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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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Prediction of olive ripening degree combining image analysis and FT-NIR spectroscopy for virgin olive oil optimisation

TL;DR: In this article, an image analysis (IA) method was purposely developed for the objective evaluation of the maturity index, which was confirmed by the highly significant correlation with the common visual evaluation of maturity index.
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MALDI-TOF-MS integrated workflow for food authenticity investigations: An untargeted protein-based approach for rapid detection of PDO feta cheese adulteration

TL;DR: In this article , an integrated untargeted protein-based workflow in combination with advanced chemometrics is presented, to address authenticity challenges in PDO feta cheese which is legally manufactured by the mixture of sheep/goat milk.
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Near infrared spectroscopy to rapid assess the rubber tree clone and the influence of maturation and disease at the leaves

TL;DR: To predict the Hevea rubber tree clones, detect and discriminate leaves at different stages of development (young or adult), as well as to discriminate healthy and affected adult leaves by South American Leaf Blight, through the spectroscopic technique of visible to infrared (Vis-NIR) combined with chemometrics.
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Direct and indirect alcohol biomarkers data collected in hair samples - multivariate data analysis and likelihood ratio interpretation perspectives.

TL;DR: The concentration values of direct and indirect biomarkers of ethanol consumption were detected in blood (indirect) or hair (direct) samples from a pool of 125 individuals classified as either chronic (i.e. positive) and non-chronic alcohol drinkers.
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Exploring the chemical space for freeze-drying excipients.

TL;DR: A translation of the molecular descriptors on pharmaceutical performance was more successful for lyoprotectants than for bulking agents, and in the course of the experimental screening, several new potential bulking agent and lyoprotsectants were identified.
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.
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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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