Multivariate Calibration Models Research Articles

Evaluation of a Multivariate Calibration Model for the WET Sensor That Incorporates Apparent Dielectric Permittivity and Bulk Soil Electrical Conductivity

The measurement of apparent dielectric permittivity (εs) by low-frequency capacitance sensors and its conversion to the volumetric water content of soil (θ) through a factory calibration is a valuable tool in precision irrigation. Under certain soil conditions, however, εs readings are substantially affected by the bulk soil electrical conductivity (ECb) variability, which is omitted in default calibration, leading to inaccurate θ estimations. This poses a challenge to the reliability of the capacitance sensors that require soil-specific calibrations, considering the ECb impact to ensure the accuracy in θ measurements. In this work, a multivariate calibration equation (multivariate) incorporating both εs and ECb for the determination of θ by the capacitance WET sensor (Delta-T Devices Ltd., Cambridge, UK) is examined. The experiments were conducted in the laboratory using the WET sensor, which measured θ, εs, and ECb simultaneously over a range of soil types with a predetermined actual volumetric water content value (θm) ranging from θ = 0 to saturation, which were obtained by wetting the soils with four water solutions of different electrical conductivities (ECi). The multivariate model’s performance was evaluated against the univariate CAL and the manufacturer’s (Manuf) calibration methods with the Root Mean Square Error (RMSE). According to the results, the multivariate model provided the most accurate θ estimations, (RMSE ≤ 0.022 m3m−3) compared to CAL (RMSE ≤ 0.027 m3m−3) and Manuf (RMSE ≤ 0.042 m3m−3), across all the examined soils. This study validates the effects of ECb on θ for the WET and recommends the multivariate approach for improving the capacitance sensors’ accuracy in soil moisture measurements.

Elemental analyses of feldspathic to basaltic soils and rocks on the moon using laser-induced breakdown spectroscopy

In-situ analysis of major elements using laser-induced breakdown spectroscopy (LIBS) is essential for future lunar landing missions, yet its performance under lunar conditions remains not fully understood. This uncertainty arises from the absence of an atmosphere and the diverse range of surface materials, which vary in chemical composition from anorthosites to basalts, and in physical properties from fine regolith to boulders. To address these challenges, we developed and cross-validated a multivariate LIBS calibration model by measuring 169 compressed fine powders of geologic samples under vacuum. These samples fully encompass the bulk composition range of lunar meteorites. We investigated the applicability of the model to a wider range of samples by measuring lunar meteorites, terrestrial anorthites, and lunar simulants in various physical forms, including rock chips and soils with different grain sizes and bulk densities. For powder samples, the quantification accuracy, assessed using root mean squared error (RMSE), resulted in 2.5 wt% SiO2, 0.25 wt% TiO2, 1.2 wt% Al2O3, 1.3 wt% MgO, 1.2 wt% CaO, 0.33 wt% Na2O, 0.47 wt% K2O (0.060 wt% K2O in the <1 wt% range), and 1.5 wt% T-Fe2O3. For rock chip samples, the RMSEs were 3.1 wt% SiO2, 0.32 wt% TiO2, 2.2 wt% Al2O3, 2.5 wt% MgO, 2.0 wt% CaO, 0.33 wt% Na2O, 0.089 wt% K2O, and 2.1 wt% T-Fe2O3. Despite significant differences in physical conditions between powders and rocks, their RMSEs remained consistent within a factor of two. Changes in grain size or bulk density of soils had relatively minor effects on the RMSE. These RMSEs confirm that the quantification accuracy of LIBS is sufficient to distinguish the subgroups within the lunar anorthosite suite (e.g., anorthosites vs. norites) and basalts (e.g., high-Ti vs. low-Ti) across a range of soil types, from coarse to fine and from loose to compact, as well as rocks. Furthermore, our analysis shows that LIBS can differentiate between “purest” and “pure” anorthosites (98 and 95 vol% plagioclase, respectively) based on the 3σ detection limits of Mg and Fe lines. These capabilities of LIBS align well with the goals of future lunar exploration, such as locating ilmenite-rich soils for resource extraction, detecting purest anorthosites to understand early lunar evolution, and identifying noritic impact melts to refine lunar chronology. Overall, our results demonstrate that LIBS serves as a versatile tool for rapid geochemical characterization on the Moon.

Enhancing quantitative 1H NMR model generalizability on honey from different years through partial least squares subspace and optimal transport based unsupervised domain adaptation

Honey is a nourishing and natural food product that is widely favored by a diverse group of consumers. Proton Nuclear Magnetic Resonance (1H NMR) is a powerful tool for quantitative analysis of honey and plays a crucial role in ensuring its quality. The 1H NMR technique necessitates the utilization of multivariate calibration models to facilitate the quantitative analysis of key compounds present in honey. However, maintaining consistent measurement conditions across different years is scarcely possible, which can significantly impact the distribution of training and test spectra, ultimately leading to reduced performance of predictive models. Unsupervised domain adaptation (UDA) methods have gained considerable attention for their ability to match distribution differences between the labeled source spectra and the unlabeled target spectra without costly annotation. To enhance the quantitative model generalizability on honey from different years, we propose a UDA method known as partial least squares subspace and optimal transport-based UDA (PLSS-OT-UDA). This approach eliminates distribution differences between the source subspace and target subspace via partial least squares (PLS) dimensionality reduction and OT. Firstly, the optimal latent variable weight matrix from the source domain (i.e., labeled 1H NMR data in 2017) is extracted with PLS. Next, the dimension of both source and target domains (i.e., unlabeled 1H NMR data in 2018) is reduced and their corresponding subspaces are obtained with weight matrix of the source domain. Finally, OT is then employed to align the distribution of the source and target domains within the subspace. Experimental results on the honey dataset demonstrate that the PLSS-OT-UDA outperforms traditional methods, including transfer component analysis (TCA), optimal transport for domain adaptation (OTDA), domain adaptation based on principal component analysis and optimal transport (PCA-OTDA), and subspace alignment (SA), with respect to generalization performance on three components: baume degree, sugar content, and water content.

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

BackgroundRapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance.ResultsWe develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model.ConclusionsWe demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

In Situ Eutectic Formation in a Polymeric Matrix via Hot-Melt Reactive Extrusion and the Use of Partial Least Squares Regression Modeling for Reaction Yield Determination.

There has been a significant volume of work investigating the design and synthesis of new crystalline multicomponent systems via examining complementary functional groups that can reliably interact through the formation of noncovalent bonds, such as hydrogen bonds (H-bonds). Crystalline multicomponent molecular adducts formed using this approach, such as cocrystals, salts, and eutectics, have emerged as drug product intermediates that can lead to effective drug property modifications. Recent advancement in the production for these multicomponent molecular adducts has moved from batch techniques that rely upon intensive solvent use to those that are solvent-free, continuous, and industry-ready, such as reactive extrusion. In this study, a novel eutectic system was found when processing albendazole and maleic acid at a 1:2 molar ratio and successfully prepared using mechanochemical methods including liquid-assisted grinding and hot-melt reactive extrusion. The produced eutectic was characterized to exhibit a 100 °C reduction in melting temperature and enhanced dissolution performance (>12-fold increase at 2 h point), when compared to the native drug compound. To remove handling of the eutectic as a formulation intermediate, an end-to-end continuous-manufacturing-ready process enables feeding of the raw parent reagents in their respective natural forms along with a chosen polymeric excipient, Eudragit EPO. The formation of the eutectic was confirmed to have taken place in situ in the presence of the polymer, with the reaction yield determined using a multivariate calibration model constructed by combining spectroscopic analysis with partial least-squares regression modeling. The ternary extrudates exhibited a dissolution profile similar to that of the 1:2 prepared eutectic, suggesting a physical distribution (or suspension) of the in situ synthesized eutectic contents within the polymeric matrix.

Adjusted Pareto Scaling for Multivariate Calibration Models

ABSTRACTThe performance of multivariate calibration models ŷ = f(x) for the prediction of a numerical property y from a set of x‐variables depends on the type of scaling of the x‐variables. Common scaling methods are autoscaling (dividing the centered x by its standard deviation s) and Pareto scaling (dividing the centered x by sP with P = 0.5). The adjusted Pareto scaling presented here varies the exponent P between 0 (no scaling) and 1 (autoscaling) with the aim of obtaining an optimum prediction performance for ŷ. Related scaling methods based on the variable spread are range scaling and vast scaling; while level scaling is based on the location (central value) of the variable. These scaling methods and robust versions are compared for models created by partial least‐squares (PLS) regression. The applied strategy repeated double cross validation (rdCV) evaluates the model performance for test set objects and considers its variability. Results with three data sets from chemistry show: (a) the efficacy of the different scaling methods depends on the data structure; (b) optimization of the Pareto exponent P is recommended; (c) range scaling or vast scaling may be better than adjusted Pareto scaling; (d) in general a heuristic search for the best scaling method is advisable. Overall, the consideration of different variants of scaling allow for a flexible adjustment of the variable contributions to the calibration model.

Supervised Factor Analysis Transfer: Calibration transfer with noise modeling and response variable integration

Multivariate calibration models often encounter challenges in extrapolating beyond the calibration instruments due to variations in hardware configurations, signal processing algorithms, or environmental conditions. Calibration transfer techniques have been developed to mitigate this issue. In this study, we introduce a novel methodology known as Supervised Factor Analysis Transfer (SFAT) aimed at achieving robust and interpretable calibration transfer. SFAT operates from a probabilistic framework and integrates response variables into its transfer process to effectively align data from the target instrument to that of the source instrument. Within the SFAT model, the data from the source instrument, the target instrument, and the response variables are collectively projected onto a shared set of latent variables. These latent variables serve as the conduit for information transfer between the three distinct domains, thereby facilitating effective spectra transfer. Moreover, SFAT explicitly models the noise variances associated with each variable, thereby minimizing the transfer of non-informative noise. Furthermore, we provide empirical evidence showcasing the efficacy of SFAT across three real-world datasets, demonstrating its superior performance in calibration transfer scenarios.

Quantification of API content in pharmaceutical tablets within milliseconds by time-stretch near-infrared transmission spectroscopy

We explored the feasibility of high-speed and high-accuracy quantification of active pharmaceutical ingredient (API) content in tablet products by near-infrared (NIR) spectroscopy to improve the reliability of pharmaceuticals. For this purpose, we employed a high-power NIR time-stretch transmission spectrometer recently developed by us. By using this transmission spectrometer with a multivariate calibration model, we demonstrated the ability to quantify API content with a short measurement time of 3.9 ms per tablet for model pharmaceuticals. For the model tablet, the quantification ability of our spectrometer was comparable to that achieved by a commonly used Fourier-transform NIR (FT-NIR) spectrometer with a measurement time of several seconds. We also confirmed that the effect of irradiating tablets with the NIR pulses used in our spectrometer was negligible.

Strategies for multivariate characterization and classification of pulps and papers by near-infrared spectroscopy

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.

Real-time determination of combustion degree by laser-induced breakdown spectroscopy

The combustion of fuels in industries is one of the sources of greenhouse gases, posing a considerable threat to human health and the natural environment. The monitoring of the combustion process and the measurement of combustion degrees, therefore, are crucial and significant. In this work, an experimental system was developed based on Laser-induced breakdown spectroscopy (LIBS). Charcoals with different combustion degrees were taken as samples, and in the LIBS spectra of charcoals, the characteristic line of C and the molecular bands of CN and CaO were observed. Moreover, a univariate calibration model based on the exponential fitting curve was constructed to predict the combustion degree according to the variation of the C I line intensity under different combustion degrees. Furthermore, different algorithms including support vector machine (SVM), partial least squares (PLS), random forest (RF), and convolutional neural network (CNN) were applied to establish the multivariate calibration model, and the prediction performance was compared according to the 10-fold cross-validation. Particle swarm optimization (PSO) was used to optimize hyperparameters of the selected SVM-based multivariate calibration model. Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to extract spectral features and reduce dimensions for the improvement of the model's performance, with the final root mean square error of prediction (RMSEP) and mean relative error (MRE) of the LDA-PSO-SVM calibration model of 0.035 and 3.877%, respectively. Finally, c. The results prove feasible to realize the real-time determination of combustion degree to control carbon emissions and monitor carbon concentration through the LIBS-based method with machine learning, which also provides new ideas for the application of LIBS in the atmospheric field.

Use of Constant-Analyte Samples in Updating Quantitative Near-Infrared Calibration Models for Physiological Levels of Glucose

Updating procedures were investigated for multivariate calibration models for physiological levels of glucose in aqueous samples based on near-infrared spectroscopy. The methodology evaluated used samples with constant glucose concentrations collected on the prediction day to augment previously collected calibration spectra for the purpose of computing an updated model for use only on that day. Models were based on partial-least squares (PLS) regression in conjunction with spectral preprocessing using direct orthogonal signal correction (DOSC). The use of constant-analyte samples allowed the procedure to be compatible with samples collected at the beginning of a chemical reaction or from a fasting patient in a blood glucose monitoring application. The methodology was tested with nine sets of prediction spectra collected from 368 to 617 days after the collection of the calibration data. The updated PLS models based on four augmented samples exhibited an approximately 50% improvement in prediction accuracy relative to models that were based solely on the original calibration data. The use of DOSC in conjunction with updating was found to be beneficial overall, but only led on average to less than 10% improvement in prediction performance.

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