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 (R 2 > 0.90) and the minimal root mean square error criteria. The correction coefficient analysis shows a better prediction performance (R 2 > 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.

中文翻译：

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基于光声光谱法测定土壤重金属污染物

土壤中的重金属 (HM) 污染对人类健康和环境安全构成威胁。本文介绍了一种通过强大的定制光声光谱系统进行 HM 评估的红外光声光谱无损测试。镉 (Cd) 被选为混合在土壤样品中的目标污染物。两层前馈人工神经网络 (ANN) 模型被开发用于 HM 浓度估计。结果表明，标准正态变量和连续统去除方法是关于最大校正系数（R 2 > 0.90）和最小均方根误差准则的最佳预处理算法。校正系数分析表明两种预处理方法对 HM 浓度估计具有更好的预测性能 (R 2 > 0.95)。综上所述，通过实施特定的预处理和训练方法，可以显着提高预测精度。基于 ANN 模型的红外光声光谱方法具有用于土壤中无特征 HM 污染估计的未来潜力。