Determination of Heavy Metal Soil Contaminants Based on Photoacoustic Spectroscopy

Heavy metal (HM) contamination in soil is a threat to human health and environmental safety. This paper presents an infrared photoacoustic spectroscopic non-destructive testing for HM evaluation by a robust customized photoacoustic spectrometric system. Cadmium (Cd) was selected as the target contamination which was blended in soil samples. A two-layer feed-forward artificial neural network (ANN) model was developed for HM concentration estimation. The results show that the standard normal variate and continuum removal methods are the best preprocessing algorithms regarding the maximal correction coefficient (R2 > 0.90) and the minimal root mean square error criteria. The correction coefficient analysis shows a better prediction performance (R2 > 0.95) for a HM concentration estimation with the two preprocessing methods. In conclusion, the prediction accuracy can be significantly improved by implementing specific preprocessing and training methods. The ANN model-based infrared photoacoustic spectroscopic method has a future potential for the featureless HM contamination estimation in soil.