A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis

Allegrini, Franco; Olivieri, Alejandro C.

doi:10.1016/j.aca.2011.04.061

Cited by 105 publications

(59 citation statements)

References 35 publications

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“…At this stage in the data analysis process, it is often beneficial to try to increase model sensitivity for a specific analyte by removing any excess (unrelated) spectral variables. As with all other aspects of chemometrics, there are many different algorithms that can be used for variable selection, [112][113][114][115] in our group ant colony optimization (ACO) [116][117][118] has been used most often. 4.…”

Section: Sample Variance Analysis: This Third Stage Generally Involvesmentioning

confidence: 99%

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

2017

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The production of active pharmaceutical ingredients (APIs) is currently undergoing its biggest transformation in a century. The changes are based on the rapid and dramatic introduction of protein-and macromolecule-based drugs (collectively known as biopharmaceuticals) and can be traced back to the huge investment in biomedical science (in particular in genomics and proteomics) that has been ongoing since the 1970s. Biopharmaceuticals (or biologics) are manufactured using biological-expression systems (such as mammalian, bacterial, insect cells, etc.) and have spawned a large (>E35 billion sales annually in Europe) and growing biopharmaceutical industry (BioPharma). The structural and chemical complexity of biologics, combined with the intricacy of cell-based manufacturing, imposes a huge analytical burden to correctly characterize and quantify both processes (upstream) and products (downstream). In small molecule manufacturing, advances in analytical and computational methods have been extensively exploited to generate process analytical technologies (PAT) that are now used for routine process control, leading to more efficient processes and safer medicines. In the analytical domain, biologic manufacturing is considerably behind and there is both a huge scope and need to produce relevant PAT tools with which to better control processes, and better characterize product macromolecules. Raman spectroscopy, a vibrational spectroscopy with a number of useful properties (nondestructive, non-contact, robustness) has significant potential advantages in BioPharma. Key among them are intrinsically high molecular specificity, the ability to measure in water, the requirement for minimal (or no) sample pre-treatment, the flexibility of sampling configurations, and suitability for automation. Here, we review and discuss a representative selection of the more important Raman applications in BioPharma (with particular emphasis on mammalian cell culture). The review shows that the properties of Raman have been successfully exploited to deliver unique and useful analytical solutions, particularly for online process monitoring. However, it also shows that its inherent susceptibility to fluorescence interference and the weakness of the Raman effect mean that it can never be a panacea. In particular, Raman-based methods are intrinsically limited by the chemical complexity and wide analyte-concentration-profiles of cell culture media/bioprocessing broths which limit their use for quantitative analysis. Nevertheless, with appropriate foreknowledge of these limitations and good experimental design, robust analytical methods can be produced. In addition, new technological developments such as time-resolved detectors, advanced lasers, and plasmonics offer potential of new Raman-based methods to resolve existing limitations and/or provide new analytical insights.

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Section: Sample Variance Analysis: This Third Stage Generally Involvesmentioning

confidence: 99%

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

2017

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“…For that, in this work, we apply the ant colony optimization (ACO) method. The benefits obtained from this optimization approach are the stability of the model in terms of collinearity in multivariate spectra and the interpretability of relationship between spectral data and sample compositions as initially shown by Allegrini and Olivieri [20].…”

Section: Pure Spectra Chemometric Modeling -Pscm/acomentioning

confidence: 98%

NIR pre-selection data using modified changeable size moving window partial least squares and pure spectral chemometrical modeling with ant colony optimization for wheat flour characterization

Ranzan

Hitzmann

Trierweiler

2015

Chemometrics and Intelligent Laboratory Systems

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“…[25][26] With these variables, and the 0~100% piracetam samples, new PLS models were recalculated for all 10 channels. ACO produced a ca.…”

Section: Piracetam Quantificationmentioning

confidence: 99%

“…Good quality spectra were obtained using 1 second exposure (50 minute acquisition time per sample). MATLAB was used for data pretreatment prior to quantitative analysis, and codes for ant colony optimization (ACO) [25][26] and calculating PLS limits of detection 27 were generously provided by Prof. A.C. Olivieri (Universidad Nacional de Rosario, Argentina).…”

Section: Methodsmentioning

confidence: 99%

Low-Content Quantification in Powders Using Raman Spectroscopy: A Facile Chemometric Approach to Sub 0.1% Limits of Detection

et al. 2015

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Publication InformationLi, B.; Calvet, A.; Casamayou-Boucau, Y.; Morris, C.; Ryder, A.G. (2015) 'Low-content quantification in powders using Raman spectroscopy: a facile chemometric approach to sub 0.1% limits of detection'. Analytical Chemistry, 87 (6):3419-3428. PublisherAmerican Chemical Society Low-content quantification in powders using Raman spectroscopy: a facile chemometric approach to sub 0.1% limits of detection.Boyan Li, Amandine Calvet, Yannick Casamayou-Boucau, Cheryl Morris, and Alan G. Ryder* Nanoscale Biophotonics Laboratory, School of Chemistry, National University of Ireland, Galway, Galway, Ireland.* To whom correspondence should be addressed.Tel: +3539149 2943 Email: alan.ryder@nuigalway.ie ABSTRACT A robust and accurate analytical methodology for low-content (<0.1%) quantification in the solid-state using Raman spectroscopy, sub-sampling, and chemometrics was demonstrated using a piracetam-proline model. The method involved a 5-step process: collection of relatively large number of spectra (8410) from each sample by Raman mapping, meticulous data pretreatment to remove spectral artefacts, use of a 0-100% concentration range partial least squares (PLS) regression model to estimate concentration at each pixel, use of a more-accurate, reduced concentration range PLS model to accurately calculate analyte concentration at each pixel, and finally statistical analysis of all 8000+ concentration predictions to produce an accurate overall sample concentration. The relative prediction accuracy was ~2.4% for a 0.05~1.0% concentration range and the limit of detection was comparable to high performance liquid chromatography (0.03% versus 0.041%). For data pretreatment, we developed a unique cosmic ray removal method and used an automated baseline correction method, neither of which required subjective user intervention and thus were fully automatable. The method is applicable to systems, which cannot be easily analyzed chromatographically such as hydrate, polymorph, or solvate contamination.

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A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis

Cited by 105 publications

References 35 publications

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

NIR pre-selection data using modified changeable size moving window partial least squares and pure spectral chemometrical modeling with ant colony optimization for wheat flour characterization

Low-Content Quantification in Powders Using Raman Spectroscopy: A Facile Chemometric Approach to Sub 0.1% Limits of Detection

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