Anionic forensic signatures for sample matching of potassium cyanide using high performance ion chromatography and chemometrics

Potassium cyanide was used as a model toxicant to determine the feasibility of using anionic impurities as a forensic signature for matching cyanide salts back to their source. In this study, portions of eight KCN stocks originating from four countries were separately dissolved in water and analyzed by high performance ion chromatography (HPIC) using an anion exchange column and conductivity detection. Sixty KCN aqueous samples were produced from the eight stocks and analyzed for 11 anionic impurities. Hierarchal cluster analysis and principal component analysis were used to demonstrate that KCN samples cluster according to source based on the concentrations of their anionic impurities. The Fisher-ratio method and degree-of-class separation (DCS) were used for feature selection on a training set of KCN samples in order to optimize sample clustering. The optimal subset of anions needed for sample classification was determined to be sulfate, oxalate, phosphate, and an unknown anion named unk5. Using K-nearest neighbors (KNN) and the optimal subset of anions, KCN test samples from different KCN stocks were correctly determined to be manufactured in the United States. In addition, KCN samples from stocks manufactured in Belgium, Germany, and the Czech Republic were all correctly matched back to their original stocks because each stock had a unique anionic impurity profile. The application of the Fisher-ratio method and DCS for feature selection improved the accuracy and confidence of sample classification by KNN.     

Determination of impurities in illicit methamphetamine seized in Korea and Japan

Impurities produced during synthesis of methamphetamine (MA) show different patterns under various synthetic conditions. Valuable information on the origins and smuggling routes can be obtained by using impurities as chemical fingerprints. We have detected more than 100 compounds from 436 MA samples seized in Korea by gas chromatography-flame ionization detector and gas chromatography-mass spectrometer, among which 31 impurities and three additives were identified. Twenty-six impurity peaks including unknowns were selected as the indicators of similarity, and were used as variables for cluster analysis. Cluster analysis result showed that part of the MA samples seized in Japan might have the same origin as those seized in Korea. It means that broad-based cooperation is necessary for efficient regulation of MA. Synthetic trends of the MA seizures of Korea were monitored by cluster analysis with 16 MA samples synthesized by three different methods in the previous work. We could find comparable changes of synthetic trends, which might have been influenced by domestic regulations and international situations.     

Evaluation method for linking methamphetamine seizures using stable carbon and nitrogen isotopic compositions: a complementary study with impurity profiling

Drug profiling, extraction of physical and/or chemical profiles from abused drug samples, is useful for inferring and characterizing links between samples originating from the same and different seizures, and supports drug crime investigations. We describe an evaluation method for linking methamphetamine (MA) seizures using stable carbon and nitrogen isotopic compositions concurrently with gas chromatographic impurity profiling, which is one of the major methods of drug profiling. Several sets of MA seized in Japan, whose investigative information indicated linkages, were analyzed. The impurity profile of each set of seizures was quite similar and hierarchical cluster analysis showed a sample classification that was relatively consistent with the investigative information. The stable carbon and nitrogen isotopic compositions of the MA seizures varied between -29.40 and -24.90 (delta(13)C) and -2.29 and 5.94 (delta(15)N), respectively. In the delta(13)C-delta(15)N graph, MA seizures were classified into seven groups, probably reflecting different origins. The size of the cluster in the isotopic-composition graph was determined by pooled standard deviations (s(p)), the pooled estimates of measurement uncertainty. The sizes of the clusters were less than 6s(p) and the linkages between the MA seizures from the isotopic compositions were consistent with the impurity profiling and investigative information. The results showed that complementary use of stable-isotopic compositions with impurity profiling provides useful information for evaluating the links between seizures.     

五味子优良品种优选的模式识别方法

通过超高效液相色谱法,对不同生长特征的五味子样品中5种主要的木脂素成分:五味子醇甲、五味子醇乙、五味子酯甲、五味子甲素和五味子乙素进行测定,同时考察其抗氧化活性。 对所得到的5种木脂素成分的含量及抗氧化数据进行聚类分析和主成分分析,并且通过逐步判别分析建立判别函数,从而筛选五味子优良品种。 主成分分析和聚类结果基本一致,均将样品分为3大类,逐步判别分析的正确率高达90%。     

Pattern recognition applied to mineral characterization of Brazilian coffees and sugar-cane spirits

Aluminium, Ca, Cu, Fe, K, Mg, Mn, Na, Pb, S, Se, Si, Sn, Sr, and Zn were determined in coffee and sugar-cane spirit (cachaça) samples by axial viewing inductively coupled plasma optical emission spectrometry (ICP OES). Pattern recognition techniques such as principal component analysis and cluster analysis were applied to data sets in order to characterize samples with relation to their geographical origin and production mode (industrial or homemade and organically or conventionally produced). Attempts to correlate metal ion content with the geographical origin of coffee and the production mode (organic or conventional) of cachaça were not successful. Some differentiation was suggested for the geographical origin of cachaça of three regions (Northeast, Central, and South), and for coffee samples, related to the production mode. Clear separations were only obtained for differentiation between industrial and homemade cachaças, and between instant soluble and roasted coffees.     

Multivariate analysis of reflectance spectra from propolis: Geographical variation in Romanian samples

The present study described reflectance spectroscopy as a suitable analytical tool to discriminate the floral origin of 39 Romanian propolis samples. Relevant differences between the UV-vis reflectance spectra of the investigated propolis samples within the 220-850 nm spectral range were found. The results obtained applying cluster analysis, principal component analysis and linear discriminant analysis to the digitized data of zero order, zero order normalized and first order derivative spectra support the reliability of this technique. In addition, the application of the linear discriminant analysis to the score matrices corresponding to the first principal components appeared to be an illuminating solution. Generally, the samples have been assigned to two large groups in a good agreement with their vegetal sampling location, samples originating from predominant forest area and samples originating from meadows. Within the first group, two subgroups were identified according to the dominant type of the forest, deciduous or resinous, while within the last group three subgroups were found according to the extend and variety of the meadow.     

Evaluation of nutritional parameters in infant formulas and powdered milk by Raman spectroscopy

It has been made a critical evaluation of the application of near infrared Fourier transform-Raman spectroscopy for the simultaneous determination of the most important nutritional parameters such as energetic value, carbohydrate, protein and fat contents of infant formula and powdered milk samples based on the use of partial least squares (PLS) regression analysis. A highly heterogeneous population of 23 samples, covering a wide range of infant food formula and powdered milk, were obtained from the Spanish market. Raman spectra, obtained by excitation with a Nd:YAG laser at 1064 nm, show no disturbing fluorescence effects; therefore sample spectra can be recorded without any previous preparation step. After correcting the spectra, hierarchical cluster analysis was performed in order to select a representative calibration set and the corresponding validation sample set. Different PLS models and several spectral windows were tested in order to evaluate their prediction capabilities for the validation set. Considering a calibration set comprised of three replicate spectra of 15 samples and a validation data set of eight samples, the procedure developed provided figures of merit which complied with the statutory values declared by the United States Food and Drug Administration (US FDA).     

Classification of gasoline by octane number and light gas condensate fractions by origin with using dielectric or gas-chromatographic data and chemometrics tools

The approach for classification of gasoline by octane number and light gas condensate fractions by origin with using dielectric permeability data has been proposed and compared with classification of same samples on the basis of gas-chromatographic data. The precision of dielectric permeability measurements was investigated by using ANOVA. The relative standard deviation of dielectric permeability was in the range from 0.3 to 0.5% for the range of dielectric permeability from 1.8 to 4.4. The application of exploratory chemometrics tools (cluster analysis and principal component analysis) allow to explicitly differentiate the gasoline and light gas condensate fractions into groups of samples related to specific octane number or origin. The neural networks allow to perfectly classifying the gasoline and light gas condensate fractions.     

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。     

Bottle-neck type of neural network as a mapping device towards food specifications

A novel methodology is proposed for food specifications associated with the origin of food. The methodology was tested on honey samples collected within the TRACE EU project. The data were sampled in various regions in Europe and analysed for the trace elements content. The sampling sites were characterized by different geological origins, such as limestone, shale, or magmatic. We have chosen 14 elements, B, Na, Mg, A, K, Ca, Mn, Co, Ni, Cu, Zn, Rb, Sr, and Ba, due to their influence on the separation of samples regarding the geology of the sampling sites. A special architecture of an error back-propagation neural network, so called bottle-neck type of neural network was used to project the data into a 2D plane. The data were fed into the 14-nodes input layer and then transferred through the 2-nodes hidden layer (compared to a bottle-neck) to the 14-nodes output layer. The two hidden nodes representing the two coordinates of the projection plane enable us to map the samples used for training of the bottle-neck network. With the knowledge about the classes of individual samples we determine the clusters in the projection plane and consequently obtain the coordinates of the centroid (gravity point) of a particular cluster. The clusters are characterized with an ellipse shape borders spanning the length of up to 3σ in each dimension. Since the data were classified as regard to the geology, three main clusters were sought: (i) limestone, (ii) shale/mudstone/clay/loess, and (iii) acid-magmatic origin of honey samples. The novel methodology proposed for food specifications was demonstrated on a reduced set of samples, which shows good clustering of all three classes in the projection plane, and on the third class of the original data set.     

Characterization of ceramics from the archaeological site of San Miguel Ixtapan,Mexico State,Mexico, using NAA,SEM, XRD and PIXE techniques

Summary Ceramic samples found in San Miguel Ixtapan, Mexico State, Mexico, were analyzed by means of NAA, PIXE, SEM and XRD. Statistical treatments such as bivariate cluster and principal-components analysis were applied to the data set. The origins of these ceramic samples were classified as local, regional and foreign.     

Authentication of Galician (N.W. Spain) quality brand potatoes using metal analysis. Classical pattern recognition techniques versus a new vector quantization-based classification procedure

The objective of this work was to develop a classification system in order to confirm the authenticity of Galician potatoes with a Certified Brand of Origin and Quality (CBOQ) and to differentiate them from other potatoes that did not have this quality brand. Elemental analysis (K, Na, Rb, Li, Zn, Fe, Mn, Cu, Mg and Ca) of potatoes was performed by atomic spectroscopy in 307 samples belonging to two categories, CBOQ and Non-CBOQ potatoes. The 307 x 10 data set was evaluated employing multivariate chemometric techniques, such as cluster analysis and principal component analysis in order to perform a preliminary study of the data structure. Different classification systems for the two categories on the basis of the chemical data were obtained applying several commonly supervised pattern recognition procedures [such as linear discriminant analysis, K-nearest neighbours (KNN), soft independent modelling of class analogy and multilayer feed-forward neural networks]. In spite of the fact that some of these classification methods produced satisfactory results, the particular data distribution in the 10-dimensional space led to the proposal of a new vector quantization-based classification procedure (VQBCP). The results achieved with this new approach (percentages of recognition and prediction abilities > 97%) were better than those attained by KNN and can be compared advantageously with those provided by LDA (linear discriminant analysis), SIMCA (soft independent modelling of class analogy) and MLF-ANN (multilayer feed-forward neural networks). The new VQBCP demonstrated good performance by carrying out adequate classifications in a data set in which the classes are subgrouped. The metal profiles of potatoes provided sufficient information to enable classification criteria to be developed for classifying samples on the basis of their origin and brand.     

Authenticity assessment of beef origin by principal component analysis of matrix-assisted laser desorption/ionization mass spectrometric data

It has become necessary to assess the authenticity of beef origin because of concerns regarding human health hazards. In this study, we used a metabolomic approach involving matrix-assisted laser desorption/ionization imaging mass spectrometry to assess the authenticity of beef origin. Highly accurate data were obtained for samples of extracted lipids from beef of different origin; the samples were grouped according to their origin. The analysis of extracted lipids in this study ended within 10 min, suggesting this approach can be used as a simple authenticity assessment before a definitive identification by isotope analysis.     

Geographical classification of Epimedium based on HPLC fingerprint analysis combined with multi‐ingredients quantitative analysis

A reliable and comprehensive method for identifying the origin and assessing the quality of Epimedium has been developed. The method is based on analysis of HPLC fingerprints, combined with similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and multi‐ingredient quantitative analysis. Nineteen batches of Epimedium , collected from different areas in the western regions of China, were used to establish the fingerprints and 18 peaks were selected for the analysis. Similarity analysis, HCA and PCA all classified the 19 areas into three groups. Simultaneous quantification of the five major bioactive ingredients in the Epimedium samples was also carried out to confirm the consistency of the quality tests. These methods were successfully used to identify the geographical origin of the Epimedium samples and to evaluate their quality.     

Investigation of the origin of archaeological glass artefacts by means of pattern recognition

The origin of medieval glass artefacts is studied by using a supervised learning technique, which is shown to be helpful when samples cannot be identified by typical design and appearance. A set of seventy pieces of glass was analyzed for ten trace elements by optical emission spectrography. The data matrix of 33 known objects from five origins was evaluated by multivariate variance and discriminant analysis in a training step. The extracted non-elementary discriminant functions were used to classify the 37 unidentified samples. The classification result is discussed in terms of its cultural/historical information content.     

Determination of Selected Metals in Niger Delta Oils by Graphite Furnace Atomic Absorption Spectrometry

Abstract

Graphite furnace atomic absorption spectroscopic (GFAAS) analysis of oil samples from the onshore and offshore fields in the Niger Delta was carried out in order to determine their trace element contents. The concentrations of the elements (Cr, Co, Cu, Fe, Mn, Ni, Pb, and V) determined ranged from 0.151–941.199 ppb with an average of 30.190 ppb (% RSD<5) for the oil samples analyzed. The pattern of occurrence of each element was in agreement with studies conducted employing neutron activation analysis and flame atomic absorption spectroscopy. From the cluster analysis of the data, two groups of oils were evident. The groups are correlated with each other, indicating common or similar genetic origin. Trace metal parameters indicate that the oils were generated from organic matter of strong terrestrial input. GFAAS proved to be a good method for the analysis of elemental composition and classification of Niger Delta oils.     

Fingerprint Analysis of Eucommia Bark by LC-DAD and LC-MS with the Aid of Chemometrics

Chromatographic fingerprints of 46 Eucommia Bark samples were obtained by liquid chromatography-diode array detector (LC-DAD). These samples were collected from eight provinces in China, with different geographical locations, and climates. Seven common LC peaks that could be used for fingerprinting this common popular traditional Chinese medicine were found, and six were identified as substituted resinols (4 compounds), geniposidic acid and chlorogenic acid by LC-MS. Principal components analysis (PCA) indicated that samples from the Sichuan, Hubei, Shanxi and Anhui—the SHSA provinces, clustered together. The other objects from the four provinces, Guizhou, Jiangxi, Gansu and Henan, were discriminated and widely scattered on the biplot in four province clusters. The SHSA provinces are geographically close together while the others are spread out. Thus, such results suggested that the composition of the Eucommia Bark samples was dependent on their geographic location and environment. In general, the basis for discrimination on the PCA biplot from the original 46 objects× 7 variables data matrix was the same as that for the SHSA subset (36 × 7 matrix). The seven marker compound loading vectors grouped into three sets: (1) three closely correlating substituted resinol compounds and chlorogenic acid; (2) the fourth resinol compound identified by the OCH₃ substituent in the R4 position, and an unknown compound; and (3) the geniposidic acid, which was independent of the set 1 variables, and which negatively correlated with the set 2 ones above. These observations from the PCA biplot were supported by hierarchical cluster analysis, and indicated that Eucommia Bark preparations may be successfully compared with the use of the HPLC responses from the seven marker compounds and chemometric methods such as PCA and the complementary hierarchical cluster analysis (HCA).     

Determination of the iron content of aluminate liquors from the Bayer alumina process by neutron activation analysis

The behaviour of iron in the Bayer process has been investigated by neutron activation analysis and Ge(Li) spectrometry, using bauxite samples of various origin. The amount of iron impurities in alumina was found to be independent of the origin of the bauxites. Alumina is contaminated by iron in the process of decomposition of aluminate liquors; regardless whether iron is present in ‘dissolved’ form or as a floating impurity, it will pass into the alumina almost completely.     

Land-Based Classification of Herb's Origin Based on Supervised and Unsupervised Pattern Recognition of Plant and Soil Chemical Profiling

In this work, a series of supervised and unsupervised pattern recognition techniques are assessed for the first time as a tool in order to elaborate a common approach able to classify different plant species based on a common pattern determined by their geographical origin. For this reason, soil samples and extracts from various parts of 7 self-sown indigenous Greek plants were collected in nature from two regions (highlands and insular) in NW Greece. Based on the chemical profiling of each species and the respective soil matrix composition, the identification of similarities and dissimilarities on a regional basis were pursued. The results indicate that hierarchical cluster classification and discriminant analysis using the chemical profile of inorganic elements and soil matrix composition provide the basis for the provision of an area-specific geographic marker that enables the classification of different herbs based on their origin with high discrimination efficiency.