大口径毛细管气相色谱法测定油脂、果蔬中20种有机氯农药的残留量

 对水果、蔬菜样品采用混合溶剂提取 ,对油脂样品则采用乙腈分配处理 ,然后运用柱色谱净化方式 ,以HP 1 0 1大口径弹性石英毛细管柱为分离柱 ,以电子捕获检测器检测 ,用气相色谱法测定油脂、水果、蔬菜中 2 0种有机氯农药的残留量。方法的检测限为 1 .0ng/g～ 2 0 .0ng/g ,回收率为 83.2 %～ 1 0 6.8% ,RSD为 2 .0 %～9.5%。     

珠江三角洲蔬菜中的锰

珠江三角洲蔬菜中锰的含量范围为５．０１＊１０＾－６－３４６．８＊１０＾－６,平均６２．９１＊１０＾－６,大多数蔬菜中锰的含量为２０＊１０＾－６－７０＊１０＾－６,叶菜类锰的平均含量为６９．２０＊１０＾－６,头菜类平均为１９．８５＊１０＾－６,果蔬菜类,花菜类平均为３８．９０＊１０＾－６。蔬菜中锰的含量与蔬菜种类和土壤性质有密切关系。     

土壤和灌溉水中邻苯二甲酸酯的气相色谱-质谱测定

采用气相色谱-质谱联用方法分析了赣州市4个典型蔬菜基地的土壤和灌溉水中6种邻苯二甲酸酯(PAEs)的含量.结果表明,邻苯二甲酸二(2-乙基)己酯是土壤和灌溉水中的主要污染物;赣州市蔬菜基地受PAEs的污染很小.     

Preparation and Characterization of Polymeric Nanocapsules Produced by in Situ Polymerization From Nanoemulsions Produced by Direct Emulsification

Polymeric nanoparticles constitute an important drug delivery system with controlled release profile. This article describes a new way to produce polymeric nanocapsules using a vegetable oil nanoemulsion as template. The process occurs in two steps: First, a nanoemulsion was obtained with a low-energy method based on phase inversion emulsification, using 2-ethylhexyl acrylate as lipophilic monomer. The in situ polymerization of the nanoemulsion droplets is induced by the addition of polymerization catalyzers. The mean size of the polymeric nanoparticles was evaluated by photon correlation spectroscopy and atomic force microscopy. Both techniques showed the formation of polymeric nanocapsules with a mean particle size less than 300 nm.     

Development of Vegetable Oil Emulsions with Lamellar Liquid-Crystalline Structures

Emulsions containing vegetable oils and anisotropic phases have especially attractive properties in pharmaceutical technology. They are use as vehicle for different kind of drugs, especially those of topical application. Apart from that, many vegetable oil have pharmacological activity, increasing the necessity for the development of new delivery systems for them. We developed emulsions with vegetable oils at a fixed surfactant ratio and observed the formation of liquid crystalline phases. Nine vegetable oils: Andiroba, Apricot, Avocado, Brazil Nut, Buriti, Cupuassu, Marigold, Passion Fruit and Pequi and mineral oil were tested. Surfactant system was consisted by Steareth-2 and Ceteareth-5. Emulsions were prepared by the emulsion phase inversion (EPI) method, presenting high stability independent on the HLB value. Results indicate that this method could be employed to attain stable emulsions, even if the required HLB value is not known.     

Natural Brazilian Raw Material to Develop O/W Emulsions Containing Lamellar Gel Phase (Development and Analysis of Emulsion with Vegetable Oils)

Oil-in-water emulsions were developed employing the HLB system and emulsion phase inversion (EPI) method. X-ray diffraction revealed that the anisotropic structures around the inner phase globules were lamellar gel network phases. The calculated distances between the lamellae made after preparation and 3 month latter showed that there was no swelling of the lamellar gel network indicating good stability and few changes during storage. The developed emulsions were stable and have potential to be employed for cosmetic and pharmaceutical purposes. The gel phase network and vegetal components seemed to be contributing factors.      

全二维气相色谱-四极杆质谱法检测植物油脂中脂肪酸

建立了植物油脂中31种脂肪酸成分的全二维气相色谱-四极杆质谱(GC×GC-qMS)分析方法。样品经甲酯化衍生后,以DB-1柱(30 m×0.25 mm×0.25 μm)作为一维柱、DB-Wax柱(3.2 m×0.1 mm×0.1 μm)作为二维柱组成柱系统进行分离,在调制周期为3.5 s、四极杆质量扫描范围为m/z 40～350的条件下,植物油脂中31种脂肪酸成分可以在50 min内得到准确和灵敏的检测。将本方法应用于实际样品的分析,灵敏度较传统的气相色谱-质谱法提高了100倍以上,一些植物油中微量的脂肪酸成分也因此被检出。该研究不仅为植物油中脂肪酸成分的分析提供了新的技术手段,同时对于确保食用植物油的质量安全、消除食用植物油的掺假伪劣等均有重要意义。     

Determination of the Polar Compounds in Vegetable Oil by Ultra-Performance Liquid Chromatography–Quadrupole-Time-of-Flight-Mass Spectrometry with Chemometrics

High-throughput ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry was combined with chemometric tools for the rapid determination of polar components in camellia oil, rapeseed oil, and waste cooking oil. The results were analyzed by two unsupervised methods: principal component analysis (one-way ANOVA, p<.05) and volcano plot analysis (p<.05, fold change ≥2) and supervised method: partial least squares discriminant analysis. The results showed that the oils were correctly classified based on their polar components. The first three principal components reflected most detail with a cumulative contribution rate of 84.67% using principal component analysis. The prediction accuracy was close to 100% using partial least squares discriminant analysis. Nineteen components were screened by principal component analysis; twelve were preliminary identified as palmitamide, phytosphingosine, eicosasphinganine, 1-monopalmitin, glyceryl monooleate, glyceryl monostearate, 1α-hydroxyvitamin D2, 1-linoleoyl glycerol, oleamide, sphinganine, stearamide, and linoleic acid. The proposed method may be applied to effectively and accurately authenticate edible oils.     

食用植物油中反式脂肪酸含量的激光拉曼光谱检测

油脂中的反式脂肪酸(TFA)有害人们的身体健康,有必要对其含量进行监测。共收集各类食用植物油样本79个,涉及9个品种和27个品牌,分配到校正集和预测集的样本数分别为53个和26个。采用QE65000拉曼光谱仪采集79个样本的拉曼光谱,利用自适应迭代惩罚最小二乘法去除样本拉曼光谱的荧光背景;在此基础上,采用多种归一化方法对样本拉曼光谱进行处理,并对拉曼光谱的建模波数范围进行初选;再利用竞争性自适应重加权采样(CARS)方法筛选与食用植物油TFA含量相关的光谱变量,并应用偏最小二乘(PLS)回归将食用植物油TFA的特征变量光谱强度与气相色谱测定的TFA真实含量进行关联,建立食用植物油中TFA含量的定量预测模型。研究结果表明,多种归一化方法中,有4种归一化方法均能提高PLS定量预测模型的性能,其中Area normalization方法的效果最优;经建模波数范围初选,波数范围由686~2 301 cm-1缩减为737~1 787 cm-1,确定较优的建模波数范围为737~1 787 cm-1;经CARS方法筛选,共有31个光谱变量被选择,其选择的光谱变量主要分布在1 265,1 303,1 442及1 658 cm-1拉曼振动峰附近,且974 cm-1拉曼振动峰两侧均有光谱变量被选择;此外,CARS方法的PLS建模结果优于常用的无信息变量消除及连续投影算法。由此可知,激光拉曼光谱技术结合化学计量学方法检测食用植物油中的TFA含量是可行的。归一化方法、建模波数范围初选及竞争性自适应重加权采样(CARS)方法能有效提高TFA定量预测模型的预测精度和稳定性,优化后的TFA定量预测模型的校正集及预测集的相关系数和均方根误差分别为0.949,0.953和0.188%,0.191%。与未优化的预测模型相比,预测均方根误差由0.361%下降为0.191%,下降幅度为47.1%;建模所用的变量数由683个下降为31个,仅占原变量数的4.54%。     

拉曼光谱和MLS-SVR的食用油脂肪酸含量预测研究

为实现食用植物油中饱和脂肪酸、油酸、亚油酸含量的快速预测,对一批纯食用油以及不同比例两两混合油共91个样品进行了拉曼光谱检测,在800～2 000 cm-1范围内,通过基于寻峰算法的自动确定支点的基线拟合方法,对获得的光谱数据进行预处理,提取八个特征峰作为拉曼光谱的特征值。以这些特征值为输入,以样品油中实际饱和脂肪酸、油酸、亚油酸含量为输出,运用偏最小二乘回归(PLS)和多输出最小二乘支持向量回归机(MLS-SVR)方法,分别建立了可以同时预测三种脂肪酸含量的数学模型,结果表明MLS-SVR方法具有较好的效果。将MLS-SVR模型的预测结果与气相色谱法结果相比较,可得到三种脂肪酸的预测均方根误差分别为0.496 7%,0.840 0%和1.019 9%,相关系数分别为0.813 3,0.999 2和0.998 1;对未知样品三种脂肪酸的预测均方根误差不超过5%。表明,拉曼光谱和MLS-SVR相结合的食用油脂肪酸含量预测方法,具有快速、简便、无损、准确等优点,为食用油脂肪酸含量分析提供了一种可行的方法。