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Determination of the Biodiesel Content in Petrodiesel/Biodiesel Blends: A Method Based on Uv-Visible Spectroscopy and Chemometrics Tools
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作者 Armando Guerrero Francisco Anguebes +4 位作者 Mepivoseth Castelán Victorino Morales Ismael León José C. Zavala Atl V. Córdova 《American Journal of Analytical Chemistry》 2013年第6期273-276,共4页
In this work, we developed an analytical method based on UV-visible spectroscopy to determine the concentration of biodiesel from African palm in blends of petrodiesel. Seventy-five samples with biodiesel concentratio... In this work, we developed an analytical method based on UV-visible spectroscopy to determine the concentration of biodiesel from African palm in blends of petrodiesel. Seventy-five samples with biodiesel concentrations between 0-100 wt% were prepared. The spectral fingerprints that were obtained from the analysis of the samples by UV-visible spectroscopy were used to build predictive model using PLS regression. The predictive ability of the models was evaluated through statistical parameters: the standard error of calibration (SEC), the standard error of validation (SEV), the correlation coefficient of calibration (r Cal) and validation (r Val), the ratio (SEC/SEV), the coefficient of determination R2, the paired data Student’s t-test, cross-validation and external validation. The results indicate that the PLS model predicts the concentration of biodiesel from African palm with high precision in mixtures with petrodiesel. The method developed in this study can be applied to determine the concentration of biodiesel African palm in mixtures of petrodiesel in a more rapid and economical way. Moreover, this method has less analytical errors and is more environmentally friendly than the conventional methods. 展开更多
关键词 AFRICAN PALM Petrodiesel/Biodiesel BLENDS regression model pls Spectroscopy UV-VISIBLE
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光谱分辨率对土壤组分建模影响分析
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作者 陈玉 魏永明 +3 位作者 王钦军 LI Lin 雷少华 路春燕 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2021年第3期865-870,共6页
实验室可见-近红外高光谱数据(VIS-NIR)具有快速、高效、无损等技术优势,被越来越多应用于土壤组分反演中。光谱分辨率越高所能表达的土壤信息越丰富,但也带来了数据冗余。目前,对于不同光谱分辨率对土壤组分建模影响效应分析的研究相... 实验室可见-近红外高光谱数据(VIS-NIR)具有快速、高效、无损等技术优势,被越来越多应用于土壤组分反演中。光谱分辨率越高所能表达的土壤信息越丰富,但也带来了数据冗余。目前,对于不同光谱分辨率对土壤组分建模影响效应分析的研究相对较少。以欧洲土壤中心数据集19036个土壤样本为数据源,以土壤总氮(N)、有机碳(OC)、碳酸钙(CaCO_(3))、粘土(Clay)为例,基于偏最小二乘回归方法(PLS)并选择30%的随机样本独立验证的方式开展相关研究。首先将所有样本原始0.5 nm分辨率4200个波段的高光谱数据采用等间距取均值方法分别重采样到2,4,8,…,1024 nm开展分析。结果表明:随着光谱分辨率的降低,土壤各类组分反演精度均呈下降趋势,光谱分辨率在64 nm以上,4类土壤组分普遍具有较高的模型验证精度(R^(2)>0.65,RPD>1.7),光谱分辨率在128 nm以下CaCO 3和Clay组分精度显著变差;4类组分中,CaCO_(3)对光谱分辨率敏感性最强,在高光谱分辨率下反演精度较高(R 2>0.86,RPD>2.72),但随光谱分辨率降低精度下降最快。此外,基于光谱响应函数将样本光谱重采样到GF2,S3A,L8,Aster,Modis和S3OLCI六种常见卫星传感器的光谱分辨率展开评价。结果表明:土壤N、OC在各传感器中均可获得较高的精度,甚至在GF2传感器仅有4个波段情况下,也具有不错的验证精度(R^(2)=0.56;RPD=1.51),而土壤CaCO_(3)及Clay反演精度普遍较差;除传感器光谱波段数量外,波段位置对土壤组分的反演能力的影响也很显著,拥有近红外长波(1100~2500 nm)光谱范围的传感器对土壤组分的反演能力优于缺少该光谱波段的传感器,特别是粘土矿物的吸收峰多位于近红外长波段,S3A,L8,Aster和Modis传感器的Clay反演能力均优于光谱波段数更多的S3OLCI。该研究成果对土壤组分高光谱数据预处理、卫星数据源的选择及未来传感器光谱通道的设计具有指导意义� 展开更多
关键词 土壤组分 实验室可见近红外光谱 卫星传感器 光谱分辨率 偏最小二乘法
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