Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines.Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data.The journal deals with the following topics:1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.)2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered.3) Development of new software that provides novel tools or truly advances the use of chemometrical methods.4) Well characterized data sets to test performance for the new methods and software.The journal complies with International Committee of Medical Journal Editors' Uniform requirements for manuscripts.
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。
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