KEYWORDS: Ultraviolet radiation, Analytical research, Principal component analysis, Data modeling, Error analysis, Statistical analysis, Water contamination, Chemical analysis, Matrices, Lithium
Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR’s analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR’s result better than PLSR.
In recent years, with the development of the Flat-Field Holographic Concave Grating, they are adopted by all kinds of
UV spectrometers. By means of single optical surface, the Flat-Field Holographic Concave Grating can implement
dispersion and imaging that make the UV spectrometer system design quite compact. However, the calibration of the
Flat-Field Holographic Concave Grating is very difficult. Various factors make its imaging quality difficult to be
guaranteed. So we have to process the spectrum signal with signal restoration before using it. Guiding by the theory of
signals and systems, and after a series of experiments, we found that our UV spectrometer system is a Linear Space-
Variant System. It means that we have to measure PSF of every pixel of the system which contains thousands of pixels.
Obviously, that's a large amount of calculation .For dealing with this problem, we proposes a novel signal restoration
method. This method divides the system into several Linear Space-Invariant subsystems and then makes signal
restoration with PSFs. Our experiments turn out that this method is effective and inexpensive.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.