Application of hyperspectral imaging technology for rapid identification of contaminated by heavy metals.

Human beings are confronted with a serious health hazard when ingesting contaminated with heavy metals, and thus it is significantly necessary to identify heavy metal contaminated . This study investigates the feasibility of hyperspectral imaging to identify heavy metal contamination in rapidly. To reduce the effects of noise, four different spectral pretreatments were performed on the original spectra. To select characteristic wavebands for identification, four waveband selection algorithms based on neighbourhood rough set theory were proposed, namely, mutual information, consistency measure, dependency measure, and variable precision. The selected wavebands were input to an extreme learning machine to construct classification models. The results demonstrated that multiplicative scatter correction pretreatment was suitable for hyperspectral imaging datasets. The identification models exhibited satisfactory performance to distinguish healthy from those contaminated by both individual and multiple heavy metals. The identification results of Cd and Pb contaminated samples were more accurate than those of Cu and Zn contaminated samples. When the number of training samples decreased the identification performance decreased, but not significantly. The results showed that combined with pattern recognition analysis hyperspectral imaging technology can be used to distinguish healthy samples from those contaminated by heavy metals, even with only a small number of training samples. This model is suitable for applications in analysing many shellfish rapidly and non-destructively.