Journal of Chemometrics最新文献

{"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":"https://doi.org/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":"https://doi.org/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":"https://doi.org/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":"https://doi.org/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":"https://doi.org/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":"https://doi.org/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":"https://doi.org/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":"On the Equivalence Between Null Space and Orthogonal Space in Latent Variable Regression Modeling","authors":"Sergio García-Carrión,&nbsp;Francesco Sartori,&nbsp;Joan Borràs-Ferrís,&nbsp;Pierantonio Facco,&nbsp;Massimiliano Barolo,&nbsp;Alberto Ferrer","doi":"10.1002/cem.70057","DOIUrl":"https://doi.org/10.1002/cem.70057","url":null,"abstract":"<p>The concepts of null space and orthogonal space have been developed in independent contexts and with different purposes: the former arises in the inversion of partial least-squares (PLS) regression models, and the latter in orthogonal PLS (O-PLS) modeling. In this study, we bridge PLS model inversion and O-PLS modeling by mathematically proving that the null space and the orthogonal space are the same space. We also provide a graphical interpretation of the equivalence between the two spaces, using both a simulated and a real case study.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773865","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":"Predictive QSAR Models Followed by Toxicity, Molecular Docking, and Molecular Dynamics Simulation in Search of Azole Derivatives as AChE Inhibitors for the Treatment of Alzheimer's Disease","authors":"Kajal Gupta,&nbsp;Akshay Kumar,&nbsp;Richa Patel,&nbsp;Piyush Ghode,&nbsp;Himanchal Kumar,&nbsp;Anjali Murmu,&nbsp;Nemdas Sahu,&nbsp;Geeteshwari Verma,&nbsp;Seema Sahu,&nbsp;Sonali Soni,&nbsp;Shakuntala Pal,&nbsp;Jagadish Singh,&nbsp;Partha Pratim Roy,&nbsp;Purusottam Banjare","doi":"10.1002/cem.70049","DOIUrl":"https://doi.org/10.1002/cem.70049","url":null,"abstract":"<div>\u0000 \u0000 <p>The present study aims to find azole-containing acetylcholinesterase (AChE) inhibitors for the treatment of Alzheimer's disease (AD) through a mixed in silico approach. The first step involved the collection of azole derivatives and predictive quantitative structure–activity relationship (QSAR) model development for their AChE inhibition activity, using multiple linear regressions (MLRs) with the genetic algorithm (GA) for feature selection. The developed GA-MLR models were statistically robust enough internally (<i>R</i>²<i>_a_dj</i> = 0.643–0.640, <i>Q</i>²_LOO = 0.616–0.621) as well as externally (<i>R</i>²_pred = 0.626–0.658, <i>R</i>²<i>M</i> = 0.562–0.601). The prediction reliability of the models was assured through the leverage approach of the applicability domain. The most significant models were applied to azole-containing PubChem database compounds, which were classified as active and inactive based on theoretical predictions. The toxicity analysis was also performed for the active compounds by the online web server Protox-II. The less or nontoxic compounds were subjected to molecular docking, along with donepezil as a standard. Docking analysis revealed that the four compounds have better binding affinity (binding energy = −11.6 to −11.2 kcal/mol) as compared to donepezil (binding energy = −11 kcal/mol). Apart from binding energy, donepezil was observed to be toxic by the prediction from the Protox-II. Finally, molecular dynamics (MD) analysis of two compounds (Compound 5, having the lowest IC₅₀, and Compound 25, having the highest IC₅₀ among the top 4 docked compounds) not only differentiated them based on final interactions but also exhibited that the toxicity of donepezil might be due to hydrogen bonding with the active site.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714680","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":"Influence of a Measurement Procedure and Evaluation of Transflectance Sensing System for Quantifying Sunflower Oil Adulterations in Olive Oil. A Proof of Concept","authors":"D. Castro-Reigía,&nbsp;M. Sierra,&nbsp;I. García,&nbsp;S. Sanllorente,&nbsp;L. A. Sarabia,&nbsp;M. C. Ortiz","doi":"10.1002/cem.70054","DOIUrl":"https://doi.org/10.1002/cem.70054","url":null,"abstract":"<p>The development of NIR instruments and/or their modification to adapt the measurements for each problem and improve its performance are crucial steps for the optimal measurement procedures. In this work, it is presented the development of an accessory for cuvettes designed to have the possibility to collect NIR spectra in transflectance mode. In that sense, it is aimed to investigate how different factors in the measurement procedure using this accessory influence both the NIR spectra and the subsequent calibration models for detecting adulterations with sunflower oil in olive oil. The purpose is to show how a proof of concept can be developed using chemometric tools. For that, every measurement condition influencing the spectra was evaluated with ASCA, visualizing how the use of different NIR devices, the sensor arrangement regarding the cuvette, the activation of the internal compensation system of temperature of the sensor, or the concentration levels of the adulterant affected the resulting spectra. Afterwards, every possible combination of the factors was explored through eight different PLS calibration models and their validation to examine if the factors also influenced the calibration models built for quantifying the sunflower oil present in the olive oil. It was found that not only were all factors significant regarding NIR measurements but also when quantifying adulterants. The best results of this proof of concept were obtained by arranging the sensor in a horizontal disposition regarding the cuvette and activating the internal compensation system of temperature. The capability of detection of the method for the particular oils used was 1.4% for probabilities of false positive and false negative of 0.05.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705444","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}