Abstract
In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis, with detailed algorithm to classify the samples. The method can correct the matrix effect effectively through classifying the samples automatically, and influence of X-ray absorption and enhancement by major elements of the samples is reduced. Experiments for the complex matrix effect correction in EDXRF analysis of samples in Pangang showed improved accuracy of the elemental analysis result.
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