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Method Article
Camilo LM Morais
University of Central Lancashire
Corresponding Author
Francis L Martin
University of Central Lancashire
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Infrared (IR) spectroscopy is a powerful analytical technique that can be applied to investigate a wide range of biological materials (e.g., biofluids, cells, tissues), where a specific biochemical signature is obtained representing the ‘fingerprint’ signal of the sample being analysed. This chemical information can be used as an input data for classification models in order to distinguish or predict samples groups based on computational algorithms. One fundamental step towards building such computational models is sample selection, where a fraction of the samples measured during an experiment are used for building the classifier, whereas the remaining ones are used for evaluating the model classification performance. This protocol shows how sample selection can be performed in a computational environment (MATLAB) by using a combination of Euclidian-distance calculation and random selection, named Morais-Lima-Martin (MLM) algorithm, as a previous step before building classification models in biological-derived IR datasets.
Keywords
infrared spectroscopy, sample selection, data splitting, pre-processing, computational analysis, chemometrics, algorithm
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The authors declare no competing financial interests
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Table 1 Classification performance of PCA-LDA and PLS-DA algorithms applied to the sample dataset.
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Method Article
Camilo LM Morais
University of Central Lancashire
Corresponding Author
Francis L Martin
University of Central Lancashire
Corresponding Author
Version History
CURRENT STATUS: Posted
Version 1
Posted 20 Dec, 2018
No community comments so far
Metrics
Comments: 0
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Infrared (IR) spectroscopy is a powerful analytical technique that can be applied to investigate a wide range of biological materials (e.g., biofluids, cells, tissues), where a specific biochemical signature is obtained representing the ‘fingerprint’ signal of the sample being analysed. This chemical information can be used as an input data for classification models in order to distinguish or predict samples groups based on computational algorithms. One fundamental step towards building such computational models is sample selection, where a fraction of the samples measured during an experiment are used for building the classifier, whereas the remaining ones are used for evaluating the model classification performance. This protocol shows how sample selection can be performed in a computational environment (MATLAB) by using a combination of Euclidian-distance calculation and random selection, named Morais-Lima-Martin (MLM) algorithm, as a previous step before building classification models in biological-derived IR datasets.
Figure 1
Figure 2
Figure 3
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