烟草脱外壁花粉与完整花粉电激导入效果的比较研究

利用膜不通透的荧光染料钙黄素作为指示剂，比较了烟草脱外壁花粉与完整的未萌发花粉和萌发花粉电激导入的效果．研究了电激过程中的电场强度、脉冲持续时间、电激液的介质成分和浓度对三者的导入率及电激后花粉萌发率的影响．结果表明，从未萌发花粉、萌发花粉到脱外壁花粉，导入率逐渐升高，证实了脱外壁花粉由于摆脱了外壁的阻碍而有利于外源物质的电激导入．但电激后花粉萌发率的变化规律相反．荧光指示剂的应用便于优化选择适宜电激参数，为外源基因的导入实验提供了参考依据．     

A projected subgradient method for solving generalized mixed variational inequalities

We consider the projected subgradient method for solving generalized mixed variational inequalities. In each step, we choose an ε_k-subgradient u^k of the function f and w^k in a set-valued mapping T, followed by an orthogonal projection onto the feasible set. We prove that the sequence is weakly convergent.     

Study of a new derivatizing reagent that improves the analysis of amino acids by HPLC with fluorescence detection: application to hydrolyzed rape bee pollen

A simple and sensitive method for evaluating the chemical compositions of protein amino acids, including cystine (Cys)₂ and tryptophane (Try) has been developed, based on the use of a sensitive labeling reagent 2-(11H-benzo[α]-carbazol-11-yl) ethyl chloroformate (BCEC–Cl) along with fluorescence detection. The chromophore of the 1,2-benzo-3,4-dihydrocarbazole-ethyl chloroformate (BCEOC-Cl) molecule was replaced with the 2-(11H-benzo[α]-carbazol-11-yl) ethyl functional group, yielding the sensitive fluorescence molecule BCEC–Cl. The new reagent BCEC–Cl could then be substituted for labeling reagents commonly used in amino acid derivatization. The BCEC–amino acid derivatives exhibited very high detection sensitivities, particularly in the cases of (Cys)₂ and Try, which cannot be determined using traditional labeling reagents such as 9-fluorenyl methylchloroformate (FMOC-Cl) and ortho-phthaldialdehyde (OPA). The fluorescence detection intensities for the BCEC derivatives were compared to those obtained when using FMOC-Cl and BCEOC-Cl as labeling reagents. The ratios I _BCEC/I _BCEOC = 1.17–3.57, I _BCEC/I _FMOC = 1.13–8.21, and UV_BCEC/UV_BCEOC = 1.67–4.90 (where I is the fluorescence intensity and UV is the ultraviolet absorbance). Derivative separation was optimized on a Hypersil BDS C₁₈ column. The detection limits calculated from 1.0 pmol injections, at a signal-to-noise ratio of 3, ranged from 7.2 fmol for Try to 8.4 fmol for (Cys)₂. Excellent linear responses were observed, with coefficients of >0.9994. When coupled with high-performance liquid chromatography, the method established here allowed the development of a highly sensitive and specific method for the quantitative analysis of trace levels of amino acids including (Cys)₂ and Try from bee-collected pollen (bee pollen) samples.     

蜂王信息素的改进合成

早在50年代,Butler等^[1]发现蜂王的上颚腺能分泌一种影响工蜂活动的物质,并提出了蜂群是靠蜂王分泌的该种化学物质传递各种个体间的信息的论断,Barbier等^[2]测定其结构为9-氧代-2E-癸烯酸1(蜂王物质),并首次进行了人工合成.     

基于ABC-SV R算法的拉曼光谱检测混合油脂肪酸含量

提出了一种基于激光拉曼光谱和人工蜂群智能优化支持向量回归机(ABC-SVR)算法的快速定量检测三组分混和油中3种脂肪酸含量的方法。该方法针对光谱数据信息与样本之间非线性、高维度的关系,建立了预测精度及建模效率均高于同类对比算法的数学模型,同时避免了气相色谱法、液相色谱法等对混合油脂肪酸含量的检测方式,根据纯种油中3种脂肪酸含量的国际标准,由油品配置体积得到脂肪酸质量,有效降低了检测成本与实验复杂程度,提高了检测工作的实用价值。首先根据一定梯度配置66组混合油检测样品,使用便携式拉曼光谱仪采集样本的拉曼光谱信息,扣除背景噪声;观察多组样本的拉曼光谱图可知,由于官能团浓度的差异,食用油的拉曼特征峰位移基本相同,特征峰的峰值明显不同,因此基于特征峰信息可以区分食用调和油的不同混合物;其次对拉曼光谱做背景扣除、光谱平滑、最大值谱线归一化三步预处理,以降低实验中不可控的外界因素及背景荧光的影响,准确提取光谱特征峰强度信息;然后根据纯种油中3种脂肪酸的国际标准含量,结合国家食品法典委员会标准CODEX STAN210-1999《指定的植物油法典标准》中规定的纯种油密度中值,由油品体积得到脂肪酸质量数;随机选取56组样本数据作为训练集,剩余10组样本数据作为预测集;以训练集光谱特征峰强度和脂肪酸质量分别作为回归模型的输入及输出值,建立SVR和PSO-SVR,ABC-SVR三种混合优化算法对比的定量分析模型,对测试集的3种脂肪酸含量分别进行预测;最后通过均方误差(MSE)、相关系数(r)及建模时间(Elapsed time)分别进行对比,建立数据表对模型精准度进行检验。实验结果表明,通过ABC-SVR定量分析模型效果最佳,3种脂肪酸含量预测值与真实值的均方差分别为0.88×10-4,16×10-4和8×10-4,均低于0.002;相关系数分别为93.43%,99.65%和99.43%,均高于93%;预测时间(Elapsed time)分别为1.26,2.42和2.14 s。因此,所提出的检测方法,具备较高的精确度、较快的建模时间,且在理论上的类似条件下可适用于其他样品检测工作,可为振动光谱学对食用油掺伪分析的进一步工作提供可行的理论依据。     

梨花粉红外波段复折射率测定与分析

花粉是生物气溶胶重要的组成部分,复折射率是研究花粉光学特性以及探测、识别生物气溶胶成分的重要参数。采用压片法对梨花粉2.5~15 μm波段的反射光谱进行了测量,利用Krames-Kronig(K-K)关系计算了复折射率,并就傅里叶红外光谱仪测试压片的入射角和复折射率长波长、短波长区外推两方面对结果作了误差分析。结果表明,测试时18°入射角以及长波长、短波长区外推对梨花粉复折射率的计算结果影响不大,利用反射光谱计算花粉复折射率的方法是可行的。计算得到的复折射率谱对梨花粉光学特性的研究以及生物气溶胶成分的探测、识别有一定的参考价值。     

石墨炉原子吸收光谱法测定保健品松花粉中痕量铅

实验通过采用优级纯以上试剂降低本底,多道程序清洗器皿控制污染,通过控制酸量改善测定铅的信噪比,降低检出限,引入基体改进剂磷酸二氢铵消除基体干扰.优化石墨炉检测条件下,松花粉样品中铅的加入平均回收率达到90％以上.使用标准物质(茶叶GBW10016)对该方法进行了验证,结果表明铅的测定值在标准物质的保证值范围内.该方法能...     

六种蜂花粉的红外光谱三级鉴别研究

采用傅里叶变换红外光谱(FTIR)结合二阶导数谱和热扰动下的二维相关红外光谱技术对6种不同花粉,即杏花花粉、油菜花粉、茶花花粉、西瓜花粉、荷花花粉和虞美人花粉,进行了快速无损的鉴别。结果表明,在一维红外光谱图上,不同花粉的蛋白质、脂肪和糖类物质的特征吸收峰在相对峰强和峰位上均存在一定的差异,在二阶导数谱上差异很明显。而在二维红外谱图上,由于6种花粉的自动峰及相关峰峰簇的位置和数量不同,其差别体现得更为明显和直观。因此,三级红外宏观指纹图谱法是鉴别不同蜂花粉种类的一种有效和快速检测方法。     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.