The Optimum Number of Latent Variables
The relevant issue of optimizing the number of latent variables in full-spectral inverse models is discussed, with emphasis on interpretation rather on statistical and mathematical issues.
KeywordsLatent variables Explained variance Loading inspection Leave-one-out cross validation Monte Carlo cross validation Physical interpretation
- Goicoechea, H.C., Olivieri, A.C.: Determination of bromhexine in cough–cold syrups by absorption spectrophotometry and multivariate calibration using partial least-squares and hybrid linear analyses. Application of a novel method of wavelength selection. Talanta. 49, 793–800 (1999)CrossRefPubMedGoogle Scholar
- Method 4500 F D: Standard Methods for the Examination of Water and Wastewater, 20th edn, pp. 4–62. American Public Health Association, Washington, DC (1998)Google Scholar
© Springer Nature Switzerland AG 2018