Qi Meixiang, Cao Liqin, Zhao Yunliang, Jia Feifei, Song Shaoxian, He Xinfang, Yan Xiao, Huang Lixue, Yin Zize
Hubei Key Laboratory of Mineral Resources Processing and Environment, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China.
School of Resources and Environmental Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China.
Materials (Basel). 2023 Mar 29;16(7):2743. doi: 10.3390/ma16072743.
It is crucial but challenging to detect intermediate or end products promptly. Traditional chemical detection methods are time-consuming and cannot detect mineral phase content. Thermal infrared hyperspectral (TIH) technology is an effective means of real-time imaging and can precisely capture the emissivity characteristics of objects. This study introduces TIH to estimate the content of potassium salts, with a model based on Competitive Adaptive Reweighted Sampling (CARS) and Partial Least Squares Regression (PLSR). The model takes the emissivity spectrum of potassium salt into account and accurately predicts the content of Mixing Potassium (MP), a mineral mixture produced in Lop Nur, Xinjiang. The main mineral content in MP was measured by Mineral Liberation Analyzer (MLA), mainly including picromerite, potassium chloride, magnesium sulfate, and less sodium chloride. 129 configured MP samples were divided into calibration (97 samples) and prediction (32 samples) sets. The CARS-PLSR method achieved good prediction results for MP mineral content (picromerite: correlation coefficient of correction set (Rp2) = 0.943, predicted root mean square error (RMSEP) = 2.72%, relative predictive deviation (RPD) = 4.24; potassium chloride: Rp2 = 0.948, RMSEP = 2.86%, RPD = 4.42). Experimental results convey that TIH technology can effectively identify the emissivity characteristics of MP minerals, facilitating quantitative detection of MP mineral content.
及时检测中间产物或最终产物至关重要但具有挑战性。传统的化学检测方法耗时且无法检测矿物相含量。热红外高光谱（TIH）技术是一种实时成像的有效手段，能够精确捕捉物体的发射率特征。本研究引入TIH技术来估算钾盐含量，采用基于竞争自适应重加权采样（CARS）和偏最小二乘回归（PLSR）的模型。该模型考虑了钾盐的发射率光谱，并准确预测了新疆罗布泊产出的一种矿物混合物混合钾（MP）的含量。通过矿物解离分析仪（MLA）测量了MP中的主要矿物含量，主要包括软钾镁矾、氯化钾、硫酸镁，氯化钠较少。将129个配置好的MP样品分为校正集（97个样品）和预测集（32个样品）。CARS-PLSR方法对MP矿物含量取得了良好的预测结果（软钾镁矾：校正集相关系数（Rp2）=0.943，预测均方根误差（RMSEP）=2.72%，相对预测偏差（RPD）=4.24；氯化钾：Rp2 = 0.948，RMSEP = 2.86%，RPD = 4.42）。实验结果表明，TIH技术能够有效识别MP矿物的发射率特征，便于对MP矿物含量进行定量检测。
