Journal of Chemometrics最新文献

{"title":"Comparison Between Portable and Bench-Top Near-Infrared Spectroscopy for Corn Silage Characterization Using Partial Least Square and Support Vector Regression Methods","authors":"Jefferson Tales Oliva,&nbsp;Vinicius Herique Kieling,&nbsp;Felipe Augusto Bueno Rossi,&nbsp;Erick Oliveira Rodrigues,&nbsp;Giovanni Alfredo Guarneri,&nbsp;Larissa Macedo dos Santos Tonial","doi":"10.1002/cem.70073","DOIUrl":"https://doi.org/10.1002/cem.70073","url":null,"abstract":"<p>In this work, bench-top and portable near-infrared (NIR) spectroscopy equipment are compared in the context of generating predictive models for the quantification of phosphorus (P), potassium (K), and nitrogen (N) components from corn silage samples. For this, 200 spectral samples resulting from bench-top and portable NIR are preprocessed by the following sequence of approaches: mean centering application for removing the spectral bias/offset, Savitzky–Golay filter for highlighting signal energy absorption in relation to spectral noise, interval partial least square (iPLS) for selection of spectral region, and Monte Carlo method for outlier detection and removal. Then, from the preprocessed spectra, predictive models were built using the partial least squares (PLS) and support vector regression (SVR) methods for each chemical component and NIR equipment. In this sense, six models are generated, three for each NIR spectroscopy (or two for each element). As a result, considering all components and machine learning (ML) methods, bench-top models achieved <i>R</i>² values between 0.66 (quantification of P using PLS or SVR) and 0.81 (prediction of K and N using SVR regressors) during the validation, whereas portable ones achieved values between 0.50 (prediction of K using SVR) and 0.67 (quantification of N using PLS). Our results can be considered competitive, as robust and accurate predictors are also generated.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding Multivariate Analysis Principles in Conventional Chemometric Processes","authors":"John H. Kalivas,&nbsp;Robert C. Spiers","doi":"10.1002/cem.70067","DOIUrl":"10.1002/cem.70067","url":null,"abstract":"<div>\u0000 \u0000 <p>Chemometrics encompasses numerous facets such as experimental design, data collection and analysis, and many others. This paper, in honor of Paul Geladi, provides our perspective on growing the scientific intuition of multivariate analysis in conventional chemometric directions not generally practicing multivariate principles. The motivation for this perspective is to express our opinion on the need for chemometrics to expand the role of the Rashomon effect beyond “many models predict well” by integrating a more comprehensive consideration of the multivariate nature of matrix effects. Described are multiple chemometric techniques that have already been enhanced by broadening the application the Rashomon effect including model selection and explanation, figures of merit (FOM), sample similarity assessment for model reliability, outlier detection, and classification—all recent research topics from the authors. This expository discussion revolves around spectroscopic data such as near infrared and fluorescence, but the concepts are relevant to other chemometric data structures.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Similarity Measures for Binary Data in Clustering: The Role of Rare Events and Matching Absences","authors":"Tânia F. G. G. Cova,&nbsp;Alberto A. C. C. Pais","doi":"10.1002/cem.70061","DOIUrl":"10.1002/cem.70061","url":null,"abstract":"<div>\u0000 \u0000 <p>Clustering of binary data is central to various applications, particularly in the fields of medical diagnostics, chemistry, and chemoinformatics. However, standard similarity measures often fail to capture the informative value of rare features and matching absences, treating all attributes as equally relevant. This can lead to suboptimal clustering, especially when informative patterns are hidden in low-frequency features. This study proposes a probability-weighted approach to measuring similarity, which gives more weight to rare features and accounts for the value of shared absences based on their occurrence probabilities. We analyze how this adjustment impacts clustering results, using visual comparisons and experiments on real datasets. The results show consistent gains in clustering precision and stability compared to standard measures. Our findings suggest that incorporating the rarity of features into similarity computation can offer a more reliable basis for clustering binary data, especially in domains where rare signals carry meaningful information.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Grading Accuracy by Optimizing the Logistic Loss Function in PLS Modelling","authors":"Zhonghai He,&nbsp;Huilong Sheng,&nbsp;Yi Zhang,&nbsp;Xiaofang Zhang","doi":"10.1002/cem.70064","DOIUrl":"10.1002/cem.70064","url":null,"abstract":"<div>\u0000 \u0000 <p>The prediction results from Partial Least Squares (PLS) model are commonly used to assess whether a product meets quality standards, or whether adjustments are needed in production process parameters. It's easy to understand that misgrading is mostly occurred for marginal samples (samples near the threshold). We propose Logistic-Enhanced PLS (LE-PLS) model, which defines a logistic loss function and minimizes it via gradient descent to optimize the PLS projection vector. The prediction result of LE-PLS for marginal samples tends to be far away from the threshold value. This optimization enables LE-PLS to enhance grading capability while largely maintaining the regression accuracy of the PLS. LE-PLS was evaluated on two real-world datasets (bean pulp and corn gluten meal) and one simulated dataset, correcting 10 out of 19 misgraded samples, 6 out of 7, and 6 out of 12, respectively. Statistical analysis using paired <i>t</i>-tests confirmed that these improvements were significant. Although RMSEP increased slightly, the change remained within an acceptable range considering the substantial enhancement in grading performance. The algorithm has a computational complexity of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>O</mi>\u0000 <mfenced>\u0000 <mrow>\u0000 <mtext>iteration</mtext>\u0000 <mo>*</mo>\u0000 <mtext>samples</mtext>\u0000 <mo>*</mo>\u0000 <mtext>variables</mtext>\u0000 </mrow>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation>$$ mathrm{O}left({mathrm{iteration}}^{ast }{mathrm{samples}}^{ast}mathrm{variables}right) $$</annotation>\u0000 </semantics></math> during modeling training. However, its prediction-phase complexity is only <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>O</mi>\u0000 <mfenced>\u0000 <mrow>\u0000 <mtext>samples</mtext>\u0000 <mo>*</mo>\u0000 <mtext>variables</mtext>\u0000 </mrow>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation>$$ mathrm{O}left({mathrm{samples}}^{ast}mathrm{variables}right) $$</annotation>\u0000 </semantics></math>. Given these advantages, LE-PLS is well-suited for practical applications in NIR-based quality grading of products.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Paul Geladi Legacy: Pioneering Chemometrics for the Future","authors":"Beatriz Galindo-Prieto","doi":"10.1002/cem.70065","DOIUrl":"10.1002/cem.70065","url":null,"abstract":"&lt;p&gt;This special issue, entitled ‘Paul Geladi Legacy: Pioneering Chemometrics for the Future’, is a tribute to the remarkable scientific contributions of Professor Paul Geladi to the field of chemometrics. This very special issue brings together a comprehensive collection of topics that reflect the breadth and depth of Paul's work in chemometrics. While nice memories and Paul's interests in science have been shared by some of his friends and colleagues in recent publications, this editorial and its related special issue will focus on some of the most relevant scientific areas that Professor Paul Geladi explored throughout his prolific career. The title of this special issue honouring Paul is not trivial. For many years, Paul emphasized the future of chemometrics as an important and in-depth topic that should be part of scientific meetings, conferences and specialized literature. Indeed, as Paul remarked on several occasions, pioneering chemometrics for the future, not only by adapting its methodologies and advances to new challenges and technologies but also creating new chemometric research directions according to evolving trends in science, is crucial for the field of chemometrics to succeed. To achieve this, high-quality teaching and the education of the next generations in chemometrics is especially important, as well as fostering collaboration across research groups. An exemplar of the latter was the initiative led by Paul called ‘The Laboratory Profile’ (published at &lt;i&gt;Journal of Chemometrics&lt;/i&gt; in the 90s), which strengthened the global network of chemometric laboratories and showcased the wide array of scientific activities taking place across university, research institutions and industry. The breadth of Paul's knowledge, enhanced from a rich network of scientists, enabled him to successfully apply the most suitable chemometric techniques across various applications.&lt;/p&gt;&lt;p&gt;Professor Paul Geladi was a dedicated educator. In 1986, when audiovisual resources were still rarely used in statistical lectures, Paul was ahead of his time publishing an article on the use of videotapes as pedagogic tools in chemometrics education. Besides, Paul wrote several tutorials on chemometric methods, two of which stand out as his most cited work. The first is his tutorial on principal component analysis (co-authored with Wold and Esbensen), which covers the most relevant aspects of PCA and its application, whilst the second tutorial focuses on partial least squares regression (co-authored with Kowalski) and covers the concept and algebra of the PLS algorithm. These tutorials published in international journals remain foundational references in the field. In addition, Paul authored three books of high relevance in the field of chemometrics. His book &lt;i&gt;Multi-Way Analysis with Applications in the Chemical Sciences&lt;/i&gt; (co-authored with Smilde and Bro) provides chemometricians with the mathematical foundations needed to understand multi-way approaches and pra","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Simultaneous Determination of Astragalus Polysaccharides and Calycosin-7-O-β-D-Glucoside in Astragali Radix Percolate Based on Near-Infrared Spectroscopy Technology","authors":"Li Zha,&nbsp;Kaiqi Zhang,&nbsp;Die Xie,&nbsp;Yongming Luo,&nbsp;Xin Che,&nbsp;Lihong Wang","doi":"10.1002/cem.70062","DOIUrl":"10.1002/cem.70062","url":null,"abstract":"<div>\u0000 \u0000 <p>As a crucial extraction process in traditional Chinese medicine, quality control of percolation still faces challenges in real-time monitoring methods. To address this challenge, this study focused on the Astragalus percolation process and established an NIRS-based method for synchronous online monitoring of two bioactive markers in Astragalus percolates: Astragalus polysaccharides (APSs) and calycosin-7-O-β-D-glucoside (CG), achieving rapid and nondestructive analysis. In this study, near-infrared (NIR) spectra were collected online at different time points during percolation to determine APS and CG concentrations by means of NIRS technology, with high-performance liquid chromatography (HPLC) and ultraviolet–visible spectrophotometry (UV–Vis) used as reference methods. Two modeling approaches—partial least squares regression (PLSR) and support vector regression (SVR)—were employed to establish quantitative analytical models for these bioactive components, with model performance optimized through spectral preprocessing and feature variable selection. Results demonstrated that SVR-based models achieved superior predictive accuracy compared with PLSR. The optimal APS model showed calibration and validation set <i>R</i>² values of 0.9995 and 0.9874, respectively, while the CG model yielded 0.9811 (calibration) and 0.9632 (validation). Both components exhibited residual prediction deviation (RPD) values exceeding the threshold (RPD &gt; 3), with 6.5349 for APS and 3.8357 for CG, confirming excellent predictive capability. Paired <i>t</i>-test analysis of external test sets (<i>p</i> &gt; 0.05) revealed no statistically significant difference between measured and predicted values, further validating the model's robustness for unknown sample prediction. The concentrations of APS and CG in the Astragalus percolation solution can be simultaneously determined by this method within 30 s, significantly improving analytical efficiency compared with the conventional method (60–80 min per sample), while featuring simple operation, solvent-free consumption, low cost, and pollution-free advantages. This study demonstrates that the combination of NIRS and chemometrics enables real-time monitoring of multiple key substance concentrations during the percolation process. As a green analytical technology, NIRS shows significant potential for improving production efficiency and ensuring product quality consistency.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the Distinctive Features of Alpha-D-mannopyranoside Structure From Similar Structures Against FimH Through ANN and PCA: Insights and Perspectives","authors":"M. Dhanalakshmi,&nbsp;K. R. Jinuraj,&nbsp;Muhammed Iqbal,&nbsp;D. Sruthi,&nbsp;Kajari Das,&nbsp;Sushma Dave,&nbsp;N. Muthulakshmi Andal","doi":"10.1002/cem.70063","DOIUrl":"10.1002/cem.70063","url":null,"abstract":"<div>\u0000 \u0000 <p>This computational study aimed to demonstrate distinct characteristics of alpha-D-mannopyranoside structure, leveraging D-mannose and its analogs due to their known roles in host–pathogen interactions and potential to be used as nutraceuticals. Targeting bacterial adhesion is a critical strategy to combat urinary tract infections (UTIs), especially given rising antibiotic resistance. The FimH lectin on <i>Escherichia coli</i> is a key mediator of this adhesion, making it a compelling target for novel anti-adhesive therapies. We employed a multi-stage virtual screening pipeline to efficiently explore a vast chemical space around the ligands and their binding interactions. Ligand-based virtual screening, utilizing self-organizing maps (SOMs), clustered 5256 D-mannose-similar structures, identifying a promising subset of 141 molecules with 39 known bioassay actives. This was followed by structure-based ligand docking to precisely evaluate their inhibitory impact on FimH lectin. To understand the structural features driving activity, principal component analysis (PCA) was then applied to analyze the molecular structures and their physicochemical descriptors. Our analysis revealed that 15 compounds exhibited the highest binding energy and docking scores. Crucially, the alpha-D-mannopyranoside conformation demonstrated the most effective inhibitory profile. This superior activity, despite structural similarities, was differentiated by two 3D-matrix descriptors: HRG and Wi G, highlighting their significance in predicting subtle yet impactful conformational preferences.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Monitoring Scheme Using GLPP Through Kantorovich Distance Combined With a Sliding Window Technique for Nonlinear Dynamic Process Fault Detection","authors":"Cheng Zhang,&nbsp;Lu Ren,&nbsp;Jing Zhang,&nbsp;Yuan Li","doi":"10.1002/cem.70058","DOIUrl":"10.1002/cem.70058","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the issue of insufficient fault detection performance of global–local preserving projections (GLPP) in the detection of minor faults within nonlinear dynamic processes, a novel fault detection method based on GLPP and Kantorovich distance combined with a sliding window (GLPP-KD) is proposed. Firstly, the GLPP algorithm is used to construct a weight matrix to retain the key information of the data, and the objective function containing local and global information is transformed into a generalized eigenvector problem to obtain a projection matrix. Additionally, the sliding window technique integrated with the Kantorovich distance is employed to quantify the discrepancies between probability distributions, thereby capturing the local dynamic characteristics of the data. Eventually, the fault detection task is achieved by identifying the minor distinctions between normal and faulty states. Experimental results show that compared with traditional methods, GLPP-KD improves the fault detection accuracy and effectively reduces the false alarm rate. The proposed method provides a strong guarantee for the safe and stable operation of the industry and has high application value.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding the Chemometric Data Analysis Toolbox With Immersive Analytics","authors":"John H. Kalivas","doi":"10.1002/cem.70060","DOIUrl":"10.1002/cem.70060","url":null,"abstract":"<div>\u0000 \u0000 <p>Immersive analytics is a developing field growing as technology improves. This paper presents some important points, but by no means is the discussion complete. The cited papers and books should be read to fully grasp the potential of the general field of immersive analytics. The direction of this paper is to highlight those components useful for chemometric data analyses in virtual reality.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Honoring Professor Tormod Næs—A Pillar of Chemometrics","authors":"Ingrid Måge","doi":"10.1002/cem.70059","DOIUrl":"10.1002/cem.70059","url":null,"abstract":"&lt;p&gt;It is both a privilege and a personal honor to introduce this special issue of the &lt;i&gt;Journal of Chemometrics&lt;/i&gt;, dedicated to celebrating the career of Professor Tormod Næs. As a mentor, colleague, and friend, Tormod has been a guiding light throughout my scientific journey from my earliest days as a PhD student under his supervision to our many years of working together at Nofima.&lt;/p&gt;&lt;p&gt;Tormod's contributions to the field of chemometrics are both foundational and far-reaching. His ability to bridge rigorous statistical theory with practical application is a defining feature of his work and a testament to his rare combination of intellectual depth and scientific intuition.&lt;/p&gt;&lt;p&gt;His early work in multivariate calibration, particularly in near-infrared (NIR) spectroscopy, laid the groundwork for numerous applications in food science, process modeling, and sensory analysis. His 1992 book &lt;i&gt;Multivariate Calibration&lt;/i&gt;, co-authored with Prof. Harald Martens, remains a seminal reference. It is cited nearly 9000 times, and it continues to serve as an accessible introduction to chemometrics for both students and practitioners.&lt;/p&gt;&lt;p&gt;Equally pioneering was his work in sensometrics, where he developed methods to understand individual differences in sensory and consumer data, an area that has become increasingly important in this field. Tools like PanelCheck and ConsumerCheck, which he helped develop, have empowered practitioners and researchers to apply complex statistical methods with ease and confidence.&lt;/p&gt;&lt;p&gt;My main area of collaboration with Tormod has been in multiblock modelling. His theoretical innovations in this field include methods such as SO-PLS and ROSA, in the context of prediction, interpretation, and path modelling. The methods have been widely adopted and further developed by researchers around the world and have numerous applications in process modeling, spectroscopy, sensometrics, -omics and beyond. Tormod's work in this area has opened new avenues for data fusion and interpretation across a broad range of scientific domains.&lt;/p&gt;&lt;p&gt;Tormod's scholarly achievements include over 250 peer-reviewed articles, 7 books, and more than 28,000 citations. Beyond these impressive numbers, the most important part of his legacy is, in my view, the community he has nurtured. He has supervised 25 PhD students and mentored countless others, always prioritizing their development. Tormod is known for his remarkable ability to encourage young scientists and consistently push them forward. His constructive, thorough, and insightful feedback is always delivered with kindness. His mentorship has shaped not only the scientific work but also the confidence and careers of many young researchers.&lt;/p&gt;&lt;p&gt;Tormod's international collaborations have enriched the field globally. His affiliations with institutions such as the University of Oslo and the University of Copenhagen, along with long-standing partnerships across Europe, the United States and South Afric","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}