Application of hyperspectral imaging technology for rapid identification of Ruditapes philippinarum contaminated by heavy metals. Issue 54 (15th November 2021) - Liu, Yao
Abstract : Combined with pattern recognition analysis hyperspectral imaging technology can be used to identify heavy metal contamination in Ruditapes philippinarum rapidly and non-destructively, even with only a small number of training samples. Abstract : Human beings are confronted with a serious health hazard when ingesting Ruditapes philippinarum contaminated with heavy metals, and thus it is significantly necessary to identify heavy metal contaminated Ruditapes philippinarum . This study investigates the feasibility of hyperspectral imaging to identify heavy metal contamination in Ruditapes philippinarum 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 Ruditapes philippinarum hyperspectral imaging datasets. The identification models exhibited satisfactory performance to distinguish healthy Ruditapes philippinarum 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 Ruditapes philippinarum 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.