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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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Evaluation of FT-IR spectroscopy combined with SIMCA and PLS‑DA for detection of adulterants in pistachio butter

TL;DR: In this article , Fourier transform infrared spectroscopy (FT-IR) and multivariate classification methods were used for detecting adulteration of pistachio butter with three potential edible oils.
Journal ArticleDOI

Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data.

TL;DR: In this paper, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation.
Journal ArticleDOI

Scott test associated with multivariate image analysis: A more selective alternative for cocaine research in forensic laboratories.

TL;DR: In this paper , the authors evaluated the frequency of these results and the spectral behavior and color development of false positive substances and proposed the application of the partial least squares discriminant analysis (PLS-DA) method associated with photographic images obtained by a smartphone camera to increase the selectivity of the Scott test.
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Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data

TL;DR: In this paper , a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years was proposed, where the combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signalto-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain.
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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