A Review of Genetic Algorithms in near Infrared Spectroscopy and Chemometrics: Past and Future

Koljonen, Janne; Nordling, Torbjörn E. M.; Alander, Jarmo T.

doi:10.1255/jnirs.778

Cited by 59 publications

(39 citation statements)

References 39 publications

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“…In 1998, Spiegelman provided, by using a mathematical demonstration, that a well‐judged variable selection enhances regression model predictive capacities. Since the importance of such techniques is established, a wide range of algorithms is proposed in review articles and tutorials . However, considering papers dealing with selection algorithms and cell culture monitoring, there is no option that stands out.…”

Section: Introductionmentioning

confidence: 99%

Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

André

Lagresle

Hannas³

et al. 2017

Biotechnology Progress

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In recent years, as a result of the process analytical technology initiative of the US Food and Drug Administration, many different works have been carried out on direct and in situ monitoring of critical parameters for mammalian cell cultures by Raman spectroscopy and multivariate regression techniques. However, despite interesting results, it cannot be said that the proposed monitoring strategies, which will reduce errors of the regression models and thus confidence limits of the predictions, are really optimized. Hence, the aim of this article is to optimize some critical steps of spectroscopic acquisition and data treatment in order to reach a higher level of accuracy and robustness of bioprocess monitoring. In this way, we propose first an original strategy to assess the most suited Raman acquisition time for the processes involved. In a second part, we demonstrate the importance of the interbatch variability on the accuracy of the predictive models with a particular focus on the optical probes adjustment. Finally, we propose a methodology for the optimization of the spectral variables selection in order to decrease prediction errors of multivariate regressions. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:308-316, 2017.

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Section: Introductionmentioning

confidence: 99%

Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

André

Lagresle

Hannas³

et al. 2017

Biotechnology Progress

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“…Many authors have stated that even though global multivariate methods were able to cope with a large part of the noise, the selection of variables could bring robustness and higher calibration performances [2,3]. These same authors have presented techniques such as genetic algorithm (GA) [4][5][6][7], simulated annealing [8], interval-PLS [9], and particle swarm optimization (PSO) [10], to extract relevant spectral features.…”

Section: Introductionmentioning

confidence: 99%

Local chemometrics for samples and variables: optimizing calibration and standardization processes

Igne

Hurburgh

2010

Journal of Chemometrics

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The ability of five sample selection methods for local chemometrics and three variable selection algorithms were compared for the development and the transfer of whole soybeans protein and oil near infrared prediction models. Two new methods based on a similarity index considering Euclidian distance among Fourier coefficients were introduced and tested against more common approaches (locally weighted regression, LOCAL). Genetic algorithms were also challenged with the development of models based on particle swarm optimization (PSO). A modification to the original PSO model was introduced. Sample and variable selection methods, as well as their combinations, were tested in the transfer of models in intra-and inter-brand situations using two Foss Infratecs and two Bruins OmegAnalyzerGs. For each brand, a master was designated and its models transferred onto the second unit of its network and the two units of the second brand. Calibration models were proven transferable from brand to brand with similar or better precisions than when all instruments were calibrated on their own calibration sets (relative predictive determinant (RPD) improving from 10.42 to 12.76 and 12.39 in intra-brand standardization for Infratec network with local and variable selection methods respectively). These methods provided contrasted results depending on the instrument, the parameter, and the variability of interest.

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“…Chemometrics has become an established technique for handling chemical data. Good reviews of chemometrics are provided by Geladi and Grahn [18], and Workman and Springsteen [19], and Koljonen et al [20], which present a review of genetic algorithm optimization in chemometry. The next text is a short introduction for the subject, and for a deeper insight, the previous reviews are recommended.…”

Section: Chemometricsmentioning

confidence: 99%

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Alander

Bochko

Martinkauppi

et al. 2013

International Journal of Spectroscopy

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128

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This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.

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A Review of Genetic Algorithms in near Infrared Spectroscopy and Chemometrics: Past and Future

Cited by 59 publications

References 39 publications

Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

Local chemometrics for samples and variables: optimizing calibration and standardization processes

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

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