葡萄糖漫反射近红外光谱研究

首先采用一维漫反射近红外(NIR)光谱开展了葡萄糖的结构研究,实验发现:葡萄糖一维漫反射NIR光谱包括3种红外吸收模式(ν1-一维,ν2-一维和ν3-一维).进一步开展了葡萄糖的二阶导数漫反射NIR光谱研究.选择不同的数量平滑点(包括:5个平滑点、9个平滑点、13个平滑点、19个平滑点、25个平滑点、37个平滑点、49个平滑点和149个平滑点)进行实验,结果发现:37个平滑点是适合的选择,而葡萄糖二阶导数漫反射NIR光谱包括4种红外吸收模式(ν1-二阶导数,ν2-二阶导数,ν3-二阶导数和ν4-二阶导数).研究葡萄糖一维漫反射NIR光谱和二阶导数漫反射NIR光谱的吸收频率及标准差,实验发现:葡萄糖二阶导数漫反射NIR光谱的谱图分辨能力及数据平行性要优于相应的一维漫反射NIR光谱.

光程补偿近红外光透射反射干涉重构微结构内部形貌

高深宽比微结构的底部及侧壁形貌重构是微机电系统领域亟待解决的一个问题。本文提出光程补偿近红外光透射反射干涉技术重构微结构内部形貌的方法,所采用的近红外光干涉技术将白光干涉系统中的光源扩展至近红外光源,将反射干涉技术扩展至透射反射干涉技术,近红外光干涉测量系统由近红外光光源、干涉显微镜、红外光CCD、高精度压电陶瓷和数据采集系统组成。设计了具有两个台阶的GaAs半导体微结构待测样品,采用近红外光垂直扫描干涉法并通过光程补偿,重构了微结构的内部三维形貌,并与扫描电镜结果进行对比。光程补偿近红外光透射反射干涉技术测量的台阶相对高度分别为2. 132μm和0. 766μm,与扫描电镜和近红外光反射干涉测量结果基本一致,分别对应2. 16%和2. 68%的相对误差。测量结果表明,该测量系统能够测量高深宽比微结构底部及侧壁形貌。

基于可见光/近红外光谱技术的倒伏水稻识别研究

运用可见光/近红外光谱仪获取正常的和受稻飞虱、穗颈瘟危害而倒伏的水稻冠层光谱反射率,采用主成分分析(PCA)方法对反射率光谱进行降维处理,提取2个主分量光谱.其中,第一主分量PC1代表了水稻冠层的光谱特性,第二主分量PC2反映了倒伏水稻的冠层光谱变化信息.将前2个主分量作为支持向量分类机(SVC)的输入向量,建立分类模型.结果表明,对受稻飞虱危害倒伏的水稻验证数据的识别精度为100%,对受穗颈瘟危害倒伏的水稻验证数据的识别精度为90.9%.研究表明可见光/近红外光谱可能是一种有效的倒伏水稻识别方法.

用于食品成分分析的双探测器近红外光谱仪

研制了双探测器结构的滤光片型漫反射近红外光谱仪。用分束镜将单色光分成两束,一束光直接被一个探测器接收,另一束光经过积分球后照射到样品上,样品的漫散射光被积分球上的另一个探测器接收。将两个探测器的测量值相比较,计算出样品的吸光度。应用该仪器检测了漫反射标准板的漫反射率,测量值与标称值的相关系数平均为0.995,标准偏差平均为0.01。另外,使用该仪器测定了奶粉、牛奶及酱油相关成分含量,近红外光谱与奶粉的蛋白质和脂肪含量的相关系数>0.9,与酱油及原奶的相关成分含量的相关系数>0.8。检测结果表明,自制的双探测器结构滤光片型近红外光谱仪可以准确获得样品的漫反射率。

近红外光谱在淀粉糖生产中的应用

本文论述了采用近红外光谱漫反射技术检测葡萄糖浆中主要成分含量的方法。讨论了采用偏最小二乘法(PLS)建立校正模型过程中样品预处理及利用常规吸收峰优选波长的方法。经验证:DE、DP分布测量值同浓度参考值具有良好相关性(相关系数大于0.9),测量重复性变异系数(CV)优于2%。结果表明:近红外光谱法可以满足葡萄糖浆中主要成分的实际测量要求,为玉米深加工企业提供了淀粉糖生产过程控制的参考方法。

近红外反射光谱在冬小麦抗旱性鉴定中的应用

为评价近红外反射法对冬小麦抗旱性的测定效果,通过2 a冬小麦抗旱鉴定材料的近红外分析与相应抗旱指数比对,建立了线性回归模型。初步认为,不同水处理方法、不同地点的小麦种子,近红外图谱较为稳定;不同抗旱性差异的种子,近红外反射图谱有明显特异性差异。通过建立回归模型,可以用近红外技术预测抗旱指数,决定系数为0.632,低于田间鉴定决定系数(0.804),预测抗旱指数与田间鉴定的平均误差为0.06,可用于亲本鉴定与后代选择。

近红外漫反射光谱法快速测定天然纤维素清洁浆料α-纤维素含量

针对天然纤维素清洁制浆新型连续生产工艺,提出了采用近红外漫反射光谱测定天然纤维素(棉、木浆粕)清洁浆料中α-纤维素含量。收集了142个天然纤维素清洁浆料样品,采用GB/T 9107—1999方法(化学分析方法)测定其α-纤维素含量。通过粉碎预处理提高样品的均匀性,继而压入旋转杯采集光谱。采用簇类独立软模式(SIMCA)方法建立了有效的棉浆粕和木浆粕的分类模型,模型识别率达到100%。基于偏最小二乘(PLS)法分别建立的全部样品以及分类棉、木浆粕的α-纤维素含量定量校正模型相关系数分别为0.954,0.911和0.839,SEP分别为2.4%,1.2%和1.6%,模型预测精密度与GB方法的允差接近,表明该方法是可行的,且操作简单,分析速度快,对提高天然纤维素清洁浆料α-纤维素含量分析效率和指导其连续生产具有积极意义。

近红外漫反射光谱法测定天然纤维素浆粕聚合度

提出一种采用近红外光谱测定天然纤维素(棉、木)浆粕聚合度的方法。实验收集了195个天然纤维素浆粕样品,采用GB/T 9107-1999方法测定了其聚合度,对样品进行粉碎预处理后,放入旋转杯中采集相应的近红外漫反射光谱。通过化学计量学偏最小二乘方法(PLS)将聚合度数据与近红外光谱关联,分别建立了棉木浆粕混合样品、棉浆粕样品和木浆粕样品的聚合度定量模型。最优模型相关系数分别为0.980,0.993,和0.886,预测均方根误差(RMSEP)分别为147,143和53。研究了近红外方法精密度和准确性。结果表明棉浆粕和木浆粕分类模型预测准确性优于混合模型,且其预测精密度符合国标(GB)方法要求。另外,采用主成分分析方法建立了棉浆粕和木浆粕的识别模型,结果表明该模型可以有效识别棉浆粕和木浆粕。该方法操作简单、分析速度快,能够满足天然纤维素浆粕聚合度在线检测要求,为实现清洁制浆新工艺聚合度在线监控目标提供了理论和技术依据。

现场收购冻玉米中各成分含量的近红外光谱（NIR）分析

论述了采用近红外光谱漫反射技术检测冻玉米中主要成分含量的方法。讨论了采用偏最小二乘法(PLS)建立校正模型过程中样品预处理及利用常规吸收峰优选波长的方法。经验证：水分、淀粉、蛋白测量值同浓度参考值具有良好相关性(相关系数大于0．9)，测量重复性变并系数(CV)优于2％。结果表明，近红外光谱法可以满足冻玉米中主要成分的实际到量要求，为玉米深加工企业提供了冬季原料玉米按质收购的参考方法。

Al-Sm2O3/聚氨酯复合涂层的近红外低反射与814μm低发射率性能

以Al粉与Sm2O3为复合颜料,聚氨酯（PU）为黏合剂,制备了Al-Sm2O3/PU复合涂层。系统研究了涂层的微结构、红外发射率、近红外反射性能及力学性能。研究结果表明：Sm2O3的存在可有效降低涂层对1.06μm近红外光的反射率,Al粉的存在可有效降低涂层在8～14μm波段的红外发射率。通过调节Al粉与Sm2O3质量比,涂层在8～14μm波段的红外发射率可在0.556～0.834范围内调节,涂层对1.06μm近红外光的反射率可在35.1%～57.7%范围内调节。所制备Al-Sm2O3/PU涂层具备优良的力学性能,在不同Al-Sm2O3质量比下,其附着力与耐冲击强度分别可达到1级和50kg·cm。Al-Sm2O3/PU复合涂层有望成为一种新型的具备低近红外光反射率与优良力学性能的低红外发射率涂层材料。

Construction of universal quantitative models for the determination of cefoperazone sodium/sulbactam sodium for injection from different manufacturers using near-infrared reflectance spectroscopy

To develop near-infrared （NIR） reflectance spectroscopic methods for the quantitative analysis of cefoperazone sodium/ sulbactam sodium from different manufacturers for injection powder medicaments. Various powders of cefoperazone sodium/ sulbactam sodium were directly analyzed by non-destructive NIR reflectance spectroscopy using the spectrometer EQUINOX55. Two quantitative methods via integrating sphere （IS） and fiberoptic probe （FOP） models were explored from 6 batches of commercial samples and 42 batches of laboratory samples at a content ranging from 30% to 70% for cefoperazone and 60% to 20% for sulbactam. The root mean square errors of cross validation （RMSECV） and the root mean square errors of prediction （RMSEP） of IS were 1.79% and 2.85%, respectively, for cefoperazone sodium, and were 1.86% and 3.08%, respectively, for sulbactam sodium; and those of FOP were 2.93% and 2.92%, respectively, for cefoperazone sodium, and were 2.23% and 3.01%, respectively, for sulbactam sodium. Based on the ICH guidelines and Ref. 12, the quantitative models were then evaluated in terms of specificity, linearity, accuracy, precision, robustness and model transferability. The non-destructive quantitative NIR methods used in this study are applicable for rapid analysis of injectable powdered drugs from different manufacturers.

一种高效热反射聚合物薄膜的制备与性能研究

采用线型低密度聚乙烯(LLDPE)作为基材,加入双组分近红外反射添加剂,通过熔融共混技术和流延工艺制备LLDPE近红外反射复合薄膜。研究双组分添加剂对LLDPE薄膜的结构、热反射性、光学性能、力学性能的影响。结果表明:添加剂在聚合物基材中促进薄膜结晶与取向,当添加剂质量分数逐渐增加,薄膜的光学性能逐渐降低,而力学性能呈现先上升后降低的趋势。通过小角计算,颗粒的回转半径随添加剂质量分数的增加逐渐增大。当添加剂质量分数为1%时,颗粒的回转半径增至57.2 nm。添加剂团聚情况随添加剂质量分数的增加而增多。添加剂能够大幅提高薄膜热反射性能,添加剂质量分数为0.5%时,薄膜反射率达到峰值。该研究对优化热反射薄膜的综合性能具有指导意义。

Classification of Guizhou Aspidistra Plants by Near-infrared Diffuse Reflectance Spectroscopy

[Objective] This study was conducted to establish a near-infrared diffuse reflectance spectroscopy of Guizhou Aspidistra plants. [Method] Twenty three batch- es of Guizhou Aspidistra plants including A. chishuiensis, A. spinula, A. Caespitosa, A. sichuanensis, A. ebianensis, A. retusa, A. guizhouensis and A. liboensis were subjected to drying, pulverization and sieving and then directly determined for near- infrared reflectance spectrums; and the plants in this genus were classified by clus- ter analysis and principal component analysis （PCA）. [Result] The near-infrared re- flectance spectrums of the 23 batches of Guizhou Aspidistra plants showed very high similarity. The spectrums were processed by first derivative method, and the spectral range of 4 000-7 500 cm-1 was selected as the analytical range. Cluster analysis and PCA were employed to mass spectrum variables of plants in Aspidis- tra, fewer new variables became the linear combination of primary variables, and small differences between different varieties were enlarged, thereby facilitating intu- itive classification of plants in this genus. [Conclusion] Near-infrared diffuse re- flectance spectroscopy is nondestructive and rapid for determination of solid sam- pies, and provides a new method for the classification of Guizhou Aspidistra plants combined by information processing techniques.

The Study of Using Near-infrared Diffuse Reflectance Spectroscopy Rapid Identify Wheat Drought Resistance-Ⅱ

[Objective] The aim was to build an evaluation method rapidly identifying wheat drought tolerance with near infrared diffuse reflectance spectroscopy. [Method] In the research, 36 wheat varieties in 2007-2009 were chosen and drought-tolerance degrees of wheat were graded and identified according to Winter-wheat Drought Tol- erance Evaluation Technical Standards （GB/T 21127-2007）, and harvest wheat grains underwent spectrum collection, with a full-spectrum analyzer, to establish a database. [Result] Based on qualitative analysis and full-spectrum correlation research, the coef- ficient of determination （RSQ） and cross-validation coefficient of determination （1-VR） were concluded at 0.697 5 and 0.600 2, showing near-infrared diffuse reflectance spectroscopy is of significant differences among wheat varieties and of significant or extremely significant correlation with drought-tolerance indices. [Conclusion] The re- search indicates that to evaluate drought-tolerance of wheat with near-infrared diffuse reflectance spectroscopy is a rapid and feasible way, which is simple, convenient without damages on grains, and of practical values for construction wheat drought-tol- erance evaluation index system and identification of breeding materials.

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.

Rapid Identification for Drought Resistance of Wheat Using Near-infrared Diffuse Reflectance Spectroscopy

[Objective] This study aimed to establish an identification system for drought-resistance in wheat by using near-infrared diffuse reflectance spectroscopy. [Method] In 2006-2007, 36 wheat varieties with different drought resistance were selected and were classified according to their drought resistance grades determined by the Technical Specification of Identification and Evaluation for Drought Resistance in Wheat （GB/T 21127-2007）. In addition, the harvested wheat seed samples were spectrally analyzed with FOSS NIRSystems5000 near-infrared spectrum analyzer for grain quality （full spectrum analyzer） and then the forecasted regression equations were established. [Result] After the establishment of a database and validation, dis- criminated functions were obtained. The determination coefficient （RSQ） and coeffi- cients of determination for cross validation （1-VR） in the discriminant function built with seed samples from water stress area were 0.846 0 and 0.781 8, respectively, which indicated that the consistency between drought resistance and spectral charac- teristics in wheat varieties was good, and there was high correlation between the near-infrared diffuse reflectance spectra of seeds and the drought resistance in wheat. [Conclusiou] Under water stress condition, it is feasible to establish a conve- nient, rapid and no-damage identification system for the drought resistance in wheat by using the near-infrared diffuse reflectance spectrum technique to scan wheat seeds.

Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful for acquiring data for soil digital mapping,precision agriculture and soil survey.In this study,1581 soil samples were collected from 14 provinces in China,including Tibet,Xinjiang,Heilongjiang,and Hainan.The samples represent 16 soil groups of the Genetic Soil Classification of China.After air-drying and sieving,the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer.All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses.The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification.The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils.The results on the classification of the spectra are comparable to the results of other similar research.Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression(PLSR).This combination significantly improved the predictions of soil organic matter(R2=0.899;RPD=3.158)compared with using PLSR alone(R2=0.697;RPD=1.817).

Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Prediction of Soil Properties near a Copper Smelter

Spatial and temporal monitoring of soil properties in smelting regions requires collection of a large number of sam- ples followed by laboratory cumbersome and time-consuming measurements. Visible and near-infrared diffuse reflectance spectroscopy （VNIR-DRS） provides a rapid and inexpensive tool to predict various soil properties simultaneously. This study evaluated the suitability of VNIR-DRS for predicting soil properties, including organic matter （OM）, pH, and heavy metals （Cu, Pb, Zn, Cd, and Fe）, using a total of 254 samples collected in soil profiles near a large copper smelter in China. Partial least square regression （PLSR） with cross-validation was used to relate soil property data to the reflectance spectral data by applying different preprocessing strategies. The performance of VNIR-DRS calibration models was evaluated using the coefficient of determination in cross-validation （R^2cv） and the ratio of standard deviation to the root mean standard error of cross-validation （SD/RMSEcv）. The models provided fairly accurate predictions for OM and Fe （R2v 〉 0.80, SD/RMSEcv 〉 2.00）, less accurate but acceptable for screening purposes for pH, Cu, Pb, and Cd （0.50 〈 Rcv 〈 0.80, 1.40 〈 SD/RMSEcv 〈 2.00）, and poor accuracy for Zn （R2v 〈 0.50, SD/RMSEcv 〈 1.40）. Because soil properties in conta- minated areas generally show large variation, a comparative large number of calibrating samples, which are variable enough and uniformly distributed, are necessary to create more accurate and robust VNIR-DRS calibration models. This study indicated that VNIR-DRS technique combined with continuously enriched soil spectral library could be a nondestructive alternative for soil environment monitoring.

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 cor- rection 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 predic- tion=0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.

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 loquats 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 rim）, 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.