近红外光谱技术在药物分析中的应用

近红外光谱技术作为一种简单、快速、无损的检测手段,已经成为药物分析中新兴的方法.本文介绍了近红外光谱分析技术的类型、方法特点及其基本原理,综述了近红外光谱分析技术在药物分析中的应用,并对近红外光谱技术作了展望.     

近红外光谱在药品领域中应用及前景

近红外光谱作为一种简单、快速、无损伤、无污染的检测手段,在检测原料、在线检测以及产品质量检测等方面有独特的优势．因此在药品领域得到了广泛的应用。     

铁观音中茶多酚含量的近红外光谱检测模型分析

利用近红外光谱分析(NIR)技术对安徽产地铁观音名茶茶多酚含量模型进行定量分析,通过不同的光谱预处理方法比较,得出最优的光谱预处理方法,同时结合偏最小二乘法(PLS)在近红外特征吸收峰波段1 872 nm与2 218 nm建立茶叶的茶多酚含量模型.从建立的样本校正集模型相关系数R和均方根误差RMSEP分析发现,1 872 nm处的模型R=0.9624、RMSEP=0.01648精度较好,以此模型为标准得出预测集样本茶多酚含量,并建立茶多酚化学值和预测值的相关关系图.该法可对茶叶中茶多酚含量的快速无损检测提供参考.     

近红外光谱分析技术的研究与应用

基于重庆市高校创新团队＂近红外光谱检测技术研究及其应用＂的建设与发展,重点介绍了近红外光谱分析技术的发展,研究了近红外光谱分析技术在三峡库区特色资源的快速检测的应用.     

基于判定区间伸缩因子的决策融合算法研究

以近红外光谱分析技术为基础,结合化学计量学分析方法,对西湖龙井和普通龙井进行鉴别研究,提出一种基于判定区间伸缩因子决策融合的鉴别方法。该方法采用支持向量机（SVM）结合向后区间偏最小二乘法（BiPLS）进行决策融合,并引入判定区间伸缩因子对判定区间进行调节,找到最优判定区间。以西湖龙井和普通龙井样本为材料,采集样本的近红外光谱,使用二阶导数对原始光谱进行预处理,采用该方法建立分类模型,对西湖龙井和普通龙井进行分类。结果表明,对72份训练集样本和38份预测集样本进行分类,训练集样本和预测集样本分类的准确率均达到100%,证明该方法对西湖龙井和普通龙井能进行有效的分类鉴别。     

尿液中葡萄糖浓度的近红外光谱检测研究

探索一种方便、快速、可靠的分析手段对于尿糖检测来说具有十分重要的意义.利用近红外光谱测试分析葡萄糖浓度为0 mg/dL-1 200 mg/dL的尿液样本,并通过偏最小二乘回归结合一阶导数光谱法构建尿糖浓度的预测模型,用于建模的光谱区域为900 nm-1 600 nm.研究表明,主成分个数为6时,训练集的线性相关系数达0.999 0,总体方均根误差为9.85 mg/dL,校验样本的总体方均根误差为26.51 mg/dL.鉴于近红外光谱技术具有方便、准确、廉价等优点,其可以为尿糖检测提供一种快速分析手段.     

银黄颗粒剂的近红外光谱鉴别分析

利用近红外光谱与判别分析相结合的方法对不同生产厂家的银黄颗粒剂进行分类鉴别。采用多元散射校正(MSC)+一阶导数对银黄颗粒剂的近红外光谱进行预处理,在7400～4000cm-1光谱区间内建立近红外判别分析模型,并用三重交叉验证的方法对模型的稳定性进行验证。所建判别分析模型对校正集样品的分类鉴别准确率为100%,对验证集样品的预测鉴别准确率为100%。结果表明,该方法准确、快速、简便,可用于不同生产厂家银黄颗粒剂的分类鉴别。     

KCNJ11基因多态性与2型糖尿病的关联研究

目的：探讨β-细胞腺苷三磷酸一敏感性钾通道（potassium inwardly—rectifying channel,subfamilyl,member 11,KCNJ11）基因E23K多态性与湖北汉族人群2型糖尿病的相关性。方法：采用同胞对（家系内对照）和随机病例一对照两种实验设计,利用聚合酶链式反应-限制性片段长度多态性（PCR—RFLP）技术分析湖北地区443例样本KCNJ11基因E23K多态性。并测定身高、体重、腰围、臀围、血压和空腹血糖等生理生化指标。结果：随机病例一对照实验设计中,病例组与对照组的基因型频率与等位基因频率有显著性差异（P〈0．05或P〈0.01）。结论：在湖北汉族人群中,2型糖尿病的发生发展与KCNJ11基因E23K多态性相关联,KCNJ11基因是湖北汉族人的一个2型糖尿病易感基因。     

基于近红外的葡萄酒品质快速识别方法研究

为建立葡萄酒综合品质的快速识别方法,研究采用近红外对不同产地、葡萄品种及品牌的葡萄酒样本进行检测分析,结合极限学习机(Extreme learning machine,ELM)构建葡萄酒品质的快速识别模型.结果显示,所建ELM模型测试集对不同产地、葡萄品种及品牌葡萄酒样本的正确识别率分别为83.33%,91.67%和83.33%,效果良好.研究表明,近红外结合ELM可用于葡萄酒综合品质的快速识别.     

基于XGBoost算法的社交网络链路预测

提升链路预测精度是复杂网路研究的基础问题之一。传统基于局部信息相似性、基于全局信息相似性与基于随机游走相似性的链路预测都是基于单个相似性指标进行预测的,而没有充分利用这些相似性指标的综合信息。将链路预测问题看作机器学习中的二分类问题,将有连接的样本标签记为1,无连接的样本标签记为0,将基于局部信息、基于全局信息与基于随机游走相似性等15个指标作为样本特征。综合考虑以上信息,使用XGBoost算法,选取AUC作为模型评价准则,在facebook真实数据集上进行实验。结果表明,该算法在测试集上的AUC高于基于单个相似性指标链路预测的AUC。     

On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy

The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207).In this study,82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode.Discriminant models were developed using principal component analysis (PCA),discriminant analysis (DA),and discriminant partial least squares (DPLS) regression methods.After outliers detection,the samples were randomly split into two sets,one used as a calibration set (n=82) and the remaining samples as a validation set (n=82).When predicting the variety of the samples in validation set,the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%.The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (Rc)=0.920,root mean square errors of calibration=0.196,and root mean square errors of prediction=0.216).The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.     

Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice

Near-infrared (NIR) transmittance spectroscopy combined with least-squares support vector machine (LS-SVM) was investigated to study the quality change of tomato juice during the storage. A total of 100 tomato juice samples were used. The spectrum of each tomato juice was collected twice: the first measurement was taken when the tomato juice was fresh and had not undergone any changes, and the second measurement was taken after a month. Principal component analysis (PCA) was used to examine a potential capability of separating juice before and after the storage. The soluble solid content (SSC) and pH of the juice samples were determined. The results show that changes in certain compounds between tomato juice before and after the storage period were obvious. An excellent precision was achieved by LS-SVM model compared with discriminant partial least-squares (DPLS), soft independent modeling of class analogy (SIMCA), and discriminant analysis (DA) models, with 100% of a total accuracy. It can be found that N1R spectroscopy coupled with LS-SVM, DPLS, SIMCA, and DA can be used to control the quality change of tomato juice during the storage.     

Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy

The near infrared (NIR) spectroscopy technique has been applied in many fields because of its advantages of simple preparation,fast response,and non-destructiveness.We investigated the potential of NIR spectroscopy in diffuse reflectance mode for determining the soluble solid content (SSC) and acidity (pH) of intact loquats.Two cultivars of loquats (Dahongpao and Jiajiaozhong) harvested from two orchards (Tangxi and Chun'an,Zhejiang,China) were used for the measurement of NIR spectra between 800 and 2500 nm.A total of 400 loquats (100 samples of each cultivar from each orchard) were used in this study.Relationships between NIR spectra and SSC and acidity of ioquats were evaluated using partial least square (PLS) method.Spectra preprocessing options included the first and second derivatives,multiple scatter correction (MSC),and the standard normal variate (SNV).Three separate spectral windows identified as full NIR (800-2500 nm),short NIR (800～1100 nm),and long NIR (1100～2500 nm) were studied in factorial combination with the preprocessing options.The models gave relatively good predictions of the SSC of loquats,with root mean square error of prediction (RMSEP) values of 1.21,1.00,0.965,and 1.16 °Brix for Tangxi-Dahongpao,Tangxi-Jiajiaozhong,Chun'an-Dahongpao,and Chun'an-Jiajiaozhong,respectively.The acidity prediction was not satisfactory,with the RMSEP of 0.382,0.194,0.388,and 0.361 for the above four loquats,respectively.The results indicate that NIR diffuse reflectance spectroscopy can be used to predict the SSC and acidity of loquat fruit.     

Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits

INTRODUCTION Soluble solids content (SSC) is a major charac- teristic used for assessing citrus fruit quality. Near-infrared spectroscopy (NIRS) has been used as a rapid and nondestructive technique for determining the soluble solids content of fruit. Kawano et al.(1992) measured sugar content of peaches in the wavelength region of 680~1235 nm. Their experiments indicated good correlation between the NIR spectra and the sugar content (r=0.97, SEP=0.05 °Brix). Slaughter (1995) devel…     

Application of NIR spectroscopy for firmness evaluation of peaches

The use of near infrared (NIR) spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800～2500 nm by lnGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts (including three latitudes and three longitudes) were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes (i,ii, iii) and latitudes (A, B, C). For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression (PLSR) models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction (MSC) pretreatment, with correlation coefficient of cross-validation rcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of cali-bration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.     

小波去噪在基于近红外光谱的砂糖橘水分检测的应用

水分含量是衡量砂糖橘营养品质的重要指标之一,其快速无损检测显得越来越重要。本文基于小波变换的方法,对砂糖橘的500-2500nm区间的漫反射光谱,利用正交小波函数DBn(n=2,3,…10)分别进行2-6五个水平分解和消噪,并比较了不同小波函数和不同分解水平的消噪效果。结果表明,小波消噪有利于消除导数光谱中的噪声,提高建模精度,基于小波函数DB3(分解尺度为3)消噪后的导数光谱建立的PLS模型的预测相关系数为0.8725,预测均方根误差为0.6767。     

镧离子掺杂类普鲁士蓝复合汞膜电极的制备及电分析测定铅

在镧离子掺杂类普鲁士蓝（La-PB）修饰的玻碳电极表面电沉积汞膜,制备了一种新型化学修饰复合汞膜电极La-PB/MFE/GC;研究了修饰层厚度、镀汞方式和汞膜成长过程对复合汞膜形成的影响;并以Pb2＋为探针离子,对镧、钴离子掺杂类普鲁士蓝复合汞膜电极和常规玻碳汞膜电极的汞膜稳定性和金属离子的溶出伏安行为等进行了对比研究;同时,应用该电极结合示差脉冲阳极溶出伏安法对实际水样中微量Pb2＋的质量浓度进行了测定.结果表明,Pb2＋在该复合汞膜电极上的阳极溶出氧化峰电流在4.82×10-9～4.82×10-7mol/L范围有良好的线性关系（r=0.995 5,n=13）,检出限为9.06×10-10mol/L,RSD值为2.6%,加标回收率为97%～102%.可用于实际样品的测定.     

Measurement and analysis of soil nitrogen and organic matter content using near-infrared spectroscopy techniques

Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.     

催化溴酸钾氧化吖啶橙褪色光度法测定痕量Cr（Ⅵ）

基于H2SO4介质中,沸水浴加热10min的条件下,铬（Ⅵ）能显著催化溴酸钾氧化吖啶橙（R）的褪色反应,据此建立了催化溴酸钾氧化吖啶橙褪色光度法测定痕量Cr（Ⅵ）的新方法．其线性范围为0．02—0．40μg／L,工作曲线的回归方程为△A=0．07684＋0．14971CCr（Ⅵ）（μg／L）,相关系数r=0．9966,检出限为1．1×10^-11g／mL．本法成功用于江水、人发中的痕量铬（Ⅵ）的测定,结果满意．     

毛细管气相色谱法同时测定工业废水中微量的萘、α-萘酚及蒽

本文建立了同时测定工业废水中微量的萘、α-萘酚及蒽的毛细管气相色谱方法。以二氯乙烷和氯化钠的混合液萃取水样,利用毛细管柱气相色谱-FID进行检测,以外标法定量。该方法测定的萘、α-萘酚、蒽的标准工作曲线方程分别为y=23.091x＋0.688（r=0.9996）,y=17.788x-13.136（r=0.9992）,y=25.123x-10.756（r=0.9991）,检出限（S/N=3）分别是5.3×10-4μg.mL^-1,6.9×10-4μg.mL^-1,4.9×10-4μg.m^L-1,线性范围分别是1.8×10^-3 - 40μg.mL^-1、2.3×10^-3 - 60μg.mL^-1、1.6×10^-3 - 50μg.mL^-1。对此多环芳烃体系标准混合物溶液进行了3个不同水平的添加,每个水平重复6次进样,萘、α-萘酚、蒽的平均加标回收率分别为89%-104%、97%-113%、90%-106%,相对标准偏差分别为1.7%-2.6%、1.9%-3.7%、1.8%-3.5%。相同条件下,实际工业废水样品经富集后检测,萘,α-萘酚,蒽的平均浓度分别为196.7μg.mL^-1、20.65μg.mL^-1、100.8μg.mL^-1。实验结果表明：本方法操作简便、快速、准确、灵敏度高、线性范围较宽,测定结果令人满意。