基于近红外反射光谱与机器学习的磷矿石品位检测OA

In order to realize the green and efficient development and utilization of phosphate rock resources,a phos-phate rock grade detection and analysis method combining near-infrared reflectance spectroscopy and machine learning is proposed.High-grade apatite and low-grade dolomite were crushed and ground,and mixed in a variety of propor-tions as test samples.The near-infrared reflectance spectra of the test samples were collected using a fiber optic spec-trometer in the 900-2 600 nm band.The relationship between the characteristic absorption and scattering response characteristics of near-infrared light and the mineral components in the test samples was explored,and the quantitative analysis model of spectroscopy and phosphate rock grade was constructed.The results show that the combination of partial least squares regression and random forest has the highest accuracy of 99.79％and the strongest stability in predicting the grade of phosphate rock on the verification set.The combination of machine learning and near-infrared spectroscopy technology can effectively extract potential spectral information,realize real-time and accurate detection of phosphate rock grade,and provide a scientific detection method for efficient separation of phosphate rock.