Feasibility of estimating heavy metal concentrations in water column using hyperspectral data and partial least squares regression

Yiyun Chen; Yaolin Liu; Dun Wang; XueSong Kong; Chen Zeng

doi:10.1117/12.838559

13 October 2009 Feasibility of estimating heavy metal concentrations in water column using hyperspectral data and partial least squares regression

Yiyun Chen, Yaolin Liu, Dun Wang, XueSong Kong, Chen Zeng

Author Affiliations +

Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 74920M (2009) https://doi.org/10.1117/12.838559
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China

Abstract

Mining and smelting often produce acidic wastes that can cause severe biogeochemical changes downstream from these mines. Dexing copper mine, as the largest open cast mine in China, is connected to Poyang Lake by Le An river. Water and spectra samples were taken from Le An River and two of its branches, and afterward the concentrations of Cd, Cu, Pb and Zn were measured in the lab. Different spectral pre-processing methods were applied to the spectra, including Savitzky-Golay spectral smoothing, SNV, first derivative, second derivative spectral transforming. On the purpose of estimating metal concentrations from differently pre-processed spectra, partial least squares regression was then used in model calibrations. For deciding the optimal number of PLS factors included in the PLS model, the model with the lowest root mean square error of validation is chosen. The coefficient of determination (R²_v) between the predicted and the reference values from the test set are used as an evaluation mean. For estimating Pb concentration, R²_v = 0.915, which is acceptable. For Cd concentration, R²_v = 0.697 and 0.683 for Zn. PLS model seems to failed in estimating Cu concentration, for the best R²_v for PLS model of Cu is lower than 0.5. From the aspects of spectral pre-processing methods, first derivative after Savitzky-Golay smoothing performs superior to others. In conclusion, PLS models based on carefully pre-processed hyperspectral data turn out to be a promising solution for detecting certain heavy metals concentrations in river.

Citation Download Citation

Yiyun Chen, Yaolin Liu, Dun Wang, XueSong Kong, and Chen Zeng "Feasibility of estimating heavy metal concentrations in water column using hyperspectral data and partial least squares regression", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74920M (13 October 2009); https://doi.org/10.1117/12.838559

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available