近红外光谱技术在液化石油气检测中的应用研究

本研究采用实验室近红外光谱仪测定气态液化石油气的组成,自行研制了可让气流稳定流过的比色皿,并制作了配套的铝合金样品槽,研究了光程、光谱波段、扫描时间等因素对气态液化石油气近红外模型的影响,确定了最佳的实验参数,并对液化石油气各组分的近红外模型进行了优化.结果表明:近红外光谱技术结合自制比色皿适用于液化石油气组成分析,所...     

近五年我国近红外光谱分析技术研究与应用进展

评述了近五年来(2014～2018)我国近红外光谱分析技术的研究与应用进展,内容涉及方法研究、软硬件研发、应用特点和趋势等方面,并对今后我国近红外光谱技术的发展方向进行了展望。引用文献97篇。     

近红外与中红外光谱技术在土壤分析中的应用

传统的土壤分析方法周期长、成本高,不能在短时间内获取所需的土壤信息,如何快速、准确地获取土壤信息成为农业现代化的必然要求。漫反射光谱技术以其快速、廉价、非破坏与无污染等特点,已成为环境研究中获取相关土壤信息的重要技术手段,其中的近红外(NIR,780~2 500 nm)和中红外(MIR,2 500~25 000 nm)光谱技术预测土壤理化特性逐渐成为国内外学者研究的重点领域。文章介绍了近红外和中红外光谱技术的基本原理和分析方法,综述了该技术在土壤分析方面的具体应用,并对此提出了一些观点和展望。     

软件技术在近红外光谱定量分析中的应用

为了更好地运用化学计量学方法提高近红外光谱定量分析的质量,全面研究了相关算法技术与软件工具的应用。根据近红外光谱定量分析的一般流程,依次探讨了光谱数据预处理、异常样本检测、样本集划分、波长选择、模型建立及其参数优化的常用算法,并给出了其中各种主流算法的基本步骤;然后介绍了OMNIC、OPUS、Origin、The Unscrambler X和MATLAB等软件工具的功能特点和主要用途,可为相关技术研究和分析检测工作提供参考。     

近红外光谱分析技术在快速分析上的应用

简要介绍了近红外光谱的原理、特点,综述了近红外光谱在农业、食品、制药、石油化工、高分子等领域快速分析上的研究及应用现状,并对近红外光谱的应用前景进行了展望．     

烟草近红外光谱分析网络化及其应用进展

该文回顾了近红外光谱(NIRS)分析技术的应用历程以及"近红外光谱分析+互联网"模式在烟草领域中的应用研究与实践,探讨了在近红外光谱分析网络化环境中,近红外光谱仪器设备存在的硬件差异以及常规化学计量学方法(算法)在建模、数据处理存在的不足对近红外光谱的深度应用产生的影响,并提出了近红外光谱分析云计算应用的解决思路。最后,对大数据时代近红外光谱分析网络化模式的应用前景进行了展望。     

近红外光谱快检技术在口岸安全监管领域的应用展望

近红外快速检测技术在全球范围内作为一项新兴的快速检测手段,已在农业、食品、饮料、石油、材料、制药、医学、动植物检疫及其工业化过程等领域得到了广泛应用。该文结合近红外光谱快速检测原理及其应用现状,针对口岸安全监管中的商品智能归类、材料成分快速鉴定、动植物产品快速检疫鉴定、商品装卸载过程的在线监测等需求,提出引入近红外光谱检测技术应用于口岸安全监管领域的设想,力求为实现口岸贸易安全与贸易便利化双重目标奠定科技基础,并给出了近红外在口岸安全监管研究方面的应用展望。     

主成分—人工神经网络在近红外光谱定量分析中的应用

近红外光谱的主成分由非线性迭代偏最小二乘法（ＮＩＰＡＬＳ）求出,主成分作标准化处理后,作为Ｂ－Ｐ神经网络的输入结点进行非线性迭代。该法的优点是,充分利用了全光谱的数据,得到消除噪声后的最佳主成分,能建立非线性模型,Ｂ－Ｐ神经网络迭代时间显著缩短,用该法对大麦中的淀粉含量进行了定量分析研究。结果为：校准和预测的相关系数分别为０．９８１和０．９５３,校准和预测的相对标准偏差分别为１．７０％和２．４８％     

鱼粉近红外光谱模型传递应用研究

为探讨光栅型与傅里叶变换型近红外分析仪之间模型传递的应用效果,选取国产鱼粉为近红外光谱样本,DS2500F型近红外分析仪为源仪器,MPA型近红外分析仪为目标仪器,采用分段直接校正(PDS)方法实现近红外光谱传递。分别建立水分、粗蛋白质、粗脂肪、蛋氨酸和赖氨酸等组分的预测模型,通过交互验证决定系数(R_cv²)、交互验证标准误差(RMSECV)、马氏距离(MD)、系统偏差(Bias)、预测均方根误差(RMSEP)和相对分析误差(RPD)等参数,多维度评估光谱传递后所建预测模型的效果。结果表明,DS2500F仪器的近红外光谱传递到MPA型仪器时,所建国产鱼粉的水分、粗蛋白质、粗脂肪、蛋氨酸、赖氨酸的预测模型与MPA型仪器原始预测模型各参数对比无显著差异,预测效果基本一致,说明国产鱼粉在DS2500F仪器上的近红外光谱通过传递可以替代MPA型仪器的原始光谱,间接实现了模型传递,且具有良好的适用性和共享性,可提高近红外预测模型的应用效率。     

近红外光谱技术在川麦冬原位检测中的应用研究

原药材检测费时费力一直是川麦冬质量分析中亟待解决的问题。该文将近红外光谱分析技术应用于川麦冬原药材的质量分析,以漫反射模式对麦冬颗粒进行无损原位检测后,采用光谱预处理方法减少因粒径造成的干扰,通过变量筛选方法提取有效信息,最终建立了快速定量分析模型。结果表明,水分、浸出物和总皂苷含量模型的验证均方根误差（RMSEP）分别为0.165 5%、0.401 9%、0.078 4%,验证集决定系数（Rv2al）分别为0.965 1、0.696 5、0.803 6,相对分析误差（RPD）分别为4.50、2.68、2.22,3个质量指标的RPD均大于2,说明模型性能较好,能够满足川麦冬质量分析的要求。该法通过近红外光谱技术采集川麦冬颗粒的原位光谱,避免了粉碎,真正意义上实现了川麦冬无损、绿色、快速的含量分析,为川麦冬的质量分析提供了参考。     

高效液相色谱-串联质谱法测定动物源食品中同化激素的研究进展

综述了动物源食品样品的提取与净化方法(包括固相萃取、凝胶渗透色谱和基质固相分散)和高效液相色谱-串联质谱法测定其中同化激素的研究进展(引用文献39篇)。     

Development of Near-Infrared Reflectance Spectroscopy Models for Quantitative Determination of Water-Soluble Carbohydrate Content in Wheat Stem and Glume

Water-soluble carbohydrate (WSC) reserved in stem is an important agronomic trait for crop improvement. The intact samples and pieces of chipped samples were employed to determine WSC content by near-infrared reflectance spectroscopy (NIRS). Three NIRS models were developed to predict WSC content in wheat stem lower internode, upmost internode, and wheat glume, respectively. Moreover, a mixed model was developed for WSC quantitative analysis in the mixed sample of the three wheat organs. Statistics analysis indicated that the four models showed a high determination coefficient (R² ≥ 0.97) and ratio of standard deviation to RMSECV (RPD ≥ 5.99). The NIRS models would allow rapid and high throughout assessments and selections of WSC contents in wheat genetics and breeding programs.     

近红外反射光谱法分析玉米秸秆纤维素含量的研究

利用近红外反射光谱分析技术和偏最小二乘回归法（PLS）,通过比较不同光谱范围和光谱预处理方法,采用二阶导数光谱预处理,在7540．3-5361．1cm^-1和4882.9—4504．9cm^-1谱区内建立了近红外光谱测定玉米秸秆纤维素含量的校正模型。利用15个玉米秸秆样品对所建模型的实际预测效果进行了验证,预测值与化学值的相关系数（r）可达0．9953,最大相对误差仅为5．20。结果表明,近红外光谱技术可以快速、准确地测定玉米秸秆纤维素,该结果对玉米秸秆材料的快速鉴定和筛选利用具有重要的意义。     

近红外漫反射光谱法测定黄连浸膏粉中生物碱含量

采用近红外漫反射光谱法对黄连浸膏粉中小檗碱、巴马亭、药根碱和总生物碱含量进行快速无损检测。以HPLC分析值作参比,采用偏最小二乘回归算法建立二阶导数光谱信息与各组分含量间的定量校正模型,并对未知样品中各组分含量进行预测。小檗碱、巴马亭、药根碱和总生物碱的预测均方差(RMSEP)分别为0．184、0．109、0．054和0．325;加样回收率分别为97．67％～99．59％、96．63％～100．70％、95．15％～101．15％和97．41％～99．89％,重现性实验相对标准偏差(RSD)分别为0．3％、0．6％、1．8％和0．3％。该方法结果准确可靠,重现性、稳定性均良好,适用于工业现场的原位和在线检测。     

Characterization of Automobile Plastics by Principal Component Analysis and Near-Infrared Spectroscopy

Based on near-infrared diffuse reflection spectroscopy, multivariate calibration models for discarded automobile plastic were constructed using principal component analysis and clustering analysis to rapidly characterize four widely employed materials: polypropylene, polyethylene, acrylonitrile butadiene styrene, and polymethylmethacrylate with an accuracy rate of 97%. The method was shown to rapidly discriminate waste automobile plastic.     

Screening Inorganic Arsenic in Rice by Visible and Near-Infrared Spectroscopy

The potential of near-infrared spectroscopy (NIRS) for screening the inorganic arsenic (i-As) content in commercial rice was assessed. Forty samples of rice were freeze-dried and scanned by NIRS. The i-As contents of the samples were obtained by acid digestion-solvent extraction followed by hydride generation atomic absorption spectrometry, and were regressed against different spectral transformations by modified partial least square (MPLS) regression. The second derivative transformation equation of the raw optical data, previously standardized by applying standard normal variate (SNV) and De-trending (DT) algorithms, resulted in a coefficient of determination in the cross-validation (1-VR) of 0.65, indicative of equations useful for correct separation of the samples in low, medium and high groups. The standard deviation (SD) to standard error of cross-validation (SECV) ratio, expressed in the second derivative equation, was similar to those obtained for other trace metal calibrations reported in NIRS reflectance. Spectral information relating to starch, lipids and fiber in the rice grain, and also pigments in the caryopsis, were the main components used by MPLS for modeling the selected prediction equation. This pioneering use of NIRS to predict the i-As content in rice represents an important reduction in labor input and cost of analysis.     

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.     

NIRS分析技术在饲料品质检测中的应用

简介近红外光谱（Near Infrared Spectroscopy,NIRS)分析技术的优点，原理和应用，并重点对其在饲料品质检测中的应用进行综述（引用文献共51篇）。     

Progress in Research and Application of Micro-Laser-Induced Breakdown Spectroscopy

The technique of micro-laser-induced breakdown spectroscopy (μLIBS) usually refers to analyzing the surface of the sample using a compact focused laser beam in the optical microscope range. Compared to conventional laser-induced breakdown spectroscopy (LIBS), it can provide more abundant form, structure, and content information on a smaller, less sample condition. Hence, the application field is very wide. In this paper, the basic status of μLIBS in the selection of laser source and the structure of optical path system is introduced, and its application in metal, semiconductor, animal and plant fields is reviewed. The existing problems and potential development direction is pointed out.     

Determination of Bovine Hemoglobin by Near-Infrared Diffuse Reflectance Spectroscopy with Novel Adsorption Preconcentration

A method for the quantitative determination of bovine hemoglobin in dilute solution was developed using adsorption preconcentration and near-infrared diffuse reflectance spectroscopy. An adsorbent, designated as multicarbonyl polymer-grafted silica particles, was prepared for the preconcentration of bovine hemoglobin in dilute solution. Under neutral conditions, the adsorption efficiency exceeded 98% within 20?min. After the preconcentration of bovine hemoglobin on the adsorbent, the near-infrared spectrum was measured in diffuse reflectance mode and a partial least squares model was constructed for quantitative prediction. Samples were analyzed in the presence of amino acid, albumin bovine V, D-glucose, and metal ions as potential interferences. The results show that bovine hemoglobin was selectively determined. The correlation coefficient between the predicted concentrations and the reference values was 0.9911, and the recoveries were from 86.4 to 111.2% for validation samples with concentrations between 2.1 and 30.0?mg?L^?1. Therefore, the determination of bovine hemoglobin was achieved by near-infrared diffuse reflectance spectroscopy combined with preconcentration and chemometric modeling.