Multivariate Calibration Models Research Articles

A voltammetric method coupled with chemometrics for determination of a ternary antiparkinson mixture in its dosage form: greenness assessment

An electroanalytical methodology was developed by direct differential pulse voltammetric (DPV) measurement of Levodopa (LD), Carbidopa (CD) and Entacapone (ENT) mixture using bare glassy carbon electrode (GCE) in Britton Robinson (BR) buffer (pH = 2.0). A multivariate calibration model was then applied to the exported preprocessed voltammetric data using partial least square (PLS) as a chemometric tool. Additionally, the model was cross-validated and the number of latent variables (LVs) were determined to produce a reliable model for simultaneous quantitation of the three drugs either in their synthetic mixtures or in their marketed pharmaceutical formulation with high accuracy and precision. Data preprocessing was used to tackle the problem of lacking bi-linearity which is commonly found in electrochemical data. The proposed chemometric model was able to provide fast and reliable technique for quantitative determination of antiparkinson drugs in their dosage forms. This was successfully achieved by utilizing sixteen mixtures as calibration set and nine mixtures as validation set. The percent recoveries for LD, CD and ENT were found to be 100.05% ± 1.28%, 100.04% ± 0.53% and 99.99% ± 1.25%, respectively. The obtained results of the proposed method were statistically compared to those of a previously reported High Performance Liquid Chromatography (HPLC) methodology. Finally, the presented analytical method strongly supports green analytical chemistry regarding the minimization of potentially dangerous chemicals and solvents, as well as reducing energy utilization and waste generation.

Tapping the Full Potential of Infrared Spectroscopy for the Analysis of Technical Lignins.

We present an approach to overcome the challenges associated with the increasing demand of high-throughput characterization of technical lignins, a key resource in emerging bioeconomies. Our approach offers a resort from the lack of direct, simple, and low-cost analytical techniques for lignin characterization by employing multivariate calibration models based on infrared (IR) spectroscopy to predict structural properties of lignins (i. e., functionality, molar mass). By leveraging a comprehensive database of over 500 well-characterized technical lignin samples - a factor of 10 larger than previously used sets - our chemometric models achieved high levels of quality and statistical confidence for the determination of different functional group contents (RMSEPs of 4-16 %). However, the statistical moments of the molar mass distribution are still best determined by size-exclusion chromatography. Analyses of over 500 technical lignins offered also a great opportunity to provide information on the general variability in kraft lignins and lignosulfonates (from different origins). Overall, the effected savings in analysis time (>7 h), resources, and required sample mass combined with non-destructiveness of the measurement satisfy key demands for efficient high-throughput lignin analyses. Finally, we discuss the advantages, disadvantages, and limitations of our approach, along with critical insights into the associated chemical-analytical and spectroscopic challenges.

Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms

Recent work has validated a new method for estimating the grain size of microgranular materials in the range of tens to hundreds of micrometers using laser-induced breakdown spectroscopy (LIBS). In this situation, a piecewise univariate model must be constructed to estimate grain size due to the complex dependence of the plasma formation environment on grain size. In the present work, we tentatively construct a unified calibration model suitable for LIBS-based estimation of those grain sizes. Specifically, two unified multivariate calibration models are constructed based on back-propagation neural network (BPNN) algorithms using feature selection strategies with and without considering prior information. By detailed analysis of the performances of the two multivariate models, it was found that a unified calibration model can be successfully constructed based on BPNN algorithms for estimating the grain size in the range of tens to hundreds of micrometers. It was also found that the model constructed with a prior-guided feature selection strategy had better prediction performance. This study has practical significance in developing the technology for material analysis using LIBS, especially when the LIBS signal exhibits a complex dependence on the material parameter to be estimated.

Nutrient quantification of livestock slurry and digestate using a low-cost handheld NIR spectrometer and multi-biomass calibration models

Real-time analysis during slurry field application can potentially ensure higher efficiency in crop fertilisation and reduce related environmental problems. Near Infrared Spectroscopy (NIRS) can predict quickly and adequately the nutrient content of the slurry but the devices are still costly. In this study, a low-cost portable device has been tested to predict total solids, total Kjeldahl nitrogen, total ammonia nitrogen, and phosphorus contents in cattle and pig slurries and digestate. Multivariate calibration models were developed for different biomass types (multi-biomass) by comparing support vector machine (SVM) and partial least square (PLS) regressions. The developed SVM and PLS models showed satisfactory and similar results, with performance to deviation ratio (RPD) values ranging between 2.22 and 2.80. The use of the handheld device with the obtained models also meets the requirements of a certification protocol for commercial NIR sensors and therefore can ensure a better slurry management at farm level.

Investigation of long-term stability of a transmission Raman calibration model using orthogonal projection methods

Transmission Raman Spectroscopy (TRS) was implemented as an ‘Extended’ Content Uniformity (ECU) method for un-coated tablets for a commercial pharmaceutical product. By sampling un-coated tablets throughout the duration of the tablet compression stage, it can be demonstrated that the material from the preceding blend step was of uniform composition, and therefore the blend and compression unit-operations were in a state of control. TRS was selected as a rapid, non-destructive measurement that can be automated through the use of a sample tray that can hold many tablets. In this work, the performance of a multivariate calibration model (PLS) deployed to two Transmission Raman Spectrometers co-located within the same QC laboratory was studied using data obtained over a 3-year period. The aim of the investigation was to assess the impact of various annual instrument maintenance events, and to evaluate several chemometric methods for reducing or eliminating the spectral effects that led to deterioration of a models performance. Linear orthogonal projection approaches such as Transfer by Orthogonal Projection (TOP), Dynamic Orthogonal Projection (DOP) and Unsupervised Dynamic Orthogonal Projection (uDOP) were applied, along with a more recent, non-linear method called Transfer Component Analysis-Orthogonal Projection (TCA-OP). This works shows that each method has merits, depending on the nature of the spectral/model correction required. In most cases, the model performance could be fully restored, or significantly improved. This work also highlights how these various methods can be useful tools to better understand the root-cause for a deterioration in model performance.

Application of smart chemometric models for spectra resolution and determination of challenging multi-action quaternary mixture: statistical comparison with greenness assessment

A multivariate spectrophotometric method is a potential approach that enables discrimination of spectra of components in complex matrices (e.g., pharmaceutical formulation) serving as a substitution method for chromatography. Four green smart multivariate spectrophotometric models were proposed and validated, including principal component regression (PCR), partial least-squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN). The developed chemometric models were compared to resolve highly overlapping spectra of Paracetamol (PARA), Chlorpheniramine maleate (CPM), Caffeine (CAF), and Ascorbic acid (ASC). The four multivariate calibration models were assessed via recoveries percent, and root mean square error of prediction. Hence, the proposed models were efficiently applied with no need for any preliminary separation step. The models were utilized to analyze the studied components in their combined pharmaceutical formulation (Grippostad® C capsules). Analytical GREEnness Metric Approach (AGREE) and eco-scale tools were applied to assess the greenness of the established models and found to be 0.77 and 85, respectively. Moreover, the proposed models have been compared to official ones showing no considerable variations in accuracy and precision. Therefore, these models can be highly advantageous for conducting standard pharmaceutical analysis of the substances researched within product testing laboratories.

Rapid detection and quantification of milk adulteration using MALDI-MS protein profiling and multivariate calibration

The adulteration of high-value milks with low-value milks is a common fraudulent practice in the dairy industry. This not only results in economic or quality prejudice, but also poses a potential threat to individuals with sensitivities or allergies. Profiling the major whey and casein milk proteins can be a crucial tool in combating this malpractice, given that each mammal species exhibits a unique protein fingerprint. In this study, we employed matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-MS) as a rapid and straightforward method for profiling proteins from bovine, caprine, and ovine milks. Subsequently, we analyzed binary mixtures of goat and sheep milks with bovine milk to establish multivariate calibration models using partial least-squares (PLS) to quantify the adulteration of goat and sheep milks. Proteins were identified according to their molecular mass (Mr) and their intensities were considered as multivariate data. To achieve satisfactory calibration and validation results, normalization through variable augmentation was necessary, enabling the quantification of bovine milk across the entire adulteration range. This uncomplicated approach based on MALDI-MS protein profiling and PLS shows significant potential for qualitative and quantitative assessments of milk adulteration.

Transfer of near infrared calibration for gasoline octane number based on screening consistent wavelengths combined with direct standardization algorithm

In order to share multivariate calibration models of gasoline research octane number between different near infrared spectrometers, a novel calibration transfer method, namely combination of screening consistent wavelengths and direct standardization (SWCSS-DS) was proposed. Firstly, screening wavelengths with consistent and stable signals (SWCSS) between instruments was used to select the wavelengths with best stability, and then direct standardization (DS) further corrected the systematic errors that still exist after the SWCSS was implemented. The spectra of 120 standard gasoline samples collected on two near infrared spectrometers of the same type were investigated in detail to verify the validity of the new algorithm. Compared results of other transfer methods such as SWCSS, Slope/Bias (S/B), direct standardisation (DS), and piecewise direct standardization (PDS), the root mean squared error for prediction (RMSEP) of SWCSS-DS algorithm for target samples was decreased from 5.75 to 0.295, and the Akaike information criterion value decreased from 1516 to 640, which were better than those of the SWCSS, S/B, DS and PDS algorithms. Therefore, the joint algorithm of SWCSS-DS has not only improved the universality of the master model, but also reduced the dimension of the spectral matrix and calibration equation, that would provide a more efficient model transfer strategy for the practical applications.

Simultaneous Determination of Levamisole and Triclabendazole by Multivariate Calibration Models using Second Derivative Spectrophotometry in Veterinary Pharmaceutical Formulation

Simultaneous Determination of Levamisole and Triclabendazole by Multivariate Calibration Models using Second Derivative Spectrophotometry in Veterinary Pharmaceutical Formulation

Extraction of polymeric hemicelluloses from okoumé sapwood by steam explosion: Use of non-destructive characterization methods

The hemicelluloses, which represent an important family of polysaccharides constituting ≈ 20–30% of the plant wall are currently one of the most poorly valorized fractions of the biomass. Extracted in polymeric form, hemicelluloses could be used for innovative material applications, replacing fossil materials. Five cumulative pretreatments involving bleaching and/or steam explosion (SE) and/or alkaline extraction processes have been studied for polymeric hemicelluloses extraction from Okoumé sapwood. Morphological analysis of the cell wall of untreated and steam exploded wood was performed by optical and fluorescence microscopy after staining. Near infrared spectroscopy combined with multivariate calibration modeling were also performed for evaluating hemicellulose extraction. Hemicelluloses extracts were characterized by HPEAC/PAD ion chromatography, size exclusion chromatography (SEC), and high-performance liquid chromatography (HPLC), FTIR and HSQC NMR spectroscopy analyses. Alkali extraction of bleached wood led to high yields (≈ 19%) of high molecular weight hemicelluloses (Mw ≈ 65 kDa) while neutral SE pretreatment allowed the recovery of highly acetylated polymers with a low degree of branching. A bleaching step prior to SE increased accessibility to hemicelluloses extraction but weakened the pulp and led to the removal of micro-particles of wood during the SE.

Impact of Spectral Resolution and Signal-to-Noise Ratio in Vis–NIR Spectrometry on Soil Organic Matter Estimation

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and R2 obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels > spectral resolutions > estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics.

Determination of basic nitrogen content in diesel oil: A miniaturized method by digital image-based colorimetry in a portable device

A miniaturized method using a portable device with digital image acquisition and PhotoMetrix PRO app data treatment was developed for the determination of basic nitrogen content in diesel oil. The method was based on the colorimetric titration described in the UOP 269-10 standard protocol. A homemade 3D-printed chamber with controlled light intensity equipped with an USB camera was used for image acquisition after an acid-base titration reaction, carried out in a miniaturized device. After mixing reagents and diesel oil, the images were obtained and converted into RGB (red, green, and blue) histograms, and a partial least squares (PLS) multivariate calibration model was constructed. Parameters of the regression model were evaluated, by the coefficient of determination (R2), the root mean squared error of calibration (RMSEC), the root mean squared error of cross-validation (RMSECV), and the root mean squared error of prediction (RMSEP). Some conditions for the acid-base titration were optimized, such as the concentration of the indicator (68.0 to 272 µmol L-1) and the titrating (HClO4, 0.179 to 1.79 mmol L-1), as well as the volume of diesel oil. With 60 µL of 2.54 mmol L-1 indicator solution, 20 µL of 20 mmol L-1 HClO4 as titrating and using 50 to 1000 µL of diesel oil, optimal conditions were obtained for calibration (RMSEP of 0.377 mg kg-1, RMSECV of 0.307 mg kg-1 with 4 factors). It is important to mention that no differences were observed (p < 0.05) when comparing reference values with the results by the proposed protocol. This proved to be advantageous in relation to the methods described in the UOP 269-10 standard since it was possible to reduce the consumption of reagents and waste generation, in agreement with green analytical chemistry. In addition, this alternative protocol combines simplicity and speed to obtain results with good accuracy, precision and suitable limit of quantification (1 mg kg-1) using a miniaturized system.

Effect of laser wavelength on soil carbon measurements using laser-induced breakdown spectroscopy.

We investigate the effect of laser wavelength on laser-induced breakdown spectroscopy (LIBS) on the measurement of carbon in agricultural soils. Two laser wavelengths, 1064 nm and 532 nm, were used to determine soil carbon concentration. No chemical pretreatment, grinding, or pelletization was performed on soil samples to simulate in-field conditions. A multivariate calibration model with outlier filtering and optimized parameters in partial least squared regression (PLSR) was established and validated. The calibration model estimated carbon content in soils with an average prediction error of 4.7% at a laser wavelength of 1064 nm and 2.7% at 532 nm. The limit of detection (LOD) range for 532 nm was 0.34-0.5 w/w%, approximately half of the LOD range for 1064 nm laser wavelength. The improvement in prediction error and LOD of LIBS measurements is attributed to the increase in plasma density achieved at 532 nm.

Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra.

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky-Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch.

Raman spectroscopy applied to online monitoring of a bioreactor: Tackling the limit of detection

An in-situ monitoring model of alcoholic fermentation based on Raman spectroscopy was developed in this study. The optimized acquisition parameters were an 80 s exposure time with three accumulations. Standard solutions were prepared and used to populate a learning database. Two groups of mixed solutions were prepared for a validation database to simulate fermentation at different conditions. First, all spectra of the standards were evaluated by principal component analysis (PCA) to identify the spectral features of the target substances and observe their distribution and outliers. Second, three multivariate calibration models for prediction were developed using the partial least squares (PLS) method, either on the whole learning database or subsets. The limit of detection (LOD) of each model was estimated by using the root mean square error of cross validation (RMSECV), and the prediction ability was further tested with both validation datasets. As a result, improved LODs were obtained: 0.42 and 1.55 g·L−1 for ethanol and glucose using a sub-learning dataset with a concentration range of 0.5 to 10 g·L−1. An interesting prediction result was obtained from a cross-mixed validation set, which had a root mean square error of prediction (RMSEP) for ethanol and glucose of only 3.21 and 1.69, even with large differences in mixture concentrations. This result not only indicates that a model based on standard solutions can predict the concentration of a mixed solution in a complex matrix but also offers good prospects for applying the model in real bioreactors.

Robust principal component analysis-multivariate adaptive regression splines (rPCA-MARS) model for determining total acid number (TAN) and total base number (TBN) of crude oil samples using attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy

Rapid assessments of Total Acid Number (TAN) and Total Base Number (TBN) in crude oil samples are significant in the oil industry. The analysis of TAN and TBN was conducted using the standard methods of the American Society for Testing and Materials (ASTM). However, the standard methods are costly and require a large number of samples for analysis. Therefore, providing analytical methods for the rapid crude oil analysis is very essential. In the current research, we report an application of Attenuated Total Reflection Fourier-Transform Infrared (ATR-FTIR) spectroscopy, based on robust Principal Component Analysis-Multivariate Adaptive Regression Splines (rPCA-MARS), for crude oil analysis. In the rPCA-MARS model, the ATR-FTIR spectral matrix data was decomposed into PCs scores using the rPCA method as an input data for the MARS model. The multivariate calibration models were applied to the analysis of crude oil samples based on the quantitative determination of total acid number (TAN) and total base number (TBN). An analytical method was developed for determination of TAN and TBN of crude oil samples. The ATR-FTIR spectroscopy coupled with multivariate calibration methods can be applied as a novel analytical method for crude oil samples. The result of partial least square regression (PLS-R), principle component analysis (PCR), Piecewise-Linear rPCA-MARS and Piecewise-cubic rPCA-MARS models were compared for analysis of total acid and base number of crude oil samples. The squared correlation coefficient (R2) and root mean square error (RMSE) for calibration and prediction sets were calculated to evaluate multivariate calibration models. The importance of methods was studied and discussed. The mean square error (MSE) values of Piecewise-Linear rPCA-MARS for TAN and TBN were 1.587 * 10−4 and 0.009, respectively. The Piecewise-Linear rPCA-MARS model can be successfully applied for the analysis of TAN and TBN of crude oil samples based on the obtained results. The fast and easy prediction capability of the proposed Piecewise-Linear rPCA-MARS model, as compared to standard methods, for determining TAN and TBN without any sample preparation step is important in the oil industry

A Simple UV Spectrophotometric Method Based on Continuous Wavelet Transform and Multivariate Calibration Model for the Concurrent Analysis of Three Water-Soluble Vitamins in Fertility Supplements.

Owing to the presence of overlapping spectra in pharmaceutical components, classical spectrophotometry is hard for concurrent determination. The advance of chemometrics along with UV-Vis spectrophotometry has contributed to solving this problem. In this study, a fast, easy, precise, accurate, low-cost, and eco-friendly spectrophotometric technique was introduced and validated for the simultaneous analysis of vitamin B6, vitamin B12, and vitamin C in fertility supplements for men and women using continuous wavelet transform (CWT) and partial least squares (PLS) techniques without using time-consuming extraction process and organic solvents. In the CWT method, the zero-crossing technique was applied to obtain the optimum points for plotting calibration curves for each component. The validation of both methods was evaluated by analyzing several mixtures with different concentrations. The efficiency of the proposed methods was also surveyed on commercial capsules. Wavelet families, including Symlet (sym2) at 230, Biorthogonal (bior1.3) at 378 nm, and Daubechies (db2) at 261, were considered for vitamins B6, B12, and C, respectively. The linear range was found to be 8-20, 8-20, and 10-25 μg/mL with the coefficient of determination (R2) equal to 0.9982, 0.9978, and 0.9701 for B6, B12, and C, respectively. Low limit of detection (LOD) (<0.09 μg/mL) and limit of quantification (LOQ) <0.9 μg/mL were achieved. The mean recovery values in synthetic mixtures were from 98.38 to 98.89% and from 99.83 to 99.99%, where root-mean-square error (RMSE) of not more than 0.4 and 0.05 using the CWT and PLS methods, respectively. The obtained results from the commercial capsules, applying the suggested techniques, were compared to those yielded by the high-performance liquid chromatography (HPLC) method using the analysis of variance (ANOVA) test. According to the results, there are no significant differences, and they were in good agreement. According to all the mentioned cases, the proposed approaches can replace the time-consuming and costly HPLC method in quality control laboratories. Green spectrophotometry coupling chemometrics methods were proposed. Simultaneous determination of three water-soluble vitamins in fertility supplements was done using these approaches. Rapidity, simplicity, low cost, and accuracy are the benefits of the proposed methods. A HPLC technique was used as a reference method to compare with the chemometrics methods.

Binary adsorption isotherms of methylene blue and crystal violet on mandarin peels: prediction via detailed multivariate calibration and density functional theory (DFT) calculations.

The multicomponent adsorption of synthetic dyes has great relevance in the treatment of effluents due to the complexity of the adsorbate-adsorbent interactions. Therefore, this study provides useful information about the adsorption capacity of methylene blue (MB) and crystal violet (CV) in a bioadsorbent (mandarin peels) in a single-component and competitive system using detailed multivariate calibration analysis. The PLS1 multivariate calibration model was used to quantify the adsorbates. In mono and two-component systems, the adsorption capacity of CV (1.26-1.36 mg g-1) was superior when compared to MB (0.925-0.913 mg g-1), characterizing synergistic adsorption for CV and antagonistic adsorption for MB. The Sips model was effective for describing single-component systems, suggesting that adsorption did not occur in the monolayer. For competitive adsorption, modified, unmodified, and extended models were used to understand the interactions between the dyes and the bioadsorbent. The modified Redlich-Peterson (MRP) model was effective in describing the behavior of the binary system, indicating that the interaction forces with the adsorbate were significant. Thus, the bioadsorbent showed promising results for competitive adsorption, thus being of relevance to the industrial sector. Density functional calculations were also performed to characterize the atomic interactions for the removal of both dyes on mandarin peels.

Application of stable consistency wavelength in optimizing gasoline RON near‐infrared analysis model transfer

AbstractThe purpose of model transfer is to solve the problem that multivariate calibration models cannot be shared among different near‐infrared spectrometers. Taking gasoline as the research object, the transfer analysis of its octane number model was carried out. The gasoline samples collected by two near‐infrared spectrometers of the same type were used as the research object. The screening wavelengths with consistent and stable signals (SWCSS) combined with competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projections algorithm (SPA) were used to reduce the adverse effects of invalid wavelengths in the SWCSS method; therefore, the analysis ability of the master model to the slave samples was improved. Partial least squares regression (PLSR) models based on SWCSS‐UVE, SWCSS‐CARS, and SWCSS‐SPA algorithms were established, and comparison was made between their analytical capabilities for slave samples and those of SWCSS, direct standardization (DS) algorithm , piecewise direct standardization (PDS) algorithm, and slope/bias (S/B) algorithm. The results shown that the SWCSS‐UVE and SWCSS‐CARS methods can be used to establish models from the 231 and 6 wavelengths selected from the consistent wavelengths, respectively. The root mean square error of prediction (RMSEP) of the gasoline octane number (RON) content of the direct analysis of the spectrum measured by the slave machine was reduced from 5.7490 to 0.3226 and 0.3250, respectively, which was better than the single SWCSS and DS, PDS, and SWCSS‐SPA methods, and was close to the model transfer accuracy of the S/B algorithm. The transfer accuracy of SWCSS‐UVE and SWCSS‐CARS was not much different, but the wavelength variable involved in the model transfer of the latter was much smaller than that of the former, and the AIC value of SWCSS‐CARS was −59.59, which was much smaller than the akaike information criterion (AIC) value of SWCSS‐UVE 398.42.

Evaluation of greenness and whiteness assessment of chemometric assisted techniques for simultaneous determination of canagliflozin, sitagliptin, metformin, pioglitazone, and glimepiride in a quinary mixture

An assortment of lifestyle and genetic characteristics contribute to the eventual appearance of type 2 diabetes. Diabetes can be triggered by a variety of drugs and other health issues. Five anti-diabetic medications, Metformin, Glimepiride, Canagliflozin, Pioglitazone, and Sitagliptin, were identified for simultaneous evaluation in their synthetic mixtures applying multivariate calibration models Principal Component Regression (PCR) and Partial Least Square (PLS-2) to resolve the extensively overlapping spectrum.For extracting important information and enhancing the accuracy of the techniques, both techniques were used for deciding on the variables. The most beneficial spectral ranges and combinations were identified by considering the least values of the Correlation Coefficient (R2 ≥ 0.9998), the Root Mean Square Error of Prediction (RMSEP) values between (0.02782–0.08277), and the Relative Error of Prediction (SEC) values between (0.20693–0.90161).The two multivariate calibration approaches were successful in determining all five components in their quinary mixture simultaneously. As a result, the suggested techniques can easily be employed without a separation stage and effectively implemented in pharmaceutical formulation evaluation. In addition, statistical assessments between the suggested chemometric techniques and the reference revealed no significant differences.Furthermore, the suggested techniques succeeded in the guidelines of green analytical chemistry, and their eco-friendliness was assessed using four tools, namely, the Analytical Eco-Scale, the National Environmental Methods Index (NEMI), the Green Analytical Procedure Index (GAPI), and the Analytical Greenness Metric (AGREE), which confirmed the proposed methods' eco-friendliness. In addition, the newly generated Red-Green-Blue (RGB12 paradigm) was employed to investigate the whiteness properties. The suggested methods' acceptable observations, as well as their long-term sustainability, straightforwardness, cost-effectiveness, and inexpensive cost, stimulate adoption in quality control laboratories.