碳纳米管修饰电极的孔性界面对电分离多巴胺和抗坏血酸的影响

制备了碳纳米管修饰电极(镶嵌、涂层),并研究了电分离多巴胺(DA)和抗坏血酸(AA)的机理.结果表明,该电极的表面有一个多孔性的立体界面层,不仅对DA和AA具有较强的电催化作用,而且二者的氧化峰电位差(ΔE_pa)达250mV以上.用循环伏安法和差示脉冲伏安法研究了影响ΔE_pa的因素.分散剂的种类对ΔE_pa有很大的影响;多层碳纳米管的管径对ΔE_pa的影响较小,碳纳米管的种类(单层、多层、螺旋型多层)则有一定的区别;同一种碳纳米管经不同氧化强度的酸截短后,对ΔE_pa产生了明显影响.这说明修饰电极的界面性质对电分离DA和AA有较大的影响.     

利用傅里叶变换红外光谱法鉴别菊花中硫磺残留

利用傅里叶变换(FTIR)红外光谱法对不同加工方法的祁菊花和大怀菊进行了快速鉴别分析。结果表明,不同加工方法可能会导致菊花内部某些有效成分间相对含量有所变化,某些化学结构或许有所改变,反映在FTIR谱图上就有所差异。经过硫磺熏制后晾干的菊花,在1 600,1 060 cm-1波数附近其吸收峰的形状和强度与其他样品不同,在922,818,777 cm-1三处有固定的特征吸收,而且吸收强度相对较强;不同加工方法和不同品种的菊花在FTIR谱图上都有差异。因此,利用FTIR光谱法不需要对样品进行分离提取,不仅能够快速、准确、方便、直观地鉴别菊花是否有硫残留,同时还能鉴别不同品种的菊花。     

近红外光谱技术在肉品检测中的应用和研究进展

近红外光谱(NIRS)作为新型光学检测技术在食品行业中得到广泛应用。该技术能实现肉品在线、快速、无损检测,是肉和肉制品品质分析的重要技术之一。文章综述了近红外光谱技术在肉类行业中的重要应用以及近年来的研究进展,主要包括蛋白质、脂肪及水分等影响肉类品质的化学组成成分分析,肉品感官品质如嫩度、保水性、肉色及新鲜度等指标的评价以及肉品的产地、品种等方面的鉴定。同时列举了近红外光谱技术在几种常见肉制品品质检测中的应用实例,并针对目前发展趋势展望了该技术的前景：近红外光谱技术在进一步深入研究提高肉品检测精度的基础上,通过与机器视觉技术等新型无损检测技术的融合以实现全面评价肉类品质的目标。     

PLS-DA法判别分析木材生物腐朽的研究

利用近红外光谱结合PLS-DA判别分析方法可用于食品、药品和农产品等的快速识别或检测,因此,研究利用近红外光谱结合PLS-DA方法来检测木材的生物腐朽。研究结果表明：应用近红外光谱结合PLS-DA方法对培训集样本建立的判别模型,其校正及验证结果与实际分类变量的相关系数均超过0.94,SEC和SEP都低于0.17; 利用模型对未参与建模的样本进行检测,发现该模型对未腐朽、白腐和褐腐三种类型样本的判别准确率均为100%(偏差均小于0.5); 与SIMCA法相比,PLS-DA法对木材生物腐朽样本的判别准确率更高,说明应用近红外光谱结合PLS-DA方法能快速地检测到木材的生物腐朽,并能准确地判别出木材的生物腐朽类型。     

应用近红外光谱技术快速检别酱油品牌的研究

提出了一种采用近红外光谱快速鉴别酱油品牌的新方法,对不同品牌的酱油建立相应的指纹模型。对市场上8种典型品牌的酱油,通过近红外透射获取光谱曲线,选择了其中噪声较小的7 625～3 684 cm-1共3 942个波段作为建模分析数据。为了减少原始数据量,提高数据处理效率,对原始数据进行了多项式平滑拟合等预处理,采取主成分分析法,得到能反映酱油99.99%光谱信息的8个主成分。由这8个主成分得到的得分图,可以区分其中某几个品牌,但是不能做到区分全部品种,因此选取了人工神经网络进行了进一步信息提取与种类判别。将8个主成分作为人工神经网络的输入,对应的酱油品牌作为输出,通过不断调整参数,建立了最优的BP神经网络。8个品牌共242个样本作为建模学习样本,每个品牌各10个共80个样本作为检验样本。结果表明,在0.98的置信区间里取得了98.75%的识别正确率,为不同等级和品牌的酱油鉴别提供了一种新的方法。     

基于近红外光谱技术和人工神经网络的玉米品种鉴别方法研究

提出了一种采用近红外光谱技术结合人工神经网络对玉米品种进行鉴别的方法。在3 800～10 000 cm-1(波长1 000～2 632 nm)范围内采集四种玉米单粒完整籽粒的近红外漫反射光谱,经Savitky-Golay平滑和多重散 射校正预处理后,对数据进行主成分分析,再结合人工神经网络技术进行品种鉴别。主成分分析表明,前8个主成分的累积贡献率达到99.602%。以前8个主成分作为网络输入,品种类型作为输出,建立三层LMBP神经网络模型。每个品种 各取30粒共120个样本用于建模,10粒共40个样本用于预测。模型对建模集120个样本鉴别率为100%,对预测集40个样本的鉴别率为95%。实验结果说明该方法能快速无损地鉴别玉米品种,为玉米的品种鉴别提供了一种新方法。     

Role and Results of statistical methods in protein fold class prediction

Statistical methods of discrimination and classification are used for the prediction of protein structure from amino acid sequence data. This provides information for the establishment of new paradigms of carcinogenesis modeling on the basis of gene expression. Feed forward neural networks and standard statistical classification procedures are used to classify proteins into fold classes. Logistic regression, additive models, and projection pursuit regression from the family of methods based on a posterior probabilities; linear, quadratic, and a flexible discriminant analysis from the class of methods based on class conditional probabilities, and the nearest-neighbors classification rule are applied to a data set of 268 sequences. From analyzing the prediction error obtained with a test sample (n = 125) and with a cross validation procedure, we conclude that the standard linear discriminant analysis and nearest-neighbor methods are at the same time statistically feasible and potent competitors to the more flexible tools of feed forward neural networks. Further research is needed to explore the gain obtainable from statistical methods by the application to larger sets of protein sequence data and to compare the results with those from biophysical approaches.     

Improving the Performance of All-Optical Wavelength Converters Using Fiber Gratings

We show that spectral filtering using a fiber Bragg grating as an optical discriminator improves the performance of semiconductor optical amplifier (SOA) wavelength converters. This technique increases the operation speed and reduces the data patterning effect of SOAs for non-return-to-zero (NRZ)operation. For return-to-zero (RZ) operation the fiber grating increases extinction ratio and preserves the data polarity. We use this technique to obtain error-free wavelength conversion of NRZ data at 10 Gb s in an SOA with 6 GHz dynamic bandwidth. We also use the SOA fiber grating to convert RZ data at 10 Gb s and show that the converted pulses can propagate error free over 20,000 km in a dispersion-managed system.     

Comparative investigation of the inclusion preferences of optically pure versus racemic TADDOL hosts for pyridine and isomeric methylpyridine guests

Here we consider the effect of the optical purity of TADDOL (α,α,α′,α’-tetraphenyl-2,2-dimethyl-1,3-dioxolane-4,5-dimethanol) on its discriminatory behaviour. We synthesized optically pure and racemic TADDOL, which form complexes with pyridine and the methylpyridines, and recrystallized them from various equimolar mixtures of these guests, and showed that both TADDOLs, surprisingly, discriminate similarly between them. The selectivity of optically active TADDOL was in the order 3-methylpyridine > 4-methylpyridine > pyridine > 2-methylpyridine, while this order was 3-methylpyridine > 4-methylpyridine > 2-methylpyridine > pyridine for the racemate. For many of the experiments, the extent of selectivity displayed by the two hosts was comparable. Single crystal X-ray diffraction experiments showed that preferred guests experienced stronger π-π and, even more so, H-bonding interactions with the host. Thermal analyses confirmed reports that suggested that guests that reside in channels in the host structure form less thermally stable complexes than those where the guests are accommodated in discrete cavities.     

A bi-objective weighted model for improving the discrimination power in MCDEA

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objective multiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach.