Chemometric Methods Research Articles

Real-time monitoring of chromatic and phenolic dynamics of vinification employing UV-Vis spectroscopy, Python and chemometrics

Increasing consumer demand for high-quality wines required the introduction of more comprehensive monitoring indicators for quality management, necessitating winemakers to strive for rapid, real-time, and affordable monitoring tools during winemaking. This study investigated the feasibility of cost-efficient and easy-to-implement ultraviolet-visible (UV-Vis) spectroscopy combined with chemometric methods to predictively monitor the evolution of wine color and phenolics throughout commercial fermentation. Fermentations on a winery scale were carried out with a daily analysis of the UV-Vis spectra, color, and phenolics. Furthermore, characteristic wavelengths were established by a self-compiled Python program to reduce redundancy in spectra. The optimized partial least squares (PLS) model predicted well with a residual prediction deviation (RPD) greater than 2.54, and the robustness was further verified by a robust external Bland-Altman method. UV-Vis spectroscopy can achieve objective monitoring of the practical fermentation profile, which can better help winemakers make decisions to improve wine quality and consistency.

When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization

BackgroundSurface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems. ResultsHerein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification. SignificanceThe proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

Depth-resolved elemental and molecular analysis of polymeric multilayers with EI-MS and ICP-OES using chemometric evaluation

Polymeric composite materials with layered structures have increasingly gained importance as, for specific applications, the required properties cannot be achieved with one single material; thus, for example, multi-layer polymers or special surface coatings are employed.None of the conventionally available techniques enables a comprehensive characterization of composite materials, including depth-resolved information about their organic and elemental constituents.In this work, a previously introduced approach enabling the direct characterization of the organic and inorganic constituents of solid polymer samples was further developed and optimized for depth profiling analysis of stacked layer polymers. For this purpose, the ablation chamber’s washout behavior and transfer line’s transport efficiency were improved. Chemometric methods were applied for the data analysis to gain greater insight and enable more precise differentiation, comparison and classification.The work investigates the feasibility of the developed approach by analyzing a multi-layer stacked polymer sample created from nail polishes and using cluster analysis on the data collected with the bimodal detection. The layers of the sample could be differentiated by their organic and elemental constituents’ fingerprints, achieving a depth resolution of 7 µm.

Improvement of Qualitative Analyses of Aliphatic Alcohols Using Direct Catalytic Fuel Cell and Chemometric Analysis Format.

Direct catalytic methanol fuel cells (DCMFCs) have been studied for several years for energy conversion. Less extensive is the investigation of their analytical properties. In this paper, we demonstrate that the behavior of both the discharge and charger curves of DCMFCs depends on the chemical composition of the solution injected in the fuel cell. Their discharge and charge curves, analyzed using a chemometric data fusion method named ComDim, enable the identification of various types of aliphatic alcohols diluted in water. The results also show that the identification of alcohols can be obtained from the first portion of the discharge and charge curves. To this end, the curves have been described by a set of features related to the slope and intercept of the initial portion of the curves. The ComDim analysis of this set of features shows that the identification of alcohols can be obtained in a time that is about thirty times shorter than the time taken to achieve steady-state voltage.

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

Development and application of chemometrical methods based on 1H-NMR spectra of fruit juices – Structural approach for analysis of wide and unbalanced datasets

Analytical verification of the authenticity of fruit juices with respect to organic quality or production as direct juice (NFC) vs. juice from concentrate (FC) is challenging. To address this, the promising combination of 1H-NMR analysis with multivariate classification methods was applied to a sample collection of 506 orange juices, 649 apple juices and 224 tomato juices with the purpose to develop optimized classifying models for a subsequent use in juice monitoring. In a standardized procedure, the treatment of outliers, variable selection, the handling of unbalanced datasets and adequate model validation were addressed. The algorithms LDA, PLSDA, Random Forest, linear SVM, radial SVM and kNN were applied to each classification question. Therefore, standardized guidelines for selecting an optimal sampling method and a final model for the use in routine were developed. The differentiation of the classes orange – organic vs. non-organic, orange – NFC juices vs. FC juices, apple – NFC juices vs. FC juices and tomato – NFC juices vs. FC juices, was achieved each with a mean accuracy of above 95.0% in independent validation. These high performing models were subsequently implemented into an R-Shiny application for routine analysis. The final models for apple - organic vs. non-organic and tomato - organic vs. non-organic performed with accuracies of 82.8% and 89.8%, respectively. Ultimately, through standardization and optimization, it was possible to transparently select valid classification models that are now used in routine control analysis of orange, apple, and tomato juices.

Natural pre-treatment in raisins production: Effects on quality during storage

BRS Vitoria raisins were produced with a pre-treatment using a natural surfactant, i.e., extra-virgin olive oil (EVOO), to (i) better elucidate its action on the grape surface using scanning electron microscopy; (ii) evaluate its influence on the oleanolic and ursolic acid content of raisins through HPLC-DAD; (iii) assess raisins characteristics during 180-day storage at different temperatures by using physicochemical and microbiological analyses with multivariate-based chemometric methods (MASLT). Grapes which presented adequate physicochemical characteristics and high concentration of anthocyanins originated raisins similar to those reported in the literature. The pre-treatment accelerated the dehydration process (about 26%) mainly because of the non-uniform redistribution of the surface waxy components accompanied by the formation of microholes and cracks. The small amount of EVOO used did not affect the oleanolic acid content in raisins, yet this compound proved to resist drying, which means that there is no loss of wax during the pre-treatment. For both untreated (RW) and pre-treated raisins (RP), the main changes triggered over time were not related to microbiological characteristics. Most of the changes appeared to occur right during drying and not during storage for RW, whereas for RP the physicochemical characteristics that mostly correlated with the storage changes were total monomeric anthocyanins, total acidity, and total sugars; also, they tended to reach an equilibrium and equaled RW at the end of 180 days, especially at 35 °C. These parameters proved to be the main characteristics related to the changes triggered in raisins. For both raisins, sugar crystals were formed in the flesh, but they were not visible to the naked eye or perceived by consumers. The results show that it is highly desirable to have a high-quality product at the beginning of storage, which was possible thanks to the EVOO pre-treatment used.

Rapid screening of tuna samples for food safety issues related to histamine content using fourier-transform mid-infrared (FT-MIR) and chemometrics

Biogenic amines (BAs) generally result from the decarboxylation reaction of free amino acids as a result of the activity of different microorganisms. A build-up of these compounds can result in food being spoilt. Therefore, the rapid and precise detection of BAs like histamine is an important task for food safety. This research aimed to explore the potential of Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy combined with chemometric methods to assess histamine in fresh tuna quantitatively. Based on the FT-MIR data, partial least squares regression models for the prediction of histamine were successfully constructed with R2 > 0.90. Machine learning algorithms (partial least squares-discrimination analysis, k-nearest neighbors, and support vector machine) were applied, and excellent discrimination results were achieved based on the limits specified in two different legislations (EU and FDA). The results support the use of a rapid, economic and reliable approach for the discrimination of samples that could pose a health risk to consumers.

Rapid detection of endogenous impurities in walnuts using near-infrared hyperspectral imaging technology

Impurities in walnut-based products pose a significant risk to human health, necessitating the development of rapid and efficient methods for detecting endogenous impurities in walnut shell-breaking materials. To address this issue, this study aims to investigate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) in conjunction with conventional chemometric methods and deep learning models. First, biochemical experiments demonstrated differences in the total phenolic and flavonoid contents of different walnut kernels. An improved support vector machine model based on the butterfly optimization algorithm was constructed for classification using an NIR dataset of endogenous impurities in walnuts, achieving an accuracy of 96.12 %. In addition, this study proposed the WT-NIRSNet model, a deep neural network with an additional attention mechanism, and compared it to a traditional chemometric approach. The study demonstrated an accuracy of 99.03 % on the test set using WT-NIRSNet to solve the fast classification problem of endogenous impurities in walnuts. It can achieve desirable classification results using only raw spectral data without complex preprocessing operations and is superior to traditional chemometric models. Therefore, the NIR-HSI technique combined with deep neural networks has a significant potential for detecting endogenous impurities in walnuts.

Geographical authentication of saffron by chemometrics applied to the ion mobility spectrometry data

There is a lack of a reliable tool for quickly determining the geographical origins of saffron (SFR). Ion mobility spectrometry (IMS) has emerged as a promising method for rapid authentication. In this study, 232 Iranian SFR samples harvested in five distinct areas (Khorasan, Azerbaijan, Golestan, Fars, and Isfahan) were analyzed by IMS coupled with chemometric methods. The principal component analysis (PCA) was applied for analyzing the collected IMS data, utilizing three principle components (PCs) that accounted for 81 % of the explained variance. Moreover, the partial least squares-discriminant analysis (PLS-DA) demonstrated the average sensitivity and specificity rates, of 72.3 % to 92.5 % for the exernal test set and 75.5 % to 94.3 % for training set. The accuracy values were ≥ 85.0 % for the prediction set for all classes of samples. The results of this study revealed a successful application of IMS and chemometric methods for rapid geographical authentication of saffron samples in Iran.

Stable isotope profiling of cigarettes assisted with chemometrics for authenticity determination

The battle against tobacco fraud presents a formidable challenge, given the global proliferation of imitation and counterfeit cigarettes. This not only inflicts economic losses on governments but also poses significant health risks due to the prevalence of these fraudulent products. This research delves into the application of stable isotopes (δ13C, δ15N, δ2H, and δ18O) and bio-elements (C% and N%) to discern the authenticity of various cigarette brands. Differences in brand origin notably impacted the δ15N and δ2H values. The assessment of cigarette authenticity, as well as the specific brands, unveiled maximum variances in the δ15N and δ2H values, underscoring their potential as robust discriminators between genuine and counterfeit products. Unsupervised Principal Component Analysis results consistently corroborated the authenticity of the samples for the four brands. The integration of chemometric methods, such as Orthogonal Partial Least Squares-Discriminant Analysis, yielded highly accurate authentication for cigarettes across the four brand categories, achieving an accuracy of over 96.8 %. Additionally, this investigation revealed that the critical determinants in distinguishing authentic cigarettes were the isotopic values of δ15N, C%, and δ18O. This work paves the way for a more convenient avenue for the classification of cigarette authenticity compared to untargeted analysis that need to a comprehensive set of chemical fingerprints.

Detection of Fusarium spp. infection in potato (Solanum tuberosum L.) during postharvest storage through visible–near-infrared and shortwave–near-infrared reflectance spectroscopy

Abstract Pathogen infection can damage agricultural products, thereby reducing their economic value. Fusarium spp. is a fungal pathogen that infects potatoes (Solanum tuberosum L.) and causes dry rot. In this study, we utilized visible–near-infrared (Vis–NIR) and shortwave–near-infrared (SW–NIR) spectroscopy for the early detection of Fusarium spp. infection in potato tubers. The spectrometer used in this study analyzed the Vis–NIR (400–1,000 nm) and SW–NIR (970–1,700 nm) regions. A total of 183 potato (G2 “Granola L.” variety) samples were used. Among these, 93 samples were artificially inoculated with Fusarium solani mycelia, while 90 samples were left uninfected and considered the control group. The potato samples were stored at two different temperatures (12 and 25°C). Vis–NIR and SW–NIR spectra were analyzed by a chemometric method, namely principal component analysis with linear discriminant analysis (PCA–LDA), to differentiate healthy and infected potatoes. The PCA–LDA model based on Vis–NIR spectra exhibited a calibration accuracy of 80.26% and a reliability of 65%. Meanwhile, the PCA–LDA model based on SW–NIR spectra exhibited a calibration accuracy of 100% and a reliability of 97.30%. Overall, both methods demonstrated their suitability for differentiating potato tubers with Fusarium spp. fungal infection and healthy ones; however, the results suggest that SW–NIR spectroscopy is more effective than Vis–NIR spectroscopy.

Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI)

Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.

Discriminant analysis of a mixture of pathogenic bacteria into different types and proportions by surface-enhanced Raman scattering spectroscopy combined with chemometric methods

Accurate identification and discrimination of bacteria is crucial for ensuring food safety and reducing pathogenic infections. This study presents a novel approach that combines surface-enhanced Raman scattering spectroscopy (SERS) with chemometric methods for discriminant analysis of a mixture of pathogenic bacteria into different types and proportions. Au@Ag@SiO2 composite nanomaterials were employed as the SERS substrate to collect Raman spectra of multiple pathogenic bacteria. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) methods were combined with standard normal variate (SNV) to discriminate the different species of mixed bacteria and the multiple proportion mixed bacterial samples, respectively. The results showed that SNV-PLS-DA had good classification performance in the discriminant analysis of different species of mixed bacteria, with an accuracy of 92% for the external test set. Furthermore, both SNV-PLS-DA and SNV-OPLS-DA models exhibited excellent classification performance in the discrimination of multiple pathogenic bacteria at different mixing proportions, achieving 100% accuracy in the external test set, but except for mixed samples of Escherichia coli and Salmonella typhimurium. Our method demonstrates the accurate capability of the SERS platform combined with chemometric methods in the discriminant analysis of multiple pathogenic bacteria at different species and mixing proportions, which provides novel insights for the synchronous analysis of multiple pathogenic bacteria.

Determination of organic acids for predicting sourness intensity of tea beverage by liquid chromatography-tandem mass spectrometry and chemometrics methods.

The contents of organic acids (OAs) in tea beverage and their relationship with taste intensity have not been fully understood. In this work, a rapid (10min for a single run) and sensitive (limits of quantification: 0.0044-0.4486µg/mL) method was developed and validated for the simultaneous determination of 17 OAs in four types of tea, based on liquid chromatography-tandem mass spectrometry with multiple reaction monitoring mode. The contents of 17 OAs in 96 tea samples were measured at levels between 0.01 and 11.80g/kg (dried weight). Quinic acid, citric acid, and malic acid were determined as the major OAs in green, black, and raw pu-erh teas, while oxalic acid and tartaric acid exhibited the highest contents in ripe pu-erh tea. Taking the OAs composition as input features, a partial least squares regression model was proposed to predict the sourness intensity of tea beverages. The model achieved a root-mean-square error of 0.58 and a coefficient of determination of 0.84 for the testing set. The proposed model provides a theoretical way to evaluate the sensory quality of tea infusion based on its chemical composition.

Development of Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Coupled with Multivariate Classification Chemometric Model for Routine Screening of Paracetamol, Ibuprofen, and Aspirin Adulteration in Herbal Products

Objective: The objective of this study is to develop and validate a routine screening test for the determination of three common antipyretic-analgesic synthetic drugs (paracetamol, ibuprofen, and aspirin) adulteration in herbal products using Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) coupled with chemometric method. Methods: ATR-FTIR spectra of sixteen testing sets of herbal product samples for pain and fever indications were used for multivariate chemometrics model construction. Linear Discriminant Analysis (LDA) was selected as a method for model construction with IBM SPSS for statistical analysis. Model development employed feature selection, such as the stepwise method for variable selection. The model with a high %correct classification and cross-validation was selected and was then validated with an independent testing data set with an auto-prediction test, confusion matrix, and Receiver Operating Characteristic (ROC) curve. To validate the developed test for routine use, the result from ATR-FTIR method was compared with the standard HPLC and TLC analyses used for adulteration screening. Results: The selected model's overall %correct classification result was 97.7%, with a cross-validation of 93.8% rate in training set samples. External validation with an independent testing dataset gave an overall correct classification of 93.8%, with an area under the curve of ROC at 0.979. Comparative testing revealed that model performance was comparable with the HPLC and TLC methods, which routinely detect the presence of paracetamol, aspirin, and ibuprofen. The results of testing set samples classification were consistent with training set samples. Conclusion: Against the standard chromatographic methods, the multivariate chemometric model based on ATR-FTIR demonstrates comparable detection capability to determine adulteration of paracetamol, ibuprofen, and aspirin in herbal products.

Benchtop NMR Coupled with Chemometrics: A Workflow for Unveiling Hidden Drug Ingredients in Honey-Based Supplements.

Recently, benchtop nuclear magnetic resonance (NMR) spectrometers utilizing permanent magnets have emerged as versatile tools with applications across various fields, including food and pharmaceuticals. Their efficacy is further enhanced when coupled with chemometric methods. This study presents an innovative approach to leveraging a compact benchtop NMR spectrometer coupled with chemometrics for screening honey-based food supplements adulterated with active pharmaceutical ingredients. Initially, fifty samples seized by French customs were analyzed using a 60 MHz benchtop spectrometer. The investigation unveiled the presence of tadalafil in 37 samples, sildenafil in 5 samples, and a combination of flibanserin with tadalafil in 1 sample. After conducting comprehensive qualitative and quantitative characterization of the samples, we propose a chemometric workflow to provide an efficient screening of honey samples using the NMR dataset. This pipeline, utilizing partial least squares discriminant analysis (PLS-DA) models, enables the classification of samples as either adulterated or non-adulterated, as well as the identification of the presence of tadalafil or sildenafil. Additionally, PLS regression models are employed to predict the quantitative content of these adulterants. Through blind analysis, this workflow allows for the detection and quantification of adulterants in these honey supplements.

Chemometric assessment of electronic cigarettes based on the ICP-MS determination of multiple heavy metal concentrations

To elucidate the concentrations of heavy metal elements in commercially available electronic cigarettes and improve quality assessment. Inductively coupled plasma-mass spectrometry (ICP-MS) coupled to chemometrics was used to determine the concentrations of Cr, Ni, As, Cd, Sn, Sb, Hg, and Pb in the e-liquids and aerosols derived from 32 electronic cigarettes sold commercially under six brand names. The e-liquids contained: 4.858 to 274.658 (Cr), 17.292 to 3068.375 (Ni), 3.217 to 29.867 (As), 0.225 to 24.717 (Cd), 0.783 to 17.042 (Sn), 0.658 to 36.033 (Sb), 0.658 to 187.592 (Hg), and 2.458 to 17.417 (Pb) ng g−1. The aerosol samples contained: 276.075 to 3333.175 (Cr), 72.908 to 1150.183 (Ni), 4.567 to 86.958 (As), 0.400 to 12.842 (Cd), 1.092 to 32.142 (Sn), 0.976 to 10.633 (Sb), 3.483 to 234.708 (Hg), 27.833 to 849.100 (Pb) ng 100 puffs−1. The recovery of heavy metals ranged from 99.1% to 112.4% in the e-liquids and from 87.3% to 116.6% in the aerosols, with RSD values below 10%. Hierarchical cluster analysis grouped the e-liquids into eight clusters, and the aerosols into five, indicating differences between products within brands and between different brands. Orthogonal partial least squares discriminant analysis coupled with variable importance in projection (VIP > 1) identified As and Sb as the primary heavy metals causing differences between the e-liquids, while differences between the aerosols were caused by Hg, As, Pb, Cd, and Cr. The use of chemometric methods yields a greater depth of information that will support improvements to the quality control of e-cigarette products and the assessment of their potential risk to human health.

Environmentally safe chemometric evaluation and data analysis for the kinetic investigation of mirabegron stability

The physicochemical characteristics of different pharmaceuticals may change with transportation, storage, and usage which directly affect their efficacy, quality, and other features. It is crucial to investigate the kinetic action of pharmaceutical compounds to produce predictive data that aid in the computation of their shelf lives. Several conventional analytical methods introduce unwanted dangerous risks, like employing unsafe solvents, that harm both the analyst and the environment. This created the motive for analysts to apply green approaches that consider using safe chemicals and producing as little waste as possible. In this study, powerful green assessment methods were investigated to study the kinetic process of Mirabegron (MIR) in different hydrolytic conditions giving its main degradation product. First, the optimal solvent was identified for the given kinetic procedures with the aid of Principal Component Analysis. Then two advanced chemometric methods were applied to estimate the kinetic parameters of the MIR degradation process namely: Multivariate Curve Resolution-Alternating Least Squares and Artificial Neural Networks. By data manipulation, both algorithms could provide an explanation of the MIR kinetic process in different hydrolytic conditions. Moreover, modified National Environmental Methods Index (NEMI), eco-scale, Hexagon algorithm, Complementary Green Analytical Procedure Index (Complex GAPI), and analytical greenness metric for sample preparation (AGREEprep) were adopted to ensure the greenness of the proposed study. Eventually, the presented investigation could successfully present an eco-friendly chemometric approach for monitoring the different kinetic processes of pharmaceutical compounds with minimum effort and solvent consumption and maximum time saving and analyst safety compared to other traditional methods.

A sustainable model for the simultaneous determination of tadalafil and dapoxetine hydrochloride in their binary mixture: Overcoming the challenges of environmental, operational, and instrumental variability for spectral data

This study employed functional principal component analysis (FPCA) and covariate design of experiments (DoE) to mitigate the susceptibility of chemometric methods to unexpected variations stemming from operational and instrumental factors. A comparative analysis with partial least squares (PLS) revealed that our proposed approach effectively reduced variability across different analysts, days, and instruments. Specifically, FPCA was utilized to compress available spectral wavelength information, while covariate DoE aided in selecting an optimal training set within the experimental space. Subsequently, PLS was applied for the simultaneous determination of tadalafil (TD) and dapoxetine hydrochloride (DP) in their binary mixture. Validation of the proposed method through accuracy profiles demonstrated its reliability, paving the way for its application in pharmaceutical analysis.