@article {Lorber:1988-11-01T00:00:00:0003-7028:1572,
author = "Lorber, Avraham and Kowalski, Bruce R.",
title = "A Note on the Use of the Partial Least-Squares Method for Multivariate Calibration",
journal = "Applied Spectroscopy",
volume = "42",
number = "8",
year = "1988-11-01T00:00:00",
abstract = "The multivariate calibration problem is a problem of predicting the concentration in an unknown sample, *c*
_{un}, from the response vector of an unknown sample, **r**
_{un} (*J* responses). The predicting equation can be arranged in the form

*ĉ*
_{un}
= **r**
_{un}
^{T}
**R**
^{+}
**c.** (1)

**R**
^{+} is the pseudo-inverse of the calibration set matrix of responses, **R**, whose column indices correspond to the *J* sensors or wavelengths and row indices
correspond to the *I* samples (individuals), and **c** is the vector of concentrations for the *I* samples of the analyte in each of the calibration samples. Derivation of Eq. 1 is described in Ref. 1. The PLS regression involves solution of the predicting equation.",
pages = "1572-1574",
url = "http://www.ingentaconnect.com/content/sas/sas/1988/00000042/00000008/art00036",
doi = "doi:10.1366/0003702884429481",
keyword = "Regression, PLS, Multivariate calibration, Singular value decomposition"
}