeine, ephedrine, and fluoxetine using the developed method was reported as 101.75, 91.7, and 92.36, respectively. The results showed that to avoid negative health outcomes associated with the excessive consumption of adulterated food supplements releasing such products should be carefully regulated. The developed method was validated using statistical factors and showed acceptable and reliable results. (1) The application of MCR-ALS coupled with HPLC-Diode-Array Detection data sets allowed the simultaneous identification and quantification of three key adulterants (caffeine, ephedrine, and fluoxetine) in dietary food supplements. (2) A small amount of the novel adsorbent was successfully used to preconcentrate the trace amounts of adulterants in samples. (3) This method benefits from the chemometrics tools and experimental design to significantly reduce the use of toxic solvents and complicated instruments to propose a less time-consuming method for quantification of multicomponents in the presence of uncalibrated interferents.

Fast and non-destructive determination of histamine in tuna fish by ATR-FTIR spectroscopy combined with PLS calibration method

Herein, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy as a non-destructive, rapid and robust method (Mid, 600–4000 cm−1) has been examined for the determination of Histamine in tuna fish. For this purpose, a partial least squares regression (PLS-R) with two wavelength selection methods (IPLS and GA) was applied as a multivariate calibration method and the calibration model was developed by cross validation method. After the evaluation of the model on the unknown sample, the results showed that root mean square error of cross validation (RMSECV = 1.10) and root mean square error of prediction (RMSEP = 1.65) were smaller for GA-PLS and correlation coefficient (Rpred2 = 0.99) were higher than the i-PLS (RMSECV = 3.16, RMSEP = 3.80,Rpred2 = 0.96). The lower quantification limit was obtained, 4.68 ppm for GA-PLS in a concentration range from 5 to 100 ppm. Hence the results illustrate that ATR/FTIR spectroscopy with PLS can be a feasible method for quick and reliable quantitative analysis of Histamine in the fish products industry.

Genetic algorithm based artificial neural network and partial least squares regression methods to predict of breakdown voltage for transformer oils samples in power industry using ATR-FTIR spectroscopy

The current study proposes a novel analytical method for calculating the breakdown voltage (BV) of transformer oil samples considered as a significant method to assess the safe operation of power industry.Transformer oil samples can be analyzed using the Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with multivariate calibration methods. The partial least squares regression (PLSR) back propagation–artificial neural network (BP-ANN) methods and a genetic algorithm (GA) for variable selection are used to predict and assess breakdown voltage in transformer oil samples from various Iranian transformer oils.As a result, the root mean square error (RMSE) and correlation coefficient for the training and test sets of oil samples are also calculated. In the GA-PLS-R method, the squared correlation coefficient (R2pred) and root mean square prediction error (RMSEP) are 0.9437 and 2.6835, respectively. GA-BP-ANN, on the other hand, had a lower RMSEP value (0.2874) and a higher R2pred function (0.9891).Considering the complexity of transformer oil samples, the performance of GA-BP-ANN has resulted in an efficient approach for predicting breakdown voltage; consequently, it can be effectively used as a new method for quantitative breakdown voltage analysis of samples to evaluate the health of transformer oil..

Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy

Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (Rp2 = 0.870; RMSEP = 13 Kg/m3; RPDp = 2.774).

Performance of three handheld NIR spectrometers for predicting grass silage quality

Description of the subject. Feed is the main variable cost in dairy farming. More efficient use of forage resources is one way to reduce production costs. Improving forage resource efficiency can start with a better assessment of the dry matter content and nutritional value of forages. Currently, analytical process time is often long and analyses are not repeatable while the quality of the fodder changes over time. Being able to analyze forages directly on-farm would make it possible to adapt the animal diet according to forage variability, in order to improve the profitability of the farm. Objectives. To propose in situ rapid analysis solutions to better characterize dry matter content and the chemical composition of fodder for assessing its feeding value. Method. The performance of three recently developed spectroscopic handheld devices, namely the Viavi’s MicroNIR 1700, the Ocean Insight’s FlameNIR and the Malvern Panalytical’s ASD FieldSpec 4, are evaluated to predict dry matter content and the chemical composition of fresh and unground grass silage in the framework of precision feeding and compared to the reference benchtop Foss’s XDS instrument. The conventional global PLS and local PLS are used as multivariate calibration methods. Results. The assessed handheld devices allow the dairy farmer to obtain a relatively precise quantitative prediction of the dry matter and crude fiber content (2.5% and 1.8% respectively on average, in terms of ratios between the local PLS error on fresh forage and the reference method error) in order to adapt the livestock diet. Crude protein, even if the prediction accuracy is lower (6.4%), is still well predicted. Higher errors are obtained for ash (9.2%), crude neutral (6.8%) and acid detergent fiber (6.9%). Conclusions. The studied devices should allow the dairy farmer to obtain a relatively precise quantitative prediction of those quality parameters in order to directly adapt the quantity of forage distributed to the animals. Performances could probably be improved by including more samples/spectra into the databases.

Multivariate analysis of images in spectrophotometric methods: Quantification of soil organic matter

Soil organic matter (SOM) is usually quantified by Walkley-Black titration method or using a spectrophotometric method. This study proposes an alternative method for quantification of SOM using digital image from scanner and mathematical algorithms to replace titration and spectrophotometry procedures. For this, after SOM oxidation by potassium dichromate, digital images were acquired. Posteriorly, extraction of RGB color histograms from images have occurred, followed by the use of multivariate calibration method: partial least squares (PLS). Six soil samples were analyzed. We used the Walkley-Black method as reference. SOM was estimated by images using the PLS tool. The new method, besides being a fast, low cost, and more operational alternative, presented statistically equal results in relation to the reference method, as assessed by the Student t-test and F-test at 95 % confidence.

Artificial neural network and multivariate calibration methods assisted UV spectrophotometric technique for the simultaneous determination of metformin and Pioglitazone in anti-diabetic tablet dosage form

In this paper, for the simultaneous measurement of Metformin (MET) and Pioglitazone (PIO) in Actoplus MET as antidiabetic tablet, chemometrics and spectrophotometry methods without the need for separation steps was used. The applied chemometrics methods were artificial neural network (ANN), partial least squares regression (PLS), and principal component regression (PCR). The ANN consisting of two, four and six layers with 2, 4, 6, 8, and 10 neurons was trained using a feed forward back-propagation (FFBP) learning. The algorithms used were Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back propagation (GDX). The mean square error (MSE) of the LM algorithm was obtained 3.18 ​× ​10−30 and 1.58 ​× ​10−30 for MET and PIO, respectively, which represented that the LM algorithm performed better than the GDX algorithm. In the PLS method, lower root mean square error (RMSE) (MET ​= ​0.0558, PIO ​= ​0.3981) showed better performance compared PCR method (MET ​= ​0.0559, PIO ​= ​0.4048). Finally, the results of the proposed methods and high performance liquid chromatography (HPLC) as a reference approach were compared with one-way ANOVA test at 95% confidence level, which did not show a significant difference between the data.

Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration

Because of the nutritional advantages and customer acceptance, it is vital to ensure pork meat quality. This study examined the quantification of myoglobin proportions (deoxymyoglobin and oxymyoglobin) by coupling Raman spectroscopy with efficient variables selection chemometrics. Prior to acquiring Raman spectroscopic data, the fractions of myoglobin were determined. Afterward, multivariate calibration methods like partial least square (PLS), competitive adaptive reweighted sampling (CARS-PLS), genetic algorithm-PLS (GA-PLS), and random frog-PLS (RF-PLS) were applied and evaluated. The models’ performance was assessed using correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The RF-PLS model achieved optimal results for both deoxymyoglobin and oxymyoglobin, with Rp = 0.8936; RMSEP = 2.91 and RPD = 1.97 for the former and Rp = 0.9762; RMSEP = 1.23 and RPD = 4.47 for the latter, respectively. Therefore, this work demonstrated that Raman spectroscopy paired with RF-PLS could be employed for nondestructive, fast, and easy detection of deoxymyoglobin and oxymyoglobin.

The comparison of two Chemometric Assisted UV Spectrophotometric Techniques with High-performance Liquid Chromatography Methods for simultaneous determination of three Antiemetic drugs used in Chemotherapy Induced Nausea and Vomiting

This research work was emphasis to adopt green analytical chemistry via development of environmentally friendly methods for simultaneously estimation of drugs used for chemotherapy induced nausea and vomiting (CINV). In these study two multivariate calibration methods namely; Partial least square (PLS) models, Principal component regression (PCR) and RP-HPLC (Reverse phase- high performance liquid chromatography) method were employed for simultaneous assessment of aprepitant (APT), dexamethsone (DEX) and ondansetron (OND) in their market formuations. The chromatographic separation was achieved on Phenomenex Luna C18 column (150mm X 4.6mm in diameter with 5μ particle size) and detection was carried out by UV-Visible detector. The mobile phase comprises a mixture of ethanol and toluene in a ratio of 65:35% v/v, at the flow rate of 1.2ml/ min. The elution was monitored at 225nm and total run time required for separation was 10 min. The retention time of APT, DEX and OND were found to be 4.37 min, 6.57 and 8.11 min respectively. The Applied methods were validated as per ICH guidelines to achieve maximum sensitivity and lowest error. Linearity range for multivariate calibration methods was found to be 5-50μg/mL for APT, DEX and OND and similarly for HPLC methods was found to be 5-30μg/mL,5-30μg/mL and 5-50μg/mL of APT, DEX and OND respectively. A statistical procedure was carried out to find statistical difference among these developed methods. The results revealed that there is no significant difference between two multivariate models and HPLC methods. Therefore, it could be applied as an alternative of HPLC method in quality control laboratories lacking the required facilities for these expensive instruments. Hence, proposed method can also be applied in clinical pharmacy, toxicology and quality control analysis without the interference of commonly encountered dosage form additives.

Chemometric quality assessment of Paracetamol and Phenylephrine Hydrochloride with Paracetamol impurities; comparative UV-spectrophotometric implementation of four predictive models

Spectrophotometric data analysis using multivariate approaches has many useful applications. One of these applications is the analysis of active ingredients in presence of impurities. Four chemometric-assisted spectrophotometric methods, namely, principal component regression (PCR), partial least-squares (PLS), artificial neural networks (ANN) and multivariate curve resolution-alternating least squares (MCR-ALS) were proposed and validated. The developed chemometric methods were compared to resolve the severely overlapped spectrum of Paracetamol (PAR) and Phenylephrine HCl (PHE) along with PAR impurities namely, P-Aminophenol (PAP), P-Nitrophenol (PNP), Acetanilide (ACT) and P-Chloroacetanilide (CAC). The four multivariate calibration methods succeeded in simultaneous determination of PAR and PHE with further quantification of PAR impurities. So, the proposed methods could be used with no need of any separation step and successfully applied for pharmaceutical formulation analysis. Furthermore, statistical comparison between the results obtained by the proposed chemometric methods and the official ones showed no significant differences.

Application of Chemometrics-assisted HPLC-DAD Strategies for Simultaneous Determination of Paracetamol, Pseudoephedrine HCl, Dextromethorphan HBr, Doxylamine Succinate and Saccharin in Syrup Formulation

Introduction:This study introduces an effective strategy, which combines high performance liquid chromatography coupled with diode array detection (HPLC-DAD) with multivariate calibration methods for the simultaneous determination of paracetamol (PAR), pseudoephedrine HCl (PSE), dextromethorphan HBr (DEX) and doxylamine succinate (DOX) along with sweetener saccharin (SAC) in syrup formulation. Methods: PLS-2 and PCR calibration algorithms were selected for data processing. Based on the strategy, all target analytes were rapidly quantified within 5.3 min under the simple isocratic elution (water: methanol, 20/80, v/v) without a complete separation. The performances of the proposed methods were confirmed by analyzing a series of synthetic solutions including different concentrations of analytes. Results:The average recovery values were in the range of 100.33 to 103.70%, and the REP (relative error of prediction) values ranged from 1.96 to 4.36% showed that these methods could provide satisfactory predictions. Conclusion:Novel HPLC methods coupled with PLS and PCR algorithm enable a simple, fast and low-cost analysis of similar pharmaceutical products for simultaneous determination of the target compounds.

Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression.

The components in polymer-bonded explosive X, including cyclotetramethylene-tetranitramine, paraffin, and polytetrafluoroethylene, were determined using near-infrared (NIR) spectroscopy. Using partial least squares as the multivariate calibration method, quantitative calibration models for components in X were verified internally and externally. The possible combinations of eight general spectral pretreatment methods and different bands of the scanning spectral region (12,500–4000 cm–1) were established. The models were analyzed, evaluated, and optimized via the fitting effect. The data were combined with the mathematical meaning of the model spectral pretreatment methods and the chemical significance of the modeling spectral bands. Prediction performance offered optimal quantitative calibration models. Paired bilateral Student’s t tests show that there is no significant difference between the values obtained by NIR and chemical analysis methods, and the NIR method has good accuracy. Moreover, the precision of the NIR method is better than that of the chemical method, and the analysis time is reduced from 2 days to a few minutes.