用于疾病诊断的Gd~Ⅲ/量子点多模态成像探针的构建

结合核磁共振成像(MRI)和荧光成像技术,以钆离子、近红外低毒量子点、二氧化硅和聚丙烯酸(PAA)等为原料,采用一系列纳米载体自组装技术,构建出MRI弛豫率/荧光效率高和生物相容性好的GdⅢ/量子点多模态纳米探针.结果表明,与未螯合GdⅢ的量子点纳米探针相比,GdⅢ/量子点多模态纳米探针具有更高的弛豫率;t1-加权MRI成像也证实了GdⅢ/量子点多模态纳米探针具有很好的阳性造影功效.     

黄铁矿在维持高放废物处置库近场还原性环境中的作用

研究了黄铁矿受60Coγ射线辐照后对地下水还原性环境的影响.辐照剂量率为200Gy·min-1,总吸收剂量为5×106Gy.通过分析辐照前后以及与地下水共同辐照后黄铁矿的结构和组成,发现60Co的γ射线可使地下水发生辐解反应,从而将黄铁矿氧化;但无论是直接辐照还是与地下水共同辐照,60Co的γ辐射均不改变黄铁矿的结构.黄铁矿可以消耗地下水辐解产生的氧化性产物,有利于维持处置库近场的还原性环境,提高处置库的安全性.     

蜂蜜中果糖和葡萄糖近红外检测的差异性分析及优化研究

采集了来自全国20种单植物源和其它多植物源的101份的蜂蜜样品,分别运用傅立叶型近红外光谱仪采用光纤透反射(800～2500nm,2mm光程)和透射(800～1370nm,20mm光程)采集方式获得近红外光谱,来预测蜂蜜中结构和含量都很相近的果糖和葡萄糖含量。结果发现,两种测量方式下果糖、葡萄糖的预测准确度存在着较大的差异。为了分析这种差异产生的原因,采用支持向量机分析其非线性信息,采用遗传算法分析其特征波长,结果表明:这种差异主要来自两种糖分特征波长分布不同所导致。通过对两种糖分的检测方案进行优化,得出在利用近红外光谱技术检测蜂蜜中葡萄糖成分含量时应尽量采集短波区、长光程的光谱,或者对全谱区、短光程的光谱,进行特征波长的提取,避开水分的干扰,从而提高其预测精度;而对于果糖,则应尽量采集全谱区、短光程的光谱;采用常用线性定量建模方法PLSR就可以得到很好的预测模型,非线性的支持向量机模型未能明显提升模型性能。     

汽油族组成的近红外光谱快速测定

以荧光指示剂吸附色谱法（FIA）测定汽油族组成结果为基础，采用近红外光谱和化学计量学方法建立了快速、准确测定催化裂化馏出口汽油族组成（饱和烃、烯烃和芳烃）的分析模型；试验表明，该法分析速度快、重复性好、成本低，特别适用于生产中间控制分析。     

基于过程分析技术和设计空间的金银花醇沉加醇过程终点检测

建立了一种新的基于过程分析技术(PAT)和质量源于设计(QbD)设计空间的中药制药过程终点分析与控制方法.以近红外(NIR)光谱技术为PAT工具, 采集正常操作条件下制药过程的多批次NIR光谱; 采用主成分分析结合移动块相对标准偏差(PCA-MBRSD)法, 确定每一批次过程的理想终点样本(DEPs), 由多批DEPs的光谱信息构成过程终点设计空间; 在过程终点设计空间确定的范围内, 建立多变量统计过程控制(MSPC)模型, 利用多变量Hotelling T²和SPE控制图对过程终点进行判断.应用上述方法, 进行了金银花醇沉加醇过程终点检测研究, 结果表明该方法灵敏、准确, 适宜于中药制药过程终点检测.     

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated.Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together).In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression.Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression.Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.     

近红外光谱快速测定红花逆流提取过程中羟基红花黄色素A的含量

采用近红外光谱(NIRS)透射法对红花罐组式逆流提取过程中羟基红花黄色素A(Hydroxysafflor yellow A,HSYA)的含量进行快速无损的测定.在红花逆流提取过程中,以高效液相色谱法(HPLC)为对照分析方法,测定提取液中羟基红花黄色素A的含量,运用偏最小二乘(PLS)法建立NIR光谱与羟基红花黄色素A的HPLC分析值之间多元校正模型,并对逆流提取过程的未知样本进行含量预测.校正模型相关系数达到0.982,预测相关系数达到0.965,RMSEC和RMSEP分别为0.053和0.075,RSEC和RSEP分别为3.96%和5.25%.结果表明,NIRS可以作为一种准确、快速、无损的检测方法用于检测中药逆流提取过程有效成分含量变化规律.     

Use of NIRS technology with a remote reflectance fibre-optic probe for predicting major components in bee pollen

In the present work, we study the use of near infra-red spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for determination of the major components in bee pollen. The method allows immediate control of the bee pollen without prior sample treatment or destruction through direct application of the fibre-optic probe to the sample.The regression method employed was modified partial least squares (MPLS). The calibration results obtained using 45 samples of bee pollen allowed the measurement of protein, moisture, ash, reducing sugars, and pH with multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEPC) of 0.91, 0.56% for protein, of 0.78 and 0.49% for moisture; 0.92 and 0.049% for ash; 0.81 and 1.32 g of glucose/100 g of bee pollen; 0.84 and 0.15 for pH, respectively.The prediction capacity of the pattern was checked by applying it to samples of unknown pollen in external validation.     

Multiplexing fibre optic near infrared (NIR) spectroscopy as an emerging technology to monitor industrial bioprocesses

The application of near infrared spectroscopy in bioprocessing has been limited by its dependence on calibrations derived from single bioreactor at a given time. Here, we propose a multiplexed calibration technique which allows calibrations to be built from multiple bioreactors run in parallel. This gives the flexibility to monitor multiple vessels and facilitates calibration model transfer between bioreactors. Models have been developed for the two key analytes: glucose and lactate using Chinese hamster ovary (CHO) cell lines and using analyte specific information obtained from the feasibility studies. We observe slight model degradation for the multiplexed models in comparison to the conventional (single probe) models, decrease in r² values from 89.4% to 88% for glucose whereas for lactate from 92% to 91.8% and a simultaneous increase in the number of factors as the model incorporates the inter-probe variability, nevertheless the models were fit for purpose. The results of this particular application of implementing multiplexed-NIRS to monitor multiple bioreactor vessels are very encouraging, as successful models have been built on-line and validated externally, which proffers the prospect of reducing timelines in monitoring the vessels considerably, and in turn, providing improved control.     

Analysis of berberine and total alkaloid content in cortex phellodendri by near infrared spectroscopy (NIRS) compared with high-performance liquid chromatography coupled with ultra-visible spectrometric detection

This paper developed a rapid method using near infrared spectroscopy (NIRS) to differentiate two species of Cortex Phellodendri (CP), Cortex Phellodendri Chinensis (PCS) and Cortex Phellodendri Amurensis (PAR), and to predict quantitatively the content of berberine and total alkaloid content in all Cortex Phellodendri samples. Three alkaloids, berberine, jatrorrhizine and palmatine were analyzed simultaneously with a Thermo ODS Hypersil column by gradient elution with a new mobile phase under high-performance liquid chromatography-diode array detection (HPLC-DAD). Berberine content determined by HPLC-DAD was exploited as a critical parameter for successful discrimination between them. Multiplicative scatter correction (MSC), second derivative and Savitsky-Golay (S.G.) were utilized together to correct the scattering effect and eliminate the baseline shift in all near infrared diffuse reflectance spectra as well as to enhance spectral features in order to give a better correlation with the results obtained by HPLC-DAD. With the use of principal component analysis (PCA), samples datasets were separated successfully into two different clusters corresponding to two species. Furthermore, a partial least squares (PLS) regression method was built on the correlation model. The results showed that the correlation coefficients of the prediction models were R = 0.996 for the berberine and R = 0.994 for total alkaloid content. The influences of water absorption bands present in the NIR spectra on the models were also investigated in order to explore the practicability of NIRS in routine use. The outcome showed that NIRS possibly acts as routine screening in the quality control of Chinese herbal medicine.