Multivariate Curve Resolution-alternating Least Squares Research Articles

Analysis of the hydration water on the surface of human hair using a combination of infrared absorption vibrational spectroscopy and multivariate curve resolution

Modern society's keen regard for aesthetics made hair products an integral part of a multi‐billion‐dollar cosmetic industry. Hair care products (e.g., shampoos and conditioners) and chemical treatments (e.g., bleaching and permanent waving) result in various effects on the morphological attributes of hair. Generally, water adsorbed on the hair surface is known to significantly dictate the hair's mechanical characteristics (smoothness and friction), and hair's macroscopic wettability has been commonly used to indicate its surface properties. However, an approach to selectively characterize the hydration water in the hair surface is required to accurately understand the intermolecular events between the hair and its vicinal water. In this paper, we successfully obtained the infrared (IR) absorption spectra of the hydration water of human hair. We employed the multivariate curve resolution‐alternating least square (MCR‐ALS) method to separate the hydration and bulk water spectra from the whole spectra. Comparing the IR spectra of the hydration water of chemically untreated and bleached hair samples, we conclude that water molecules form strong hydrogen bonds with the bleached hair surface due to the destruction of the hair's hydrophobic outer layer and the consequent formation of hydrophilic residues.

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.

Simultaneous determination of meloxicam and bupivacaine via a novel modified dual wavelength method and an advanced chemometric approach

This study presents two spectrophotometric methods; a novel dual wavelength—derivative spectrophotometry and multivariate curve resolution-alternating least squares (MCR-ALS) for the simultaneous determination of a fixed dose combination of bupivacaine (BUP) and meloxicam (MEL) in a ratio of 30:1. The extended UV spectrum of MEL enables its direct determination at λmax 360 nm with no interference from BUP. The determination of BUP was unfeasible directly because the UV spectra of both drugs are moderately overlapped over the wavelength range of 250–450 nm, thus new chemometric based spectrophotometric methods should be developed for its determination. Dual wavelength-derivative method was employed based on using first derivative spectra. The selected dual wavelengths for determination BUP were 274.6 nm and 374.6 nm where the dA/dλ amplitudes differences for MET are equal to zero. MCR-ALS is advanced chemometric tool that enables analysis of multicomponent samples in complex matrices with high resolution based on the decomposition of signal/spectral data into the pure spectra and corresponding concentration profile. The figures of merits for MCR model show that there is a good agreement between the actual and predicted concentrations for MEL and BUP. The methods were validated and statistically compared with a reported HPLC method.

Quantitative and qualitative analysis of cell culture media powders for mammalian cells by Raman microscopy

Cell culture media are essential for large-scale recombinant protein production using mammalian cell cultures. The composition and quality of media significantly impact cell growth and product formation. Analyzing media poses challenges due to complex compositions and undisclosed exact compositions. Traditional methods like NMR and chromatography offer sensitivity but require time-consuming sample preparation and lack spatial information. Raman chemical mapping characterizes solids, but its use in cell culture media analysis is limited so far. We present a chemometric evaluation for Raman maps to qualify and quantify media components, evaluate powder homogeneity, and perform lot-to-lot comparisons. Three lots of a marketed cell culture media powder were measured with Raman mapping technique. Chemometrics techniques have outlined a strategy to extract information from complex data. First, a spectral library has been structured. In addition to the 23 spectra for presumed ingredients, we obtained another 9 pure components with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Then the Spectral Angle Mapper-Orthogonal Projection (SAM-OP) algorithm revealed whether references actually occur in the mapped media powders. Finally, a quantification was provided by Classical Least Squares (CLS) modelling. Quantities of 18 significant amino acids mostly correlated with the reference method. The proposed method can be generally applied even for such complicated samples. Leveraging Raman mapping and innovative chemometric methods enhance recombinant protein production by improving the understanding of the spatial distribution and composition of cell culture media in mammalian cell cultivations.

Serum Raman spectroscopy: Evaluation of tumour load variations in experimental carcinogenesis.

Several serum Raman spectroscopy (RS) studies have demonstrated its potential as an oral cancer screening tool. This study investigates influence of low tumour load (LTL) and high tumour load (HTL) on serum RS using hamster buccal pouch model of experimental oral carcinogenesis. Sera of untreated control, LTL, and HTL groups at week intervals during malignant transformation were employed. Serum Raman spectra were subjected to multivariate analyses-principal component analysis, principal component-based linear discriminant analysis (for stratification of study groups), and multivariate curve resolution-alternating least squares (MCR-ALS) (to comprehend biomolecular differences). Multivariate analysis revealed misclassifications between LTL and HTL at all week intervals. MCR-ALS components showed statistically significant abundances between control versus LTL and control versus HTL, but could not discern LTL and HTL. MCR-ALS components exhibited spectral mixtures of proteins, lipids, heme and nucleic acids. Thus, these findings support use of serum RS as a screening tool as varying tumour load is not a confounding factor influencing the technique.

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion

Data-independent acquisition–all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples.Graphical abstract

An exhaustive analysis of the use of image moments for second-order calibration. A comparison with multivariate curve resolution-alternating least-squares

Backgroundthe chemometric processing of second-order chromatographic-spectral data is usually carried out with the aid of multivariate curve resolution-alternating least-squares (MCR-ALS). Recently, an alternative procedure was described based on the estimation of image moments for each data matrix and subsequent application of multiple linear regression after suitable variable selection. ResultsThe analysis of both simulated and experimental data leads to the conclusion that the image moment method, although can cope with chromatographic lack of reproducibility across injections, it only performs well in the absence of uncalibrated interferents. MCR-ALS, on the other hand, provides good analytical results in all studied situations, whether the test samples contain uncalibrated interferents or not. SignificanceThe results are useful to assess the real usefulness of newly proposed methodologies for second-order calibration in the case of chromatographic-spectral data sets, especially when samples contain unexpected chemical constituents.

An optimized methodology for the determination of multiclass organic ultraviolet sunscreens and metabolites in human milk through chromatographic and chemometric resolution

Organic UV filters (UVFS) are used to mitigate the dermal effects associated with health risks from UV radiation, making them essential in personal care products. UVFS are frequently identified in environmental samples due to their high lipophilicity and persistence, underscoring the urgency of comprehensive assessments and regulatory measures aimed at safeguarding ecosystems and human health. The present study reports a multiclass analytical method for determining 16 UV sunscreens and metabolites in breast milk based on an ultrasound-assisted-dispersive liquid-liquid micro-extraction (UA-DLLME) with further chromatographic and chemometric resolution. The experimental conditions of the UA-DLLME were optimized through the implementation of the Design of Experiment tools. To model the responses, least-squares and artificial neural network methodologies were implemented. The optimal conditions were found by employing the desirability function. The samples were analyzed through reverse-phase liquid chromatographic separation, UV diode array, and fast-scanning fluorescence detection. The chromatographic analysis enabled the resolution of 16 analytes in a total time of 13.0 min. Multivariate curve resolution-alternating least-square (MCR-ALS) modelling was implemented to resolve analytes that were not fully resolved and to determine analytes that coeluted with endogenous components of the breast milk samples. An enrichment factor of 5-fold concentration was obtained with this methodology, reaching recoveries between 65 % and 105 % for 13 multiclass UV sunscreens and metabolites in breast milk samples with RSD % and REP % lower than 12 %.

Novel analytical method based on chemometric models applied to UV–Vis spectrophotometric data for simultaneous determination of Etoricoxib and Paracetamol in presence of Paracetamol impurities

The multivariate models that are used for spectral data analysis have many beneficial applications, and one of the important applications is the analysis of drugs and their impurities. Three Chemometrically-assisted spectrophotometric models have been proposed and validated. The proposed models are Partial Least Squares (PLS), Artificial Neural Networks (ANN), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). The advanced chemometric models were applied to resolve the significantly overlapping spectra of Etoricoxib (ETO) and Paracetamol (PCM), along with impurities of PCM namely; P-aminophenol (PAP) and P-hydroxy acetophenone (PHA). The proposed models succeeded in simultaneously analyzing the mixture of ETO and PCM along with the impurities of PCM. So, the proposed techniques can be used without requiring a separation step in the analysis of pharmaceutical formulation. Moreover, no significant differences were found when the results of the suggested and published chemometric models were compared statistically with the reported HPLC method.

Application of segmented analysis via multivariate curve resolution with alternating least squares to 1H-nuclear magnetic resonance spectroscopy to identify different sugar sources.

The different types of sugar employed in the food industry exhibit chemical similarity and are mostly dominated by sucrose. Owing to the sugar origin of and differences in production, the presence of certain minor organic compounds differs. To differentiate between sugars based on their botanical source, geographical origin, or storage conditions, commercial brown sugars and sugar beet extracts were analyzed by 1H NMR spectroscopy applying a segmented analysis by means of multivariate curve resolution-alternating least squares (MCR-ALS). Principal component analysis and partial least squares-discriminant analysis yielded excellent differentiation between sugars from different sources after the application of this preprocessing strategy; without loss of chemical information and with direct interpretation of the results. By applying a segmented analysis via MCR-ALS to 1H NMR sugar data, similar spectroscopic profiles could be differentiated. This improved the selectivity of 1H NMR spectroscopy for sugar source differentiation which can be useful for industrial sugar authentication purposes.

Nonchromatographic analysis of polyphenols in red wine samples: Evaluation of front-face fluorescence spectroscopy and third-order multivariate calibration to quantify catechin and epicatechin

In this work, an analytical approach based on third-order multivariate calibration and front-face fluorescence spectroscopy to determine the levels of (+)-catechin and (-)-epicatechin in wine samples was evaluated. An extra reaction mode (pH gradient) was incorporated into excitation-emission fluorescence second-order data, leading to higher predictivity and selectivity for catechin detection. Application of a Multivariate curve resolution-alternating least squares (MCR-ALS) algorithm resolved the systems, even in the presence of uncalibrated interferences, and yielded suitable figures of merit. Through the method, the level of (+)-catechin was directly determined, and the concentration of (-)-epicatechin was determined by the difference in total catechins (Epi + Cat) between synthetic samples. Finally, the levels of catechin were satisfactorily determined in real wine samples at mg L-1 levels with minimal sample treatment and in accordance with a reference method based on chromatographic separation.

Rapid and Highly Efficient Separation of i-Motif DNA Species by CE-UV and Multivariate Curve Resolution.

The i-motif is a class of nonstandard DNA structure with potential biological implications. A novel capillary electrophoresis with an ultraviolet absorption spectrophotometric detection (CE-UV) method has been developed for the rapid analysis of the i-motif folding equilibrium as a function of pH and temperature. The electrophoretic analyses are performed in reverse polarity of the separation voltage with 32 cm long fused silica capillaries permanently coated with hydroxypropyl cellulose (HPC), after an appropriate conditioning procedure was used to achieve good repeatability. However, the electrophoretic separation between the folded and unfolded conformers of the studied cytosine-rich i-motif sequences (i.e., TT, Py39WT, and nmy01) is compromised, especially for Py39WT and nmy01, which result in completely overlapped peaks. Therefore, deconvolution with multivariate curve resolution-alternating least-squares (MCR-ALS) has been required for the efficient separation of the folded and unfolded species found at different concentration levels at pH 6.5 and between 12 and 40 °C, taking advantage of the small dissimilarities in the electrophoretic mobilities and UV spectra levels. MCR-ALS has also provided quantitative information that has been used to estimate melting temperatures (Tm), which are similar to those determined by UV and circular dichroism (CD) spectroscopies. The obtained results demonstrate that CE-UV assisted by MCR-ALS may become a very useful tool to get novel insight into the folding of i-motifs and other complex DNA structures.

A novel multivariate curve resolution based strategy for multi-omic integration of toxicological data

Multi-omics has become a current subject of research in computational bioinformatics and chemometrics to trigger a more accurate understanding of biological processes. The Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) approach has been demonstrated to be a powerful chemometric method convenient for the analysis of multisets with multiple components with strongly overlapped contributions. However, MCR-ALS has yet to be tested to analyze multi-omic data fused at the mid-level. Hence, this study seeks to assess the efficacy of MCR-ALS when analyzing mid-level fused data, both independently and in conjunction with a complementary variance decomposition technique (Joint and Individual Variation Explained, JIVE), for the evaluation of multi-omic data within the ecotoxicology field. Particularly, we have evaluated the ecotoxicological effects of tributyltin zebrafish embryos by concatenating epigenomics, transcriptomics, and metabolomics datasets. The developed pipeline demonstrated the importance of the pre-processing and variance decomposition step for improving the MCR-ALS capability to describe the multi-omics data. Our results showed that applying the combination of JIVE and MCR-ALS to the mid-level multi-omic fused data provided a more accurate interpretation of toxicological data. Therefore, integrating these chemometric tools into the analysis of multi-omic data in ecotoxicology could allow a more comprehensive analysis of this complex data, boosting the understanding of the toxicological mechanisms of pollutants.

Operando ME-DRIFTS study of reaction intermediates during toluene oxidation on CeO2 nanoparticles supported on MgO hexagonal plates

Operando DRIFT spectroscopy was employed to investigate the intermediates involved in the oxidation of toluene on highly dispersed ceria nanoparticles supported on magnesium oxide hexagonal nanoplates. Small CeO2 nanoparticles dispersed on magnesium oxide hexagonal nanoplates showed enhanced catalytic performance to total toluene combustion compared to a high surface area polycrystalline CeO2. Selective identification of active surface species was carried out by combining concentration-modulation excitation spectroscopy (c-MES) experiments with phase-sensitive detection (PSD) spectral analysis and a chemometric multivariate curve resolution alternating least squares (MCR-ALS) method. The results revealed a stepwise reaction pathway for toluene oxidation on the catalyst surface. Initially, toluene is molecularly adsorbed and reacts with surface oxygen species to produce intermediate benzyl (C6H5CH2-). Next, benzyl is stepwise oxidized to benzyloxy/benzyl alcohol, benzaldehyde, and benzoate. Further oxidation of benzoate and the aromatic ring produces formate and carbonate species. On pure CeO2, formates accumulate on the ceria surface, and only a minor part of them reacts to produce CO2 (and CO). Instead, formates species are rapidly oxidized to CO2 on the CeO2/MgO catalyst and do not accumulate on the surface, due to the enhanced redox capacity and higher presence of oxygen vacancies of the supported nanoparticles.

Rapid authentication of intact saffron stigma thorough the package using Vis-SWNIR hyperspectral imaging coupled with chemometrics

This study presents a rapid method for detecting and authenticating intact saffron stigma through its packaging using visible-short wavelength near-infrared (Vis-SWNIR) hyperspectral imaging (HSI) in conjunction with chemometrics. A dataset containing 38 authentic saffron samples was utilized, and HSI images were captured within the spectral range of 400–950 nm. Multivariate curve resolution-alternating least squares (MCR-ALS) was used to obtain pure spatial and spectral profiles of the components. The resolved spatial profiles were then subjected to principal component analysis (PCA) to identify potential patterns in the authentic samples. Binary mixtures of pooled saffron samples and adulterants, including safflower, calendula, and saffron style, were prepared at varying levels. Data-driven soft independent modeling of class analogy (DD-SIMCA) was employed to establish a boundary between authentic and adulterated samples, resulting in robust classification performance with a sensitivity of 95 % and specificity of 100 %. Partial least squares-discriminant analysis (PLS-DA) was used for binary and multi-class classification of the MCR-ALS resolved spatial profiles. The performance of PLS-DA was evaluated using sensitivity, specificity, and accuracy measures, which exceeded 83.4 % for the calibration, cross-validation, and prediction sets across three different saffron packaging methods. These findings provide further evidence for the validity and efficacy of the proposed method.

A novel and intelligent molecularly imprinted enzymatic biosensor for biosensing of human serum albumin in the presence of gamma-globulin, and glucose as uncalibrated interference

Human serum albumin (HSA) is not electroactive, because the electroactive amino acids have been buried in its structure. Therefore, direct electrochemical determination of the HSA as the most abundant protein in human plasma cannot be performed. In this work, a novel electrochemical biosensor has been developed based on synthesis of dual templates molecularly imprinted polymers (DTMIPs) having trypsin (TRP) and HSA as template molecules onto the surface of a glassy carbon electrode (GCE) modified with graphene-ionic liquid (Gr-IL). By incubation of the biosensor with TRP, and HSA, tryptic hydrolysis of the HSA is occurred which breaks the HSA down into free amino acids. The HSA involves five electroactive amino acids including cysteine, tryptophan, tyrosine, methionine and histidine whose differential normal pulse voltammetric (DNPV) responses are overlapped and generated a single peak. In order to increase the sensitivity of the DTMIP/Gr-IL/GCE for determination of the HSA, hydrodynamic DNPV (HDNPV) data were generated, and used for analytical purposes. The second-order HDNPV data were generated at different pulse amplitudes and used to develop four second-order calibration models by multivariate curve resolution-alternating least squares (MCR-ALS), parallel factor analysis2 (PARAFAC2), multi-way partial least squares/residual bilinearization (N-PLS/RBL), and unfolded partial least squares/residual bilinearization (U-PLS/RBL) to select the best procedure for determination of the HSA in the presence of gamma-globulin and glucose as uncalibrated interferences. The results confirmed the best performance for the biosensor assisted by MCR-ALS for ultrasensitive and selective determination of the HSA in both synthetic and real matrices which was comparable with HPLC-UV as a reference method.

Identification and quantification of ciprofloxacin, enrofloxacin and difloxacin residues in milk using excitation-emission fluorescence and second-order standard addition methods

In the presence of non-modeled components and analyte-matrix interaction in real samples, a combination of second-order multivariate calibration and standard addition is desired to achieve accurate results via second-order advantage. Enrofloxacin (ENR), Difloxacin (DIF), and Ciprofloxacin (CIP) are Fluoroquinolones widely considered antibiotic residues in milk. Excitation-emission fluorescence spectra of these antibiotics at pH = 9.1 provide second-order data for identifying and quantifying them in a mixture using second-order calibration modeling.Rank annihilation factor analysis (RAFA) and multivariate curve resolution-alternating least squares (MCR-ALS) with trilinearity constraints are applied to determine the CIP, ENR, and DIF residues in synthetic mixtures and milk using second-order excitation-emission fluorescence spectroscopy. In both methods, the trilinearity constraint guarantees a unique true solution to the spectral profiles of analytes. The combination of second-order standard addition and higher-order calibration modeling with RAFA and MCR-ALS supports the second-order advantage in milk analysis.

Fingerprinting of quinoa grain protein extracts by liquid chromatography with spectrophotometric detection for chemometrics discrimination

Quinoa (Chenopodium quinoa Willd.) grain is gaining great popularity worldwide because it is a rich source of nutrients, bioactive compounds, complete essential amino acids, and high-quality proteins. The demand for quinoa-based products is on the rise, which makes them prone to adulteration with less expensive cereals. In this study, we described a rapid and simple procedure for fingerprinting of quinoa grain protein extracts based on the combination of liquid chromatography with ultraviolet absorption diode array detection (LC-UV-DAD) and chemometrics. First, we developed a novel LC-UV-DAD method to obtain distinctive multiwavelength chromatographic profiles of protein extracts from various commercial quinoa grains, which encompass different quinoa varieties sold as black, red, white (from Peru), and royal (white from Bolivia). Then, the components of the LC-UV-DAD fingerprints were deconvoluted by multivariate curve resolution alternating least squares (MCR-ALS), and principal component analysis (PCA) followed by partial least squares discriminant analysis (PLS-DA) were applied to efficiently discriminate the commercial quinoa grains according to their differential composition. The chemometrics-assisted LC-UV-DAD fingerprinting methodology demonstrated its potential to rapidly and reliably discriminate quinoa grains according to the differential composition of their protein extracts and it may be applied in food quality and food fraud control.

Tracing the identity of Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” cheese using NMR spectroscopy and multivariate data analysis

BackgroundNuclear magnetic resonance (NMR) spectroscopy is one of the well-established tools for food metabolomic analysis, as it proved to be very effective in authenticity and quality control of dairy products, as well as to follow product evolution during processing and storage. The analytical assessment of the EU mountain denomination label, specifically for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, has received limited attention. Although it was established in 2012 the EU mountain denomination label has not been much studied from an analytical point of view. Nonetheless, tracing a specific profile for the mountain products is essential to support the value chain of this specialty. ResultsThe aim of the study was to produce an identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” (Mountain-CQ) cheese, and to differentiate it from Parmigiano Reggiano PDO samples (conventional-PDO) using 1H NMR spectroscopy coupled with multivariate data analysis. Three different approaches were applied and compared. First, the spectra-as-such were analysed after proper preprocessing. For the other two approaches, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used for signals resolution and features extraction, either individually on manually-defined spectral intervals or by reapplying MCR-ALS on the whole spectra with selectivity constraints using the reconstructed “pure profiles” as initial estimates and targets. All approaches provided comparable information regarding the samples’ distribution, as in all three cases the separation between the two product categories conventional-PDO and Mountain-CQ could be highlighted. Moreover, a novel MATLAB toolbox for features extraction via MCR-ALS was developed and used in synergy with the Chenomx library, allowing for a putative identification of the selected features. SignificanceA first identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” obtained by interpreting the metabolites signals in NMR spectroscopy was obtained. Our workflow and toolbox for generating the features dataset allows a more straightforward interpretation of the results, to overcome the limitations due to dimensionality and to peaks overlapping, but also to include the signals assignment and matching since the early stages of the data processing and analysis.

Quantification of ciprofloxacin in pharmaceutical products from various brands using FT-NIR: A comparative investigation of PLS and MCR-ALS

This study aims to quantify ciprofloxacin in commercial tablets with varying excipient compositions using Fourier Transform Near-Infrared Spectroscopy (FT-NIR) and chemometric models: Partial Least Squares (PLS) and Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS).Matrix variation, arising from differences in excipient compositions among the tablets, can impact quantification accuracy. We discuss this phenomenon, emphasizing potential issues introduced by varying certain excipients and its importance in reliable ciprofloxacin quantification.We evaluated the performance of PLS and MCR-ALS models independently on two sets of tablets, each containing the same drug substance but different excipients. The statistical results revealed promising results with PLS prediction error of 0.38% w/w of the first set and 0.47% w/w of the second set, while MCR-ALS achieved prediction errors of 0.67% w/w of the first set and 1.76% w/w of the second set.To address the challenge of matrix variation, we developed single models for PLS and MCR-ALS using a dataset combining both first and second sets. The PLS single model demonstrated a prediction error of 4.3% w/w and a relative error of 6.41% w/w, while the MCR-ALS single model showed a prediction error of 1.88% w/w and a relative error of 1.29% w/w.We then assessed the performance of the single PLS and MCR-ALS models developed based on the combination of the first and the second set in quantifying ciprofloxacin in various commercial tablet brands containing new excipients. The PLS model achieved a prediction error ranging between 6.2% w/w and 8.39% w/w, with relative errors varied between 8.53% w/w and 12.82% w/w. On the other hand, the MCR-ALS model had a prediction error between 1.11% w/w and 2.66% w/w, and the relative errors ranging from 0.8% to 1.74% w/w.