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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

Classification of different animal fibers by near infrared spectroscopy and chemometric models

TL;DR: In this article, the feasibility of classifying different animal fibers with respect to their categories via near-infrared (NIR) spectroscopy along with chemometric models was investigated.
Journal ArticleDOI

Predicting the properties of biodiesel and its blends using mid-FT-IR spectroscopy and first-order multivariate calibration

TL;DR: In this paper, partial least squares regression (PLS) and support vector machine regression (SVM) were used to model the relationship between mid-FT-IR spectroscopic data and the density, refractive index and cold filter plugging point of biodiesel samples and their blends.
Journal ArticleDOI

Rapid and nondestructive detection of sorghum adulteration using optimization algorithms and hyperspectral imaging.

TL;DR: Results show that the model can rapidly and nondestructively detect sorghum adulteration and accuracy of the model identification for the validation set reached 96%, and for the adulterated samples reached 91%, and comprehensive accuracy ofThe model could reach more than 90%.
Journal ArticleDOI

Classification of different tomato seed cultivars by multispectral visible-near infrared spectroscopy and chemometrics

TL;DR: In this paper, the feasibility of rapid and non-destructive classification of five different tomato seed cultivars was investigated by using visible and short-wave near infrared (Vis-NIR) spectra combined with chemometric approaches.
Journal ArticleDOI

Authentication of apple juice categories based on multivariate analysis of the synchronous fluorescence spectra

TL;DR: In this paper, the use of synchronous fluorescence as a means for discrimination between commercial apple juice categories was studied, and partial least squares discriminant analysis (PLS-DA) was used for the development of classification models.
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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