Multivariate calibration.

By M. Forina, S. Lanteri, M. Casale. Journal of Chromatography A, 2007

1,839 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Industrial and Manufacturing Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/16b48244fa27568e61e3ee8dca4e3e9a6a62a7e9

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Citations in the corpus

A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. (2007, methodology): “Hardness of tablets is determined in different studes with the well established regression methods (PLS, MLR). orisseau and Rhodes used MLR and PLS to predict the hardess of tablets.”
Path modelling by sequential PLS regression (2011, methodology): “the first and second components among the different blocks that may need a re-estimation of inner relations, as an extension of Martens’ version of the two-block PLS Regression [1].”
Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry (2014, methodology): “SPA employs a simple projection operation in a vector space to select subsets of variables with a minimum of colinearity (Martens and Naes 1993).”
Mathematical modelling of anaerobic digestion of biomass and waste: Power and limitations (2013, methodology): “Examples of some tools for the development of black-box models are (i) Principle Component Regression (PCR) [141] (ii) Partial Least Squares (PLS) [142], (iii) Artificial Neural Networks (ANN) [143], (iv) Neuro-fuzzy Systems [144] and (v) Support Vector Machines (SVM) [145].”
The pls Package: Principal Component and Partial Least Squares Regression in R (2007, methodology): “NIPALS algorithm) (Martens and Næs 1989) and the SIMPLS algorithm (de Jong 1993).”
Advances in Geometric Morphometrics (2009, background): “Previously unknown shape features can be inferred from visualizations such as TPS deformation grids (e.g., Bookstein 2000) and vague notions of a signal can be ''calibrated'' (Martens and Naes 1989) with the present data.”
PLS post‐processing by similarity transformation (PLS + ST): a simple alternative to OPLS (2005, positive): “(13) follows directly from the step-wise NIPALS algorithm [3], it is just a matter of where to end the last step.”
Comparison of Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) Methods for Protein and Hardness Predictions using the Near-Infrared (NIR) Hyperspectral Images of Bulk Samples of Canadian Wheat (2014, background): “Martens and Naes (1992) explained necessary system calibrations to be done before acquiring the NIR hyperspectral images.”
Two standard-free approaches to correct for external influences on near-infrared spectra to make models widely applicable (2020, methodology): “Unlike other analytical techniques such as nuclear magnetic resonance and chromatography which can directly provide the concentration of the analytes, NIR spectroscopy usually requires a multivariate calibration step before it can be used for any application (Saeys et al., 2019; Walsh et al., 2020).”
Physical and Socioeconomic Driving Forces of Land‐Use and Land‐Cover Changes: A Case Study of Wuhan City, China (2016, neutral): “If there is linear relationship between 𝑡 1 and 𝑢 1 , indicating 𝑋 significantly correlated to 𝑌 , it is reasonable to construct PLS linear model between 𝑋 and 𝑌 [33].”
Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations (2011, methodology): “…analysis, i.e. partial least square regression (PLSR) (Naes and Martens 1985; Geladi and Kowalski 1986; Naes et al. 1986; William and Norris 1987; Martens and Naes 1989; Ehsani et al. 1999; Geladi et al. 1999; Martens and Naes 2001; Viscarra Rossel 2008) and stepwise multiple linear regression…”
Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging (2016, methodology): “Therefore, a 218 classification model is built by using a training data set of samples with known Class affiliation 219 and is then evaluated using external samples (Martens & Naes, 1989).”
On the Validity of the Bootstrap in Non‐Parametric Functional Regression (2009, background): “Sometimes, this kind of statistical problem is called ‘calibration problem’ © 2009 Board of the Foundation of the Scandinavian Journal of Statistics. in the literature (see, e.g. Martens & Næs, 1989).”
Nondestructive firmness prediction of apple fruit using acoustic vibration response (2020, neutral): “, 2003), mango (Jarimopas and Kitthawee, 2007; Valente et al., 2009), tomato (Kataoka et al.”