Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"A novel statistical framework to address FTIR spectral challenges: Hybrid MARS–PCA/KPCA models for pollutants analysis in honey samples","authors":"Sughra Sarwar,&nbsp;Tahir Mehmood,&nbsp;Mudassir Iqbal","doi":"10.1016/j.chemolab.2025.105625","DOIUrl":"10.1016/j.chemolab.2025.105625","url":null,"abstract":"<div><div>Fourier Transform Infrared (FTIR) spectroscopy enables the rapid and non-destructive examination of complex materials; however, challenges associated with its high dimensional data include dimensionality, noise, and outliers. Conventional regression methods, such as Multivariate Adaptive Regression Splines (MARS), often struggle with these problems, which frequently involve small sample sizes and numerous variables, particularly in chemometric studies. This study proposed a hybrid framework that combines MARS with Principal Component Analysis (PCA) and Kernel PCA (KPCA) and linear regression to overcome these drawbacks. FTIR was used to investigate 30 honey samples from various geographical areas in Pakistan. Before modeling, outlier detection was conducted using the Mahalanobis distance, computed in both PCA and KPCA-transformed spaces, with Minimum Covariance Determinant (MCD) estimators applied to identify and remove statistical outliers. This preprocessing step ensured that anomalous samples did not influence the model’s accuracy. The model consistently identified chemically relevant wave numbers, especially in the high-energy C–H, O–H, and N–H stretching zones (e.g., 2966, 3010, and 3584 cm<span><math><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span>). These wavenumbers correspond to important functional groups involved in the absorption and bioaccumulation of pollutants in honey. To assess model performance, we used a 70:30 train-test split. The MARS-PCA-LR model, with RMSE <span><math><mo>=</mo></math></span> 1.2905, MAE <span><math><mo>=</mo></math></span> 1.2725 and MSE <span><math><mo>=</mo></math></span> 1.2887, outperformed the normal MARS baseline (RMSE <span><math><mo>=</mo></math></span> 4.5860, MAE <span><math><mo>=</mo></math></span> 3.4267 and MSE <span><math><mo>=</mo></math></span> 21.0319) and the MARS-KPCA-LR model (RMSE <span><math><mo>=</mo></math></span> 1.5017, MAE <span><math><mo>=</mo></math></span> 1.3300 and MSE <span><math><mo>=</mo></math></span> 1.5013) in terms of prediction accuracy. These results suggest that the proposed MARS-PCA-LR and MARS-KPCA-LR models offer improved interpretability and robustness, making them strong and reliable techniques for analyzing high-dimensional spectral data.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105625"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-scale approach integrating hyperspectral system for tracing the origin of agricultural products","authors":"Yuqi Ren ,&nbsp;He Wang ,&nbsp;Chongbo Yin ,&nbsp;Hong Men ,&nbsp;Yan Shi ,&nbsp;Jingjing Liu","doi":"10.1016/j.chemolab.2025.105592","DOIUrl":"10.1016/j.chemolab.2025.105592","url":null,"abstract":"<div><div>Agricultural products of the same variety can differ in quality, appearance, and nutritional value due to variations in climate, soil, and other growth conditions. To support reliable and sustainable origin traceability, we propose a non-destructive framework using hyperspectral data. Spectral information for rice and peanut samples from multiple production regions was acquired using a GaiaSorter hyperspectral imaging system. This method can rapidly detect chemical bonds and functional groups, with differences in these features reflecting the overall microstructural quality of agricultural products from different origins. A novel Quadrangle Attention with Deformation (QAD) module was designed to enhance multi-scale feature learning. The module applies geometric transformations within local windows and incorporates relative positional encoding to capture multi-scale receptive-field information, thereby improving spectral-band relationships. By embedding the QAD module into a separable-convolution backbone, we developed the Quadrangle Attention with Deformation Network (QAD-Net) for precise origin identification. On two benchmark datasets, QAD-Net achieved state-of-the-art accuracy, reaching 99.66 ± 0.57 % on peanuts and 99.57 ± 0.65 % on rice, outperforming existing models. This work demonstrates the potential of QAD-Net as a fast, accurate, and non-destructive tool for hyperspectral origin traceability, with significant implications for on-site quality supervision, authenticity verification, and sustainable market regulation.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105592"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Raman hyperspectral imaging using RS-NMF: a novel Regularized Sparse Non-negative Matrix Factorization for spectral unmixing","authors":"Marc Offroy ,&nbsp;Amir Ayadi ,&nbsp;Léon Govohetchan ,&nbsp;Janette Ayoub ,&nbsp;Thomas M. Hancewicz ,&nbsp;Ludovic Duponchel ,&nbsp;Mario Marchetti","doi":"10.1016/j.chemolab.2025.105602","DOIUrl":"10.1016/j.chemolab.2025.105602","url":null,"abstract":"<div><div>Molecular spectroscopy is a powerful, non-destructive technique for chemical analysis, as the sample remains unaltered during the measurement. Although it is essential for getting meaningful information, it often suffers from spectral overlap, making it challenging to identify individual components within a sample. Therefore, for over fifty years, a plethora of mathematical approaches have been developed to unmix complex signals and push the detection limits of spectroscopic instruments, such as Blind Source Separation (BSS) or Multivariate Curve Resolution (MCR), to name but a few. However, despite these numerous advances, and even as the amount of data increases – potentially providing more information – they continue to face inherent limitations (i.e., selectivity problems), particularly when dealing with contemporary samples, making their thorough characterization an increasingly intricate challenge, especially with diminishing prior knowledge. This article presents a novel signal unmixing method applied to hyperspectral Raman imaging designed to overcome these limitations. Our approach, based on a Non-Negative Matrix Factorization (NMF), addresses critical challenges such as rotational ambiguity and noise sensitivity, which often prevent accurate pure component spectral unmixing. First, we introduce our methodology and explain how it differs from existing mathematical methods. We then evaluate its performance on a well-known real-world dataset in the chemometrics community called “emulsion” from hyperspectral Raman imaging. To further challenge our method, we apply it to a complex simulated molecular signal dataset. Finally, we compare our results with those obtained using the standard MCR-ALS approach. Our initial results demonstrate that this RS-NMF approach improves the unmixing of complex signals.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105602"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of parametric and permutation t spectral representations for determining individual metabolite abundances from factorial design spectra","authors":"Leonardo J. Duarte ,&nbsp;Gustavo G. Marcheafave ,&nbsp;Elis D. Pauli ,&nbsp;Ieda S. Scarminio ,&nbsp;Roy E. Bruns","doi":"10.1016/j.chemolab.2025.105622","DOIUrl":"10.1016/j.chemolab.2025.105622","url":null,"abstract":"<div><div>Two level factorial design spectra can be transformed into t spectral representations to analyze changes in metabolic abundances owing to environmental impacts. This transformation involves performing statistically paired <em>t</em>-test for each spectroscopic variable. These tests are sensitive to deviations from normality of the spectral data as well as heterogeneous variances of data at different factorial design levels. Although existing spectral information for metabolites can help guide interpretive efforts, permutation calculations can be performed to obtain statistical significance and t values of metabolic peaks that are expected to be less sensitive to these assumptions. The results of these calculations are reported here and compared with results from parametric statistical values for 13,501 NMR spectral variables for two level factorial design data of ethanol, dichloromethane and ethanol-dichloromethane (1:1) mixture extracts of yerba mate leaf samples. All t-representation peaks found to be statistically significant by parametric calculations are confirmed by the permutation calculations. Permutation results do not indicate any new significant peaks that were not predicted by the parametric results. As such, permutation calculations are recommended to validate results obtained from parametric determinations of statistical significance.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105622"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LRADA: An adaptive global-local fault diagnosis via low-rank subspace representation with prior-constrained discriminative framework","authors":"Yi Luo ,&nbsp;Jian Cheng ,&nbsp;Si-Yu Chen ,&nbsp;Lin-Hao Nie ,&nbsp;Qian Cheng ,&nbsp;Cheng-Shu Ye ,&nbsp;Yuan Xu ,&nbsp;Yang Zhao","doi":"10.1016/j.chemolab.2025.105608","DOIUrl":"10.1016/j.chemolab.2025.105608","url":null,"abstract":"<div><div>Fault diagnosis of industrial processes is of great significance for reducing the risk of sensor damage and improving the safety and smooth operation of the plants. The data-driven fault diagnosis methods show promise in eliminating redundant signals captured by sensors, enabling superior anomaly detection performance. However, current data-driven approaches face many challenges in industrial plants signal processing and feature extraction, such as insufficient robustness, poor interpretability of the projection space, and inadequate representation of nonlinear local structures. To address these challenges, we propose a novel diagnostic framework, the low-rank approximation fusion discriminant analysis (LRADA) model, to enhance fault diagnosis for industrial processes. In the LRADA method, the prior information of the low-rank subspace was used to learn the global low-rank attributes of the original space. Then these attributes are embedded into the improved local linear discriminant analysis framework to enhance the discrimination between different classes. In addition, specific norm constraints are imposed on the projection space to facilitate sparse feature extraction and suppress noise interference. Finally, the effectiveness of the proposed diagnosis method is verified by experimental analysis on three benchmark data sets of industrial processes. The experimental results show the superiority of the proposed method in solving the above challenges and improving the fault diagnosis accuracy in industrial environments. LRADA is available at <span><span>https://github.com/gitcodelist/LRADA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105608"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on quality evaluation system and grade classification of Angelica dahurica based on artificial intelligence and multispectral technology","authors":"Wei Nie ,&nbsp;Xulong Huang ,&nbsp;Jin Pei ,&nbsp;Chaoxiang Ren ,&nbsp;Tao Zhou ,&nbsp;Jinyu Du ,&nbsp;Huajuan Jiang ,&nbsp;HanYi Zhang ,&nbsp;Xin Li ,&nbsp;Juan Li ,&nbsp;Yuhang Li ,&nbsp;Yueying Hu ,&nbsp;Zhiyu Hao","doi":"10.1016/j.chemolab.2025.105610","DOIUrl":"10.1016/j.chemolab.2025.105610","url":null,"abstract":"<div><div><em>Angelica dahurica</em> (AD) is both a widely used spice and a precious traditional Chinese medicine. Currently, its quality evaluation predominantly depends on traditional identification methods and physicochemical assessments, which are often subjective or time-consuming, thus limiting their suitability for rapid, non-destructive, and accurate quality evaluation. Therefore, this study constructed a quality evaluation system based on key criteria: shape, color, odour and texture. Experienced traditional medicine experts scored 611 samples according to this system, categorizing them into three quality grades. Imperatorin and isoimperatorin in 30 randomly selected batches were quantified by HPLC, revealing a positive correlation with quality grade and confirming the system's accuracy and reliability. Moreover, quality grading models were established by integrating multispectral imaging technology with artificial intelligence technologies such as CNN and Transformer. The Transformer model achieved the highest accuracy of 88.71 %. Overall, this study improves the objectivity and reproducibility of traditional identification methods. It also demonstrates that integrating artificial intelligence with multispectral imaging enables non-destructive, rapid, and precise classification of AD, offering a novel approach for quality control of medicinal materials.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105610"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble robust SIMPLS with block-penalized smoothing for scalar-on-function regression","authors":"Aylin Alin","doi":"10.1016/j.chemolab.2025.105626","DOIUrl":"10.1016/j.chemolab.2025.105626","url":null,"abstract":"<div><div>We propose a robust penalized smooth partial least squares approach that (i) smooths high-dimensional discretized functional predictors via blockwise B-spline bases, (ii) applies robust SIMPLS to obtain latent scores, (iii) fits a penalized regression in the latent space whose penalty is exactly a block-diagonal roughness penalty on the coefficient function(s), and (iv) aggregates models through bootstrap ensembles (classical/sufficient resampling; mean/median aggregation). The method supports multiple functional predictors through a block-diagonal construction and yields interpretable smooth coefficient functions. Our method demonstrates competitive or superior performance under collinearity and contamination.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105626"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online monitoring of phytochemical dynamics in black tea processing using MIP-driven classifier models","authors":"Debanjana Ghosh ,&nbsp;Debangana Das ,&nbsp;Shreya Nag ,&nbsp;Runu Banerjee Roy","doi":"10.1016/j.chemolab.2025.105611","DOIUrl":"10.1016/j.chemolab.2025.105611","url":null,"abstract":"<div><div>—Black tea processing involves variation in phytochemical constituents through multiple stages, with the tea quality index varying according to these biomarkers. In this treatise, modified classifier models were used to monitor two key biomarkers, catechin and epigallocatechin gallate (EGCG), throughout the five distinct stages of black tea processing for tea quality estimation. Catechin and EGCG selective molecularly imprinted polymer (MIP) electrodes were prepared for differential pulse voltammetry (DPV) responses at five different processing stages of black tea. The DPV responses were analyzed for the discrimination of the processing stages based on the content of catechin and EGCG using a stacked model incorporating four classification algorithms—Random forest, K-nearest neighbors, Gaussian Naive Bayes (NB), and Gradient boosting and an Artificial Neural Network (ANN) classifier model. The proposed models exhibited satisfactory performance in classifying five different stages of fermentation for four different tea samples, with accuracies of 98 % for catechin and 95 % for EGCG. Principal Component Analysis (PCA) plots show the capability of the sensors to identify each stage of tea processing as a distinct cluster. The sensor response also exhibited a consistent pattern of change in catechin and EGCG contents across various stages of tea processing.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105611"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting and classifying palladium nanoparticles in microscopic images using neutrosophic deep learning","authors":"Mohamed El-dosuky ,&nbsp;Aboul Ella Hassanien ,&nbsp;Heba Alshater ,&nbsp;Rania Ahmed ,&nbsp;Sameh H. Basha ,&nbsp;Heba AboulElla ,&nbsp;Ashraf Darwish ,&nbsp;Sara Abdelghafar","doi":"10.1016/j.chemolab.2025.105619","DOIUrl":"10.1016/j.chemolab.2025.105619","url":null,"abstract":"<div><div>This paper introduces a neutrosophic deep learning model for automated detection and classification of palladium nanoparticles in scanning electron microscopy (SEM) images, distinguishing between ordered and disordered structures for accurate nanoparticle characterization. The model follows a five-phase pipeline for enhanced accuracy and efficiency. It begins with data augmentation, applying transformations like rotation and flipping to improve dataset diversity. The second phase uses neutrosophic image segmentation to manage uncertainty and noise in SEM images, allowing for the precise isolation of nanoparticle regions. In the third phase, the VGG-19 deep neural network extracts high-level features, initially identifying 25,088 features. In the fourth phase, a hybrid approach combining Gini importance and Genetic Optimized Rough Sets (GORS) reduces the number of features to 2454. The refined feature set is then classified using a Random Forest classifier, which effectively distinguishes between ordered and disordered palladium nanoparticles. To validate its performance, the proposed model was evaluated on a dataset of 1000 SEM images of carbon-based materials with deposited palladium nanoparticles, which was then expanded to 1500 images to address class imbalance and minimize overfitting. The experimental results highlight the model's strong potential as a high-performance classification tool for nanoparticle analysis in SEM images, achieving an overall accuracy of 99.67 %. To evaluate the impact of the introduced phases on the proposed model's performance, four ablation experiments were conducted, demonstrating the significance of each phase. Dropping data augmentation and feature reduction reduced accuracy approximately to 97.5 %, while dropping the feature extraction phase reduced it further to 94.17 %, highlighting the critical impact of these processes on performance and robustness.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105619"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scientific trends in spectroscopy and regression chemometric modelling for the estimation of whole fruit quality: A systematic review","authors":"Vicente Amirpasha Tirado-Kulieva ,&nbsp;Fidel A. Torres-Guevara ,&nbsp;Jhony Alberto Gonzales-Malca ,&nbsp;Wilson Castro ,&nbsp;Lucía Seguí","doi":"10.1016/j.chemolab.2025.105612","DOIUrl":"10.1016/j.chemolab.2025.105612","url":null,"abstract":"<div><div>Spectroscopic techniques, supported by chemometrics, provide rapid, non-destructive, and sustainable solutions for assessing the quality of whole fruits. This study systematically reviews and analyzes research on spectroscopy and regression chemometric modelling for the estimation of whole fruits from 1997 to 2025. A total of 389 English-language articles were retrieved from Scopus using a hybrid strategy that combined an initial search and snowballing. Geographical analysis identified China as the leading country in scientific output, followed by Spain, Italy, and the United States, while Africa and Oceania showed limited participation. Apples, grapes, pears, and mangoes were the most frequently studied fruits, and commonly modeled attributes included SSC, physicochemical properties, and bioactive compounds. NIR and Vis-NIR were the predominant techniques, complemented by emerging methods such as HSI and Raman. Among chemometric approaches, preprocessing relied mainly on hybrid strategies followed by SNV and derivatives. For dimensionality reduction, CARS, SPA, and hybrid methods were the most relevant. PLSR remained dominant for modeling, although there was an increasing use of advanced algorithms such as SVMR and deep neural networks. The review also examined current trends and future directions, highlighting progress in robust modeling algorithms, portable and online detection systems, and multimodal spectroscopy with data fusion. Key priorities include methodological harmonization, open data practices, and large-scale field validation. Overall, the findings highlight a transition toward more precise and adaptable systems while underscoring persistent challenges in standardization, real-world validation, and equitable access. This review provides a strategic foundation for advancing non-destructive technologies across the fruit value chain.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"269 ","pages":"Article 105612"},"PeriodicalIF":3.8,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}