Chemometric Techniques Research Articles

Year-to-year differentiation of black tea through spectroscopic and chemometric analysis

Abstract The compositions of food products such as tea can vary significantly from one harvest year to another, primarily due to factors such as shifting climatic conditions, and plant periodicity. These fluctuations in composition can significantly affect the overall product quality. Spectral methods combined with chemometric techniques can provide efficient tools to monitor and assess these variations. In this study, 205 black tea samples from two consecutive harvest years were analyzed using mid-infrared, UV–visible, and fluorescence spectroscopy. Mid-infrared spectra were collected for both infused and powdered samples, while only the infused samples were used for the other spectroscopic methods. The study used partial least-square discriminant (PLS-DA) and orthogonal partial least-square discriminant analyses (OPLS-DA) to differentiate samples by harvest year. These models, applied after various data transformations, achieved high correct classification rates. Mid-infrared spectroscopic data yielded rates of 93.33% and 90.33% for powdered and infused samples, respectively. Fluorescence and UV–visible spectra also showed excellent prediction accuracy, with success rates of 98.3% and 100%. The results indicate that these spectroscopic methods, combined with chemometric differentiation, are valuable tools for monitoring year-to-year changes in black tea.

Discovery-Based Analysis for Chemical Trends in Chromatographic Data Sets Using Alteration Analysis and Two-Dimensional Correlation Analysis.

Alteration analysis (ALA), an unsupervised chemometric technique, was evaluated for its ability to discover statistically significant trends in chromatographic data sets. Recently introduced, adoption of ALA has been limited due to uncertainty regarding its sensitivity to minor changes, and there are no rules implementing ALA especially for multivariate data sets such as liquid or gas chromatography coupled to mass spectrometry. Using in-silico data sets, ALA limits of discovery for various signal-to-noises (S/Ns), rates of change across samples, and a number of samples were assessed. For 10 samples, ALA discovered changes of ∼2% across each sample for low S/Ns (15-50), ∼1% change across each sample for moderate S/Ns (65-200), and as little as a 0.1% change at high S/Ns. ALA was also evaluated for unresolved chromatographic peaks, detecting changes down to a resolution of 0.01. In tandem with ALA, two-dimensional correlation analysis (2DCOR), a nonquantitative technique, was employed post-ALA processing to provide unique insights into the relationships between the chemical changes across simulated data sets. Finally, ALA and 2DCOR were applied to the pyrolysis gas chromatography-mass spectrometry (pyGC-MS) of Kraton G1650, a styrene-ethylene-butylene-stryrene (SEBS) polymer, pyrolyzed at temperatures ranging from 350 to 700 °C. A total of 523 statistically significant chemical compounds were discovered. The ALA output was fed into 2DCOR, and a subset of the data was evaluated to understand the relationship between the chemical changes of four selected statistically significant compounds.

Chemometric Methods-A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and Food.

Developing antifungal drugs with lower potential for interactions with food may help to optimize treatment and reduce the risk of antimicrobial resistance. Chemometrics uses statistical and mathematical methods to analyze multivariate chemical data, enabling the identification of key correlations and simplifying data interpretation. We used the partial least squares (PLS) approach to explore the correlations between various characteristics of oral antifungal drugs (including antifungal antibiotics) and dietary interventions, aiming to identify patterns that could inform the optimization of antifungal therapy. We analyzed 15 oral antifungal drugs, including azoles (8), antifungal antibiotics (4), antifungal antimetabolites (1), squalene epoxidase inhibitors (1), and glucan synthase inhibitors (1). The input dataset comprised information from published clinical trials, chemical records, and calculations. We constructed PLS models with changes in the pharmacokinetic parameters (∆AUC, area under the curve; ∆Cmax, maximum drug concentration; and ∆Tmax, time to reach maximum drug concentration) after dietary intervention as the response parameters and eight groups of molecular descriptors (M1-M8) as the predictor parameters. We performed separate analyses for the different nutritional interventions. In the final PLS model with food as an intervention, we effectively reduced the dimensionality of the dataset while retaining a substantial percentage of the original information (variance), as significant components explained 69.8% and 17.5% of the predictor and response parameter variances, respectively. The PLS model was significant because its components met the cross-validation criteria. We obtained six significant positive and negative correlations between the descriptors related to atoms and the postprandial ∆Tmax. The PLS method is valuable for investigating interactions between antifungal drugs (including antifungal antibiotics) and food. The correlations obtained can be used in drug modeling to predict interactions with dietary interventions based on the antifungal drug's chemical structure. Incorporating chemometric techniques into the early drug development stages could facilitate the design of antifungal antibiotics and other antifungal agents with optimized absorption in the presence of dietary components.

A novel chemometric coupling technique combined with HPLC-DAD for rapid quantification and source apportionment of monoaromatic hydrocarbons in soil samples

A novel chemometric coupling technique combined with HPLC-DAD for rapid quantification and source apportionment of monoaromatic hydrocarbons in soil samples

Identification and discrimination of human keratinized tissues using ATR-FTIR and chemometrics.

In forensic investigations, human keratinized tissues like skin and nails are commonly encountered as trace evidence, yet the use of vibrational spectroscopy for their identification and differentiation has been underexplored. This research utilized ATR-FTIR to distinguish between human nails and skin samples collected from a group of 50 participants, employing advanced chemometric analysis techniques. The spectral signatures of human keratinized tissues, such as nails and skin, exhibit similarities consistent with previous studies. Chemometric analysis aimed at distinguishing these tissues showed that the PLS-DA model achieved an overall accuracy of 67 % with an AUC score of 0.65, while the SVM model had an overall accuracy of 56 % with an AUC score of 0.71. For sex identification, the PLS-DA model demonstrated an overall accuracy of 83 % with an AUC value of 1, whereas the SVM model achieved an overall accuracy of 100 % with an AUC score of 1. The study underscores the potential of ATR-FTIR coupled with chemometrics in the precise identification and differentiation of human keratinized tissue, thereby enhancing the capabilities of forensic investigations.

Laser Desorption-Ion Mobility Spectrometry of Explosives for Forensic and Security Applications.

The detection of explosives in crime scene investigations is critical for forensic science. This study explores the application of laser desorption (LD) ion mobility spectrometry (IMS) as a novel method for this purpose utilising a new IMS prototype developed by MaSaTECH. The LD sampling technique employs a laser diode module to vaporise explosive traces on surfaces, allowing immediate analysis by IMS without sample preparation. Chemometric approaches, including multivariate data analysis, were utilised for data processing and interpretation, including pre-processing of raw IMS plasmagrams and various pattern recognition techniques, such as linear discriminant analysis (LDA) and support vector machines (SVMs). The IMS prototype was validated through experiments with pure explosives (TNT, RDX, PETN) and explosive products (SEMTEX 1A, C4) on different materials. The study found that the pre-processing method significantly impacts classification accuracy, with the PCA-LDA model demonstrating the best performance for real-world applications. The LD-IMS prototype, coupled with effective chemometric techniques, presents a promising methodology for the detection of explosives in forensic investigations, enhancing the reliability of field applications.

Standardized Chromatographic and Computational Approaches for Lipophilicity Analysis of Five Gliflozin Antidiabetic Drugs in Relation to Their Biological Activity.

This study represents the first-time experimental analysis of lipophilicity for antidiabetic drugs from the gliflozin class using chromatographic methods alongside computational approaches. The lipophilicity of five gliflozins (canagliflozin (CANA), dapagliflozin (DAPA), empagliflozin (EMPA), ertugliflozin (ERTU), and sotagliflozin (SOTA)) was assessed using RMW and log kW parameters with RP8, RP18, and CN coatings, while methanol and acetonitrile were used as organic modifiers. To enhance the reliability, six reference substances with established lipophilicity values (0.62-3.5) were used for standardization. For computational analyses, the methods ALOGP, iLOGP, MLOGP, SILICOS-IT, WLOGP, XLOGP3, and Consensus. Log P were applied. Descriptive statistics, correlation analyses, and chemometric techniques were employed to compare the results. Experimental lipophilicity values showed strong correlations, indicating that RMW and log kW are reliable parameters for evaluating the lipophilicity of these therapeutically valuable drugs. However, computational lipophilicity values were less consistent, both among themselves and compared to experimental data. Finally, the experimental lipophilicity of gliflozins was analyzed in relation to their pharmacological properties, including protein binding, renal clearance, volume of distribution, half-life, potency (IC50), and lipophilic ligand efficiency (LLE). Our results allow for a confident proposal of a model to experimentally determine the lipophilicity of gliflozin drugs including new derivatives.

Characterization of the aromatic profile of Ruché wine from Piedmont (Italy) with gas chromatography-mass spectrometry and unsupervised machine learning techniques.

The volatile composition of Ruché, a red wine produced from a native grape in Piedmont (Italy), was investigated using headspace-solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC/MS). The main volatile compounds of the wine were identified and quantified. Chemometric techniques were applied to identify features and possible clusters among the different samples. Forty volatile compounds were unambiguously identified in the 36 wine samples from different producers. The aroma profile was mainly composed of different alcohols and esters but also featured appreciable concentrations of terpenes, aldehydes, and octanoic acid. 2-Methyl benzaldehyde was identified for the first time. A high concentration of isoamyl alcohol significantly contributed to the aroma complexity. Differences between producers are highlighted. The present work is the first report about the volatile profile of Ruché wines investigated by chemometric methods with quantitative results. Multivariate exploratory approaches revealed minor but distinct differences among the studied wines, likely attributable to variations in winemaking procedures. This study could be developed in future by investigating possible differences between wines according to different production vintages. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Mayan Medicinal Plants Bignonia potosina and Thouinia paucidentata Demonstrate Anti-Infective Properties Against the Priority Antibiotic-Resistant Bacteria Acinetobacter baumannii and Pseudomonas aeruginosa.

(1) Background: Carbapenem-resistant Acinetobacter baumannii (CBRAB) and Pseudomonas aeruginosa (CBRPA) are critical and high-priority pathogens that require new therapeutic developments. Medicinal plants are valuable pharmaceutical resources. This study explored the anti-infective properties of Mayan plants, Bignonia potosina, and Thouinia paucidentata. (2) Methods: Plant parts were extracted using n-hexane, and their ability to inhibit bacterial growth and counteract resistance mechanisms and virulence factors in CBRAB and CBRPA was assessed. GC-MS analysis of the composition of the non-polar extracts and chemometric techniques correlated the phytoconstituents with anti-infective properties. (3) Results: Bignonia potosina liana and flower extracts exhibited potent antibacterial activity against A. baumannii strains (MIC 15.7 to 250 µg/mL) and moderate activity against P. aeruginosa strains (MIC 250 to 1000 µg/mL). Thouinia paucidentata leaf extract at 1000 µg/mL reduced imipenem MIC by 2048-fold for CBRAB, and B. potosina flower extract significantly inhibited A. baumannii catalase activity (at 62.5 µg/mL) and reduced P. aeruginosa pyocyanin production (at 1000 µg/mL). Chemometric analysis identified fatty acids, fatty acid amides, terpenes, and higher alkanes as contributors to their anti-infective properties. (4) Conclusions: This study highlights the potential of medicinal plants in the development of novel anti-infective therapies against CBRAB and CBRPA with various targets.

Impact of meteorological and processing factors on metabolite composition of Darjeeling tea

Meteorological and edaphic factors significantly affect the first flush metabolite composition of Darjeeling tea, impacting flavor, nutrition, and customer preferences. The Camellia sinensis var. sinensis grown in the Darjeeling hills was sampled at different stages of processing. 75 metabolites were identified using a GC/MS-based metabolomic and chemometric technique. The study aimed to pinpoint quality markers and create a database for Darjeeling tea authenticity, India’s first geographical indication product. Principal component analysis and partial least squares discriminant analysis revealed changes in tea metabolites during processing, while the chemometric study demonstrated variations in key compounds. Antioxidant activity exhibited significant variations among the samples with final processed showing highest antioxidant potential. The concentration of amino acids increased significantly (p < 0.05) from fresh tea leaves to the final processed product. Conversely, the content of organic acids, inorganic acids, and fatty acids was observed to decrease. Additionally, caffeic acids, epicatechin, epigallocatechin, chlorogenic acid, 3, 4-dihydroxybenzoic acid, and 4-hydroxycinnamic acid increased in concentration, while catechin, (-)-epicatechin, gallic acid, pyrogallol, quinic acid, shikimic acid, and arbutin decreased significantly in concentration in the final processed tea. Meteorological parameters were documented during the study period, showing variation in the levels of PM2.5, PM10, O3, NO2, and SO2. The findings of this study offer insightful information on the variables influencing Darjeeling tea metabolite composition, which tea producers and processors can use to raise the standard and authenticity of this beverage.Graphical abstract

A Comparative Study of the Molecular Constituent Profile Among Brussels Sprout Leaf Layers by Mid-Infrared Spectroscopy and Chemometrics

Brussels sprouts (Brassica oleracea) are of great nutritional interest due to their functional ingredients and health benefits. This study aimed to investigate the molecular composition of different leaf layers of Brussels sprouts using ultraviolet-visible spectrophotometry and attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy combined with chemometric techniques. Results revealed significant differences in chlorophyll and carotenoid content, with the outer leaves exhibiting the highest levels. ATR-FTIR analysis identified distinct molecular constituents, including glucosinolates, phenolics, lipids, and polysaccharides, that varied across leaf layers. Discriminant analysis effectively classified the leaf layers based on their spectral data with high accuracy, highlighting distinct molecular ingredients.

Chemical profiling and clustering of various dried cannabis flowers revealed by volatilomics and chemometric processing

Cannabis flower scent is one of the key characteristics of the cannabis plant. The diverse scents impact user experiences and offer medicinal benefits. These scents originate from volatile compounds, particularly terpenes and terpenoids. This study characterized the volatile profile of 19 different dried cannabis flowers using gas chromatography-mass spectrometry coupled with headspace-solid phase microextraction (HS-SPME-GC-MS). A total of 75 compounds were identified, including alcohols, aldehydes, benzenes, esters, ketone, monoterpenes, monoterpenoids, sesquiterpenes, and sesquiterpenoids. Cluster analysis was able to group the 19 cannabis cultivars into five clusters based on volatile chemotypes using chemometric techniques of hierarchical cluster analysis (HCA) and principal component analysis (PCA). Potential discriminant markers of each cultivar were then analyzed using a supervised partial least squares discriminant analysis (PLS-DA) verified through Variable Importance in Projection values (VIP), identifying twenty discriminant markers. In addition, the correlations among 75 volatile compounds were also obtained. The findings of this study provide a valuable database of single cannabis cultivars, useful for identifying individual strains and verifying their quality. Clustering the cultivars by volatile chemotype can be used for the classification of cannabis in the market. The results of this study are expected to be a starting point for further cannabis breeding programs to expand knowledge of this plant. Furthermore, the proposed method is applicable to other aroma plants in the future.

Unveiling the nutritional value and potentially toxic elements in fish species from Miliç Wetland, Türkiye: A probabilistic human health risk assessment using Monte Carlo simulation

This study evaluates the nutritional value and health risks of fish from Miliç Wetland, Türkiye, focusing on potentially toxic elements (PTEs) in Esox lucius, Squalius cephalus, and Carassius gibelio. Using ICP-MS, mean PTE concentrations were determined, including Zn (4979 μg/kg), Fe (4241 μg/kg), and As (125 μg/kg). Macro elements like K, P, and Ca were also assessed for nutritional profiling. A Monte Carlo-based risk assessment confirmed that PTE levels were below safety limits, indicating safe consumption. Chemometric techniques (PCA, PCC, HCA) helped trace contamination sources, identifying residential, agricultural, and lithogenic inputs. Esox lucius showed the highest essential nutrient levels. This research highlights the importance of combining chemometric analysis with regular monitoring for food safety and public health protection.

Advances in Vibrational Spectroscopic Techniques for the Detection of Bio-Active Compounds in Virgin Olive Oils: A Comprehensive Review.

Vibrational spectroscopic techniques have gained significant attention in recent years for their potential in the rapid and efficient analysis of virgin olive oils, offering a distinct advantage over traditional methods. These techniques are particularly valuable for detecting and quantifying bio-active compounds that contribute to the nutritional and health benefits of virgin olive oils. This comprehensive review explores the latest advancements in vibrational spectroscopic techniques applied to virgin olive oils, focusing on the detection and measurement of key bio-active compounds such as unsaturated fatty acids, phenolic compounds, and other antioxidant compounds. The review highlights the improvements in vibrational spectroscopy, data processing, and chemometric techniques that have significantly enhanced the ability to accurately identify these compounds compared to conventional analytical methods. Additionally, it addresses current challenges, including the need for standardized methodologies and the potential for integrating vibrational spectroscopy with other analytical techniques to improve accuracy and reliability. Finally, findings over the last two decades, in which vibrational spectroscopy techniques were effectively used for the detailed characterization of bio-active compounds in virgin olive oils, are discussed.

Chemometrics and digital image colorimetry approaches applied to paper-based analytical devices: A review.

Colorimetric paper-based analytical devices (CPADs) are cost-efficient and high-throughput technologies that use readily available materials for point-of-need (PON) applications by leveraging color changes in response to target analytes. However, the complexity of samples can limit the precision and accuracy of CPAD applications. Therefore, CPADs have been combined with chemometric approaches to enhance analytical performance and provide simple solutions to complex systems. The integration of formal optimization techniques, such as Design of Experiments (DoE), classification tools, quantification methods, and other advanced algorithms enables optimal experimental analytical conditions and extracting meaningful information from the complex colorimetric data generated by CPADs. These approaches facilitate robust calibration and prediction models, enabling reliable quantifications or sample differentiation. In addition, chemometrics combined with CPADs contributes to Green Analytical Chemistry once they have the potential to minimize the number of experiments, provide optimal designs and consumption of reagents, and decrease waste generation. The synergy between digital colorimetry using CPADs and smart devices with chemometric techniques holds great promise for portable analysis in resource-limited settings, with applications ranging from environmental monitoring to point-of-care diagnostics. Herein, we review recent advances in the development of CPADs, ranging from manufacturing methods to extraction of color patterns and data treatment using several chemometric tools, performance assessment, and potential transfer to onsite applications for relevant analytical problems.

How Machine Learning and Gas Chromatography-Ion Mobility Spectrometry Form an Optimal Team for Benchtop Volatilomics.

This invited feature article discusses the potential of gas chromatography-ion mobility spectrometry (GC-IMS) as a point-of-need alternative for volatilomics. Furthermore, the capabilities and versatility of machine learning (ML) (chemometric) techniques used in the framework of GC-IMS analysis are also discussed. Modern ML techniques allow for addressing advanced GC-IMS challenges to meet the demands of modern chromatographic research. We will demonstrate workflows based on available tools that can be used with a clear focus on open-source packages to ensure that every researcher can follow our feature article. In addition, we will provide insights and perspectives on the typical issues of the GC-IMS along with a discussion of the process necessary to obtain more reliable qualitative and quantitative analytical results.

Online Monitoring of Catalytic Processes by Fiber-Enhanced Raman Spectroscopy.

An innovative solution for real-time monitoring of reactions within confined spaces, optimized for Raman spectroscopy applications, is presented. This approach involves the utilization of a hollow-core waveguide configured as a compact flow cell, serving both as a conduit for Raman excitation and scattering and seamlessly integrating into the effluent stream of a cracking catalytic reactor. The analytical technique, encompassing device and optical design, ensures robustness, compactness, and cost-effectiveness for implementation into process facilities. Notably, the modularity of the approach empowers customization for diverse gas monitoring needs, as it readily adapts to the specific requirements of various sensing scenarios. As a proof of concept, the efficacy of a spectroscopic approach is shown by monitoring two catalytic processes: CO2 methanation (CO2 + 4H2 → CH4 + 2H2O) and ammonia cracking (2NH3 → N2 + 3H2). Leveraging chemometric data processing techniques, spectral signatures of the individual components involved in these reactions are effectively disentangled and the results are compared to mass spectrometry data. This robust methodology underscores the versatility and reliability of this monitoring system in complex chemical environments.

Toward Smart Manufacturing By Introducing Redundancy to Analytical Systems for Controlling of Damascene and 3D Packaging Electrodeposition Processesa

Accurate monitoring of the chemical composition of electrodeposition baths used in semiconductor manufacturing of interconnects in integrated circuits and 3D packaging is of fundamental importance. It achieves cost minimization and satisfies process specifications regarding feature filling and morphology control for manufacturing of electronic components. To meet the Smart Manufacturing/Industry 4.0 standards, the currently-implemented process control methods need to undergo a thorough revision of their analytical approach and configuration capabilities. The chief prerequisites for upgrading to Smart Manufacturing/Industry 4.0 standards include a combination of reliability, stability of performance over time; simplicity of design; no consumables and low maintenance. The imperative factor guaranteeing a reliable, uninterrupted process control data flow is the analytical system’s redundancy. Additionally, the future Industry 4.0 compliant analytical systems must significantly reduce or completely eliminate the need for reagents or chemical operations and must not generate hazardous waste as a move toward environmentally friendly green technologies.Most of the currently used automated analytical systems for monitoring electroplating solutions in damascene and 3D packaging processes are of complex design. Typically, they consolidate numerous zero- and first-order analytical instruments employing fundamentally different methods such as titration, chromatography, spectrometry, and electroanalysis in a single, integrated unit. These complicated analytical systems require sophisticated plumbing and multiple sensors. Such a high degree of complexity adversely affects their reliability. The majority of sensors used for monitoring of electroplating solution needs to be frequently (daily or in some cases before each measurement) calibrated. This requires additional chemical standards, and increases the complexity of the entire hardware, and inflates the dimensions of the analytical system. Such a spatial burden is of critical importance in the majority of production areas and practically eliminates any notion about creating a system’s redundancy. The compactness of an analytical system is a prerequisite to consider its multiplication toward achieving the redundancy.The metrology presented in this paper features a mechanically-simple and compact device that is widely used for plating bath control. Its small-size electroanalytical sensor can be submersed in the plating bath tank without disrupting the damascene or 3D packaging electrodeposition process operation. The integrated, in-situ sensor is capable of monitoring all components solely by utilizing electroanalytical techniques including DC and multi-frequency AC voltammetry. Superimposing of several sinusoidal perturbations of various frequencies and amplitudes on DC waveform upgrades the first-order instrument to the second order without compromising the time of analysis. The solution sample is analyzed without any pretreatment and returned unaltered into the plating tank upon the conclusion of the measurement with no waste being created. This approach provides a total insight into a plating bath by uniquely combining advanced DC and AC voltammetric measurements with extensive application of chemometric techniques. Multiway chemometrics is utilized not only for concentration prediction but also for fault detection and classification (FDC). The consolidation of all analytical functions into a single, compact sensor allows for redundancy, without compromising spatial and temporal limitations. The resulting multiple advantages of operational redundancy relevant to damascene and 3D packaging electrodeposition process control will be presented.

Integration of FTIR Spectroscopy, Volatile Compound Profiling, and Chemometric Techniques for Advanced Geographical and Varietal Analysis of Moroccan Eucalyptus Essential Oils.

Eucalyptus essential oil is widely valued for its therapeutic properties and extensive commercial applications, with its chemical composition significantly influenced by species variety, geographical origin, and environmental conditions. This study aims to develop a reliable method for identifying the geographical origin and variety of eucalyptus oil samples through the application of advanced analytical techniques combined with chemometric methods. Essential oils from Eucalyptus globulus and Eucalyptus camaldulensis were analyzed using Gas Chromatography-Flame Ionization Detection (GC-FID) and Fourier Transform Infrared (FTIR) Spectroscopy. Chemometric analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (O2PLS-DA) and Hierarchical Cluster Analysis (HCA), were utilized to classify the oils based on their volatile compound profiles. Notably, O2PLS-DA was applied directly to the raw FTIR data without additional spectral processing, showcasing its robustness in handling unprocessed data. For geographical origin determination, the GC-FID model achieved a Correct Classification Rate (CCR) of 100%, with 100% specificity and 100% sensitivity for both calibration and validation sets. FTIR spectroscopy achieved a CCR of 100%, specificity of 100%, and sensitivity of 100% for the calibration set, while the validation set yielded a CCR of 95.83%, specificity of 99.02%, and sensitivity of 94.44%. In contrast, the analysis based on species variety demonstrated 100% accuracy across all metrics CCR, specificity, and sensitivity-for both calibration and validation using both techniques. These findings underscore the effectiveness of volatile and infrared spectroscopy profiling for quality control and authentication, providing robust tools for ensuring the consistency and reliability of eucalyptus essential oils in various industrial and therapeutic applications.

A Review of Non-Destructive Raman Spectroscopy and Chemometric Techniques in the Analysis of Cultural Heritage.

Today, there is an increasing concern and effort for protection, conservation, and restoration of cultural heritage materials. Non-invasive analytical methodologies such as Raman spectroscopy offers various advantages such as high speed, robust identification, low cost, and in-site analysis. Previous contributions highlighted the potential of Raman spectroscopy combined with multivariate statistics for identification and quality evaluation of cultural heritage materials such as paints, fiber, dyes, woods, stones, inks, and textile materials. Especially, application of chemometrics and multivariate statistics algorithms opens new horizons for scientists and inspectors. In conclusion, the paper provided an overview of the state-of-the-art uses of multivariate statistically equipped Raman spectroscopy methods for evaluation of cultural heritage and art materials with illustrations from previous research studies.