CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
ISSN:0169-7439

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

CHEMOMETR INTELL LAB
学科领域:化学
是否预警:不在预警名单内
是否OA:
录用周期:约3.0个月
新锐分区:化学2区
年发文量:162
影响因子:3.8
JCR分区:Q1

基本信息

化学计量学和智能实验室系统出版原始研究论文,短通讯,评论,教程和原始软件出版物报告在化学和相关学科的新的统计,数学,或计算机技术的发展。化学计量学是化学学科,使用数学和统计方法来设计或选择最佳程序和实验,通过分析化学数据提供最大限度的化学信息。该杂志涉及以下主题:1)为化学及相关领域(环境化学、生物化学、毒理学、系统生物学、组学等)开发新的统计、数学和化学计量学方法。2)化学计量学在化学和相关领域的所有分支中的新应用(感兴趣的典型领域是:过程数据分析、实验设计、数据挖掘、信号处理、监督建模、决策制定、稳健统计、混合物分析、多变量校准等)已建立的化学计量学技术的常规应用将不被考虑。3)开发新的软件,提供新的工具或真正促进化学计量学方法的使用。4)良好表征的数据集,以测试新方法和软件的性能。
0169-7439SCIE/Scopus收录
3.8
3.5
2026年3月发布
点击查看历史分区趋势    >
大类学科小类学科Top期刊综述期刊
化学2区
AUTOMATION & CONTROL SYSTEMS 自动化与控制系统
3区
CHEMISTRY, ANALYTICAL 分析化学
2区
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能
3区
INSTRUMENTS & INSTRUMENTATION 仪器仪表
3区
MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 数学跨学科应用
3区
STATISTICS & PROBABILITY 统计学与概率论
2区
N/A
WOS期刊SCI分区  2024-2025最新升级版
按JIF指标学科分区收集子录JIF分区JIF排名百分位
学科:AUTOMATION & CONTROL SYSTEMS
SCIE
Q2
29/89
学科:CHEMISTRY, ANALYTICAL
SCIE
Q2
31/111
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
SCIE
Q2
77/204
学科:INSTRUMENTS & INSTRUMENTATION
SCIE
Q1
19/79
学科:MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
SCIE
Q1
16/136
学科:STATISTICS & PROBABILITY
SCIE
Q1
7/169
按JCR指标学科分区收集子录JCR分区JCR排名百分位
学科:AUTOMATION & CONTROL SYSTEMS
SCIE
Q1
16/89
学科:CHEMISTRY, ANALYTICAL
SCIE
Q1
22/111
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
SCIE
Q1
46/204
学科:INSTRUMENTS & INSTRUMENTATION
SCIE
Q1
13/79
学科:MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
SCIE
Q1
23/136
学科:STATISTICS & PROBABILITY
SCIE
Q1
18/169
109
162
28%较易约3.0个月-工程技术-分析化学
5.3%
时间预警情况
2026年03月发布的新锐学术版不在预警名单中
2025年03月发布的2025版不在预警名单中
2024年02月发布的2024版不在预警名单中
2023年01月发布的2023版不在预警名单中
2021年12月发布的2021版不在预警名单中
2020年12月发布的2020版不在预警名单中
98.15%21.12%-
CiteScore:7.40
SJR:0.654
SNIP:1.200
学科类别分区排名百分位
大类:Chemistry
小类:Spectroscopy
Q1
15 / 79
大类:Chemistry
小类:Computer Science Applications
Q1
209 / 947
大类:Chemistry
小类:Software
Q1
112 / 490
大类:Chemistry
小类:Analytical Chemistry
Q1
38 / 160
大类:Chemistry
小类:Process Chemistry and Technology
Q2
28 / 70

期刊高被引文献

A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2018.12.003
Recognition and sensing of organic compounds using analytical methods, chemical sensors, and pattern recognition approaches
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2018.12.008
Image-based process monitoring using deep learning framework
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.03.008
A conceptual view to the area correlation constraint in multivariate curve resolution
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.04.009
iPredCNC: Computational prediction model for cancerlectins and non-cancerlectins using novel cascade features subset selection
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103876
Data-driven supervised fault diagnosis methods based on latent variable models: a comparative study
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.02.006
Using polarized Total Synchronous Fluorescence Spectroscopy (pTSFS) with PARAFAC analysis for characterizing intrinsic protein emission
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103871
Probe technique-based generalized multivariate standard addition strategy for the analysis of fluorescence signals with matrix effects
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.05.006
Automatic segmentation method for CFU counting in single plate-serial dilution
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103889
Spectra data classification with kernel extreme learning machine
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.103815
EEMlab: A graphical user-friendly interface for fluorimetry experiments based on the drEEM toolbox
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.03.001
RMet: An automated R based software for analyzing GC-MS and GC×GC-MS untargeted metabolomic data
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103866
Comparison of multi-response prediction methods
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.05.004
Variable selection using statistical non-parametric tests for classifying production batches into multiple classes
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103830
Introducing the monotonicity constraint as an effective chemistry-based condition in self-modeling curve resolution
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.04.002
Rock lithological classification by hyperspectral, range 3D and color images
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.04.006
A user-friendly excel spreadsheet for dealing with spectroscopic and chromatographic data
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103816
A method for gene essentiality in miRNA-TF-mRNA co-regulatory network and its application on prostate cancer
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.05.007
Supervised classification of monomodal and multimodal hyperspectral data in vibrational microspectroscopy: A comprehensive comparison
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2018.11.013
Basil leaves disease classification and identification by incorporating survival of fittest approach
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.01.006
Supervised projection pursuit - A dimensionality reduction technique optimized for probabilistic classification
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103867
On the internal correlations of protein sequences probed by non-alignment methods: Novel signatures for drug and antibody targets via the Burrows-Wheeler Transform
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.07.008
Ridge regression with self - Paced learning algorithm in interpretation of voltammetric signals
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.06.008
Semi-supervised learning in multivariate calibration
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103868

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