红外, 2015, 36 (7): 38, 网络出版: 2015-09-08  

人体头发中重金属元素铬含量的 高光谱检测

Estimation of Heavy Metal Chromium Content in Human Hair Based on Hyperspectral Data
作者单位
武汉大学遥感信息工程学院, 武汉 430079
摘要
头发中的重金属含量可以反映出人体健康的变化。提出了运用高光谱数据检测头发中重金属元素铬含量的方法。对头发的透射率波长曲线进行了包络线消除、吸收特征参量化等处理。以化学检测的铬含量作为标准数据,化学检测精度可达90%以上。然后训练人工神经网络,通过调节网络的隐含层层数、结点数和激活函数来优化模型。实验计算表明,隐含层层数为1,结点数为7或9的人工神经网络的预测效果较好。利用统计实验结果对人工神经网络的内部精度和外部精度进行评价。人体头发中铬的敏感波段为1380 nm~1550 nm、1880 nm~2100 nm、2120 nm~2210 nm;训练后的神经网络预测的均方根误差为13%,精度达87%。实验结果表明,应用高光谱技术可以快速无损地检测人体头发中的重金属元素铬的含量。
Abstract
The heavy metal content in human hair can reflect the health status of a human body. A method for using hyperspectral data to detect the heavy metal element chromium content in human hair is put forward. The processing such as continuum removing and absorption characteristic parametrization is performed on the transmittance wavelength curves of some hair samples. Taking the chromium content detected by a chemical method as the standard data, the accuracy of chemical detection can reach above 90%. Then, the BP artificial neural network is trained and the model is optimized by adjusting the network number of hidden layer nodes, the number of nodes and the activated function. The experimental calculation results show that the artificial network which has one hidden layer and seven or nine nodes has a better prediction result. Both internal precision and external precision of the artificial network are evaluated by using the statistical experimental results. The Cr metal in human hair is sensitive in the wavebands from 1380 nm to 1550 nm, from 1880 nm to 2100 nm and from 2120 nm to 2210 nm. The trained BP artificial neural network has a prediction error RMS of 13% and an accuracy of 87%. The experimental results show that the hyperspectral technology can be used to measure the chromium content in human hair nondestructively and quickly.

夏芷玥, 刘浩, 林志恒, 梅根, 毛喆. 人体头发中重金属元素铬含量的 高光谱检测[J]. 红外, 2015, 36(7): 38. XIA Zhi-yue, LIU Hao, LIN Zhi-heng, MEI Gen, MAO Zhe. Estimation of Heavy Metal Chromium Content in Human Hair Based on Hyperspectral Data[J]. INFRARED, 2015, 36(7): 38.

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