含苝酰亚胺聚氨酯的合成、力学特性及近红外反射性能

在聚合物基体中掺杂苝酰亚胺（PDI）是制备近红外（NIR）反射材料的常用方法,但PDI与聚合物的宏观相分离通常会严重影响材料的力学性能和近红外反射性能。本研究通过共价键将PDI分子引入到聚氨酯高分子链,合成了一种具有本征近红外反射性能的新型聚合物PU-PDI,并对其光物理特性、力学性能和近红外反射性能开展了详细研究。结果表明,共价接入的方法可有效抑制PDI单元的聚集,使PU-PDI具有优异的力学性能。PU-PDI的断裂伸长率可达700%以上,断裂强度为34.11 MPa。此外,与共混体系相比,由于PDI具有更好的分散性,PU-PDI具有更优异的近红外反射性能。     

用近红外高光谱图像区分不同品质的建筑涂料

市场上出售的建筑涂料品种繁多,型号和品质不尽相同。利用高光谱技术区分不同品质的建筑涂料。获取同一颜色四个不同品质、不同品牌建筑涂料（品牌A,B,C,D）的近红外高光谱图像,利用ANOVA(analysis of variance)方法发现1 283和2 447 nm为区分四个品牌涂料的最优波段。构建比值指数R_{1 283}/R_{2 447}并对其结果进行阈值分割,将分割结果与最大似然分类精度进行了对比。结果表明除品牌C与D之间J-M距离外,其他涂料间J-M距离均大于1.8; 而R_{1 283}/R_{2 447}指数分割精度最低为87.54%,相应最大似然分类精度为95.63%,其他品牌涂料阈值分割与最大似然分类精度均达到90%以上。因此,R_{1 283}/R_{2 447}指数能够较好地区分不同品牌的建筑涂料。该研究结果可为建筑涂料识别、装修质量验收、合格评定提供技术支持。     

一种快速有效鉴定CRISPR/Cas9诱导水稻突变体的方法

突变体的筛选与鉴定是育种工作中的重要环节。该研究基于高光谱成像技术实现了水稻CRISPR/Cas9突变体种子的可视化鉴别。采集了水稻HD野生型和CRISPR/Cas9突变体种子共1 200粒样本的高光谱图像数据,通过Kennard-Stone算法,按照2∶1的比例构建了建模集(800)和预测集(400)。对水稻种子的原始光谱经过WT预处理后,通过2nd derivative提取了24个特征波长,分别基于全谱和特征波长建立径向基函数神经网络(RBFNN),极限学习机(ELM)和K最邻近法(KNN)模型。试验结果表明,无论是基于全谱还是特征波长神经网络模型都取得了良好的识别能力。通过2nd derivative提取的特征波长结合RBFNN模型也取得了较好的鉴别结果,其建模集和预测集分别达到了92.25%和89.50%。基于2nd derivative-RBFNN结合图像处理技术,可以实现水稻CRISPR/Cas9突变体种子的可视化鉴别,实现种子的定位和识别。结果表明应用高光谱成像技术,结合化学计量学方法和图像处理技术对水稻CRISPR/Cas9突变体的鉴别具有可行性,可为水稻育种中大量突变体的快速、准确地筛选和鉴定提供技术手段。     

紫外/可见光下海洋溢油油种光谱识别方法

为了考察高光谱技术对海洋溢油种类的鉴别能力,进行了模拟溢油实验。利用紫外三种波长光源(254nm、302nm、365nm)和可见光两种光源(日光灯和阳光)照射海水上的油墨,使用AvaSpec-2048地物光谱仪采集5个油种(汽油、柴油、煤油、机油、花生油)的高光谱数据(200~1160nm),基于这些数据,通过PCA进行特征优化,使用前10个主分量构建了支持向量机的油种识别模型,使用网格法对支持向量机参数C和gamma进行参数寻优,通过5折交叉验证法进行了结果测试。结果表明,阳光下的光谱识别率最高,日光灯下的识别效果最差,三种紫外灯在波长302nm透射下识别率较高,在254nm和365nm下的识别效果相当。由此可见,在油种鉴别过程中光源的类型、照射方式、光强都会影响到油种的识别效果。     

可见/近红外高光谱成像技术对鸡蛋种类无损判别

利用高光谱技术对鸡蛋种类判别进行研究,为鸡蛋种类无损判别提供科学方法。本研究利用400~1000nm高光谱系统采集3种鸡蛋样本的高光谱图像,对原始光谱进行预处理;应用CARS、GAPLS和IRF对预处理后的光谱数据提取特征波长;分别建立基于全光谱和特征波长的KNN和PLS-DA鸡蛋判别模型。结果表明：Detrend法为最优预处理方法;利用CARS、GAPLS和IRF分别选出31、52和71个特征波长;基于IRF提取的特征波长的PLS-DA模型最优,校正集正确率97.02%,预测集正确率85.71%。表明基于高光谱成像技术采集的鸡蛋反射光谱对种类无损判别是可行的。

     

Comparative study of albumen pH and whole egg pH for the evaluation of egg freshness

The Haugh unit (HU) is the most widely used measure to assess egg freshness. It is crucial and difficult to measure albumen height as a variable of the HU, especially for stale eggs. Albumen pH is strongly correlated to the HU and rarely affected by age or strain, except for storage. The prediction of albumen pH is a better indicator of egg freshness than the HU. Albumen is separated from the yolk in the albumen pH test, while in the whole egg pH measurement, it is not necessary to separate the albumen from the yolk. The objective of this research was to compare the albumen pH with the whole egg pH using visible near-infrared spectroscopy. Spectra were acquired using transmitted light on white-shelled eggs. Pretreatment methods containing multiplicative scatter correction, the standard normal variate (SNV), the first derivative, and the second derivative were used. A partial least square regression (PLSR) was used to set up prediction models. The results illustrated that PLSR models preprocessed by the SNV of albumen pH and whole egg pH were superior to other models. In the best prediction models, the correlation coefficients for albumen pH and whole egg pH were 0.923 and 0.752, and the root mean square error values were 0.170 and 0.265, respectively. The prediction models of albumen pH were better than those of whole egg pH using the PLSR method. In addition, the relevant information concerning albumen pH and whole egg pH ranged from 550 to 850?nm.     

基于高光谱成像技术的菜青虫生命状态检测研究

采用近红外高光谱成像技术对菜青虫的存活与死亡状态进行了研究,通过提取菜青虫不同状态的光谱信息,建立判别分析模型。以不同预处理方法对所提取的951.5～1 649.2 nm光谱进行预处理,并建立偏最小二乘判别分析(partial least square-discriminant analysis, PLS-DA)模型对菜青虫的生死状态进行判别分析,判别正确率接近或达到100%。用移动平均(moving average,MA)5点平滑光谱分别采用连续投影算法(successive projections algorithm, SPA)以及加权回归系数(weighted regression coefficient,B_w)分别选取了17和20个特征波长进行生与死状态的判别。基于特征波长建立了PLS-DA, K最邻近节点算法(K-nearest neighbor,KNN),BP神经网络(back propagation neural network,BPNN)以及支持向量机(support vector machine,SVM)模型,判别正确率接近100%。结果表明采用近红外高光谱成像技术对菜青虫生命状态的研究是可行的,为作物虫害的快速诊断提供了新方法。     

一种新型近红外荧光探针用于Aβ40聚集体的检测

β⁃淀粉样蛋白（Aβ）形成的老年斑块是阿尔茨海默症（Alzheimer'sdisease,AD）最重要的病理性标志物之一,在AD的发病中起到了非常重要的作用。在AD的发病前期,Aβ40聚集体是最为主要且大量存在的蛋白形式。因此,对Aβ40聚集体的检测有利于实现对AD的早期诊断。本工作设计合成了一种具有D⁃π⁃A结构的小分子近红外荧光探针C⁃1用于Aβ40聚集体的荧光检测。探针C⁃1具有良好的荧光发射波长（λ_em=640nm）,对Aβ40聚集体表现出了较好的结合能力（K_d=3.072μmol/L）,能够实现在溶液中对Aβ40聚集体的特异性检测。除此之外,该探针合成简单,成本低廉,灵敏度较高,响应速度极快。     

一种分光型温室覆盖结构的光学特性研究

本文针对温室设计了一种分光型覆盖结构,该结构可以使太阳光中的可见光进入温室提供植物光合作用所需光能,近红外光反射到薄膜电池转化成电能.本文利用光学软件模拟不同入射角下该结构的光学特性,室外试验结果表明,该结构全天可见光透过率在36％～48.9％之间,室内照度为20～30 klx,基本满足植物生长需求;输出电功率呈现早晚...     

高热稳定性近红外荧光粉BaY2Al2Ga2SiO12∶Cr3+的合成及发光性质

近年来,近红外荧光粉转换发光二极管（NIRpc⁃LED）在夜视、生物成像和无损检测等领域引起了广泛关注。然而,获得兼具高量子效率和优异热稳定性的近红外荧光粉仍然是一个巨大挑战。本文利用高温固相法合成了一种新型近红外荧光粉BaY₂Al₂Ga₂SiO₁₂∶Cr³⁺（BYAGSO∶Cr³⁺）,并系统研究了材料的结构和发光性质。在440nm蓝光激发下,BYAGSO∶Cr³⁺荧光粉的发射光谱在650~850nm范围内呈现锐线和宽带的混合发射,源于Cr³⁺：²E→⁴A₂自旋禁戒跃迁和⁴T₂→⁴A₂自旋允许跃迁发射。该近红外发光表现出可观的量子效率和良好的热稳定性,最优化样品的外量子效率可达30.3%,在200℃时样品的发光强度可保持其在室温时强度的99%。通过将BYAGSO∶Cr³⁺荧光粉与450nm蓝光LED芯片结合,我们封装了一个NIRpc⁃LED器件。该器件在300mA驱动电流下,输出功率为70.83mW;在20mA驱动电流下,光电转换效率为11.20%。研究结果表明,BYAGSO∶Cr³⁺在NIRpc⁃LED领域具有良好的应用前景。     

Nondestructive discrimination of internal defects in jujube (Huizao) of Xinjiang based on visible and near-infrared spectroscopy

Abstract

During harvest and transport, defects are most likely to affect the interior of jujubes and thus shorten their storage period. This study applied visible and near-infrared transmission spectroscopy to detect such internal defects. Spectra were acquired on the equator area at 0, 90, 180, and 270 degrees of each sample, and a model was constructed to obtain three-dimensional damage and defect detection model. The first derivative, multiplicative scatter correction, standard normal variate, and median filtering were used for preprocessing. Modeling by mean spectra achieved a better effect than using unidirectional spectra. Then, naive Bayes classifier and support vector machine were employed for the model establishment at 600–950?nm and 680–950?nm bands, respectively, using mean spectra. Median filtering effectively improved the signal to noise ratio and the discrimination accuracy of the support vector machine model at 600–950?nm reached 96.77%, which was the best value among all models. This result indicates that the support vector machine model was the optimum model and 600–950?nm was a suitable data range for the detection of internal defects. This research confirms the feasibility of implementing visible and near-infrared spectroscopy for the detection of internal defects in jujubes.     

Rapid nondestructive evaluation of duck meat pH and color using visible and near-infrared reflectance spectroscopy

In the present study, visible and near-infrared reflectance spectroscopy were applied to predict quality attributes of duck breast meat. The real color (expressed as lightness, redness, and yellowness) and pH of duck samples were recorded using traditional contact methods and then modeled with their corresponding spectral data by partial least square regression. Before the establishment of prediction models, three different spectral preprocessing methods including first derivative, standard normalized variate, and Savitzky–Golay smoothing were used. Compared to the models obtained from original spectral data set, the predictive capabilities of models based on the spectra after preprocessing were improved effectively. As a result, the determination coefficient of calibration set and prediction set of the best models for lightness, redness, yellowness, and pH parameters were 0.96 and 0.85, 0.94 and 0.94, 0.96 and 0.94, 0.81 and 0.76, respectively. Results demonstrated that visible and near-infrared spectroscopy can become a useful tool for rapid prediction of duck color and pH quality attributes.     

可见/近红外实时成像光谱仪控制系统设计

针对高光谱成像需求,设计了一套可见/近红外实时成像光谱仪.光谱仪基于声光可调谐滤波器(Acousto-Optic Tunable Filter,AOTF)分光器件进行设计,光谱带宽为1.3μm,其中可见光相机工作在400～1000 nm波段,近红外相机工作在1000～1700 nm波段.光谱仪控制系统以现场可编程门阵列...     

Hyperspectral estimation of soil organic matter based on different spectral preprocessing techniques

In this study, the potentiality of visible and near-infrared reflectance spectroscopy to estimate soil organic matter was assessed. Six preprocessing methods were implemented to process the original spectra. The partial least-squares regression approach was also applied to construct predictive models and evaluate the optimal spectral preprocessing method. The significant wavelengths of soil organic matter were determined by using the correlation analysis and the partial least-squares regression analysis. The results were: (i) visible and near-infrared reflectance spectroscopy was proved to be an ideal approach in the soil organic matter estimation; (ii) different preprocessed spectra could improve their correlation with soil organic matter; the combination of first-order derivative and Savitzky–Golay smoothing method outperformed other preprocessing methods; (iii) the soil organic matter predictive models based on spectra processed by derivatives and Savitzky–Golay smoothing together presented a satisfactory accuracy, yielding the determination coefficient and root mean square error values of 0.986 and 0.077, respectively, for first-order derivative; and 0.973 and 0.105, respectively, for second-order derivative. The combination of first-order derivative and Savitzky–Golay smoothing was ultimately recommended the preferable preprocessing method; and (iv) the wavelengths of 417, 1853, 1000, and 2412?nm were determined as the significant wavelengths associated with soil organic matter. The study will provide a reference for the site specific management of agricultural inputs by using the visible and near-infrared reflectance spectroscopy technology.     

便携式中波近红外光谱仪在线无损检测生鲜猪肉胆固醇

应用便携式近红外光谱仪采集320份生鲜猪肉在近红外光谱中波区的光谱信息,采用不同优化方法建立猪肉胆固醇预测模型,并对异常样品的剔除及组合预处理方法对模型性能的改善进行了分析。研究表明：通过对异常值的二次剔除,并使用SG一阶导数(savitzky-golay first derivative, SG 1stD)、SG平滑(savitzky-golay smoothing, SGS)和正交信号校正(OSC)的组合预处理方法,可获得最佳生鲜猪肉胆固醇预测模型,其参数如下：校正集相关系数(R_c)=0.9137,校正标准差(standard error of calibration, SEC)=2.5607,验证集相关系数(R_p)=0.656 7,预测标准差(standard error of prediction, SEP)=4.985 5,主因子数(principal factor, PF)=4,范围误差比(ratio of performance to standard deviation, RPD)=2.5032,相对预测标准差(relative standard error of prediction, RSEP)=8.625 4%,SEP/SEC=1.946 8,说明模型在近红外光谱中波区对猪肉胆固醇的分辨能力和预测准确度较好,通过向校正集中补充代表性样品可使模型稳健性进一步改善。对检验集样品预测值(prediction value, PV)与参比值(reference value, RV)的t检验显示二者之间无显著性差异(p＞0.05),检验集样品总体预测准确率为62.5%,其中50～70 mg·(100 g)-1区段的局部预测准确率达到91.7%,可以用于生鲜猪肉胆固醇浓度的在线快速初步定量分析。该研究将便携式近红外光谱用于在近红外中波区对生鲜猪肉及肉制品中胆固醇浓度的分析和检测,通过进一步的研究和改进,可将其应用于产品的原料分级、品质和过程控制及市售产品的抽检等。