Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such ...Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.展开更多
There are two infrared beamlines at the Shanghai synchrotron radiation facility(SSRF)-BL01B and BL06B.BL01B was the first infrared beamline of the National Facility for Protein Science in Shanghai at SSRF,which is ded...There are two infrared beamlines at the Shanghai synchrotron radiation facility(SSRF)-BL01B and BL06B.BL01B was the first infrared beamline of the National Facility for Protein Science in Shanghai at SSRF,which is dedicated to synchrotron infrared microspectroscopy.It utilizes bending magnet radiation and edge radiation as light sources.Diffraction-limited spatial resolution is reached in the infrared microspectroscopy experiment.BL01B has been in operation for approximately five years since it opened in January 2015.In the past few years,many meaningful results have been published by user groups from various disciplines,such as biomacromolecule materials and pharmaceutical,environmental,and biomedical sciences.In addition,a new infrared beamline station BL06B is under construction,which is optimized for the mid-infrared and far-infrared band.BL06B is equipped with a vacuum-type Fourier transform infrared spectrometer,infrared microscope,custom longworking-distance infrared microscope,infrared scanning near-field optical microscope,and mid-infrared Mueller ellipsometer.The purpose is to serve experiments with high vacuum requirements and spatial resolution experiments,as well as those that are in situ and time-sensitive,such as high-pressure and atomic force microscopy infrared experiments.The station can be used for research in biomaterials,pharmacy,geophysics,nanotechnology,and semiconductor materials.展开更多
基金supported by JKW Program(No.M102-03)National Program(No.E0F80246).
文摘Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.
基金supported by the National Natural Science Foundation of China(Nos.U1732130,U1632273,11505267,and 11605281)
文摘There are two infrared beamlines at the Shanghai synchrotron radiation facility(SSRF)-BL01B and BL06B.BL01B was the first infrared beamline of the National Facility for Protein Science in Shanghai at SSRF,which is dedicated to synchrotron infrared microspectroscopy.It utilizes bending magnet radiation and edge radiation as light sources.Diffraction-limited spatial resolution is reached in the infrared microspectroscopy experiment.BL01B has been in operation for approximately five years since it opened in January 2015.In the past few years,many meaningful results have been published by user groups from various disciplines,such as biomacromolecule materials and pharmaceutical,environmental,and biomedical sciences.In addition,a new infrared beamline station BL06B is under construction,which is optimized for the mid-infrared and far-infrared band.BL06B is equipped with a vacuum-type Fourier transform infrared spectrometer,infrared microscope,custom longworking-distance infrared microscope,infrared scanning near-field optical microscope,and mid-infrared Mueller ellipsometer.The purpose is to serve experiments with high vacuum requirements and spatial resolution experiments,as well as those that are in situ and time-sensitive,such as high-pressure and atomic force microscopy infrared experiments.The station can be used for research in biomaterials,pharmacy,geophysics,nanotechnology,and semiconductor materials.
文摘本文以76份青稞为研究对象,利用近红外光谱仪采集青稞4000~10000 cm-1波段光谱,并联合其水分、β-葡聚糖、直链淀粉、蛋白质实测含量数值,构建了基于近红外光谱技术的青稞特征营养成分含量快速检测模型。结果显示,SG卷积平滑(Savitzky Golay,SG)是水分、直链淀粉、β-葡聚糖含量的偏最小二乘法(Partial Least Squares,PLS)预测模型的最优光谱预处理方法,而SG卷积平滑+多元散射校正(Multiplicative Scatter Correction,MSC)是蛋白质含量的偏最小二乘法(PLS)预测模型的最优光谱预处理方法。为进一步提高青稞各成分含量预测模型的准确性,考察了竞争性自适应重加权法(Competitive Adaptive Reweighted Sampling,CARS)、连续投影算法(Successive Projections Algorithm,SPA)和变量组合集群分析混合迭代保留信息变量法(Variables Combination Population Analysis and Iterative Retained Information Variable,VCPA-IRIV)特征波长选择算法对模型预测结果的影响。结果表明,VCPA-IRIV处理可有效提高水分、直链淀粉、蛋白质含量预测模型的预测决定系数,降低预测均方根误差;CARS对β-葡聚糖含量预测模型优化效果显著。基于上述最优方法建立的青稞水分、β-葡聚糖、直链淀粉、蛋白质实测含量预测模型,其预测相关系数分别为0.9868、0.9808、0.9701、0.9879;预测均方根误差分别为0.2042、0.1846、0.8135、0.2095。综上,本研究建立的基于近红外光谱的青稞特征营养成分含量快速检测模型具有较高的准确性,对加工企业快速了解原料品质及高效筛选合格原料有一定指导意义。
