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"chemometrics"
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Chemometric analysis in Raman spectroscopy from experimental design to machine learning–based modeling
Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning–based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.
Raman spectroscopy is increasingly being used in biological assays and studies. This protocol provides guidance for performing chemometric analysis to detect and extract information relating to the chemical differences between biological samples.
Journal Article
Chemometric-assisted fingerprinting profiling of the Pagoda (Clerodendrum paniculatum L.) extract variation using proton nuclear magnetic resonance (1H NMR) method, compound isolation, and cytotoxic activity
One of the best solutions to understanding the chemical data of complex natural substances is to use chemometric techniques. This research aims to apply chemometric techniques, specifically principal component analysis (PCA) and cluster analysis (CA), to determine the fingerprint profiles of nine pagoda extracts (PCP) and their isolated compounds using 1H NMR data and to conduct initial cytotoxicity tests on the extracts. PCP flowers were extracted using various solvents and extraction methods, resulting in 9 types of extracts. The methanol-extracted flower portion was subjected to maceration and the compounds were then isolated using various techniques, including silica gel, column chromatography, and preparative thin layer chromatography (PTLC), which yielded 3 types of compounds. The structures were identified using 1D and 2D NMR and mass spectrometry. Meanwhile, their cytotoxic activity was tested on MCF-7, A549, KB, KB-VIN, and MDA-MB-231 cancer cells using the sulforhodamine B (SRB) assay. The research results revealed that compounds (1) stigmasta-5,22,25-trien-3β-ol, (2) 6-nonadecenoic acid, and (3) 6,9-nonadecadienoic acid, methyl ester was discovered in this plant for the first time. The fingerprinting profile of the PCP extracts and compounds showed resonance at δH 5.33 ppm (m, 1H) and δH 5.24 ppm (m, 1H). PCA of the 12 samples with eigenvalues > 1 explained 91% of the data and exhibited a normal distribution. The score plot was influenced by PC1 (82.2%) and PC2 (10.5%). The loading plot and CA combined with the linearity of (1), (2), and (3) with respect to the variation in extracts had determination coefficients (R² = 0.7550 - 0.9288) and similarities (78.26% - 98.98%). Cytotoxicity activity showed weak growth inhibition (> 89.6%) in all tested cancer cell types. In conclusion, 1H NMR spectrum and chemometrics detects the fingerprinting profile of Pagoda extract variations, clustering extracts, identifying marker compounds, and potential for cytotoxicity studies in cancer cells.
Journal Article
Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives
This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS’s role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.
Journal Article
Chemometric analysis of ethoxylated polymer products using extracted MALDI-TOF-MS peak distribution features
MALDI-TOF MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry) of ethoxylate products produces spectra with distributions of regularly spaced peaks resulting from the addition of monomer units of ethylene oxide to the oligomer. We show that overlapping peak distributions from the different ethoxylated constituents of natural raw materials can be resolved, so that features of the individual distributions ( m/z at distribution maximum, intensity at the distribution maximum, width of the distribution at half height, and ratio of the distribution to the major peak distribution) can be extracted and used with statistical pattern recognition techniques to study ethoxylated products. Crucially, we weight the extracted features, so that features from a distribution with a high ratio to the main distribution are given more importance (‘ratio-scaled’). We exemplify the method by characterizing the structural variation between types of compositionally diverse Polysorbate 80, PEG castor oil and Oleth-20, and compare the chemometric analysis using our extracted features with analysis of the full spectra. We demonstrate that using ratio-scaled extracted features gives superior results to the full spectrum, both in terms of identifying subtle compositional differences that would otherwise be missed, and in interpretability. Importantly, the integrated auto-assignment of peak distributions to possible compounds allows the results to be reported in terms of the most abundant oligomers of the raw material constituents. This simplification facilitates interpretation of the results and allows the comparison of closely related products.
Journal Article
Classification of Organic and Conventional Cocoa Beans Using Laser-Induced Fluorescence Spectroscopy Combined with Chemometric Techniques
The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain.
Journal Article
Geographical Authentication of Aquilaria sinensis Using Integrated C and O Stable Isotope Analysis Coupled with Chemometric Profiling
Multivariate carbon and oxygen stable isotope analyses combined with chemometric methods were employed to investigate Aquilaria sinensis samples collected from six major regions in China (Honghe Hani and Yi Autonomous Prefecture and Xishuangbanna Dai Autonomous Prefecture in Yunnan Province; Zhongshan City and Maoming City in Guangdong Province; and Danzhou City and Chengmai County in Hainan Province). Isotopic δ-values were analyzed across different wood parts (longitudinal and north–south orientations), chemical fractions (de-extracted wood and α-cellulose), and geographical origins. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree, and Random Forest were applied to screen and classify the samples. Four discriminant models were successfully established, achieving a maximum accuracy of 85.7% for distinguishing Aquilaria sinensis from different regions, and 88.1% for discrimination at the provincial level. These results demonstrate that stable isotope signatures, when combined with chemometrics, provide a reliable technical approach for the traceability of incense wood and offer a reference framework for verifying the authenticity of Agarwood and related plant-derived materials.
Journal Article
FTIR-ATR Spectroscopy and Chemometrics for Varietal Screening of PDO Douro Monovarietal Wines: An Exploratory Feasibility Study
The authentication of wines with Protected Designation of Origin (PDO) status is a key requirement for quality assurance, traceability, and consumer trust, particularly in traditional wine-producing regions such as the Douro Demarcated Region (Portugal). Among the certification criteria, the reliable identification of grape varieties remains technically challenging, especially when rapid and non-destructive analytical approaches are required. In this study, Fourier-transform infrared spectroscopy coupled with chemometric analysis was evaluated as a rapid screening approach for the differentiation of monovarietal Douro wines produced under standardized microvinification conditions. Twenty-one monovarietal wines were analyzed using mid-infrared spectra (1800–1000 cm−1) and classification models were developed using Partial Least Squares Discriminant Analysis (PLS-DA). The PLS-DA models showed preliminary discriminatory capacity, with apparent error rates of 10.2% for calibration and 19.3% under leave-one-out cross-validation. The results indicate that FTIR-ATR spectroscopy combined with chemometrics captures chemically relevant spectral variability associated with grape varietal differences and shows potential as a rapid exploratory screening approach within PDO traceability frameworks. Although the study is based on a limited number of biological replicates from a single vintage and sub-region, the findings provide a methodological baseline for future multi-vintage and multi-region investigations aimed at consolidating FTIR-based approaches for varietal authentication of Douro wines.
Journal Article
Abelmoschus esculentus (L.): Bioactive Components’ Beneficial Properties—Focused on Antidiabetic Role—For Sustainable Health Applications
The main features of the okra, Abelmoschus esculentus (L.), are highlighted. The evaluation of interactions between biologically active compounds and other components of the food matrix can be considered as the first action in the investigation of potential benefits of this annual herb. Moreover, updated examples of current and innovative directions in an integrated and multidisciplinary approach are discussed, with particular attention to chemometrics. Among the main effects attributed to okra, its antidiabetic property is the focus. Finally, the use of okra in different fields will be discussed.
Journal Article