Abstract
It is of great significance to detect the components of turbid solutions using hyperspectral imaging technology in analytical chemistry. To solve the problems including complex computations and poor interpretations in previous researches, this study proposed a novel quantitative detection model based on contour extraction and ellipse fitting for turbid solutions. A wedge-shaped sample reservoir was firstly designed to increase the dimensions of light spot information. Subsequently, the visual features of the spot were extracted from their hyperspectral images using ellipse fitting. Partial least squares regression was performed for the concentrations of Intralipid-20% and the ellipse eigenvectors, and it gave a good prediction ability with the correlation coefficient (Rp) of 0.98 and the root-mean-square error (RMSEP) of 0.07%. Experimental results indicate that ellipse fitting model shows excellent performances in more reasonable interpretation, better stability, less computation, clearer visualization effect and lower requirements for data acquisition process, compared with conventional light intensity model and abstract textural features model. It can be concluded that using ellipse fitting method based on hyperspectral imaging to detect compositions of complex mixed solutions is a potential progress.
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