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Separations
Special Issues
Computer-Aided Separation Analysis
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Computer-Aided Separation Analysis

A special issue of Separations (ISSN 2297-8739).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6523

Special Issue Editor

Dr. Guillaume L. Erny

E-Mail Website
Guest Editor
LEPABE—Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Interests: analytical chemistry; mathematic separation; data mining; Finnee; high-resolution mass spectrometry; hyphenated techniques; MATLAB; metabolomics; open-source software and hardware

Special Issue Information

Dear Colleagues,

Computer-aided separation analysis refers to the use of software and algorithms designed to improve and automatize peak detection or measurement in separation profiles (chromatogram, electropherogram, etc.). Typical algorithms include peak picking, baseline drift correction, smoothing and filtering, mathematical peak models and fitting, and spectral deconvolution. These techniques have increasingly been used in untargeted analyses of hyphenated data, where thousands of peaks need to be detected and accurately measured. Computer-aided separation analysis also allows quantifying non-baseline separated peaks with a mathematical model. Peak modeling is also particularly useful to measure and understand physicochemical processes that occur during separation and, thus, to optimize the separation efficiency.

This Special Issue invites contributions in the form of original research articles or reviews on the current advances or evaluation of computer-aided separation analysis. Manuscripts focusing on computer-aided separation optimization are also welcome.

Dr. Guillaume L. Erny
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Separations is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • alignment
  • baseline drift correction
  • chemometrics
  • mathematical modeling and computer simulation of separative transport
  • peak modeling and fitting
  • separation optimization
  • spectral deconvolution

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Published Papers (2 papers)

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Research

11 pages, 2304 KiB  
Article
Iterative Multivariate Peaks Fitting—A Robust Approach for The Analysis of Non-Baseline Resolved Chromatographic Peaks
by Guillaume Laurent Erny, Marzieh Moeenfard and Arminda Alves
Separations 2021, 8(10), 178; https://doi.org/10.3390/separations8100178 - 8 Oct 2021
Cited by 1 | Viewed by 2285
Abstract
Selectivity in separation science is defined as the extent to which a method can determine the target analyte free of interference. It is the backbone of any method and can be enhanced at various steps, including sample preparation, separation optimization and detection. Significant [...] Read more.
Selectivity in separation science is defined as the extent to which a method can determine the target analyte free of interference. It is the backbone of any method and can be enhanced at various steps, including sample preparation, separation optimization and detection. Significant improvement in selectivity can also be achieved in the data analysis step with the mathematical treatment of the signals. In this manuscript, we present a new approach that uses mathematical functions to model chromatographic peaks. However, unlike classical peak fitting approaches where the fitting parameters are optimized with a single profile (one-way data), the parameters are optimized over multiple profiles (two-way data). Thus, it allows high confidence and robustness. Furthermore, an iterative approach where the number of peaks is increased at each step until convergence is developed in this manuscript. It is demonstrated with simulated and real data that this algorithm is: (1) capable of mathematically separating each component with minimal user input and (2) that the peak areas can be accurately measured even with resolution as low as 0.5 if the peak’s intensities does not differ by more than a factor 10. This was conclusively demonstrated with the quantification of diterpene esters in standard mixtures. Full article
(This article belongs to the Special Issue Computer-Aided Separation Analysis)
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17 pages, 19492 KiB  
Article
Design Space Calculation and Continuous Improvement Considering a Noise Parameter: A Case Study of Ethanol Precipitation Process Optimization for Carthami Flos Extract
by Yanni Tai, Haibin Qu and Xingchu Gong
Separations 2021, 8(6), 74; https://doi.org/10.3390/separations8060074 - 24 May 2021
Cited by 9 | Viewed by 3294
Abstract
The optimization of process parameters in the pharmaceutical industry is often carried out according to the Quality by Design (QbD) concept. QbD also emphasizes that continuous improvement should be performed in life cycle management. Process parameters that are difficult to control in actual [...] Read more.
The optimization of process parameters in the pharmaceutical industry is often carried out according to the Quality by Design (QbD) concept. QbD also emphasizes that continuous improvement should be performed in life cycle management. Process parameters that are difficult to control in actual production can be regarded as noise parameters. In this study, based on the QbD concept, the ethanol precipitation process of Carthami Flos extract was optimized, considering a noise parameter. The density of the concentrated extract, ethanol concentration, the volume ratio of ethanol to concentrated extract, stirring time after ethanol addition, and refrigeration temperature were selected as critical process parameters (CPPs), using a definitive screening design. The mathematical models among CPPs and evaluation indicators were established. Considering that the refrigeration temperature of industrial ethanol precipitation is often difficult to control with seasonal changes, refrigeration temperature was treated as a noise parameter. A calculation method for the design space in the presence of the noise parameter was proposed. The design space was calculated according to the probability of reaching the standards of evaluation indicators. Controlling parameters within the design space was expected to reduce the influence of noise parameter fluctuations on the quality of the ethanol precipitation supernatant. With more data obtained, the design space was updated. In industry, it is also recommended to adopt a similar idea: that is, continuing to collect industrial data and regularly updating mathematical models, which can further update the design space and make it more stable and reliable. Full article
(This article belongs to the Special Issue Computer-Aided Separation Analysis)
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