Cheng Jiehong, Sun Jun, Yao Kunshan, Xu Min, Wang Simin, Fu Lvhui
School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Oct 15;279:121479. doi: 10.1016/j.saa.2022.121479. Epub 2022 Jun 7.
Exploring the cadmium (Cd) pollution in rape is of great significance to food safety and consumer health. In this study, a rapid, nondestructive and accurate method for the determination of Cd content in rape leaves based on hyperspectral imaging (HSI) technology was proposed. The spectral data of rape leaves under different Cd stress from 431 nm to 962 nm were collected by visible-near infrared HSI spectrometer. In order to improve the robustness and accuracy of the regression model, a machine learning algorithm was proposed, named multi-disturbance bagging Extreme Learning Machine (MdbaggingELM). The prediction models of Cd content in rape leaves based on MdbaggingELM and ELM-based method (ELM and baggingELM) were established and compared. The results showed that the model of the proposed MdbaggingELM method performed significantly in the prediction of Cd content, with Rp of 0.9830 and RMSEP of 2.8963 mg/kg. The results confirmed that MdbaggingELM is an efficient regression algorithm, which played a positive role in enhancing the stability and the prediction effect of the model. The combination of MdbaggingELM and HSI technology has great potential in the detection of Cd content in rape leaves.
探究油菜中的镉（Cd）污染对食品安全和消费者健康具有重要意义。本研究提出了一种基于高光谱成像（HSI）技术的快速、无损且准确测定油菜叶片中Cd含量的方法。利用可见-近红外高光谱成像光谱仪采集了不同Cd胁迫下油菜叶片在431nm至962nm范围内的光谱数据。为提高回归模型的稳健性和准确性，提出了一种名为多干扰装袋极限学习机（MdbaggingELM）的机器学习算法。建立并比较了基于MdbaggingELM和基于极限学习机（ELM）方法（ELM和装袋ELM）的油菜叶片Cd含量预测模型。结果表明，所提出的MdbaggingELM方法模型在Cd含量预测方面表现显著，相关系数Rp为0.9830，预测均方根误差RMSEP为2.8963mg/kg。结果证实MdbaggingELM是一种高效的回归算法，对提高模型的稳定性和预测效果起到了积极作用。MdbaggingELM与HSI技术相结合在油菜叶片Cd含量检测方面具有巨大潜力。
