茶叶中微量Mo（VI）的测定及其意义

用催化光度法测定了23个茶叶样品中数量Mo(Ⅵ)的含量．结果表明，茶叶中Mo的高低与茶叶品质有一定的关联．     

茶叶中微量Mo（Ⅵ）的测定及其意义

用催化光度法测定了２３个茶叶样品中微量Ｍｏ（Ⅵ）的含量．结果表明，茶叶中Ｍｏ的高低与茶叶品质有一定的关联．     

茶叶中微量营养元素的原子吸收光谱分析

茶叶中的微量元素与茶叶的营养和药理作用有密切关系^[1],与茶树生长的生化活动也有密切关系^[2,3]。对茶叶中的微量营养元素的分析有助于了解茶叶的品质,研究茶叶的营养药理作用,促进茶叶生产的发展。为此,我们研究提出了用火焰原子吸收光谱法测定茶叶中铜、锰、铁、锌,方法简便、准确。     

陕南茶叶及其产地土壤中微量元素含量的测定

采用微波消解一火焰原子吸收光谱法测定了陕南茶叶及其栽培土壤中Zn、Fe、Cu、Cr、Pb5种微量元素的含量,以指导茶农合理施肥,提高陕南地区茶叶品质。结果表明,茶叶与土壤中矿质元素的含量呈一定的正相关性,不同产地的茶叶和土壤中微量元素的含量有一定的差异;茶叶和土壤中Zn、Fe元素的含量较高,而对人体健康有影响的Pb元素含量低于国家允许标准。     

分光光度法测定桂西茶叶中铁的含量

茶叶既是食品,又是一味传统医药用的中药,药食同源.用分光光度法直接测定了茶叶中铁的含量,方法简便、快速、准确,对指导人们合理饮用茶叶进行补铁及进一步开发茶叶提供了可靠的理论依据.     

茶叶中咖啡因的超临界流体分析

 用超临界流体法 (SFC)测定茶叶中的咖啡因 ,在二氧化碳流动相中加入体积分数为 5 %的甲醇后 ,得到了良好的分离效果。该方法具有样品前处理简单 ,共存组分不干扰测定 ,分析速度快等优点 ,可以用于茶叶中咖啡因的快速分析。     

一种基于近红外光谱技术的茶叶非法添加物鉴别方法

本发明公开了一种基于近红外光谱技术的茶叶非法添加物鉴别方法。本方法应用傅里叶变换近红外漫反射光谱分析技术结合因子法建立定性分析模型;应用傅里叶变换近红外漫反射光谱分析技术结合偏最小二乘法(PLS)建立定量分析模型,以两个模型结合应用实现茶叶中非法添加物(蔗糖、糖浆和米糊)的快速准确鉴别。本方法具有分析速度快、效率高、成本低、测试重现性好、样品无需预处理且便于在线无损检测的特点。     

HPLC法测定茶叶水提液中五种儿茶素和咖啡碱及其用于茶叶分类的研究

建立高效液相色谱/二极管阵列检测器(HPLC/DAD)同时测定茶叶中(-)-没食子儿茶素(GC),(-)-表没食子儿茶素(EGC),(-)-表没食子儿茶素没食子酸酯(EGCG),(-)-表儿茶素(EC),(-)-表儿茶素没食子酸酯(ECG),咖啡碱(caffeine)6种组分的分析方法,并采用聚类分析探讨以这6种活性成分为指标对茶叶进行分类的方法。采用C18柱,甲醇和0.05%三氟乙酸水溶液为流动相,梯度洗脱,DAD双波长(210、278 nm)同时检测,采用标准物质保留时间和电喷雾飞行时间质谱(ESI TOF-MS)双重定性。结果表明,各组分的色谱峰均达到基线分离,在210 nm对(-)-没食子儿茶素(GC)定量,278 nm对其它组分定量准确。该法重复性好,灵敏度高,回收率高,已用于不同种类的33种实际茶叶样品的测定。以这6种活性成分的含量为指标,采用聚类分析法可对33个红茶、黑茶、绿茶、乌龙茶样本进行合理分类,并能反映茶叶品质的差异。     

茶叶中锂的测定

用火焰原子吸收光谱法测定了茶叶中锂元素的含量。并比较了两种消解茶叶方法的测定结果。实验结果表明,干法比湿法效果要好得多。干法消解的回收率在９２．２％￣９６．１％之间。     

茶叶化学成分指纹识别中样品制备方法的优化

根据指纹识别实验对样品的要求, 以¹H NMR和主成分分析(PCA)为测试与评估手段, 对茶叶中化学成分提取条件的优化方法进行了研究. 针对生物指纹识别实验中样本体系复杂及化学组分含量多等特点, 利用主成分分析对检测样品质控的多因素进行综合分析, 最终确立了茶叶中化学成分提取制备的实验条件. 利用所建立的样品制备程序对4种茶叶进行预处理和测试, 结果表明, 所建立的方法稳定、可靠, 可以满足茶叶代谢物¹H NMR指纹识别研究的要求. 本文所提出的优化方法简单可靠, 可用于复杂样本体系标准样品预处理方法的建立.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

茶叶中重金属浸出规律的研究

采用火焰原子吸收光谱法测定了红茶和绿茶中铅、铜、锌的含量。考察了浸泡次数、浸泡温度和浸泡时间对铅、铜、锌3种金属元素浸出率的影响。结果表明,茶叶中铅、铜、锌的浸出率随浸泡次数的增多而减少,随浸泡温度的升高而增大,30min前随浸泡时间延长而增加。     

火焰原子吸收光谱法测定茶叶中的锰

介绍悬浮液进样-次灵敏线火焰原子吸收光谱法测定茶叶中的锰的方法。以琼脂为悬浮剂，将茶叶样品均匀、稳定地悬浮于琼脂溶胶中，直接喷入空气-乙炔火焰中，利用锰280.1nm次灵敏线，以工作曲线或标准加入法测定锰的含量，方法简便、快速，用于测定茶叶和桃叶样品中的锰，结果准确。     

离子色谱法同时测定茶叶中多种阴离子

研究了离子色谱法同时测定茶叶中F<'->、Cl<'->、NO<,2><'->、NO<'-><,3>、H<,2>PO<'-><,4>和SO<'2-><,4>6种无机阴离子含量的方法.茶叶样品中6种阴离子经95℃超声浸提20 min后,以浓度为1.92 mmol/L Na<,2>CO<,3>和1.80 mmol/L NaH...     

Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy

This paper indicates the possibility to use near infrared (NIR) spectroscopy as a rapid method to predict quantitatively the content of caffeine and total polyphenols in green tea. A partial least squares (PLS) algorithm is used to perform the calibration. To decide upon the number of PLS factors included in the PLS model, the model is chosen according to the lowest root mean square error of cross-validation (RMSECV) in training. The correlation coefficient R between the NIR predicted and the reference results for the test set is used as an evaluation parameter for the models. The result showed that the correlation coefficients of the prediction models were R = 0.9688 for the caffeine and R = 0.9299 for total polyphenols. The study demonstrates that NIR spectroscopy technology with multivariate calibration analysis can be successfully applied as a rapid method to determine the valid ingredients of tea to control industrial processes.     

ICA方法与NIR技术用于药片中活性成分含量的测定

用独立分量分析(ICA)方法提取药片近红外光谱数据矩阵的独立成分和相应的混合矩阵, 再用BP神经网络对混合矩阵和药片中活性成分的浓度矩阵进行建模, 提出了新的药片活性成分含量测定的基于独立分量分析-神经网络回归(ICA-NNR)的近红外光谱分析方法. 通过分析独立分量数和网络中间隐层的神经元数对模型性能的影响, 分别建立三类药片定量分析的最优模型. 该方法用于实测的三类药片中活性成分含量的测定, 测试样品集的化学检测值与近红外预测值的相关系数分别达到0.962, 0.980及0.979. 结果表明, 基于ICA-NNR的近红外光谱分析方法对制药业的药片进行定量分析是可行的.     

气相色谱法快速测定茶叶中的八氯二丙醚残留量

建立了快速测定茶叶中八氯二丙醚(S-421)残留量的气相色谱方法。茶叶样品中的S-421经丙酮-石油醚混合液超声波辅助提取,用浓硫酸去除杂质,正己烷定容,用外标法定量,以气相色谱法(GC-μECD)进行测定。实验结果表明,样品中加入0.01~0.08 mg/kg浓度水平的八氯二丙醚,回收率为86.6%~106.5%,检出限为0.001 mg/kg,测定结果的相对标准偏差不大于8%(n=6)。     

Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM)

Near-infrared (NIR) spectroscopy has been successfully utilized for the rapid identification of green, black and Oolong teas. The spectral features of each category are reasonably differentiated in the NIR region, and the spectral differences provided enough qualitative spectral information for identification. Support vector machine as a pattern recognition was applied to attain the differentiation of the three tea categories in this study. The top five latent variables are extracted by principal component analysis as the input of SVM classifiers. The identification results of the three tea categories were achieved by the RBF SVM classifiers and the polynomial SVM classifiers in different parameters. The best identification accuracies were up to 90%, 100% and 93.33%, respectively, when training, while, 90%, 100% and 95% when test. It was obtained using the RBF SVM classifier with sigma=0.5. The overall results ensure that NIR spectroscopy combined with SVM discrimination method can be efficiently utilized for rapid and simple identification of the different tea categories.     

气相色谱-质谱联用分析茶树油的成分

采用常规水蒸气蒸馏法从茶树枝叶中提取茶树油，经气相色谱-质谱联用分析，分离出47种成分，检索NI蚋8质谱数据库，与标准谱图对照鉴定了其中32种成分。应用Hewlett—Packard软件，按峰面积归一化法定量，所鉴定成分的馏出峰面积占总馏出峰面积的96．08％。