傅立叶变换离子回旋共振质谱法对赤灵芝的化学成分鉴定和指纹图谱研究

建立了基于傅立叶变换离子回旋共振超高分辨质谱(FTICR-MS)的赤灵芝化学成分鉴定和指纹图谱分析方法,并应用于不同产地赤灵芝样品的来源区分。样品采用50%甲醇进行回流提取后,以流动注射的进样方式进行直接质谱分析。以ESI离子源在负离子模式下进行检测,质荷比扫描范围为100～1 000 Da。采用精确分子量测定和碰撞诱导解离实验进行化学成分鉴定,通过与文献进行比对,共鉴定出63种化学成分(1种萜烯醛、3种糖、4种三萜醇、6种有机酸和49种三萜酸类成分)。采用聚类分析和主成分分析(PCA)对所获得的质谱指纹图谱进行统计学分析,在95%的置信区间下,多个批次不同产地的赤灵芝样品得到了较好的来源区分。研究结果表明该方法可实现赤灵芝的直接、快速、高效分析和指纹图谱研究,在中药分析领域有着广阔的应用前景。     

基于表面解吸常压化学电离质谱法快速评价咖啡种子活力

为快速、无损地区分不同活力的咖啡种子,采用自行研制的表面解吸常压化学电离质谱(DAPCI-MS),在无需样品预处理的前提下,获得咖啡种子表面的化学指纹图谱,并分别进行了主成分分析(PCA)、聚类分析(CA)和判别分析(DA),获得不同活力的咖啡种子样品的质谱信息特征.结果表明,在正离子模式下,DAPCI-MS结合多变量分析方法能有效区分不同活力的咖啡种子.PCA提取了3个主成分,累计贡献率达到92.2％;CA可以将相同活力的咖啡种子聚在一起,准确率为100％;DA对训练样本的回判正确率为100％,交叉验证分析成功率为100％,对外部验证样本进行DA,正确率95.7％.本方法具有无需样品预处理,分析速度快,灵敏度高,对种子无损伤等优点,能为其它种子活力测定提供参考.     

顶空固相微萃取-全二维气相色谱/飞行时间质谱法分析闽南乌龙茶中的挥发性成分及其在分类中的应用

采用顶空固相微萃取(HS-SPME)结合全二维气相色谱/飞行时间质谱(GC×GC-TOF MS)分析了闽南乌龙茶中的挥发性成分。从48份不同等级和产季的乌龙茶(铁观音、黄金桂、本山、毛蟹和梅占)中获得了2000余种挥发性化合物,经筛选得到51种共有组分,并结合质谱数据库、保留指数与结构谱图等进行了初步鉴定。在此基础上采用主成分分析法(PCA)获得得分投影图,直观给出了不同样品的分类趋势。通过逐步判别获得9种对分类结果有显著影响的组分,并以此为变量通过Fisher判别法(FDA)建立了4个判别函数,对样品的分类准确率达到97.9%。本试验证实了以挥发性成分识别闽南乌龙茶的可行性。     

表面解吸常压化学电离质谱法快速判别樟树化学型

采用表面解吸常压化学电离质谱(SDAPCI-MS)技术直接对5种化学型的樟树叶粉末片剂进行分析,获得其化学指纹谱图信息.采用主成分分析(PCA)、聚类分析(CA)和反向传输人工神经网络(BP-ANN)对谱图信息进行分析,获得各化学型樟树叶粉末片剂的特征质谱信息,进而对不同化学型样品进行判别.结果表明,在正离子模式下,SDAPCI-MS能快速获取樟树的化学指纹谱图;PCA分析中的PC1,PC2和PC3贡献率分别为79.9%,12.9%和4.2%,共计97.0%.SDAPCI-MS结合CA和BP-ANN测试样本准确率均为100%,能够快速、有效地判别出樟树化学型.     

气相色谱结合化学计量学区分大米贮藏时间与产地

香气是衡量大米质量的一个主要因素,对大米的食用品质有重要影响。该文以顶空固相微萃取(SPME)技术为基础,采用气相色谱法分别分析了不同贮藏时间和不同产地大米样本的挥发性成分,通过主成分分析法(PCA)和偏最小二乘判别分析法(PLS-DA)对大米样本进行分类和判别分析。PCA及PLS投影图显示不同储藏时间的大米明显聚为4类,通过留一交叉验证法(LOO)计算PLS预报的准确率为96%,相对标准误差为8.2%。同时,PCA投影图中可将4个不同产地的大米样本进行区分,分类效果显著;所建PLSDA模型可靠,不同产地大米样本均能被准确识别,正确率为100%。以顶空固相微萃取/气相色谱检测大米中挥发性成分,利用主成分分析法和偏最小二乘判别分析法鉴别大米新鲜程度和产地具有可行性。     

气相色谱-质谱指纹图谱法研究普洱茶醇溶物挥发性成分

以普洱茶7542为参考样本, 乙醇为溶剂, 超声提取制备普洱茶醇溶物, 建立了普洱茶7542醇溶物气相色谱-质谱(GC-MS)指纹图谱, 同时对其指纹图谱进行相似度计算和主成分分析.试验结果表明, 普洱茶醇溶物的最佳提取方法:采用50 mL 95%乙醇超声提取30 min, 顶空进样最佳条件为振荡箱温度110℃, 振荡时间20 min.通过对9个不同年限、不同批次的普洱茶7542系列样品醇溶物挥发性成分GC-MS指纹图谱进行相似度分析, 测定样品醇溶物挥发性成分图谱与对照图谱之间的相似度在0.706~0.906之间, 说明不同年限、不同批次的9个普洱茶7542醇溶物挥发性成分和参照样本相比发生了改变, 但变化较小, 但其共有成分峰面积百分含量存在差异.通过对9个不同年限、不同批次普洱茶7542醇溶物挥发性成分共有成分进行主成分分析, 提取了2个成分, 即2-羟甲基-2-甲基-吡咯烷-1-甲醛和N-丁基苯磺酰胺, 其特征值大于1, 累计方差贡献率达90.2%, 能较好的代表 9个不同年限、不同批次普洱茶7542的全部信息, 可以用主成分分析来反映样品的全部信息.因此可通过测定9个不同年限、不同批次普洱茶7542醇溶物中2-羟甲基-2-甲基-吡咯烷-1-甲醛和N-丁基苯磺酰胺的含量差异评价普洱茶7542的质量.     

基于化学模式识别技术的枳实HPLC定量指纹图谱研究

建立了枳实的高效液相色谱(HPLC)指纹图谱分析方法。色谱柱为Tnature-ACCHROM C18色谱柱(4.6 mm×250 mm,5μm);以乙腈-0.5%甲酸水溶液为流动相进行梯度洗脱,结合液相色谱-四极杆飞行时间质谱(HPLC-QTOF-MS)联用技术对枳实指纹图谱中的共有峰进行鉴定;采用相似度评价、聚类分析(CA)、主成分分析(PCA)和正交偏最小二乘判别分析(OPLS-DA)对22批枳实进行数据分析及质量评价。结果显示:指纹图谱共标定12个共有峰,HPLC-QTOF-MS分析指认出11个成分;22批枳实样品的相似度在0.9以上;CA、PCA和OPLS-DA的分析结果一致,其中江西产地聚为一类,湖南和福建产地聚为一类,并筛选出橙皮苷、新橙皮苷和柚皮苷3个差异性质量标志物。所建立的枳实HPLC指纹图谱方法稳定、可靠,可为其质量控制提供参考依据。     

矿物元素指纹图谱的建立及在西洋参产地鉴别中的应用研究

采用电感耦合等离子体质谱法测定了山东、吉林、美国和加拿大4个产地西洋参中50种矿物元素的含量,研究了不同产地西洋参矿物元素的差别和转换系数,构建了西洋参的矿物元素指纹图谱。以各产地矿物元素含量的平均值构建了山东、吉林、美国和加拿大产西洋参的矿物元素标准指纹图谱。采用SPSS 20.0计算了各西洋参矿物元素指纹图谱与其矿物元素标准指纹图谱的相似度,确定了山东、吉林、美国和加拿大产西洋参矿物元素指纹图谱的相似度阈值分别为0.93、0.91、0.98和0.93。通过比较未知产地西洋参矿物元素指纹图谱与矿物元素标准指纹图谱的相似度,进行西洋参的产地判别。采用20批未知产地西洋参样品验证模型的准确性,正确率为85%。此外,研究表明,不同生长年限和不同部位西洋参样品对所建立的西洋参产地鉴别方法无影响。     

指纹图谱结合化学计量学方法研究新疆薰衣草精油成分

采用水蒸汽蒸馏法(SD)提取薰衣草挥发油,气相色谱-质谱联用(GC-MS)技术分析其化学成分及相对含量。通过气相色谱法(GC)建立其色谱指纹图谱,并结合化学计量学方法对其进行品种鉴别。3种薰衣草精油中共检测29种挥发性化学成分,其中共有成分有18种;31批薰衣草样本的GC指纹图谱相似度均大于0.9,符合指纹图谱相似度的要求,利用主成分分析法(PCA)和聚类分析法(HCA)对GC指纹图谱进行识别,可直观地区分薰衣草品种,该方法可应用于薰衣草质量控制及品种鉴别。     

三波长融合指纹图谱结合6组分定量和主成分分析评价银翘解毒片的质量

建立了三波长融合高效液相色谱指纹图谱,并结合6组分定量和主成分分析(PCA)评价25批银翘解毒片的质量一致性。采用反相高效液相色谱法(RP-HPLC)分别于230、279、327 nm下检测。运用多波长融合指纹图谱技术建立银翘解毒片三波长融合指纹图谱,采用系统指纹定量法对其进行定性和整体定量评价。结果鉴别出25批银翘解毒片样品完全合格且区分良好。同时测定6组分含量,与指纹图谱结合,从整体和局部角度评价银翘解毒片质量。此外,运用PCA法对融合指纹图谱进行分析,通过主成分得分图可以明显区分来自两个厂家的25批银翘解毒片样品。方法综合性较强且有效,为科学评价与有效控制银翘解毒片的质量提供了可靠的参考。     

偏最小二乘法在红外光谱识别茶叶中的应用

采用漫反射傅立叶变换红外光谱(FTIR)法结合主成分分析(PCA)、偏最小二乘法(PLS)、簇类的独立软模式(SIMCA)识别法对十三种茶叶进行了分类判别研究。研究结果表明,通过多元散射校正(MSC)对原始光谱进行预处理,可以提高模式识别技术的分类判别效果。在此基础上,选取1 900~900 cm-1波长范围内的茶叶红外光谱建立识别模型,三种方法都得到了满意的分类判别效果。在对检验集中全部130个样本的判别中,PCA仅有两类样本无法判别,SIMCA的识别率和拒绝率都在90%以上,而PLS的识别效果最佳,全部样本都得到了正确的归类。这一研究结果表明傅立叶变换红外光谱法与化学计量学方法相结合可以实现茶叶品种的快速鉴别,这为茶叶的客观评审提供了一种新思路。     

Multivariate analysis for 1H-NMR spectra of two hundred kinds of tea in the world.

NMR measurements coupled with pattern-recognition analysis offer a powerful mixture-analysis tool for latent-feature extraction and sample classification. As fundamental applications of this analysis for mixtures, the 1H spectra of 176 kinds of green, black, oolong and other tea infusions were acquired by a 500 MHz NMR spectrometer. Each spectrum pattern was analyzed by a multivariate statistical pattern-recognition method where Principal Component Analysis (PCA) was used in combination with Soft Independent Modeling of Class Analogy (SIMCA). SIMCA effectively selected variables that contribute to tea categorization. The final PCA resulted in clear classification reflecting the fermentation and processing of each tea, and revealed marker variables that include catechin and theanine peaks.     

高效液相色谱-质谱法及模式识别法用于鉴别普洱生茶与熟茶

采用高效液相色谱-质谱联用技术及高效液相色谱法对生熟普洱茶中的主要成分进行定性和定量分析。鉴定出普洱茶水溶液中8种主要成分,分别为没食子酸(GA)、没食子酸儿茶素(GC)、表没食子酸儿茶素(EGC)、儿茶素(C)、咖啡因(CAF)、表儿茶素(EC)、表没食子酸儿茶素没食子酸酯(EGCG)和表儿茶素没食子酸酯(ECG)。以这8种成分的含量为指标,对普洱生茶和熟茶各20批进行主成分分析、聚类分析和判别分析,能准确地区分普洱生茶与熟茶。     

Metabonomics investigation of human urine after ingestion of green tea with gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry and (1)H NMR spectroscopy

A method using gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS) and (1)H NMR with pattern recognition tools such as principle components analysis (PCA) was used to study the human urinary metabolic profiles after the intake of green tea. From the normalized peak areas obtained from GC/MS and LC/MS and peak heights from (1)H NMR, statistical analyses were used in the identification of potential biomarkers. Metabolic profiling by GC/MS provided a different set of quantitative signatures of metabolites that can be used to characterize the molecular changes in human urine samples. A comparison of normalized metabonomics data for selected metabolites in human urine samples in the presence of potential overlapping peaks after tea ingestion from LC/MS and (1)H NMR showed the reliability of the current approach and method of normalization. The close agreements of LC/MS with (1)H NMR data showed that the effects of ion suppression in LC/MS for early eluting metabolites were not significant. Concurrently, the specificity of detecting the stated metabolites by (1)H NMR and LC/MS was demonstrated. Our data showed that a number of metabolites involved in glucose metabolism, citric acid cycle and amino acid metabolism were affected immediately after the intake of green tea. The proposed approach provided a more comprehensive picture of the metabolic changes after intake of green tea in human urine. The multiple analytical approach together with pattern recognition tools is a useful platform to study metabolic profiles after ingestion of botanicals and medicinal plants.     

基于近红外光谱分析技术的食品包装塑料的定性分析

该文基于近红外漫反射光谱分析技术对食品包装材料聚乙烯、聚丙烯进行定性判别试验研究,选取不同波段范围、采用不同光谱预处理方法,使用主成分分析法(Principal component analysis,PCA)结合SIMCA、贝叶斯判别、K-近邻3种模式识别方法建立定性预测模型,并根据正确识别率比较了各模型预测性能。结果表明：使用SIMCA方法、贝叶斯判别、K-近邻3种方法建立的定性校正模型均在1 050~1 550 nm波长范围内效果较好;采用矢量归一化、标准正态变量变换、中心化、滑动均值滤波、多项式平滑滤波、一阶微分6种光谱预处理方法和上述3种模式识别方法对塑料样品近红外光谱进行了数据处理,其中在1 050~1 550 nm范围内,主成分因子数为3,采用原始光谱建立的K-近邻定性校正模型较优,对样品校正集和预测集的正确识别率均为100%。可为食品包装材料聚乙烯、聚丙烯的快速鉴别研究提供参考。     

基于近红外光谱技术与化学计量学的绿茶无损鉴别方法研究

该文利用近红外光谱技术结合化学计量学方法开发了不同品种绿茶的无损鉴别方法。通过近红外光谱技术得到了8个品种绿茶样品的近红外光谱,比较了单一以及优化组合光谱预处理方法对光谱的影响,利用无监督的主成分分析(PCA)与有监督的线性判别分析方法(LDA)分别构建了茶叶品种鉴别模型。结果表明：对比单一预处理方法,优化组合预处理具有更优的鉴别准确性。标准正态变量变换预处理消除了茶叶样品大小不均造成的光谱散射影响,一阶导数预处理实现了变动背景的消除,减少了基线漂移的影响,突出了图谱中的有效信息,采用二者相结合的预处理方式并结合无监督的主成分分析法可实现较为准确的绿茶样品种类鉴别分析,准确率达75.0%。此外,采用有监督的线性判别分析方法处理原始光谱数据,可达到100%的鉴别准确率,但该方法需提供类别的先验知识。因此,采用近红外光谱技术和化学计量学相结合的手段可实现不同品种绿茶的快速无损鉴别。     

New opportunities in multiplexed optical bioanalyses using quantum dots and donor–acceptor interactions

In this study, we investigated the feasibility of using a novel volatile organic compound (VOC)-based metabolic profiling approach with a newly devised chemometrics methodology which combined rapid multivariate analysis on total ion currents with in-depth peak deconvolution on selected regions to characterise the spoilage progress of pork. We also tested if such approach possessed enough discriminatory information to differentiate natural spoiled pork from pork contaminated with Salmonella typhimurium, a food poisoning pathogen commonly recovered from pork products. Spoilage was monitored in this study over a 72-h period at 0-, 24-, 48- and 72-h time points after the artificial contamination with the salmonellae. At each time point, the VOCs from six individual pork chops were collected for spoiled vs. contaminated meat. Analysis of the VOCs was performed by gas chromatography/mass spectrometry (GC/MS). The data generated by GC/MS analysis were initially subjected to multivariate analysis using principal component analysis (PCA) and multi-block PCA. The loading plots were then used to identify regions in the chromatograms which appeared important to the separation shown in the PCA/multi-block PCA scores plot. Peak deconvolution was then performed only on those regions using a modified hierarchical multivariate curve resolution procedure for curve resolution to generate a concentration profiles matrix C and the corresponding pure spectra matrix S. Following this, the pure mass spectra (S) of the peaks in those region were exported to NIST 02 mass library for chemical identification. A clear separation between the two types of samples was observed from the PCA models, and after deconvolution and univariate analysis using N-way ANOVA, a total of 16 significant metabolites were identified which showed difference between natural spoiled pork and those contaminated with S. typhimurium.     

Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis

An extensive study was carried out in coal samples coming from several origins trying to establish a relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding near-infrared spectral data. This research was developed by applying both quantitative (partial least squares regression, PLS) and qualitative multivariate analysis techniques (hierarchical cluster analysis, HCA; linear discriminant analysis, LDA), to determine a methodology able to estimate property values for a new coal sample. For that, it was necessary to define homogeneous clusters, whose calibration equations could be obtained with accuracy and precision levels comparable to those provided by commercial online analysers and, study the discrimination level between these groups of samples attending only to the instrumental variables. These two steps were performed in three different situations depending on the variables used for the pattern recognition: property values, spectral data (principal component analysis, PCA) or a combination of both. The results indicated that it was the last situation what offered the best results in both two steps previously described, with the added benefit of outlier detection and removal.     

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.