基于液体阵列味觉仿生传感器鉴别白酒香型的新方法

通过模拟哺乳动物的味觉系统, 建立了交叉响应的液体阵列传感器, 为鉴别白酒香型提供了新方法. 选用7种染料和1种卟啉化合物作为传感单元, 构建液体阵列传感器, 集合8个传感单元的光谱响应信号构成分析物的指纹图谱, 达到识别的目的. 使用96孔板酶标仪采集响应数据, 结合主成分分析(PCA)、分层聚类分析(HCA)和判别分析(LDA)等模式识别方法进行数据处理, 对9种具有代表性的不同香型白酒样品进行了鉴别分析. PCA结果表明, 该方法对于白酒的检测主要基于酒体微量成分, 其中酸类物质对识别的贡献最大(贡献率达54.3%), 芳香类物质贡献率为18.6%; 同时, 仅用63.4%的数据信息量即可对白酒香型进行区分. HCA结果表明, 平行样均正确归类, 各白酒之间的相似程度在聚类图上得到体现. LDA结果表明, 该阵列对于9种白酒样品香型识别的准确率达到100%.     

基于可视化学传感阵列的农药快速检测新方法

采用可视化阵列传感技术,以卟啉及其衍生物和指示剂作为传感元件,构建了一种对农药敏感的可视化学传感阵列。该传感阵列可以在常温常压下对浓度为0.1 mg/L的12种农药快速识别和分类,反应时间仅为1.5 min。采用聚类分析(HCA)和主成分分析(PCA)等统计学分析方法对检测结果进行分析,不同种类农药样品在聚类分析和主成分分析中均可以被准确归类。     

可视化传感阵列对乌龙茶等级的快速识别研究

以金属二聚体卟啉,席夫碱过渡金属配合物和特异性染料为敏感材料,结合溶胶凝胶技术构建了一种新型可视化传感器阵列系统,对9种不同产地不同等级的乌龙茶进行了检测。采用主成分分析,聚类分析技术和欧式距离对检测结果进行分析。该传感阵列系统可以对乌龙茶的产地和等级实现准确的可视化识别与分类,检测耗时为3 min。通过主成分分析获得的前2个主成分所代表的乌龙茶63.6%的信息量即可以实现不同等级茶叶区分。9种乌龙茶的平行实验数据均能100%准确区分,且等级相同产地相同的样本优先聚成一簇。研究结果表明所构建的可视化阵列传感器是一种能快速准确区分乌龙茶等级与产地的方法,在实时快速检测茶叶品质方面有潜在的应用价值。     

快速气相色谱法分析白酒中的香味组分

中国的传统白酒里含有多种香味组分,包括醇类、醛类、酸类和酯类,它们的比率决定着白酒的香型和品质。这些组分可以使用气相色谱仪进行很好的分析并定性和定量。为了缩短分析时间,建立了一种快速检测白酒中香味组分的气相色谱法。采用该方法,用20 m×0.1 mm×0.1 μm的熔融石英毛细管柱在12 min之内完成了对白酒中香味组分的分析,分析时间只是传统色谱方法的三分之一。该方法的重现性良好。     

毛细管色谱直接进样法测定白酒中高碳脂肪酸乙酯的研究

介绍了采用ＦＦＡＰ键合毛细管柱直接进样测定白酒中５种高碳脂肪酸乙酯的方法，操作简捷，定量准确，检测限低达０４ｍｇ／Ｌ。用该法测定了各种香型近９０个白酒样品。改变色谱条件后，在一次直接进样分析中除能测定高碳脂肪酸乙酯外，而且还能对白酒中醇、酯、醛、酮以及有机酸等５２种香味组分进行定量测定，结果重现性良好。     

山东不同产地丹参的HPLC指纹图谱-化学模式识别研究

建立RPHPLC指纹图谱-化学模式识别评价丹参质量的方法.利用RPHPLC法测定不同产地丹参药材的指纹图谱及丹参酮ⅡAt和丹参酮的含量,采用主成分分析、系统聚类分析和逐步判别分析对指纹图谱信息进行化学模式识别研究.在主成分分析的基础上,以前4个主成分为聚类分析的指标进行系统聚类分析,取阈值为8时,所有样品可被分为4类;并建立了相应的判别函数,回判准确率100%;以此为依据,初步建立了丹参化学模式识别的评价方法.     

白酒年份检测鉴定技术研究进展

综述了白酒年份检测鉴定技术,包括气相色谱法、气相色谱-质谱法、荧光光谱法以及稳定同位素质谱法等技术,并对上述几类方法进行了比较。以气相色谱为基础的分析技术,通过对白酒酒基中的挥发物成分进行分析并构建指纹图谱和预测模型,与实际样品谱图进行匹配进行年份测定;以稳定同位素为基础的分析技术,通过对存储过程中白酒酒基中放射性同位素¹⁴C等成分衰减程度来进行年份测定;以光谱为基础的分析技术,通过预测模型的构建和实际样品的匹配进行年份测定;电子鼻、电子舌检测技术更依赖于鉴定人员对于分析方法的熟练使用,以科学分析方法进行数据分析。最后对白酒年份检测鉴定技术的发展进行了展望。     

GC-MS法检测生物检材中的白酒和啤酒

用GC-MS法分析白酒和啤酒中的成分,统计出白酒、啤酒的各自特征成分并加以比较。对添加不同量白酒、啤酒及白酒与啤酒混合物的胃内容物进行检验,对饮用不同量白酒、啤酒及白酒与啤酒混合物的人员分别采集血样进行检验,统计并总结出胃内容物及血液中白酒、啤酒的区分检验方法。当生物检材中乙醇含量大于0．3mg／mL时,利用该方法可以区分检材中的白酒和啤酒。     

电喷雾萃取电离质谱法用于掺假白酒的快速分析

采用自行研制的电喷雾萃取电离源(EESI)与LTQ-XL质谱仪耦合,并结合主成分分析(PCA)和聚类分析(CA),建立了一种能在无需样品预处理的条件下直接、快速、准确鉴别茅台等八种掺假白酒(混掺水和工业酒精,且通过酒精计调控掺假酒的酒精度与真酒保持一致)的新方法. 同时,利用串联质谱能够对目标组分进行准确鉴定. 研究结果表明,EESI-LTQ-MS检测单个样品的时间小于1 min,且重现性好,PCA区分正确率高达96.5%. 通过设置未知样分析进一步验证了该方法的可行性. 此外,还结合单光子电离飞行时间质谱法(SPI-TOF-MS)对检测谱图进行对比分析. 阐述了EESI和SPI两种电离技术在挥发性有机物分析上具有各自的优势,且两种检测手段具有互补的特点. 为市场上酒类饮品真假的快速鉴别及品质鉴定建立了一个综合的分析方法,对于快速筛选伪劣酒类产品具有非常重要的应用价值.     

不同口味电子烟烟液挥发性成分的主成分分析

为了对比不同口味电子烟烟液的特点,本试验采用GC/MS与主成分分析法对茉莉花、芙蓉王、中华、利群、玉溪5种不同口味的电子烟烟液挥发性成分进行分析。GC/MS结果表明,电子烟的主要成分是丙二醇、甘油和烟碱。主成分分析法结果表明影响电子烟烟液香型的香味成分可归结为4个因子,分别为茉莉花香型因子、果香香型因子、焦糖香香型因子和玫瑰香香型因子。茉莉花香型因子对茉莉花口味的电子烟烟液的香味影响较大,果香香型因子对芙蓉王香型的电子烟烟液的香味影响较大。影响茉莉花香型电子烟烟液的香气成分主要为乙酸苄酯和苯甲醇,影响芙蓉王香型电子烟烟液口味的香气成分主要为苯酚和乙基麦芽酚,影响中华香型的电子烟烟液口味的香味成分为吲哚,影响利群香型电子烟烟液口味的香味成分有2,3,5,6-四甲基吡嗪、二氢大马酮、苯乙醇等,影响玉溪香型电子烟烟液的香味成分为邻苯二甲酸二(2-戊基)酯和苯乙醇。该研究为电子烟烟液的复配提供理论支持。     

Characterization of Aromatic Liquor by Gas Chromatography and Principal Component Analysis

The differentiation of aromas of Chinese liquor is important for their unique flavors. In this work, aromas of Chinese liquor were characterized by gas chromatography and chemometrics. Ten representative aroma compounds, including three alcohols, four esters, two organic acids, and acetal in 16 Chinese liquor were determined by gas chromatography with flame ionization detection. The relationship between these compounds and six classic aromas was investigated using principal component analysis and k-means clustering. The cumulative contribution of the first three principal components reached up to 84.607%, which effectively differentiated the liquors. The variables with the highest loading absolute value were acetal and ethyl acetate for principal component 1, ethyl butanoate and ethyl hexanoate for principal component 2, and the hexanoic acid and ethyl butanoate for principal component 3. The aromas of the liquors were characterized by k-means clustering with the first three principal component scores, indicating that the acetal, ethyl acetate, ethyl butanoate, ethyl hexanoate, and hexanoic acid are important for the aroma of Chinese liquors. This work demonstrated that the gas chromatography with chemometrics is effective for the characterization of aromatic liquor.     

Multivariate comparison of concentration profiles in materials analysis

Hierarchical cluster analysis (HCA) and principal components analysis (PCA) were applied to find groups between similar depth-profiles in thin-layers investigated by Rutherford backscattering spectrometry (RBS).HCA yields in one run an objective hierarchy of similarity for several profiles. Among the similarity coefficients examined the linear measure, the Euclidean distance and the exponential measure respond with different sensitivity to overall shifts in direction of the concentration axis, whereas the correlation measure relates to parallelism of the profiles.For agglomerative HCA with Euclidean distance, a lowest significant linkage level has been defined by use of Fisher'sF-test. For divisive HCA based also on Euclidean distance, the maximum of a separating function marks the most separating cluster step. The outcomes of both proposals agree for the data set investigated.PCA is useful for verifying the results of HCA and yields additional information about the data structure. In the actual example quite different positions of features (concentrations at definite depths) in the space of the two first principal components hint at peculiarities during the metallurgical coating process.     

Structure-Activity Relationship of Fluoroquinolones Against K:pneumoniae

The structure-activity relationship of °uoroquinolones, which show anti-K. pneumoniae activity, was studied by using principal component analysis (PCA) and hierarchical cluster analysis (HCA). The PCA results showed that the lowest unoccupied molecular orbital energy, energy difference between the highest occupied and the lowest unoccupied molecular orbital, dipole moment, net atomic charge on atom I, molecular polarizability, partition coe±cient and molecular refractivity of these compounds are responsible for the separation between high-activity and low-activity groups. The HCA results were similar to those obtained with PCA. By using the chemometric results, four synthetic compounds were analyzed through PCA and HCA, and three of them are proposed as active molecules against K. pneumoniae which is consistent with the results of clinical experiments. The methodologies of PCA and HCA provide a reliable rule for classifying new °uoroquinolones with anti-K. pneumoniae activity.     

Discrimination of liquor aromas by pattern recognition analysis of responses from a gas sensor array

A sensor array composed of six semiconductor gas sensors was applied to the discrimination of liquor aromas. A semi-automatic headspace concentrator utilizing porous polymers was used for pretreatment of sample aromas in order to remove excess amounts of ethanol and to standardize aroma introduction to the sensor array. The differences in response patterns for liquor samples were not so conspicuous because of the non-selectivity of the gas sensors. After normalizing the sensor responses to eliminate the effects of absolute magnitude, pattern recognition analysis was applied to the resulting six-dimensional data matrices. Cluster analysis succeeded in classifying eight liquors. Five spirits (cognac and four different brands of whisky) were correctly classified by both linear discriminant analysis and cluster analysis.     

交叉响应传感器鉴别β受体激动剂及其混合体系

开发了一种鉴别β受体激动剂的新型阵列传感器。该传感器由8种传感物质构成,使用96孔板酶标仪采集响应数据,结合主成分分析(PCA)、分层聚类分析(HCA)、判别分析(LDA)等模式识别方法进行数据处理,对5类β受体激动剂及其混合物进行检测。PCA结果表明,该传感器主要是基于空间结构以及氢键作用实现对β受体激动剂的识别;HCA结果显示,93个分析样本归类正确;LDA结果显示,该传感器对于β受体激动剂识别的准确率达98.9%。本方法在β受体激动剂的检测中有潜在应用价值。     

Analysis of Psoralea corylifolia L. fruits in different regions

Application of multivariate data analysis has become a popular method in the last decades, mainly because it can provide information not otherwise accessible. The information includes classification, searching similarities, finding relationships, finding physical significance to principal components, etc. Twenty-two Chinese medicinal herbs containing twelve constituents were collected and determined by HPLC. The results were studied by hierarchical cluster analysis (HCA) and principal components analysis (PCA). It was shown that the samples could be clustered reasonably into three groups, hence corresponding with the typical habitats of Psoralea corylifolia L.     

Brand-dependent volatile fingerprinting of Italian wines from Valpolicella

Head-space solid-phase microextraction (HS-SPME) coupled to gas-chromatography-mass spectrometry was developed and applied to obtain the volatile aromatic fingerprints of three typical Italian wines, Valpolicella, Amarone and Recioto, all produced in the restricted geographical area of Valpolicella (Veneto, Italy) with the same grape cultivars within the regulations of a rigid disciplinary of production. Differences between the three typologies are mainly linked to the different withering times to which grapes are subjected before vinification, which strongly influences the concentration and the development of volatile aroma compounds. A total of 22 different wines (7 Valpolicella, 10 Amarone and 5 Recioto) were characterised in terms of aromatic volatile profile with the aim to distinguish the different products and to evaluate the possibility to differentiate the same product from different brands. For the chemometric evaluation of the data one-way analysis of variance (ANOVA), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were tested. All the chemometric tools employed allow to differentiate between the three products. More intriguing is the ability of the chemometric approach to differentiate between the same product (Amarone, Recioto) from different winery, thus showing the potential of this approach to characterize the brand-dependent typicality of wines, which is usually related to subtle technological differences which nevertheless have strong influences on the organoleptic characteristics of the products.     

Preliminary chemometric study of minerals and trace elements in Spanish infant formulae

The concentrations of minerals (Na, K, P, Ca and Mg) and trace elements (Fe, Zn, Cu, Mn, Se, Al, Cd and Pb) in a total of 105 different infant formulae (starter, follow-up, premature, specialised and soya formulae) marketed in Spain were determined by atomic spectrometry (flame and electrothermal) and inductively coupled plasma emission spectroscopy after acid-microwave decomposition. On the basis of the elements distribution, a preliminary chemometric study with the use of pattern recognition methods was carried out. Hierarchical cluster analysis (HCA), principal component analysis (PCA), as unsupervised exploratory techniques, and linear discriminant analysis (LDA), were applied to characterise, classify and distinguish the different types of infant formulae. The HCA results showed that mineral and trace element content data support adequate information to obtain the infant formula differentiation. PCA permitted the reduction of 13 variables to four principal components accounting for 61.9% of the total variability. This four-factor model interprets reasonably well the correlations of these studied elements. The obtained element associations may be attributed to the composition of matrix ingredients, the contamination during elaboration, the additives and mineral supplements added and the present tendency of standardization in the manufacture of infant formulae. The application of LDA gave a 77.1% of infant formulae correctly assigned with three clearly differentiated and two overlapped groups. The use of discriminant functions, as a complementary tool, to distinguish the different types depending on protein matrix of infant formula, is also discussed. This survey shows that HCA, PCA and LDA techniques appear useful tools for the characterisation and classification of infant formulae using their elemental profile.     

Colorimetric Artificial Nose for Identification of Breath Volatile Organic Compounds of Patients with Lung Cancer

A novel and highly sensitive colorimetric sensor array was developed for the detection and identification of breath volatile organic compounds（VOCs） of patients with lung cancer.Employing dimeric metalloporphyrins,metallosalphen complexes,and chemically responsive dyes as the sensing elements,the developed sensor array of artificial nose shows a unique pattern of colorific changes upon its exposure to eight less-reactive VOCs and their mixture gas at a concentration of 735 nmol/L within 3 min.Potential of quantitative analysis of VOCs samples was proved.A good linear relationship of 490-3675 nmol/L was obtained for benzene vapor with a detection limit of 49 nmol/L（S/N=3）.Data analysis was carried out by Hierarchical cluster analysis（HCA） and principal component analysis（PCA）.Each category of breath VOCs clusters together in the PCA score plot.No errors in classification by HCA were observed in 45 trials.Additionaly,the colorimetric sensor array showed good reproducibility under the cyclic sensing experiments.These results demonstrate that the developed colorimetric artificial nose system is an excellent sensing platform for the identification and quantitative analysis of breath VOCs of patients with lung cancer.