火焰原子吸收法测定小麦品种籽粒中铁锰锌含量

用火焰原子吸收分光光度法,测定6种不同类型的冬小麦品种籽粒中铁、锰、锌微量元素的含量。结果表明铁、锰、锌的回收率分别为10309%,10290%和10119%,相对标准偏差(RSD)分别在394%,167%和269%以内,并以GBW08503小麦粉标准样品进行比较测定,结果满意。该方法简便、快速、准确。测定结果为研究小麦品种籽粒中微量元素的含量与小麦品质的关系提供依据。     

硫酸高铈铵分光光度法测定葡萄籽提取物中原花青素

建立了葡萄籽提取物中原花青素测定的新方法 硫酸高铈铵分光光度法。它是基于原花青素与Ce4 在强酸性介质中反应生成无色的Ce3 ,通过测定黄色高铈盐的吸光度,间接测定原花青素,Ce4 在319nm波长处具有最大吸收。该方法的线性范围为0.12～10μg mL,RSD为0.98%～1.1%,回收率为97.2%～102.8%,相关系数r=0.9992,检出限为0.04μg mL。     

Fatty acid profile of <Emphasis Type="Italic">Trichosanthes kirilowii</Emphasis> Maxim. seed oil

Fatty acid profile data for refined cold-pressed Trichosanthes kirilowii Maxim. seed oil, in comparison to other commercially available oils — olive, rapeseed and camellia, are presented. Trichosanthes kirilowii Maxim. seed oil has high oleic and linoleic acid content and high polyunsaturated acid content. Squalene elutes as a distinct peak in the GC chromatograph. For a positive identification, MS detection was used. In the samples analyzed, squalene occurred in the range of 57.4–68.2 mg g⁻¹.     

Lipids and lipophilic components from seeds of some fruit plants

Lipids and lipophilic components from seeds of grape seeds and apricot, almond, and peach pits were studied. The compositions of fatty acids and unsaponified oil compounds were established. Translated from Khimiya Prirodnykh Soedinenii, No. 3, pp. 269–271, May–June, 2009.     

薏仁种类的近红外光谱技术快速鉴别

薏仁是一种药食两用资源,对其品质快速鉴别的需求也越来越多,近红外光谱技术(near infrared spectroscopy,NIRS)作为一种快速、 无损且环保的方法正适合这一需求。 以不同产地和品种薏仁的近红外光谱为基础,结合化学计量学方法对薏仁种类进行鉴别。 对原光谱用无监督学习算法主成分分析(principal component analysis,PCA)和有监督学习算法学习向量量化(learning vector quantization,LVQ)神经网络、 支持向量机(support vector machine,SVM)进行定性判别分析。 由于不同地区和不同品种的薏仁营养物质组成复杂且含量相近,所选两类薏仁的特征变量很相似,因而PCA得分图重叠严重,很难区分;而LVQ神经网络和SVM都能得到满意结果,LVQ神经网络的预测正确率为90.91%,SVM在经过惩罚参数和核函数参数优选后,分类准确率能达到100%。 结果表明：近红外光谱技术结合化学计量学方法可作为一种快速、 无损、 可靠的方法用于薏仁种类的鉴别,并为市场规范提供技术参考。     

基于高光谱技术的玉米种子可视化鉴别研究

种子纯度是衡量种子品质的重要指标。提出一种基于近红外(874～1 734 nm)高光谱技术实现玉米种子可视化鉴别的方法。采集4个品种共384个玉米种子样本的高光谱图像数据,随机选择288个样本作为建模集,剩余96个样本作为预测集。对玉米种子光谱曲线进行分析后,通过连续投影算法（SPA）选取7个特征波段作为输入,结合偏最小二乘法判别分析(PLS-DA)模型,对预测集进行预测,获得较好的分类效果,其中R_C=0.917 7,RMSECV=0.444 2; R_CV=0.911 5,RMSECV=0.459 9,建模集和预测集的总体鉴别率分别为78.5%和70.8%。通过图像处理技术提取高光谱图像中每个玉米颗粒的平均光谱数据,输入建立的SPA-PLS-DA模型,在计算生成的鉴别图中以不同颜色标识不同类别,实现了混杂玉米种子样本的可视化鉴别。对3份不同组成的混杂种子样本进行鉴别,达到了较好的可视化效果。结果表明,通过可视化鉴别技术,可以直观方便地观察混杂种子样本中不同品种种子的分布和数量,为农业生产中种子的纯度鉴别和筛选提供了帮助。     

One-pot synthesis of gold nanorods via autocatalytic growth of sonochemically formed gold seeds: The effect of irradiation time on the formation of seeds and nanorods

A one-pot synthesis for gold nanorods was developed using sonochemical reduction of gold ions in an aqueous solution in the presence of cetyltrimethylammonium bromide, silver nitrate, and ascorbic acid, where we focused on the autocatalytic growth of gold seeds formed by ultrasonic irradiation for short times. In growth experiments with these sonochemically formed gold seeds, sigmoidal shape growth curves were observed, and the induction period before growth began was longer for shorter irradiation times. This result indicated that the number of sonochemically formed gold seeds increased with increasing irradiation time. The average aspect ratio of the gold nanorods produced changed from 2.0 at an irradiation time of 0.5 min to 3.6 at 15 min. The gold nanorods produced were longer and wider when the irradiation time was shorter.     

高压对小麦种子发芽和幼苗生长的影响初探

 以豫麦18号小麦种子为材料，初步探索了不同压力和时间对不同处理的小麦种子发芽的影响，并对95 MPa压力、不同时间处理下发芽种子幼苗的生长情况进行测定。结果表明：在50~95 MPa压力下，直接真空包装的小麦种子均丧失发芽能力；在不同压力和时间下处理直接加水包装的小麦种子，种子发芽受到显著抑制；将水浸6 h再加水包装的小麦种子在95 MPa压力下处理4 h，能抑制小麦种子的发芽势和发芽率，但抑制强度较弱，也表明水可以有效降低高压对小麦种子的损伤。在95 MPa压力下、不同时间处理的直接加水包装的小麦种子幼苗的平均苗高和幼苗鲜重均大于对照。     

Ultrasonication as an innovative approach to tailor the apple seed proteins into nanosize: Effect on protein structural and functional properties

In this study, protein was extracted from the apple seed flour using alkali-acid precipitation method. The main objective of this study was to evaluate the impact of ultrasonication on structural and techno-functional properties of apple seed protein. Both native (N-protein) and ultra-sonicated protein (US-protein) were characterized for size, zeta potential, structure, protein pattern, crystallinity, thermal stability and functional properties. The results revealed that the hydrodynamic diameter of N-protein and US-protein was 1.2 µm and 484 nm while zeta potential was −11 and −19 mV, respectively. Fourier transform infrared-spectroscopy and X-ray diffraction analysis showed change in the conformational characteristics and functional groups of proteins after nano-reduction. SEM revealed change in the surface morphology of protein molecule upon ultrasonication. Differential scanning calorimetry showed decreased denaturation temperature for US-protein compared to N-protein . SDS-PAGE depicted no change in protein pattern upon ultrasonication. Ultrasonicated protein exhibited increased functional properties like emulsification, foaming, hydrophobicity and oil absorbing properties and hence can be efficiently used as functional ingredient in food and nutraceutical industry.     

Inclusion of interactions in mathematical modelling of implant assisted magnetic drug targeting

Drug delivery technologies are an important area within biomedicine. Targeted drug delivery aims to reduce the undesired side effects of drug usage by directing or capturing the active agents near a desired site within the body. This is particularly beneficial in, for instance, cancer chemotherapy, where the side effects of general (systemic) drug administration can be severe.One approach to targeted drug delivery uses magnetic nanoparticles as the constituents of carriers for the desired active agent. Once injected into the body, the behaviour of these magnetic carriers can be influenced and controlled by magnetic fields. In implant assisted magnetic drug targeting systems a magnetic implant, typically a stent, wire or spherical seed can be used to target sites deep within the body as the implant acts as a focus for the resulting magnetic force. This can be easily understood as the force depends on the gradient of the magnetic field and the gradient near the implant is large.In designing such a system many factors need to be considered including physical factors such as the size and nature of the implants and carriers, and the fields required. Moreover, the range of applicability of these systems in terms of the regions of the vasculature system, from low blood velocity environments, such as capillary beds to higher velocity arteries, must be considered. Furthermore, assessment criteria for these systems are needed. Mathematical modelling and simulation has a valuable role to play in informing in vitro and in vivo experiments, leading to practical system design.Specifically, the implant assisted magnetic drug targeting systems of Avilés, Ebner and Ritter are considered within this review, and two dimensional mathematical modelling is performed using the open source C++ finite volume library OpenFOAM. In the first system treated, a large ferromagnetic particle is implanted into a capillary bed as a seed to aid collection of single domain nanoparticles (radius 20-100 nm). The Langevin function is used to calculate the magnetic moment of the particles, and the model is further adapted to treat the agglomeration of particles known to occur in these systems. This agglomeration can be attributed to interparticle interactions and here the magnetic dipole-dipole and hydrodynamic interactions for two mutually interacting nanoparticles are modelled, following Mikkelsen et al. who treated two particle interactions in microfluidic systems, with low magnetic field (0.05 T). The resulting predicted performance is found to both increase and decrease significantly depending on initial positions of the particles. Secondly, a ferromagnetic, coiled wire stent is implanted in a large arterial vessel. The magnetic dipole-dipole and hydrodynamic interactions for multiple particles are included. Different initial positions are considered and the system performance is assessed. Inclusion of these interactions yields predictions that are in closer agreement with the experimental results of Avilés et al. We conclude that the discrepancies between the non interacting theoretical predictions and the corresponding experimental results can (as suggested by Avilés et al.) be largely attributed to interparticle interactions and the consequent agglomeration.