Rapid Determination of Iron Content in Hematite by Differential Raman Spectroscopy

Abstract

Online component detection is highly desirable for monitoring industrial raw materials and imported raw materials in customs. Raman spectroscopy is a possible route for in situ monitoring. However, so far, few qualitative estimations for online solid-state materials such as ore are reported due to the low accuracy of the measurements. Herein, a kind of potential online portable shifted-frequency excitation differential Raman spectroscopy (SEDRS) system was built for the first time to quantitatively determine the iron content in hematite samples with merits of no background noise and a high signal-to-noise ratio. The Raman spectra of hematite generally have strong A 1g mode at 230 and 498 cm −1 , and band intensity analysis illustrates a good positive correlation with iron content, which are used for quantitative analysis via principal component analysis and partial least squares regression. In contrast to the quantitative results based on singlefrequency excitation Raman spectra, the average relative error of the differential data treated with SEDRS was only 1.20%. The distribution of particle size for solid-state material is proven to have been the main factor for impacting the accuracy by comparing the peak intensity at 230 cm −1 of powders with different particle sizes. These findings confirm that SEDRS analysis is a high-accuracy reliable tool for analyzing iron content in hematite with uniform particle size, providing a feasible method to remotely detect the iron content of ore online.

Detection of microplastic samples based on spatial heterodyne microscopic differential Raman spectroscopy

“…The system combines spatial heterodyne technology with differential Raman spectroscopy technology. It not only has the characteristics of high luminous flux, high spectral resolution, no moving parts, small size, and low energy consumption of spatial heterodyne technology, but also the fluorescence background signal generated by microplastic samples can be well removed by differential Raman spectroscopy 16–18 . Finally, the Raman spectral curve of microplastics restored by the multiple constraint iteration method is smoother, the spectral noise is small, the SNR is high, and the Raman characteristic peak recognition is higher.…”

“…It not only has the characteristics of high luminous flux, high spectral resolution, no moving parts, small size, and low energy consumption of spatial heterodyne technology, but also the fluorescence background signal generated by microplastic samples can be well removed by differential Raman spectroscopy. [16][17][18] Finally, the Raman spectral curve of microplastics restored by the multiple constraint iteration method is smoother, the spectral noise is small, the SNR is high, and the Raman characteristic peak recognition is higher. This method can quickly, accurately, and efficiently identify the types of microplastics and provide technical support for the treatment of microplastic pollution.…”