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Classification tools in chemistry. Part 1: linear models. PLS-DA
Davide Ballabio,Viviana Consonni +1 more
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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.read more
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Heavy metal contents in bovine tissues (kidney, liver and muscle) from Central Gondar Zone, Ethiopia
Mulugeta Legesse Akele,Setegn Kassawmar Desalegn,Tilahun Belayneh Asfaw,Atnafu Guadie Assefa,Addis Kokeb Alemu,Rodrigo Rocha de Oliveira +5 more
TL;DR: In this paper , principal component analysis (PCA) and partial least squares - discriminant analysis (PLS-DA) were employed to investigate heavy metal profiles in bovine tissues and classify them based on their geographic source.
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Recovery of Joint Probability Distribution from one-way marginals: Low rank Tensors and Random Projections
TL;DR: In this paper, the authors link random projections of data to the problem of joint probability mass function estimation using ideas from tomography, and integrate this idea with the idea of low-rank tensor decomposition to show that they can estimate the joint density from just one-way marginals in a transformed space.
Journal ArticleDOI
Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods
Ian W. Burton,Mohsen Kompany-Zareh,Sophie Haverstock,Jonathan Hache,Camilo F. Martinez-Farina,Peter D. Wentzell,Fabrice Berrué +6 more
TL;DR: In this article , a quantitative NMR method was developed to analyze 424 honey samples collected across Canada as part of two surveys in 2018 and 2019 led by the Canadian Food Inspection Agency, and the data analysis led to the identification and quantification of 33 compounds characteristic of the chemical composition of honey.
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Data fusion of electronic noses and electronic tongues aids in botanical origin identification on imbalanced Codonopsis Radix samples
TL;DR: In this paper , two kinds of electronic sensory devices, electronic nose and electronic tongue, were used to establish a discrimination model to identify the botanical origin of each sample, and the optimal model built on the 88 batches of samples was selected from the models trained with all combination of two pretreatment methods and three classification methods.
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Determination of metal contents in aromatic herbs and spices from Algeria: Chemometric approach
TL;DR: In this paper , the results of ICP-AES method were used to determine trace metals (As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sr, and Zn) in acid digests of some aromatic herbs and spices (basil, fennel, laurel, mint, oregano, rosemary, thyme, black pepper, cinnamon, coriander, and cumin) were determined quantitatively using ICP•AES.
References
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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
Robert W. Kennard,L. A. Stone +1 more
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.
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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.