Supervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements

Nanoporous dialysis membranes made of regenerated cellulose are used as molecular weight cutoff standards in bioseparations. In this study, mesoporous standards with Stokes’ radii (50 kDa/2.7 nm, 100 kDa/3.4 nm and 1000 kDa/7.3 nm) and overlapping skewed distributions were characterized using AFM, w...

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Autores principales:	Alex Hadsell, Huong Chau, Richard Barber, Unyoung Kim, Maryam Mobed-Miremadi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	supervised learning atomic force microscopy regenerated cellulose Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/30e0479694cc4a5da47a4f4f0a0f00d0
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spelling	oai:doaj.org-article:30e0479694cc4a5da47a4f4f0a0f00d02021-11-11T18:12:47ZSupervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements10.3390/ma142167241996-1944https://doaj.org/article/30e0479694cc4a5da47a4f4f0a0f00d02021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6724https://doaj.org/toc/1996-1944Nanoporous dialysis membranes made of regenerated cellulose are used as molecular weight cutoff standards in bioseparations. In this study, mesoporous standards with Stokes’ radii (50 kDa/2.7 nm, 100 kDa/3.4 nm and 1000 kDa/7.3 nm) and overlapping skewed distributions were characterized using AFM, with the specific aim of generating pore size classifiers for biomimetic membranes using supervised learning. Gamma transformation was used prior to conducting discriminant analysis in terms of the area under the receiver operating curve (AUC) and classification accuracy (Acc). Monte Carlo simulations were run to generate datasets (<i>n</i> = 10) on which logistic regression was conducted using a constant ratio of 80:20 (measurement:algorithm training), followed by algorithm validation by WEKA. The proposed algorithm can classify the 1000 kDa vs. 100 kDa (AUC > 0.8) correctly, but discrimination is weak for the 100 kDa vs. 50 kDa (AUC < 0.7), the latter being attributed to the instrument accuracy errors below 5 nm. As indicated by the results of the cross-validation study, a test size equivalent to 70% (AUC<sub>tapping</sub> = 0.8341 ± 0.0519, Acc<sub>tapping</sub> = 76.8% ± 5.9%) and 80% (AUC<sub>fluid</sub> = 0.7614 ± 0.0314, Acc<sub>tfluid</sub> = 76.2% ± 1.0%) of the training sets for the tapping and fluid modes are needed for correct classification, resulting in predicted reduction of scan times.Alex HadsellHuong ChauRichard BarberUnyoung KimMaryam Mobed-MiremadiMDPI AGarticlesupervised learningatomic force microscopyregenerated celluloseTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6724, p 6724 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	supervised learning atomic force microscopy regenerated cellulose Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	supervised learning atomic force microscopy regenerated cellulose Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Alex Hadsell Huong Chau Richard Barber Unyoung Kim Maryam Mobed-Miremadi Supervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements
description	Nanoporous dialysis membranes made of regenerated cellulose are used as molecular weight cutoff standards in bioseparations. In this study, mesoporous standards with Stokes’ radii (50 kDa/2.7 nm, 100 kDa/3.4 nm and 1000 kDa/7.3 nm) and overlapping skewed distributions were characterized using AFM, with the specific aim of generating pore size classifiers for biomimetic membranes using supervised learning. Gamma transformation was used prior to conducting discriminant analysis in terms of the area under the receiver operating curve (AUC) and classification accuracy (Acc). Monte Carlo simulations were run to generate datasets (<i>n</i> = 10) on which logistic regression was conducted using a constant ratio of 80:20 (measurement:algorithm training), followed by algorithm validation by WEKA. The proposed algorithm can classify the 1000 kDa vs. 100 kDa (AUC > 0.8) correctly, but discrimination is weak for the 100 kDa vs. 50 kDa (AUC < 0.7), the latter being attributed to the instrument accuracy errors below 5 nm. As indicated by the results of the cross-validation study, a test size equivalent to 70% (AUC<sub>tapping</sub> = 0.8341 ± 0.0519, Acc<sub>tapping</sub> = 76.8% ± 5.9%) and 80% (AUC<sub>fluid</sub> = 0.7614 ± 0.0314, Acc<sub>tfluid</sub> = 76.2% ± 1.0%) of the training sets for the tapping and fluid modes are needed for correct classification, resulting in predicted reduction of scan times.
format	article
author	Alex Hadsell Huong Chau Richard Barber Unyoung Kim Maryam Mobed-Miremadi
author_facet	Alex Hadsell Huong Chau Richard Barber Unyoung Kim Maryam Mobed-Miremadi
author_sort	Alex Hadsell
title	Supervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements
title_short	Supervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements
title_full	Supervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements
title_fullStr	Supervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements
title_full_unstemmed	Supervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements
title_sort	supervised learning for predictive pore size classification of regenerated cellulose membranes based on atomic force microscopy measurements
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/30e0479694cc4a5da47a4f4f0a0f00d0
work_keys_str_mv	AT alexhadsell supervisedlearningforpredictiveporesizeclassificationofregeneratedcellulosemembranesbasedonatomicforcemicroscopymeasurements AT huongchau supervisedlearningforpredictiveporesizeclassificationofregeneratedcellulosemembranesbasedonatomicforcemicroscopymeasurements AT richardbarber supervisedlearningforpredictiveporesizeclassificationofregeneratedcellulosemembranesbasedonatomicforcemicroscopymeasurements AT unyoungkim supervisedlearningforpredictiveporesizeclassificationofregeneratedcellulosemembranesbasedonatomicforcemicroscopymeasurements AT maryammobedmiremadi supervisedlearningforpredictiveporesizeclassificationofregeneratedcellulosemembranesbasedonatomicforcemicroscopymeasurements
_version_	1718431873820000256

Supervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements

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