Multivariate Calibration Techniques Research Articles

Multivariate Calibration Techniques Using UV Spectrophotometry for Quantifying Ticagrelor in Pharmaceutical Formulations

Multivariate Calibration Techniques Using UV Spectrophotometry for Quantifying Ticagrelor in Pharmaceutical Formulations

Determination of DEET, Icaridin, and IR3535 in insect repellents using excitation-emission matrix (EEM) fluorescence spectroscopy and multiway calibration

Due to rising temperatures in recent years, there has been an increase in outbreaks of arboviral diseases like Dengue, chikungunya, Zika, and yellow fever, which are primarily transmitted by two Aedes mosquitos. The outbreak has sparked significant concern among the population and prompted countries to heighten their precautions, specially by increasing the use of insects repellents. Regarding the regulatory demands of these products, this study develops a novel analytical method for the determination of DEET, Icaridin, and IR3535 in insect repellents combining excitation-emission matrix (EEM) fluorescence spectroscopy with higher-order multivariate calibration techniques. For this purpose, a dataset comprising 21 samples containing the studied active ingredients was used to construct Parallel Factor Analysis (PARAFAC) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) models. As a result, DEET, Icaridin, and IR3535 were quantified with a RMSEP lower than 1.84% for PARAFAC and below 11.30% for MCR-ALS. The EJCR test confirmed the accuracy of the proposed methods, which agreed with the high-performance liquid chromatography reference method at a 95% confidence level. These findings suggest that developing multiway calibration models using EEM proves to be accurate and cost-effective for quality control in repellent-based products.

Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe

Chromium is widely recognized as a significant pollutant discharged into the environment by various industrial activities. The toxicity of this element is dependent on its oxidation state, making speciation analysis crucial for monitoring the quality of environmental water and assessing the potential risks associated with industrial waste. This study introduces a single-well fluorometric sensor that utilizes orange emissive thioglycolic acid stabilized CdTe quantum dots (TGA-QDs) and blue emissive carbon dots (CDs) to detect and differentiate between various chromium species, such as Cr (III) and Cr (VI) (i.e., CrO42− and Cr2O72−). The variations of fluorescence spectra of the proposed probe upon chromium species addition were analyzed using machine learning techniques such as linear discriminant analysis and partial least squares regression as a classification and multivariate calibration technique, respectively. Linear discriminant analysis (LDA) demonstrated exceptional accuracy in differentiating single-component and bicomponent samples. Additionally, the findings from the partial least squares regression (PLSR) showed that the sensor created has strong linearity within the 1.0–100.0, 1.0–100.0, and 0.1–15 μM range for Cr2O72−, CrO42−, and Cr3+, respectively. Furthermore, appropriate detection limits were successfully achieved, which were 2.6, 2.9, and 0.7 μM for Cr2O72−, CrO42−, and Cr3+, respectively. Ultimately, the successful capability of the sensing platform in the identification and quantification of chromium species in environmental water samples provides innovative insights into general speciation analytics.

Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits

The efficiency of hyper- and multispectral imaging (HSI and MSI) has gained considerable attention in research on plant phenotyping. This is due to their ease of use while being considered a nondestructive technology. Unlike current point-scanned spectroscopy, both HSI and MSI extract spatial and spectral information while covering a wide range of a plant body. Moreover, it is necessary to equip the extracted information with multivariate calibration techniques, followed by model evaluation. To date, the application of HSI and MSI for monitoring plant growth under a controlled environment is emerging and showing a good trend. Our systematic literature review discusses spectroscopy imaging techniques and their chemometric approaches as a sustainable sensor technology to detect plant traits. In conclusion, we also explore the possibility of carrying out HSI and MSI during plant trait analysis.

Machine Learning-Assisted Colorimetric Assay Based on Au@Ag Nanorods for Chromium Speciation.

The oxidation state of an element significantly controls its toxicological impacts on biological ecosystems. Therefore, design of robust sensing strategies for multiplex detection of species with respect to their oxidation states or bonding conditions, i.e., chemical speciation, is quite consequential. Chromium (Cr) species are known as the most abundant inorganic groundwater pollutants and can be quite harmful to human health depending on their oxidation states. In the present study, a multicolorimetric probe based on silver-deposition-induced color variations of gold nanorods (AuNRs) was designed for identification and quantification of Cr species including Cr (III) and Cr (VI) (i.e., CrO42- and Cr2O72-) in water samples. In fact, the presence of Cr species leads to inhibition of the silver metallization of AuNRs to various degrees depending on the concentration and identity of the analyte. This process is accompanied by the blue shift of the longitudinal peak which results in sharp-contrast rainbow-like color variations, thereby providing great opportunity for highly accurate visual detection. The gathered dataset was then statistically analyzed using two pattern recognition and regression machine learning techniques. In particular, linear discriminant analysis was used as a classification method to discriminate the unicomponent and mixture samples of Cr species with 100% accuracy. Then, a well-known multivariate calibration technique called partial least-squares regression was employed for quantitative analysis of Cr species. Responses were linearly related to Cr species concentrations over a wide range of 10.0-1000.0, 1.0-200.0, and 1.0-200.0 μmol L-1 with detection limits of 37.7, 8.7, and 2.9 μmol L-1 for Cr3+, CrO42-, and Cr2O72-, respectively. The practical applicability of the multicolorimetric probe was successfully evaluated by analyzing Cr species in several water specimens comprising tap water, mineral water, river water, and seawater. Above all, the vivid rainbow color tonality of the proposed assay further improves the accuracy of the naked eye detection, making it a practical platform for on-site monitoring of Cr contamination.

Correlation analysis of modern analytical data - a chemometric dissection of spectral and chromatographic variables.

The Standard Practices for Infrared Multivariate Quantitative Analysis (ASTM E1655) provide a guide for determining physicochemical properties of materials using multivariate calibration techniques applied to chemical sources that have high multicollinearity and correlated information. Partial least squares (PLS) is the most widely used multivariate regression method due to its excellent prediction capabilities and easy optimization. Initially applied to chromatographic data, PLS has also shown great results in near-infrared (NIR) and mid-infrared (MIR) spectroscopies. However, complex chemical matrices with low correlation may not be efficiently modeled using PLS or other multivariate analyses limited by grouping similar information (such as latent variables or principal components). Therefore, this study aims to evaluate the multicollinearity of different analytical techniques, such as high-temperature gas chromatography (HTGC), NIR, MIR, hydrogen nuclear magnetic resonance (1H NMR), carbon-13 nuclear magnetic resonance (13C NMR), and Fourier transform ion cyclotron resonance mass spectrometry coupled to the electrospray source in positive and negative ionization modes (ESI(±)FT-ICR). Descriptive statistics (coefficient of determination, R2) and principal component analysis (PCA) were used to identify the distribution of correlated information. Results showed that NIR and MIR spectroscopies exhibited a higher percentage of correlated variables, while 13C NMR and ESI(±)FT-ICR MS had more discrete profiles. Therefore, PLS development may be more effectively applied to NIR, MIR, and 1H NMR data, while 13C NMR and mass spectra may require other algorithms or variable selection methods in combination with PLS.

Quantitative analysis of terahertz signals using CWT-based spectrogram and Zernike image moments

The combination of terahertz (THz) spectroscopic measurements and multivariate calibration techniques has become a well-established technique in many research fields. However, intentional or unintentional changes in environmental conditions, THz instruments and/or of the substance itself make the established calibration model becoming insufficient and inadequate for the further application. In this article, we introduce, discuss, and evaluate a new multivariate calibration method, the CWT-ZM, that combines the merits of the Zernike moment (ZM) invariance and the continuous wavelet transform (CWT) time–frequency analysis. With the help of a wavelet time–frequency analysis, the THz pulse is expanded into a two-dimensional (2D) time–frequency plane that provides richer and more direct characteristic information in the time and frequency domain simultaneously. In addition, Zernike moments provide linearly independent descriptors for the 2D time–frequency intensity image and are invariant to THz signal affine transformations, such as peak shifting, baseline drifting, and scaling. In this manner, we obtain a set of features that exhibit a high capability to capture the concentrations of the target compounds and a high invariance of the different measuring instruments and the variable environment. This approach results in a more robust regression system with improved generalization properties with respect to standard methods. Experiments were then conducted on a THz dataset of pharmaceutical tablets acquired by two different THz instruments, and these confirmed the effectiveness of the proposed approach. Furthermore, CWT-ZM is an extensible framework that can be combined with various spectral qualitative and quantitative analysis algorithms.

Application of PLS and PCR as Multivariate Calibration Techniques for Simultaneous Estimation of Ofloxacin and Ornidazole in Binary Mixtures

Two chemometric methods PLS and PCR were used to simultaneously estimate Ofloxacin and Ornidazole in binary mixtures. Mean centering is used as a preprocessing tool and then PLS and PCR models were built with help of calibration and validation set. These methods showed good results for validation parameters including accuracy, precision, linearity, robustness etc. at the same time lower values of RMSEP, PRESS and RMSECV shows statistical significance of the developed methods. Results show that multivariate calibrations techniques can be successfully used to analyses Ofloxacin and Ornidazole in pharmaceutical formulations.

Prediction of octane numbers for commercial gasoline using distillation curves: a comparative regression analysis between principal component and partial least squares methods

In this study, it was found that limiting the number of explanatory distillation curves input variables in a multivariate regression method to the most significant ones (10%, 50%, 90%, and 100% of recovered condensate volumes) provides a reliable model that can be used as a quick and inexpensive method for determining the octane rating of commercial gasoline. Octane numbers were predicted using a combination of distillation curves and multivariate calibration techniques of partial least squares regression (PLSR) and principal component regression (PCR). The root mean square error of prediction (RMSEP) evaluation for calibration and prediction sets for PLSR technique varies between 0.545 and 0.902 for MON and RON, respectively, which is relatively acceptable when compared to earlier studies. Comparing PLSR and PCR results revealed that PCR has slightly lower RMSEP values for MON and RON, 0.527 and 0.899, respectively. The fact that measurements of studied key temperatures are a routine part of gasoline quality control makes the current technique a promising alternative to the suggested PLS distillation curves models in the literature, which require a larger number of explanatory variables. This method can also replace the costly and time-consuming spectral analysis now used to determine the octane rating of gasoline.

Quantification of Total Tocotrienol content in Pharmaceutical Formulation by UV Spectrophotometric aided Multivariate Calibration Technique

The present abstract employs the use of Multivariate Calibration Technique for the assessment of Tocotrienol in the marketed pharmaceutical formulation. The multivariate calibration technique was established by the use of linear regression equation employing the relationship at five different concentration levels and its absorbance recorded at five selected wavelengths around the λmax of the drug. The method was observed to be linear over a concentration range of 30-90 µg/mL with a correlation coefficient value more than 0.99. The λmax of Tocotrienol was found to be 293 nm. The developed method was found to be simple, rapid, accurate and precise in accordance to the ICH protocol Q2(R1). This statistical methodology gives ideal outcomes by eliminating the deviations emerging out of instrumental or experimental conditions.

Multivariate calibration technique for spectrophotometric quantification of ondansetron in bulk drug and pharmaceutical formulations

The aim of the present work to develop an accurate, simple, sensitive, and validated Ultra violet (UV) spectrophotometric procedure using multilinear regression method for the analysis of Ondansetron in its bulk and commercial dosage form. The multivariate calibration approach was based on equations constructed using linear regression analysis using the correlation between absorbance and concentration at selected five different wavelengths. Ondansetron had a maximum wavelength of 246 nm. The findings were statistically analysed. A linear plot in the concentration range of 5-15 µg/mL, with a regression co-efficient of 0.999 was obtained. The % RSD for intra-day and Inter day precision were 0.0409 and 0.0228, respectively. The assay was determined and found to be 98.38 % – 101.10 % w/w.

Multivariate UV spectrophotometric quantification of Cilnidipine in bulk drug and pharmaceutical formulations

The aim of this research work was to develop a simple, accurate, sensitive and validated Ultra Violet (UV) spectrophotometric assay using the multivariate regression method for the analysis of Cilnidipine. This multivariate calibration technique was based on equations constructed using linear regression analysis using the correlation between absorbance and concentration at five selected equidistant wavelengths. Cilnidipine had a maximum absorbance at 240 nm. The findings were statistically analyzed for significance. A linear plot in the concentration range of 3-9 μg/mL, with a regression coefficient of 0.999 was obtained. The % RSD for intra-day and Inter-day precision were 0.4558 and 0.6099, respectively. The assay was determined and found to be 99.1% - 101.67% % w/w. Keywords: Cilnidipine, Antihypertensive agent, UV spectrophotometry, Multivariate calibration, Assay, ICH guidelines).

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).

Variation in the determination of platinum group metals using ICP OES induced by the effect of complex matrices and the correction method based on multivariate calibration

We reveal the effect of matrix complexity on the quantitative determination of platinum group metals (PGMs) using inductively coupled plasma optical emission spectrometry. The variation in PGM determination is compensated by employing the multivariate calibration technique.

Introducing an interesting and novel strategy based on exploiting first-order advantage from spectrofluorimetric data for monitoring three toxic metals in living cells

In this work, we did our best to develop a novel and interesting analytical method based on coupling of spectrofluorimetry with first-order multivariate calibration techniques for simultaneous determination of lead (Pd), zinc (Zn) and cadmium (Cd) in HeLa cells. To achieve this goal, quenching of the emission of graphene (GR) was individually investigated in the presence of Pb, Zn and Cd and then, according to the linear ranges obtained from individual calibration graphs, a multivariate calibration model was developed based on modeling of the quenching of the emission of GR in the presence of the mixtures of Pb, Zn and Cd. First-order multivariate calibration models were constructed by partial least squares (PLS), principal component regression (PCR), orthogonal signal correction-PLS (OSC-PLS), continuum power regression (CPR), robust continuum regression (RCR) and partial robust M-regression (PRM) and their performances were evaluated and statistically compared. Finally, the OSC-PLS was chosen as the best model with the best practical performance for analytical purposes.

Rapid determination of three textile surfactants in environmental samples by modeling excitation-emission second-order data with multi-way calibration methods.

The textile industry is an important potential source of environmental pollution due to the use of chemical products. Dyes, hydrolyzed dyes, and surfactants, among others, are chemical compounds present in wastewater of textile plant. Moreover, the anionic surfactants have toxic effects for various aquatic organisms even in low concentrations. The methodologies investigated to quantify surfactants, in general, consume a lot of analysis time and frequently use toxic or environmentally objectionable reagents. For these reasons, the objective of this work was to develop a quick and simple method to quantify surfactants without the use of expensive reagents and equipment, avoiding extraction and preconcentration stages. The proposed method is based on fluorescent spectroscopy measurements for the acquisition of second-order data in excitation-emission matrices and multivariate calibration techniques applied to the data. The unfolded partial least squares combined to residual bilinearization (U-PLS/RBL) algorithm was better than parallel factor analysis (PARAFAC). U-PLS/RBL accurately quantified alkylnonylphenolethoxylated (APEO), dodecylbenzenesulfonic acid (ADBS), and 2-phenoxy-ethoxylated fatty alcohol (AGFE) surfactants. The chemometric model obtained good analytical figures of merit: REP% between 5 and 13 and LOQ between 0.45 and 2.77 μg mL-1. This methodology had no significant difference compared with results obtained by a HPLC-FD reference technique, in addition with a considerable reduction in analysis time, reagent consumption, and therefore lower cost. For environmental applications, APEO, ADBS, and AGFE were quantify in textile wastewater treatment and in the receiving water body. The concentrations varied from 8.73 to 73.94 μg mL-1 in the textile wastewater and were not detected in the receiving water body.

Study of the antioxidant activity of Pistacia atlantica Desf. Gall extracts and evaluation of the responsible compounds

Defensive gall traits against natural enemies, such as predators and parasitoids, attracted much attention of ecologists and evolutionary biologists. To our knowledge, naturally occurring antioxidant compounds present in Pistacia atlantica Desf. Galls have not yet been reported and characterized. The objective of this study is to evaluate the effects of the harvest period and plant gender on these antioxidant activities. Furthermore, peaks present in chromatographic fingerprints, probably showing significant antioxidant activities, were identified. Three antioxidant assays, 2,2 diphenyl-1-picrylhydrazyl (DPPH) and 2,2 azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging besides the ferric reducing antioxidant power (FRAP), were applied to determine the total antioxidant activity in galls from both genders of Pistacia atlantica. Fingerprints of ten P. atlantica gall extracts were obtained by Ultra Performance Liquid Chromatography (UPLC). Extensive pre-treatment of the data and a linear multivariate calibration technique, Partial Least Squares (PLS), were used for model construction. Our findings highlighted that gender and harvest period affected the antioxidant activities. Major peaks, potentially influencing the antioxidant activity of the samples, were indicated by studying the regression coefficients of the PLS models. Individual antioxidant compounds of P. atlantica galls were identified by UPLC coupled to a hybrid quadrupole orthogonal time-of-flight mass spectrometer (Q-TOF/MS). The phenolic compounds methyl gallate and valoneic acid dilactone were considered to be important antioxidants in the FRAP and scavenging ABTS assays, while quinic acid showed to be the main component influencing the antioxidant activity measured with the DPPH method. The findings of the present study suggest that P. atlantica gall might be used as an accessible source of natural antioxidants and for the treatment of diseases resulting from oxidative stress.

Eco-friendly green synthesis of functionalized mesoporous silica nanospheres for the determination of Al(III) ions in multiple samples of different kinds of water

The current work proposed a new green procedure that is not sophisticated to recognize and determine the Al(III) ions in multiple water samples. The suggested method was conducted based upon the direct immobilization of aurintricarboxylic acid reagent into the mesoporous silica nanospheres to shape a unique and novel solid sensor. Al(III)-ATA red-complex has been formed at pH 4.0 and spectrophotometrically measured at 525 nm. Moreover, the complexation was reversible, and the ATA sensor retained his functionality even after six-time reuse/cycles using EDTA as eluent. Univariate and multivariate (partial least squares 1, PLS-1) calibration techniques were utilized for calculating the figures of merit for the determination of the Al(III) ions. The obtained calibration curve was linear from 2.0 to 70 ppb Al(III) ions concentration. The developed method has a detection limit of 3.5 ppb. In addition, the ATA sensor showed high adsorption capacity value (118.53 mg/g) which gives it a great advantage to be applicable as nanocollector for trapping Al(III) ions. The novel ATA sensor showed high degree of selectivity, sensitivity, reproducibility, and stability. The current study explores the effectiveness of the ATA sensor for the first time to produce a green solid sensor to determine the trace amount of Al(III) in diverse water types; tap, mineral, river, well, and sea water.

SPECTROPHOTOMETRIC QUANTIFICATION OF GRANISETRON IN BULK DRUG AND PHARMACEUTICAL FORMULATIONS EMPLOYING MULTIVARIATE CALIBRATION TECHNIQUE

The study goal is to establish a sensitive, accurate, simple and validated Ultra violet (UV) spectrophotometric technique by multilinear regression method for the assessment of Granisetron in the bulk drug and pharmaceutical formulations. The multivariate calibration technique relies on linear regression equations and employs correlation between absorbance and concentration at five different wavelengths. The λmax of Granisetron was at 302 nm. The results were treated statistically. A linear plot was achieved in the concentration range of 10 - 30 μmol/L and regression coefficient was obtained to be 0.999. The percentage recovery was obtained in the range of 99.48 - 101.81 % w/w.

Forecasts of tropospheric ozone in the Metropolitan Area of Rio de Janeiro based on missing data imputation and multivariate calibration techniques.

Multivariate calibration based on partial least squares, random forest, and support vector machine methods, combined with the MissForest imputation algorithm, was used to understand the interaction between ozone and nitrogen oxides, carbon monoxide, wind speed, solar radiation, temperature, relative humidity, and others, the data of which were collected by air quality monitoring stations in the metropolitan area of Rio de Janeiro in four distinct sites between, 2014 and, 2018. These techniques provide an easy and feasible way of modeling and analyzing air pollutants and can be used when coupled with other methods. The results showed that random forest and support vector machine chemometric techniques can be used in modeling and predicting tropospheric ozone concentrations, with a coefficient of determination for making predictions up to 0.92, a root-mean square error of calibration between 4.66 and 27.15µgm-3, and a root-mean square error of prediction between 4.17 and 22.45µgm-3, depending on the air quality monitoring stations and season.