肉苁蓉保健食品原料的质量研究

目的研究保健食品原料肉苁蓉的质量情况。方法依据2015年版《中国药典》和食品安全国家标准对6批荒漠肉苁蓉和6批管花肉苁蓉的标志性成分、外源性污染物等理化指标项目进行检测。结果荒漠肉苁蓉和管花肉苁蓉标志性成分相似,含量存在差异,外源性污染物检测均符合要求。结论采集的6批荒漠肉苁蓉和6批管花肉苁蓉作为保健食品原料均符合2015年版《中国药典》和GB 16740-2014《食品安全国家标准保健食品》的要求。     

Proximate composition and fatty acid profile of three different fresh and dried commercial sea cucumbers from Turkey

Proximate composition and fatty acid (FA) profile of three commercial sea cucumbers; Holothuria tubulosa, Holothuria polii and Holothuria mammata caught from Aegean Sea of Turkey were analysed. The effects of regional variation and drying process on FA composition were also investigated. Moisture, protein, fat and ash contents of the species ranged between 81.24% and 85.24%, 7.88% and 8.82%, 0.09% and 0.18%, and 5.13% and 7.85%, respectively, with a significant changes among species (P < 0.05) with some exceptions. Although FA values varied significantly (P < 0.05) among species and regions, the changes for most FA types representing the same region for different species were not significantly different. Total polyunsaturated fatty acids (PUFA) were found to be higher than total saturated (SFA) and monosaturated FAs (MUFA) accounting for 53.0–62.12% for ∑PUFA, 13.28–16.41% for ∑MUFA and 13.99–19.21% for ∑SFA. While some individual SFAs and PUFAs decreased after drying process, various individual FAs of MUFA increased in their amounts (P < 0.05). Among PUFAs, the highest mean value of eicosapentaenoic acid and docosahexaenoic acid for all regions were determined for H. polii as 7.25% and H. tubulosa as 12.37% in fresh samples, respectively. This study represents new information relating to FA contents and drying effect on FA profile for these species.     

响应面法优化管萼山豆根总生物碱提取工艺

采用微波控温辅助超声波的方法提取管萼山豆根茎中总生物碱,通过单因素法和响应面法优化了提取工艺,考察了管萼山豆根茎中总生物碱对1, 1-二苯基-2-三硝基苯肼自由基(DPPH·)、2, 2-联氮-二-3-乙基-苯并噻唑-6-磺酸自由基(ABTS·)和超氧阴离子自由基(O2?·)的清除作用。结果表明,模型优化并修正的最佳工艺条件为:提取时间37.8 min,微波加热温度64.5℃,超声功率575 W,乙醇体积分数95%,在该条件下总生物碱得率为0.5418%,高于不采用微波超声辅助的0.2436%、超声辅助的0.4538%、乙醇回流的0.3884%。管萼山豆根茎中总生物碱在DPPH·、ABTS·、邻苯三酚浓度分别为0.1、7.0、8.0 mmol/L条件下,其半数抑制质量浓度(IC50)分别为0.891、0.552和0.390 g/L。     

玛咖片缓解体力疲劳作用的研究

目的研究以玛咖、管花肉苁蓉为主要原料的保健食品对缓解体力疲劳的作用。方法将SPF级ICR雄性小鼠按体重随机分组,每天摄入成品推荐摄入量的5、10、30倍作为低、中、高剂量,即0.25、0.50、1.50 g/(kg·bw),设蒸馏水为阴性对照组,灌胃给予受试物连续30d后,测定小鼠尾根部负重的游泳时间,于不同实验组分别检测小鼠运动后血清尿素含量、肝糖原、运动前后的血乳酸含量。结果连续灌胃玛咖片30d后,高剂量组1.50g/(kg·bw)能明显延长小鼠尾部负重游泳时间,显著降低小鼠游泳后血清尿素含量,以及小鼠游泳后血乳酸含量,并且与阴性对照组相比具有显著性差异(P0.05)。结论玛咖片作为保健食品具有缓解体力疲劳功能。     

顶空固相微萃取-气相色谱/质谱法分析野藿香不同部位挥发油化学成分

采用顶空固相微萃取法(headspace solid-phase micro-extraction,HS-SPME)提取野藿香中不同部位的挥发油,通过气相色谱-质谱法与Kovats色谱保留指数相结合进行定性定量分析,以峰面积归一化法计算各组分的相对含量。结果表明:在野藿香的茎、叶和花3个部位中分别鉴定出74、105种和85种化合物,分别占挥发油总峰面积的97.34%、98.80%和97.96%,其中在茎中主要成分是乙酸橙花酯(5.63%)、β-石竹烯(17.32%)、α-佛手柑油烯(7.56%)、反式-β-金合欢烯(10.2%)和氧化石竹烯(5.21%);在叶中主要是Z-柠檬醛(11.64%)、香叶醛(16.35%)、乙酸橙花酯(8.44%)、β-石竹烯(13.08%)、反式-β-金合欢烯(10.88%)和氧化石竹烯(8.34%);而在花中主要是Z-柠檬醛(21.49%)、香叶醛(27.5%)、乙酸橙花酯(9.99%)、β-石竹烯(5.47%)和反式-β-金合欢烯(10.11%),且相对含量有显著差异。     

大孔树脂对管花肉苁蓉中苯乙醇苷的吸附分离特性研究

选用7种大孔吸附树脂，比较其对管花肉苁蓉中苯乙醇苷的吸附量和解吸率，通过筛选确定AB-8吸附树脂的吸附效果较理想，吸附量剃解析率分别为57．12mg／g和90．32％。通过进一步对AB-8吸附树脂的吸附性能研究，确定了试样溶液pH=3．43，上样浓度为3．6～6．0mg／ml，控制上样流速为1．5BV／h时，AB-8树脂对苯乙醇苷的吸附量较大。当采用40％乙醇水溶液作为树脂的洗脱液时，解析率达到94．15％，洗脱峰集中，无明显拖尾现象。     

深山含笑和乐昌含笑叶片挥发油的成分分析及性能测定

采用超临界二氧化碳萃取法提取深山含笑和乐昌含笑叶片挥发油,应用GC-MS技术测定挥发油的成分并对其DPPH自由基清除能力和还原能力等抗氧化指标进行测定.结果表明:深山含笑萜类为35.35%,酯类为15.12%,醇类为9.09%,其余37.02%为萜烯类含氧衍生物.乐昌含笑萜类为28.49%,酯类为11.12%,醇类为1.94%,其余54.55%为萜烯类含氧衍生物.两种含笑叶片挥发油质量浓度为50mg/L时DPPH自由基清除能力达到饱和,且挥发油质量浓度越大还原能力越强.通过与常用抗氧化剂进行比较,发现两种含笑叶片挥发油的抗氧化能力均强于植酸PA、没食子酸丙酯PG和抗坏血酸Vc.     

Biochemistry Department,University of Oxford

Howard Watson describes how Hawkins\Brown's new Biochemistry Department for the University of Oxford represents an innovative new model for scientific research institutes. Spatially, its interior not only positively embraces and encourages social interaction and the fertilisation of ideas, but also brings art, architecture and science together under a single roof. Copyright © 2009 John Wiley & Sons, Ltd.     

栽培管花肉苁蓉中苯乙醇苷的乙醇提取工艺

探索了用乙醇回流法从栽培管花肉苁蓉中提取苯乙醇苷类物质的工艺流程.通过单因素和响应面实验,对影响苯乙醇苷提取的因素进行了探讨和研究,运用响应面分析法确定了提取工艺的最优化条件：提取温度为60 ℃,料液比（g：mL）为1：12.8,提取时间为3.75 h,乙醇体积分数为68.2 %,提取次数2次.在此条件下苯乙醇苷的得率为5.76 %.     

Optimal reduction of noise in image processing using collaborative inpainting filtering with Pillar K-Mean clustering

Digital image processing is a mechanism for analysing and modifying the image in order to improve the quality and also to manage the unwanted involvement of noises. In image processing, noise is characterized as an unwanted disturbance which occurs while capturing the actual image thus affecting the quality of the image. Hence, noise formation is considered as a perilous issue and the reduction of noise is considered as an awkward process. Nowadays, almost in all fields of science and technology, digital image processing is increasing rapidly, so there arises the need for de-noising to cure the noised image. The main objective of this paper is to overcome the issue of noise and also to increase the quality and pixel value of the image. An advanced methodology known as collaborative filtering and Pillar K-Mean clustering is discussed in this paper to overcome the abovementioned problem. Initially, distinct pure images are taken as the dataset and three types of noises are added to the corresponding image to make it as a noised one. Hence, the unspecified noise is resolved on the basis of a hybrid combination of algorithms of collaborative filtering with the image inpainting method. Sequentially, the low-density noises, such as random noise and poison noise, are recovered by the implementation of collaborative filtering, and the high-density salt and pepper noise are recovered by the image inpainting method. Based on the GLCM (Grey Level Co-occurrence Matrix) feature, the normal image and the noised image are used for the clustering process. Then the de-noised image is evaluated to find the efficiency on the basis of few parameters such as SNR (Signal to Noise Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SSI (Structural Similarity Index). Accordingly, the evaluated images are further withstood for clustering to differentiate the noises by applying the proposed clustering methodology. Then the evaluated images are verified on the basis of a few parameters such as Silhouette Width, Davies–Bouldin Index and Dunn Index. The proposed methodology is run on the platform of Mat Lab. Finally, the proposed methodology is considered as an efficient method for settling the issue in digital image de-noising.