Journal ArticleDOI
PLS-regression: a basic tool of chemometrics
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TLDR
PLS-regression (PLSR) as mentioned in this paper is the PLS approach in its simplest, and in chemistry and technology, most used form (two-block predictive PLS) is a method for relating two data matrices, X and Y, by a linear multivariate model.About:
This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 2001-10-28. It has received 7861 citations till now. The article focuses on the topics: Partial least squares regression.read more
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Assessment and Monitoring of Soil Degradation during Land Use Change Using Multivariate Analysis
Yones Khaledian,Yones Khaledian,Farshad Kiani,Soheila Ebrahimi,Eric C. Brevik,Jacqueline A. Aitkenhead-Peterson +5 more
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Flavonoid interactions during digestion, absorption, distribution and metabolism: a sequential structure-activity/property relationship-based approach in the study of bioavailability and bioactivity
Gerard Bryan Gonzales,Guy Smagghe,Charlotte Grootaert,Moisés João Zotti,Katleen Raes,John Van Camp +5 more
TL;DR: This approach reveals specific bioactive properties of flavonoids in the gastrointestinal tract as well as various barriers for their bioavailability, and determines the effect of different structural characteristics on the overall bioavailability of Flavonoids.
Journal ArticleDOI
Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints
Olexandr Isayev,Denis Fourches,Eugene N. Muratov,Corey Oses,Kevin Rasch,Alexander Tropscha,Stefano Curtarolo +6 more
TL;DR: In this article, the authors address the issue of scientific discovery in materials databases by introducing novel analytical approaches based on structural and electronic materials fingerprints, which are employed to query large databases of materials using similarity concepts, map the connectivity of the materials space (i.e., as a materials cartogram) for rapidly identifying regions with unique organizations/properties, and develop predictive Quantitative Materials Structure-Property Relation- Ships (QMSPR) models for guiding materials design.
Journal ArticleDOI
Jasmonic acid-induced volatiles of Brassica oleracea attract parasitoids: effects of time and dose, and comparison with induction by herbivores
Maaike Bruinsma,Maarten A. Posthumus,Roland Mumm,Martin J. Mueller,Joop J. A. van Loon,Marcel Dicke +5 more
TL;DR: Three species of parasitoid wasps, Cotesia glomerata, C. rubecula, and Diadegma semiclausum, differing in host range and host specificity, were tested for their behavioural responses to volatiles from herbivore-induced, JA- induced, and non-induced plants, indicating that not only quantity, but also quality of the volatile blend is important in the host-location behaviour of the wasps.
Journal ArticleDOI
Analysis of solid-state transformations of pharmaceutical compounds using vibrational spectroscopy.
TL;DR: Early detection and quantification of transformations during the manufacture of solid dosage forms is important since the physical form of an active pharmaceutical ingredient can significantly influence its processing behaviour, and biopharmaceutical properties such as solubility, dissolution rate and bioavailability.
References
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Book
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
TL;DR: In this article, the authors present a method for detecting and assessing Collinearity of observations and outliers in the context of extensions to the Wikipedia corpus, based on the concept of Influential Observations.
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A User's Guide to Principal Components
TL;DR: In this paper, the authors present a directory of Symbols and Definitions for PCA, as well as some classic examples of PCA applications, such as: linear models, regression PCA of predictor variables, and analysis of variance PCA for Response Variables.
Journal ArticleDOI
A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation
Bradley Efron,Gail Gong +1 more
TL;DR: This paper reviewed the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule, at a relaxed mathematical level, omitting most proofs, regularity conditions and technical details.