Journal ArticleDOI
Classification tools in chemistry. Part 1: linear models. PLS-DA
Davide Ballabio,Viviana Consonni +1 more
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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.read more
Citations
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Nondestructive Identification of Rice Varieties by the Data Fusion of Raman and Near-Infrared (NIR) Spectroscopies
TL;DR: In this paper , the authors explored the application of near-infrared (NIR) and Raman spectroscopies and low-level and mid-level data fusion for the classification of four types of rice from similar origins.
Journal ArticleDOI
Infrared spectroscopic and chemometric approach for identifying morphology in embryo culture medium samples
Shima Zandbaaf,Mohammad Reza Khanmohammadi Khorrami,Amir Bagheri Garmarudi,Batool Hossein Rashidi +3 more
TL;DR: The proposed method to examine embryo culture medium (ECM) samples in in vitro fertilization (IVF) using Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy coupled with the pattern recognition methods is rapid, simple and accurate for the discrimination of the ECM samples based on their morphology results.
Journal ArticleDOI
Exploring the Analytical Complexities in Insect Powder Analysis Using Miniaturized NIR Spectroscopy
TL;DR: In this article , the feasibility of portable near-infrared spectroscopy as a reliable and fast candidate for the classification of insect powder samples and the prediction of their major components was ascertained.
Book ChapterDOI
Emerging nondestructive technologies for quality assessment of fruits, vegetables, and cereals
E. J. Rifna,Madhuresh Dwivedi +1 more
TL;DR: In this paper, the use of non-invasive techniques in the fruit, vegetable, cereals, and cereal processing industries are discussed and summarized in this chapter, where the authors take into consideration the acceptance of nondestructive methods recommended by the US Food and Drug Administration to assist food products with improved safety, process efficacy, and product quality.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.