Poisonous elements such as lead (Pb), arsenic (As), cadmium (Cd), and chromium (Cr) are commonly observed in polluted soil but hard to detect due to low concentration. Currently, the quantitative content of elements can be determined by an x-ray fluorescence (XRF) analyzer if the limit of detection (LOD) is known. However, to ensure that elements with extremely low content can be detected, the LOD needs to be reduced and determined. Firstly, the authors utilized the traditional method detection limit (MDL) formula to calculate the LOD for benchmark purposes. Secondly, in order to improve the signal to noise ratios of the characteristic peaks, the wavelet threshold denoising method and iterative discrete wavelet transform algorithm were utilized for spectral denoising and background noises deduction of XRF spectra based on 57 certified reference soil samples. Thirdly, a novel Adaboost back propagation neural network model was proposed to reduce and determine the values of LOD. The results showed that the LODs of trace elements determined from the proposed algorithm decreased greatly compared to the traditional MDL method. Finally, the calibration curve of elemental concentration versus counting rate was established and regressed using the proposed multivariate partial least squares model. The goodness of fit (R 2 ) was significantly increased. Therefore, the proposed algorithm is effective to determine the LOD and can yield optimal performance in instrument calibration of trace elements in soil.

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