High concentration of nitrate will cause many problems, such as water eutrophication and compromise of human beings’ health. A fast and stable approach was applied to predict nitrate concentration in solutions using the dual optical active correction continuous spectrum analyzer designed by our research group. Firstly, standard normal variate (SNV) was used to pretreat the spectral data. Then characteristic wavelengths of spectral curve were selected by using successive projections algorithm (SPA) and genetic algorithm (GA) respectively. Finally, partial least-squares regression (PLSR) was applied to build the spectral prediction model to predict nitrate concentration, and coefficient of determination (R2) and root mean square error of prediction (RMSEP) were introduced as the evaluation indicators of prediction models. For SNV-GA-PLS model, R2=0.9966 and RMSEP=0.1712, which outperformed than SNV-UVE-SPA-PLS model (R2=0.9896, RMSEP=0.3952). It demonstrated that he model which selects spectral characteristic wavelengths by GA can decrease the complexity of prediction model building and ensure the accuracy as well. Hence, SNV-GA-PLS model can be used to predict nitrate concentration in water with quick and steady performance.
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.