Chemometrics and Intelligent Laboratory Systems最新文献

A flame image soft sensor for oxygen content prediction based on denoising diffusion probabilistic model 基于去噪扩散概率模型的氧气含量预测火焰图像软传感器

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-12 DOI: 10.1016/j.chemolab.2024.105269

Yi Liu , Angpeng Liu , Shuang Gao

引用次数: 0

Prediction of potential antitumor components in Ganoderma lucidum: A combined approach using machine learning and molecular docking 灵芝中潜在抗肿瘤成分的预测：利用机器学习和分子对接的综合方法

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-08 DOI: 10.1016/j.chemolab.2024.105271

Qi Yang , Lihao Yao , Fang Jia , Guiyuan Pang , Meiyu Huang , Chengxiang Liu , Hua Luo , Lili Fan

{"title":"Prediction of potential antitumor components in Ganoderma lucidum: A combined approach using machine learning and molecular docking","authors":"Qi Yang , Lihao Yao , Fang Jia , Guiyuan Pang , Meiyu Huang , Chengxiang Liu , Hua Luo , Lili Fan","doi":"10.1016/j.chemolab.2024.105271","DOIUrl":"10.1016/j.chemolab.2024.105271","url":null,"abstract":"<div><div>The objective of this study is to develop a reliable predictive model for antitumour activity and to identify potential antitumour components in <em>Ganoderma lucidum</em>. Four machine learning models, including Random Forest, were employed to train predictive models for antitumour activity, utilising Morgan fingerprints as molecular descriptors. The most effective model was then employed to predict the chemical composition of <em>Ganoderma lucidum</em>, identifying the four most probable compounds for molecular docking with known TNF-α-related targets. The findings of the study indicate that a Support Vector Machine (SVM) model exhibits an accuracy, F1 score, AUC, and sensitivity of 0.7638, 0.7638, 0.8332, and 0.7621, respectively. The model demonstrated an 80 % accuracy rate in predicting the antitumour activity of 10 FDA-approved drugs. Besides, the model identified 11 components in <em>Ganoderma lucidum</em>, including 3-nitroanisole, with a probability of antitumour activity exceeding 0.5, indicating their potential as antitumour agents. The results of the molecular docking procedure indicated that the four most promising antitumour compounds derived from <em>Ganoderma lucidum</em> exhibited a favourable binding affinity with the TNF-α target. In conclusion, this study incorporated a machine learning prediction step prior to molecular docking, thereby enhancing the reliability of the latter. Furthermore, it identified previously unreported compounds in <em>Ganoderma lucidum</em> with potential antitumour activity, such as 3-nitroanisole.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105271"},"PeriodicalIF":3.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652927","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}

引用次数: 0

Spectra data calibration based on deep residual modeling of independent component regression 基于独立分量回归深度残差建模的光谱数据校准

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-05 DOI: 10.1016/j.chemolab.2024.105270

Junhua Zheng , Zeyu Yang , Zhiqiang Ge

引用次数: 0

On-the-fly spectral unmixing based on Kalman filtering 基于卡尔曼滤波技术的即时光谱非混频技术

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-10-30 DOI: 10.1016/j.chemolab.2024.105252

Hugues Kouakou , José Henrique de Morais Goulart , Raffaele Vitale , Thomas Oberlin , David Rousseau , Cyril Ruckebusch , Nicolas Dobigeon

{"title":"On-the-fly spectral unmixing based on Kalman filtering","authors":"Hugues Kouakou , José Henrique de Morais Goulart , Raffaele Vitale , Thomas Oberlin , David Rousseau , Cyril Ruckebusch , Nicolas Dobigeon","doi":"10.1016/j.chemolab.2024.105252","DOIUrl":"10.1016/j.chemolab.2024.105252","url":null,"abstract":"<div><div>This work introduces an on-the-fly (i.e., online) linear spectral unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace to lighten the computational burden of the overall unmixing procedure. Experimental results obtained on synthetic and real Raman data sets show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. The proposed method constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples. The code associated with the numerical illustrations reported in this paper is freely available online at <span><span>https://github.com/HKouakou/KF-OSU</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105252"},"PeriodicalIF":3.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of soil organic carbon content using visible and near-infrared spectroscopy in the Red River Delta, Vietnam 利用可见光和近红外光谱估算越南红河三角洲的土壤有机碳含量

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-10-22 DOI: 10.1016/j.chemolab.2024.105253

Nguyen-Xuan Hau , Nguyen-Thanh Tuan , Lai-Quang Trung, Tran-Thuy Chi

{"title":"Estimation of soil organic carbon content using visible and near-infrared spectroscopy in the Red River Delta, Vietnam","authors":"Nguyen-Xuan Hau , Nguyen-Thanh Tuan , Lai-Quang Trung, Tran-Thuy Chi","doi":"10.1016/j.chemolab.2024.105253","DOIUrl":"10.1016/j.chemolab.2024.105253","url":null,"abstract":"<div><div>Accurate estimation of Soil Organic Carbon (SOC) is vital for assessing soil fertility, health, and carbon sequestration. Visible and Near-Infrared (Vis-NIR) spectroscopy has gained popularity worldwide for SOC estimation due to its cost-effectiveness and environmental benefits. However, inconsistencies arise from varying preprocessing techniques and regression models applied across different datasets and regions. Few studies explore combinations of spectral preprocessing, modeling algorithms, and resampling techniques. This study presents the first SOC estimation using Vis-NIR spectroscopy in the Red River Delta, Vietnam. We assessed estimation performances incorporating fifteen preprocessing techniques, four regression models, and three resampling methods to identify the most effective strategies. Standard Normal Variate (SNV) emerged as the top preprocessing technique, while Partial Least Squares Regression (PLSR) demonstrated the highest accuracy with minimal discrepancies between calibration and validation. Regarding resampling methods, repeated cross-validation (repeatedcv) proved most robust, with simple cross-validation as an alternative. By utilizing SNV, PLSR, and repeatedcv, we achieved the first successful Vis-NIR spectroscopy-based SOC estimation in the Red River Delta and Vietnam. This approach satisfied stringent statistical criteria for predictive models, yielding validation performance metrics of R<sup>2</sup> = 0.740, RMSE = 0.166, RPD = 2.337, and RPIQ = 2.321. Our findings highlight the importance of optimizing preprocessing, regression, and resampling techniques for accurate Vis-NIR spectroscopy-based SOC prediction.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105253"},"PeriodicalIF":3.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652926","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}

引用次数: 0

Identification of Food Compounds as inhibitors of SARS-CoV-2 main protease using molecular docking and molecular dynamics simulations 基于分子对接和分子动力学模拟的食物化合物对SARS-CoV-2主要蛋白酶抑制剂的鉴定

IF 3.9 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2021-10-15 DOI: 10.1016/j.chemolab.2021.104394

Vijay H. Masand , Md Fulbabu Sk , Parimal Kar , Vesna Rastija , Magdi E.A. Zaki

{"title":"Identification of Food Compounds as inhibitors of SARS-CoV-2 main protease using molecular docking and molecular dynamics simulations","authors":"Vijay H. Masand , Md Fulbabu Sk , Parimal Kar , Vesna Rastija , Magdi E.A. Zaki","doi":"10.1016/j.chemolab.2021.104394","DOIUrl":"10.1016/j.chemolab.2021.104394","url":null,"abstract":"<div><p>SARS-CoV-2 has rapidly emerged as a global pandemic with high infection rate. At present, there is no drug available for this deadly disease. Recently, M<sup>pro</sup> (Main Protease) enzyme has been identified as essential proteins for the survival of this virus. In the present work, Lipinski's rules and molecular docking have been performed to identify plausible inhibitors of M<sup>pro</sup> using food compounds. For virtual screening, a database of food compounds was downloaded and then filtered using Lipinski's rule of five. Then, molecular docking was accomplished to identify hits using M<sup>pro</sup> protein as the target enzyme. This led to identification of a Spermidine derivative as a hit. In the next step, Spermidine derivatives were collected from PubMed and screened for their binding with M<sup>pro</sup> protein. In addition, molecular dynamic simulations (200 ns) were executed to get additional information. Some of the compounds are found to have strong affinity for M<sup>pro</sup>, therefore these hits could be used to develop a therapeutic agent for SARS-CoV-2.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"217 ","pages":"Article 104394"},"PeriodicalIF":3.9,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chemolab.2021.104394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9177650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9