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Journal ArticleDOI

Classification tools in chemistry. Part 1: linear models. PLS-DA

Davide Ballabio, +1 more
- 26 Jul 2013 - 
- Vol. 5, Iss: 16, pp 3790-3798
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TLDR
The common steps to calibrate and validate classification models based on partial least squares discriminant analysis are discussed in the present tutorial, and issues to be evaluated during model training and validation are introduced and explained using a chemical dataset.
Abstract
The common steps to calibrate and validate classification models based on partial least squares discriminant analysis are discussed in the present tutorial. All issues to be evaluated during model training and validation are introduced and explained using a chemical dataset, composed of toxic and non-toxic sediment samples. The analysis was carried out with MATLAB routines, which are available in the ESI of this tutorial, together with the dataset and a detailed list of all MATLAB instructions used for the analysis.

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Citations
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Journal ArticleDOI

Identification of remodeled collagen fibers in tumor stroma by FTIR Micro-spectroscopy: A new approach to recognize the colon carcinoma

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Book ChapterDOI

Mid-Infrared Laser Spectroscopy Applications I: Detection of Traces of High Explosives on Reflective and Matte Substrates

TL;DR: In this article, a selection of applications, revisited under the umbrella of MIR laser-based properties, very high brightness, collimated beams, polarized sources, highly monochromatic tunable sources, and coherent sources, are discussed.
Journal ArticleDOI

Rapid Nondestructive Postharvest Potato Freshness and Cultivar Discrimination Assessment

TL;DR: In this paper, partial least square regression (PLSR) and discriminant analysis combined with feature selection techniques were implemented, in order to assess the potato freshness and to classify them into the respective genotypes.
Journal ArticleDOI

Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection

TL;DR: In this article, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized.
Proceedings ArticleDOI

Verification of the performance of Artificial Neural Networks (ANNs) versus Partial Least Squares (PLS) for spectral interference correction in optical emission spectrometry

TL;DR: In this article, Artificial Neural Networks (ANNs) and Partial Least Squares (PLS) were used to address spectral interference correction in optical emission spectrometry with low-resolution spectrometers.
References
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Journal ArticleDOI

Beware of q2

TL;DR: It is argued that the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power, which is the general property of QSAR models developed using LOO cross-validation.
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Computer Aided Design of Experiments

TL;DR: A computer oriented method which assists in the construction of response surface type experimental plans takes into account constraints met in practice that standard procedures do not consider explicitly.
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PLS regression methods

TL;DR: In this paper, the mathematical and statistical structure of PLS regression is developed and the PLS decomposition of the data matrices involved in model building is analyzed. But the PLP regression algorithm can be interpreted in a model building setting.
Journal ArticleDOI

Kohonen and counterpropagation artificial neural networks in analytical chemistry

TL;DR: The principles of the Kohonen and counterpropagation artificial neural network (K-ANN and CP-ANN) learning strategy is described and the use of both methods is explained with several examples from analytical chemistry.
Journal ArticleDOI

Calculation of the reliability of classification in discriminant partial least-squares binary classification

TL;DR: This method, called Probabilistic Discriminant Partial Least Squares (p-DPLS), integrates DPLS, density methods and Bayes decision theory in order to take into account the uncertainty of the predictions in DPLs.
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