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Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis by Nicole M. Ralbovsky; Joseph P. Smith is a Pharmacology, Toxicology and Pharmaceutics article available to read on EtoBox.

What is Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis about?

Background Enzyme immobilization is a beneficial component involved in biocatalytic strategies. Understanding and evaluating the enzyme immobilization system plays an important role in the successful development and implementation of the biocatalysis route. Ensuring the implementation of a successful enzyme immobilization process is vital for realizing a highly functioning and well suited biocatalytic process within pharmaceutical development. Aim To develop a method which can accurately and objectively identify and classify differences within enzyme immobilization systems, sample preparation methods, and data collection parameters. Methods Raman hyperspectral imaging was used to obtain a total of eight spectral data sets from enzyme immobilization samples. Partial least squares discriminant analysis (PLS-DA) was used to classify and identify the samples based on their differences. Results Several two-class, four-class, and eight-class PLS-DA models were built to classify the different sample data sets. All models reached between 92-100% accuracy after cross-validation and external validation, illustrating great success of the models for identifying differences between the samples.

Who reads Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis?

Author

Nicole M. Ralbovsky; Joseph P. Smith

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Pharmacology, Toxicology and Pharmaceutics (Life Sciences)