ParLeS: Software for chemometric analysis of spectroscopic data

By R. V. Rossel. 2008

317 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Environmental Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/2c35d0e7a550b39f6611f6fde957574f50dd3ad3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Environmental Engineering

In Industrial and Manufacturing Engineering

Citations in the corpus

In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture (2011, methodology): “The predictive contribution of each waveband can be visualised by computing the VIP, an output of PARLES (Viscarra Rossel 2008) and shown in Fig.”
Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression (2014, methodology): “The PLSR modelling was performed using software developed by Viscarra Rossel (2008).”
Digital soil mapping at local scale using a multi-depth Vis–NIR spectral library and terrain attributes (2016, methodology): “The PCA was performed using the interactive NIPALS algorithm (Martens and Naes, 1989) implemented in Parles 3.01 (Viscarra Rossel, 2008). he site and samples from the regional spectral library.”
Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy (2017, methodology): “The accuracy of the calibration and validation were assessed by correlation coefficient (r), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and ratio performance deviation (RPD) as follows:([43])”
Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations (2011, methodology): “The leave-one-out cross validation, as defined by the lowest root mean square error (RMSE), was used to determine the optimal number of factors or latent variables to be used for model development (Cho et al. 2007; Viscarra Rossel 2008; Darvishzadeh et al. 2008a).”
Improved analysis and modelling of soil diffuse reflectance spectra using wavelets (2009, methodology): “…of the spectra and the wavelet coefficients were performed for each soil property using: (i) PLSR for a single response variable (Viscarra Rossel, 2008), (ii) multiple linear regressions (MLR), (iii)MLRwith quadratic polynomials (MLR-QP), and (iv) the scores of the PLSmodel regressed…”