直接电喷雾离子阱质谱法快速表征烟叶

建立了一种快速、简单的同时检测烟叶中多酚类和非挥发性有机酸的电喷雾离子阱质谱方法。该方法采用水溶液萃取烟叶,萃取液无需色谱柱分离提纯,直接进入质谱检测,得到烟草质谱轮廓图。采用该方法对25种不同产地、不同等级的烟叶样品进行了测量,并结合模式识别的方法,对这25种烟叶进行了聚类分析,其结果基本符合烟草专家的等级划分。最后对各类烟叶进行了统计分析,得到了表征各类烟叶的平均质谱轮廓和各特征组分对区分烟叶贡献大小。表明该方法可以用于辅助烟叶等级划分及卷烟配方设计。     

Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin

A combination of mass spectrometry-based electronic nose (MS e_nose) and chemometrics was explored to classify two Australian white wines according to their varietal origin namely Riesling and unwooded Chardonnay. The MS e_nose data were analysed using principal components analysis (PCA), discriminant partial least squares (DPLS) and linear discriminant analysis (LDA) applied to principal components scores and validated using full cross validation (leave one out). DPLS gave the highest levels of correct classification for both varieties (>90%). LDA classified correctly 73% of unwooded Chardonnay and 82% of Riesling wines. Even though the conventional analysis provides fundamental information about the volatile compounds present in the wine, the MS e_nose method has a series of advantages over conventional analytical techniques due to simplicity of the sample-preparation and reduced time of analysis and might be considered as a more convenient choice for routine process control in an industrial environment. The work reported here is a feasibility study and requires further development with considerably more commercial samples of different varieties. Further studies are needed in order to improve the calibration specificity, accuracy and robustness, and to extend the discrimination to other wine varieties or blends.     

Comprehensive Evaluation of Amino Acids and Polyphenols in 69 Varieties of Green Cabbage (Brassica oleracea L. var. capitata L.) Based on Multivariate Statistical Analysis

The biological activities of the primary metabolites and secondary metabolites of 69 green cabbage varieties were tested. The LC-MS detection method was used to determine the content of 19 free amino acids (lysine, tryptophan, phenylalanine, methionine, threonine, isoleucine, leucine, valine, arginine, asparagine, glycine, proline, tyrosine, glutamine, alanine, aspartic acid, serine, and glutamate). The content of 10 polyphenols (chlorogenic acid, gallic acid, 4-coumaric acid, ferulic acid, gentisic acid, cymarin, erucic acid, benzoic acid, rutin, and kaempferol) was determined by the HPLC detection method. Considering the complexity of the data obtained, variance analysis, diversity analysis, correlation analysis, hierarchical cluster analysis (HCA), and principal component analysis (PCA) were used to process and correlate amino acid or polyphenol data, respectively. The results showed that there were significant differences between the different amino acids and polyphenols of the 69 cabbage varieties. The most abundant amino acids and polyphenols were Glu and rutin, respectively. Both amino acids and polyphenols had a high genetic diversity, and multiple groups of significant or extremely significant correlations. The 69 cabbage varieties were divided into two groups, according to 19 amino acid indexes, by PCA. Among them, seven varieties with high amino acid content all fell into the fourth quadrant. The HCA of amino acids also supports this view. Based on 10 polyphenols, the 69 cabbage varieties were divided into two groups by HCA. Based on 29 indexes of amino acids and polyphenols, 69 cabbage varieties were evaluated and ranked by PCA. Therefore, in this study, cabbage varieties were classified in accordance with the level of amino acids and polyphenols, which provided a theoretical basis for the genetic improvement of nutritional quality in cabbage.     

Combining visible and near-infrared spectroscopy with chemometrics to trace muscles from an autochthonous breed of pig produced in Uruguay: a feasibility study

Visible (Vis) and near-infrared reflectance (NIR) spectroscopy combined with chemometrics was explored as a tool to trace muscles from autochthonous and crossbreed pigs from Uruguay. Muscles were sourced from two breeds, namely, the Pampa-Rocha (PR) and the Pampa-Rocha x Duroc (PRxD) crossbreed. Minced muscles were scanned in the Vis and NIR regions (400–2,500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), discriminant partial least square regression (DPLS), linear discriminant analysis (LDA) based on PCA scores and soft independent modelling of class analogy (SIMCA) were used to identify the origin of the muscles based on Vis and NIR data. Full cross validation was used as validation method when classification models were developed. DPLS correctly classified 87% of PR and 78% of PRxD muscle samples. LDA calibration models correctly classified 87 and 67% of muscles as PR and PRxD, respectively. SIMCA correctly classified 100% of PR muscles. The results demonstrated the usefulness of Vis and NIR spectra combined with chemometrics as rapid method for authentication and identification of muscles according to the breed of pig.     

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%.     

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.     

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.     

液相色谱-电喷雾离子阱串联质谱同时分析烟草中的20种游离氨基酸

建立了液相色谱-电喷雾离子阱串联质谱(LC-ESI-IT-MS/MS)同时分析烟草中20种游离氨基酸的方法。烟草样品经萃取后过滤直接进样,无需进行衍生和固相萃取等其他前处理步骤。液相色谱采用HyPURITY C18反相色谱柱(200 mm×2.1 mm, 5 μm),采用1%(体积分数,下同)乙腈水溶液(含0.1%九氟戊酸)和90%乙腈水溶液(含0.1%九氟戊酸)为流动相进行梯度洗脱。结果表明,20种氨基酸的检出限(LOD)为0.01～0.05 μmol/L (S/N=3),线性相关系数均大于0.9977,峰面积测定的相对标准偏差(RSD)为0.78%～4.93%。该方法分析效率、灵敏度和选择性高,已成功应用于多种烟草样品中氨基酸的分析测定。     

Forensic classification of ballpoint pen inks using high performance liquid chromatography and infrared spectroscopy with principal components analysis and linear discriminant analysis

Several varieties of blue ballpoint pen inks were analyzed by high performance liquid chromatography (HPLC) and infrared spectroscopy (IR). The chromatographic data extracted at four wavelengths (254, 279, 370 and 400 nm) was analyzed individually and at a combination of these wavelengths by the soft independent modeling of class analogies (SIMCA) technique using principal components analysis (PCA) to estimate the separation between the pen samples. Linear discriminant analysis (LDA) measured the probability with which an observation could be assigned to a pen class. The best resolution was obtained by HPLC using data from all four wavelengths together, differentiating 96.4% pen pairs successfully using PCA and 97.9% pen samples by LDA. PCA separated 60.7% of the pen pairs and LDA provided a correct classification of 62.5% of the pens analyzed by IR. The results of this study indicate that HPLC coupled with chemometrics provided a better discrimination of ballpoint pen inks compared to IR. The need to develop a suitable IR method for analysing blue ballpoint pen inks has been emphasized and it is hoped that the development of such a method would indeed provide a valuable tool for the non-destructive analysis of blue ballpoint pen ink samples for forensic purposes.     

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。     

用高效液相色谱法和模式识别技术定量表征烟叶颜色相似性研究

用高效液相色谱法(HPLC)分析了13种国产烤烟型烟叶中多酚类物质,并定量测定了其中7种主要烟草多酚物质,用模式识别方法处理了HPLC测定多酚的谱图轮廓数据,计算了这些烟叶之间的颜色相似性标度参数TK值,并与人工感官结果进行了比较。结果表明,通过用模式识别技术处理多酚的HPLC轮廓数据,可以定量估计不同产地、不同等级烟叶之间颜色的相似性。模式识别方法处理与感官评价结果基本一致,但前者更客观。因此,模式识别技术可以作为客观评价烟叶颜色质量、弥补人工感官方法的工具。     

A Comparison of Three Methods of Extraction for the Determination of Polyphenols and Organic Acids in Tobacco by UPLC–MS–MS

Matrix solid phase dispersion (MSPD), ultrasonic extraction followed by a solid phase extraction (USE–SPE) and reflux extraction (REFLUX) were studied for the analysis of polyphenols and organic acids in tobacco. The analysis was by ultra-high performance liquid chromatography tandem mass spectrometry (UPLC–MS–MS). The multi-mode support sorbent Zirconia/AA12S50 in MSPD is more suitable for the extraction of tobacco polyphenols than conventional silica or C18 silica. Although the matrix effect of USE–SPE is slightly stronger than MSPD and REFLUX for most target compounds, it gave higher extraction capacity, recoveries and sensitivity.     

柱前衍生-超高效液相色谱-单重四极杆质谱法分析不同产地烤烟的20种游离氨基酸

为了分析烤烟中游离氨基酸含量与烤烟香型、产地的关系,以国内14个烟草种植省份的138个调制后的烤烟中部叶片为材料,采用柱前衍生-超高效液相色谱-单重四极杆质谱法对烤烟中的20种游离氨基酸进行分析。方法表征结果显示该方法满足分析要求,适合烤烟中游离氨基酸的检测。对调制后烤烟中部叶片的游离氨基酸分析结果显示,烤烟的游离氨基酸含量在产地间差异很大（28.50%~94.20%）,其中天冬酰胺、谷氨酰胺含量的相对标准偏差（RSD）超过80%;在3种典型香型（浓香型、清香型和中间香型）烤烟中,浓香型烤烟游离氨基酸含量的RSD大于其他两种香型。对同一香型的不同种植省份和同一省份不同香型的烤烟游离氨基酸分析结果显示,其含量呈现一定的规律。     

有机酸对卷烟感官风格和品质的影响研究

测定了25种成品卷烟的15种有机酸含量,采用逐步回归分析法建立了卷烟感官风格品质与有机酸之间的关系模型。根据逐步回归模型选择的变量,评价影响卷烟香韵表现与感官品质的关键有机酸成分。将回归模型成功地应用于试制卷烟香韵表现和感官品质得分预测,证明了模型的有效性及有机酸对卷烟香韵表现和感官品质的解释能力。研究结果表明,异戊酸、丙二酸、苯乙酸和柠檬酸等有机酸对卷烟香韵表现或感官品质存在正面或负面的不同影响。     

Classification of forensic soil evidences by application of THM-PyGC/MS and multivariate analysis

The forensic classification of soil samples was carried out by thermally assisted hydrolysis and methylation (THM) of soil organic matters (SOM) using pyrolysis-gas chromatography/mass spectrometry (PyGC/MS). In this work, thirty-three THM derivatives were detected as SOM contained in <3 mg soil. The specific ions of the mass spectra were selected to separate and minimize the interference between SOM peaks. SOM data were normalized with the sum of peak areas to correct the amounts of SOM contained in the soil, and the chemometric approach based on principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) was employed to evaluate and compare the soil classification. The first seven principal components (PCs) accounted for 94.8% of total cumulate variance and these PCs were statistically determined by multiple comparisons (Tamhane's T2 and Dunnett's T3) for the post hoc test (p-value < 0.05) and were used to construct the LDA model. It was determined that multiple comparisons were a statistically good criterion for deciding on the number of PCs for the LDA model. It was also concluded that the discrimination model correctly classified 40 soil samples into six clusters with high accuracy. Furthermore, the eleven marker compounds were investigated according to the loadings of PCs and the normalized data. These results demonstrated that lignin, fatty acid and urea can be used as potentially useful compounds to characterize soil samples for forensic purposes.     

Varietal discrimination of Australian wines by means of mid-infrared spectroscopy and multivariate analysis

This study outlines the use of mid-infrared (MIR) spectroscopy combined with principal component analysis (PCA) and linear discriminant analysis (LDA) for the varietal classification of commercial red and white table wines. Three red varieties (Cabernet Sauvignon, Shiraz and Merlot) and four white varieties (Chardonnay, Riesling, Sauvignon Blanc and Viognier) were sourced from different wine regions in Australia. Wine samples were scanned in transmission on a FOSS WineScan FT 120 from wave numbers 926 to 5012 cm⁻¹. All samples were sourced from the 2006 vintage and had not been blended with any other variety or wine from other regions. Spectral data were reduced to a small number of principal components (PCs) and LDA was then performed to successfully separate the wines into the different varieties. To test the robustness of the LDA models developed for the red wines, a set of red wines scanned in 2005 were used. Correct classification of over 95% was achieved for the validation set.     

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。     

R2 2(8) motifs in Aminopyrimidine sulfonate/carboxylate interactions: Crystal structures of pyrimethaminium benzenesulfonate monohydrate (2:2:1) and 2-amino-4,6-dimethylpyrimidinium sulfosalicylate dihydrate (4:2:2)

Background

The characterization of three types of Marche (Italy) honeys (Acacia, Multifloral, Honeydew) was carried out on the basis of the their quality parameters (pH, sugar content, humidity) and mineral content (Na, K, Ca, Mg, Cu, Fe, and Mn). Pattern recognition methods such as principal components analysis (PCA) and linear discriminant analysis (LDA) were performed in order to classify honey samples whose botanical origins were different, and identify the most discriminant parameters. Lastly, using ANOVA and correlations for all parameters, significant differences between diverse types of honey were examined.

Results

Most of the samples' water content showed good maturity (98%) whilst pH values were in the range 3.50 – 4.21 confirming the good quality of the honeys analysed. Potassium was quantitatively the most relevant mineral (mean = 643 ppm), accounting for 79% of the total mineral content. The Ca, Na and Mg contents account for 14, 3 and 3% of the total mineral content respectively, while other minerals (Cu, Mn, Fe) were present at very low levels. PCA explained 75% or more of the variance with the first two PC variables. The variables with higher discrimination power according to the multivariate statistical procedure were Mg and pH. On the other hand, all samples of acacia and honeydew, and more than 90% of samples of multifloral type have been correctly classified using the LDA. ANOVA shows significant differences between diverse floral origins for all variables except sugar, moisture and Fe.

Conclusion

In general, the analytical results obtained for the Marche honeys indicate the products' high quality. The determination of physicochemical parameters and mineral content in combination with modern statistical techniques can be a useful tool for honey classification.     

基于代谢组学技术的快速蒸发电离质谱与智能手术刀联用实时鉴别果汁掺伪方法研究

近年来,果汁掺伪问题日益突出。作为一种新型的实时鉴别方法,智能手术刀(IKnife)与快速蒸发电离质谱(REIMS)联用技术无需样品前处理即可解决这一问题。该文采用代谢组学指纹识别技术建立橙汁、苹果汁和葡萄汁的实时鉴别方法,并对其掺假水平进行预测。主成分分析-线性判别分析联用法建立的判别模型能够区分不同类型的果汁,排除20%交叉验证法的正确率为97.28%,未知样品实时鉴别正确率高达100%。筛选出不同果汁中的标志物,并对部分化合物进行鉴定。结果表明,此方法能够成功区分掺有10%-50%苹果汁和葡萄汁的橙汁。通过对比分析REIMS单极质谱和串联质谱(REIMS/MS)的检测结果,两种方法建立的偏最小二乘分析模型均能准确预测掺假水平(所有模型的R 2和Q 2均大于0.82)。与传统的REIMS方法相比,REIMS/MS的预测值更加精确。