Metabolic chemotypes of CITES protected Dalbergia timbers from Africa, Madagascar, and Asia

Visible/near-infrared hyperspectral imaging combined with machine learning for identification of ten Dalbergia species

Introduction

This study addresses the urgent need for non-destructive identification of commercially valuable Dalbergia species, which are threatened by illegal logging. Effective identification methods are crucial for ecological conservation, biodiversity preservation, and the regulation of the timber trade.

Methods

We integrate Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI) with advanced machine learning techniques to enhance the precision and efficiency of wood species identification. Our methodology employs various modeling approaches, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). These models analyze spectral data across Vis (383-982 nm), NIR (982-2386 nm), and full spectral ranges (383 nm to 2386 nm). We also assess the impact of preprocessing techniques such as Standard Normal Variate (SNV), Savitzky-Golay (SG) smoothing, normalization, and Multiplicative Scatter Correction (MSC) on model performance.

Results

With optimal preprocessing, both SVM and CNN models achieve 100% accuracy across NIR and full spectral ranges. The selection of an appropriate wavelength range is critical; utilizing the full spectrum captures a broader array of the wood's chemical and physical properties, significantly enhancing model accuracy and predictive power.

Discussion

These findings underscore the effectiveness of Vis/NIR HSI in wood species identification. They also highlight the importance of precise wavelength selection and preprocessing techniques to maximize both accuracy and cost-efficiency. This research contributes substantially to ecological conservation and the regulation of the timber trade by providing a reliable, non-destructive method for identifying threatened wood species.

Wood from Hardwood Angiosperms and Coniferous Gymnosperms Shows Distinctive Lignin Peaks in Direct Analysis in Real Time (DART) Mass Spectra

A data set was constructed consisting of 3021 mass spectra randomly selected from all available families in the ForeST© (Forensic Spectra of Trees) database of mass spectra for wood analyzed by Direct Analysis in Real Time ionization coupled with time-of-flight mass spectrometry (DART-TOFMS). Clear and reproducible differences were observed between the lignin peaks for hardwood angiosperms and coniferous gymnosperms, with DART-TOFMS spectra of angiosperms showing significantly higher relative abundances for peaks associated with syringyl subunits. Application of the method to processed wood samples demonstrated that these differences can be used to provide support for enforcing trade laws by accurately identifying the source of finished wood products from hardwood angiosperms and coniferous gymnosperms.

Analysis of natural organic matter via fourier transform ion cyclotron resonance mass spectrometry: an overview of recent non-petroleum applications

Among the different techniques for mass analysis, ultra-high-resolution Fourier transform ion cyclotron resonance (FTICR) is the method of choice for highly complex samples, as it offers unrivaled mass accuracy and resolving power, combined with a high degree of flexibility in hybrid instruments as well as for ion activation techniques. FTICR instruments are readily embraced by the biological and biomedical research communities and applied over a wide range of applications for the analysis of biomolecules such as carbohydrates, lipids, nucleic acids, and proteins. In the field of natural organic matter (NOM) analysis, petroleum-related studies currently dominate FTICR-MS applications. Recently, however, there is a growing interest in developing high-performance MS methods for the characterization of NOM samples from natural aquatic and terrestrial environments. Here, we present an overview of FTICR-MS techniques for complex, non-petroleum NOM samples, including data analysis and novel tandem mass spectrometry (MS/MS) methods for structural classifications. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd.

Opportunities in phytochemistry, ecophysiology and wood research via laser ablation direct analysis in real time imaging-mass spectrometry

Analysis of wood transects in a manner that preserves the spatial distribution of the metabolites present is highly desirable to among other things: (1) facilitate ecophysiology studies that reveal the association between chemical make-up and environmental factors or climatic events over time; and (2) investigate the mechanisms of the synthesis and trafficking of small molecules within specialised tissues. While a variety of techniques could be applied to achieve these goals, most remain challenging and impractical. Laser ablation direct analysis in real time imaging-mass spectrometry (LADI-MS) was successfully used to survey the chemical profile of wood, while also preserving the small-molecule spatial distributions. The tree species Entandrophragma candollei Harms, Millettia laurentii DeWild., Pericopsis elata (Harms) Meeuwen, Dalbergia nigra (Vell.) Benth. and Dalbergia normandii Bosser & R.Rabev were analysed. Several compounds were associated with anatomical features. A greater diversity was detected in the vessels and parenchyma compared with the fibres. Analysis of single vessels revealed that the chemical fingerprint used for timber identification is mainly determined by vessel content. Laser ablation direct analysis in real time imaging-mass spectrometry offers unprecedented opportunities to investigate the distribution of metabolites within wood samples, while circumventing the issues associated with previous methods. This technique opens up new vistas for the discovery of small-molecule biomarkers that are linked to environmental events.

Rapid detection and classification of hongmu by atmospheric pressure ionization mass spectrometry

A schematic diagram of atmospheric pressure glow discharge mass spectrometry (APGD-MS) for hongmu detection.

Rapid identification of wood species using XRF and neural network machine learning

An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.

Timber identification of Autranella, Baillonella and Tieghemella in the taxonomically challenging Sapotaceae family

BACKGROUND

To enforce timber import laws and perform timber species identification, the identity of the botanical species must be well-defined. Since the Sapotaceae family is known as a taxonomically challenging family, we focus in this study on the four most valuable Sapotaceae timber species from tropical Africa: Autranella congolensis (De Wild.) A.Chev., Baillonella toxisperma Pierre, Tieghemella africana Pierre and Tieghemella heckelii (A.Chev.) Pierre ex Dubard. The wood anatomical characteristic fiber lumen fraction and Direct Analysis in Real Time-Time of Flight Mass Spectrometry (DART-TOFMS) were used to differentiate the four species and to make inferences on species delineation and taxonomic identity.

RESULTS

We observed differences in the fiber lumen fraction measurements and discerned two groups: (1) A. congolensis and B. toxisperma, and (2) T. africana and T. heckelii. In addition, all Mann-Whitney U comparisons and differences in distributions (Kolmogorov-Smirnov) for the fiber lumen fraction measurements were significant between all species. When permutating the data between species within those two groups, significant differences were still found between the species within those groups. This could indicate that the fiber lumen fraction is not diagnostic to discern the species. DART-TOFMS analysis showed that A. congolensis and B. toxisperma have distinct chemotypes, while T. heckelii and T. africana have remarkably similar chemotypes.

CONCLUSIONS

Based on our observations of similar chemotype and weakly differentiated fiber lumen ratio, we support an alternative taxonomic hypothesis that considers Tieghemella monotypic, because of the strong resemblance between T. heckelii and T. africana. Larger sample sizes and further research is required to develop methodology for the identification of these species. A taxonomic study utilizing molecular genetics would be beneficial to assess the status of the genus and the species limits. This could have implications towards their potential inclusion on CITES appendices if there is ever need for them to be listed. If Tieghemella africana and T. heckelii remain two distinct species, they should both be listed. Screening agents should be aware that the morphological and chemical differences between T. africana and T. heckelii are minimal.

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

Microbiomes in forensic botany: a review

Fragments of botanical material can often be found at crime scenes (on live and dead bodies, or on incriminating objects) and can provide circumstantial evidence on various aspects of forensic investigations such as determining crime scene locations, times of death or possession of illegal species. Morphological and genetic analysis are the most commonly applied methods to analyze plant fragment evidence but are limited by their low capacity to differentiate between potential source locations, especially at local scales. Here, we review the current applications and limitations of current plant fragment analysis for forensic investigations and introduce the potential of microbiome analysis to complement the existing forensic plant fragment analysis toolkit. The potential for plant fragment provenance identification at geographic scales meaningful to forensic investigations warrants further investigation of the phyllosphere microbiome in this context. To that end we identify three key areas of future research: 1) Retrieval of microbial DNA of sufficient quality and quantity from botanical material; 2) Variability of the phyllosphere microbiome at different taxonomic and spatial scales, with explicit reference to assignment capacity; 3) Impacts on assignment capacity of time, seasonality and movement of fragments between locations. The development of robust microbiome analysis tools for forensic purposes in botanical material could increase the evidentiary value of the botanical evidence commonly encountered in casework, aiding in the identification of crime scene locations.

Development and technical application of SSR-based individual identification system for Chamaecyparis taiwanensis against illegal logging convictions

Chamaecyparis taiwanensis is an endemic plant suffering illegal logging in Taiwan for its high economic value. Lack of direct evidence to correlate stump and timber remains a hurdle for law enforcement. In this report, 23 polymorphic Genomic Simple Sequence Repeat (gSSR) and 12 Expressed Sequence Tag (EST)-SSR markers were developed and their transferability was assessed. The individual identification system built from selected non-linkage 30 SSR markers has a combined probability of identity as 5.596 × 10^–12 equivalents to identifying an individual in a population of up to 18 million C. taiwanensis with 99.99% confidence level. We also applied the system in an actual criminal case by selecting 19 of these markers to correlate illegally felled timbers and victim trees. Our data demonstrate that molecular signals from three timbers hit with three victim trees with confidence level more than 99.99%. This is the first example of successfully applying SSR in C. taiwanensis as a court evidence for law enforcement. The identification system adapted advanced molecular technology and exhibits its great potential for natural resource management on C. taiwanensis.

Forensic identification of the keratin fibers of South American camelids by ambient ionization mass spectrometry: Vicuña, alpaca and guanaco

RATIONALE

The keratin fleece of the endangered vicuña (Vicugna vicugna) commands a high value in international markets, and this trade has caused illegal poaching and a substantial decrease in vicuña populations. Morphological analysis of hairs does not have the resolution to determine the species of origin of camelid natural fibers. In addition, commerce in camelid fleece also includes the legal trade of alpaca (Vicugna pacos) and guanaco (Lama guanicoe) wool.

METHODS

The keratin fiber spectra of vicuña (n = 19), guanaco (n = 20) and alpaca (n = 20) were collected using X-ray fluorescence (XRF) spectrometry, Horizontal attenuated total reflectance Fourier transform infrared (HATR-FTIR) spectroscopy and direct analysis in real time time-of-flight mass spectrometry (DART-TOFMS). Analysis with each technique evaluated the data to determine if the three taxa could be separated using either descriptive or multivariate statistics.

RESULTS

XRF analysis showed that the elements detected and their relative concentrations were similar in all three species, whereas HATR-FTIR analysis could identify alpaca fleece but could not differentiate vicuña from guanaco. Ions detected by ambient ionization using DART-TOFMS, in either positive- or negative-ion mode, gave the best results and showed that each taxonomic group is distinctive. Multivariate analysis of the mass spectra created robust models which resolved each species (LOOCV = 99.9%). The analyses of eight validation samples were correctly assigned to the appropriate species and demonstrated the reliability of DART-TOFMS to infer taxonomic source.

CONCLUSIONS

The DART-TOFMS spectra of unmodified keratin fibers infer that the chemotype of each species is heavily influenced by fatty acids, cholesterol and its analogs, and that these ions are useful in separating the fleece of vicuña, alpaca and guanaco. We posit that the etiological source of these chemotype differences is consistent with genetic modulations and is less influenced by diet. Accurate taxonomic identification of fleece is important to identify violations and assists in the protection of rare species.

Chemical Fingerprinting of Wood Sampled along a Pith-to-Bark Gradient for Individual Comparison and Provenance Identification

Background and Objectives: The origin of traded timber is one of the main questions in the enforcement of regulations to combat the illegal timber trade. Substantial efforts are still needed to develop techniques that can determine the exact geographical provenance of timber and this is vital to counteract the destructive effects of illegal logging, ranging from economical loss to habitat destruction. The potential of chemical fingerprints from pith-to-bark growth rings for individual comparison and geographical provenance determination is explored. Materials and Methods: A wood sliver was sampled per growth ring from four stem disks from four individuals of Pericopsis elata (Democratic Republic of the Congo) and from 14 stem disks from 14 individuals of Terminalia superba (Côte d’Ivoire and Democratic Republic of the Congo). Chemical fingerprints were obtained by analyzing these wood slivers with Direct Analysis in Real Time Time-Of-Flight Mass Spectrometry (DART TOFMS). Results: Individual distinction for both species was achieved but the accuracy was dependent on the dataset size and number of individuals included. As this is still experimental, we can only speak of individual comparison and not individual distinction at this point. The prediction accuracy for the country of origin increases with increasing sample number and a random sample can be placed in the correct country. When a complete disk is removed from the training dataset, its rings (samples) are correctly attributed to the country with an accuracy ranging from 43% to 100%. Relative abundances of ions appear to contribute more to differentiation compared to frequency differences. Conclusions: DART TOFMS shows potential for geographical provenancing but is still experimental for individual distinction; more research is needed to make this an established method. Sampling campaigns should focus on sampling tree cores from pith-to-bark, paving the way towards a chemical fingerprint database for species provenance.

A geographical traceability system for Merbau (Intsia palembanica Miq.), an important timber species from peninsular Malaysia

To inform product users about the origin of timber, the implementation of a traceability system is necessary for the forestry industry. In this study, we developed a comprehensive genetic database for the important tropical timber species Merbau, Intsia palembanica, to trace its geographic origin within peninsular Malaysia. A total of 1373 individual trees representing 39 geographically distinct populations of I. palembanica were sampled throughout peninsular Malaysia. We analyzed the samples using a combination of four chloroplast DNA (cpDNA) markers and 14 short tandem repeat (STR) markers to establish both cpDNA haplotype and STR allele frequency databases. A haplotype map was generated through cpDNA sequencing for population identification, resulting in six unique haplotypes based on 10 informative intraspecifically variable sites. Subsequently, an STR allele frequency database was developed from 14 STRs allowing individual identification. Bayesian cluster analysis divided the individuals into two genetic clusters corresponding to the northern and southern regions of peninsular Malaysia. Tests of conservativeness showed that the databases were conservative after the adjustment of the θ values to 0.2000 and 0.2900 for the northern (f = 0.0163) and southern (f = 0.0285) regions, respectively. Using self-assignment tests, we observed that individuals were correctly assigned to populations at rates of 40.54-94.12% and to the identified regions at rates of 79.80-80.62%. Both the cpDNA and STR markers appear to be useful for tracking Merbau timber originating from peninsular Malaysia. The use of these forensic tools in addition to the existing paper-based timber tracking system will help to verify the legality of the origin of I. palembanica and to combat illegal logging issues associated with the species.

Identification of rhinoceros keratin using direct analysis in real time time-of-flight mass spectrometry and multivariate statistical analysis

RATIONALE

Trade in rhinoceros horn is regulated or banned internationally in recognition of its impact on wild populations worldwide. Enforcement of the laws and regulations depends on successfully identifying when violations occur, which is complicated by the presence of alternative/imitation rhinoceros horn keratin (e.g., bovid horn keratin). In this study, we assess the potential for Direct Analysis in Real Time (DART) ionization paired with Time-Of-Flight Mass Spectrometry (DART-TOFMS) to classify different keratin types from four taxonomic groups: rhinoceros, bovid, domestic horse, and pangolin.

METHODS

The spectra of 156 keratin samples from all five rhinoceros species (horn keratin), eight genera of bovids (horn keratin), domestic horses (hoof keratin), and all extant species of pangolins (scale keratin) were collected. Fisher ratio analysis identified the most important ions that characterized each class and these ions were used for the training model, which consisted of 143 spectra. Kernel Discriminant Analysis (KDA) was used to classify the different groups.

RESULTS

The spectra collected for each taxonomic group are distinctive. The chemotypes demonstrate that the spectra of rhinoceros, bovids, and domestic horse are similar to each other, whereas the chemotypes of pangolins show a different chemical profile. The model built by KDA resolved each taxonomic group: 95% of samples were correctly assigned using leave-one-out cross validation. The 13 blind samples not used in model development were all correctly classified to taxonomic source.

CONCLUSIONS

DART-TOFMS appears to be a reliable approach for taxonomic identification of keratin. This analysis can be carried out with a small sliver of keratin, with minimal sample preparation, inexpensively and quickly, making it a potential valuable tool for identification of rhinoceros horn and other keratin types.

Alignment-free genome comparison enables accurate geographic sourcing of white oak DNA

Background

The application of genomic data and bioinformatics for the identification of restricted or illegally-sourced natural products is urgently needed. The taxonomic identity and geographic provenance of raw and processed materials have implications in sustainable-use commercial practices, and relevance to the enforcement of laws that regulate or restrict illegally harvested materials, such as timber. Improvements in genomics make it possible to capture and sequence partial-to-complete genomes from challenging tissues, such as wood and wood products.

Results

In this paper, we report the success of an alignment-free genome comparison method, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {d}_2^{\ast }, $$\end{document}d2∗, that differentiates different geographic sources of white oak (Quercus) species with a high level of accuracy with very small amount of genomic data. The method is robust to sequencing errors, different sequencing laboratories and sequencing platforms.

Conclusions

This method offers an approach based on genome-scale data, rather than panels of pre-selected markers for specific taxa. The method provides a generalizable platform for the identification and sourcing of materials using a unified next generation sequencing and analysis framework.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5253-1) contains supplementary material, which is available to authorized users.

Source attribution profiling of five species of Amanita mushrooms from four European countries by high resolution liquid chromatography-mass spectrometry combined with multivariate statistical analysis and DNA-barcoding

Source attribution profiling of five species of Amanita mushrooms from four European countries was performed using Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) combined with multivariate statistical analysis. Initially, species determination was carried out morphologically and was verified by DNA-analysis. This data was then combined with chemical profiling, generated from LC-HRMS full scan analysis. The untargeted data was processed and the 720 most abundant peaks in the LC-HRMS chromatogram were used to build a multivariate PLS-DA model. The two independent methods for species determination showed 100% correlation, indicating the potential use of chemical profiling as a supporting technique to genetic methods. When specimens of one species were studied, significant variation related to the region of growth was found. The potential of the geo-positioning was shown for A. phalloides from Sweden, Denmark and UK and A. virosa from Sweden and Denmark. Additionally, A. virosa specimens could be attributed to three geographically different regions of Sweden.

DNA barcoding of vouchered xylarium wood specimens of nine endangered Dalbergia species

ITS2+ trnH - psbA was the best combination of DNA barcode to resolve the Dalbergia wood species studied. We demonstrate the feasibility of building a DNA barcode reference database using xylarium wood specimens. The increase in illegal logging and timber trade of CITES-listed tropical species necessitates the development of unambiguous identification methods at the species level. For these methods to be fully functional and deployable for law enforcement, they must work using wood or wood products. DNA barcoding of wood has been promoted as a promising tool for species identification; however, the main barrier to extensive application of DNA barcoding to wood is the lack of a comprehensive and reliable DNA reference library of barcodes from wood. In this study, xylarium wood specimens of nine Dalbergia species were selected from the Wood Collection of the Chinese Academy of Forestry and DNA was then extracted from them for further PCR amplification of eight potential DNA barcode sequences (ITS2, matK, trnL, trnH-psbA, trnV-trnM1, trnV-trnM2, trnC-petN, and trnS-trnG). The barcodes were tested singly and in combination for species-level discrimination ability by tree-based [neighbor-joining (NJ)] and distance-based (TaxonDNA) methods. We found that the discrimination ability of DNA barcodes in combination was higher than any single DNA marker among the Dalbergia species studied, with the best two-marker combination of ITS2+trnH-psbA analyzed with NJ trees performing the best (100% accuracy). These barcodes are relatively short regions (<350 bp) and amplification reactions were performed with high success (≥90%) using wood as the source material, a necessary factor to apply DNA barcoding to timber trade. The present results demonstrate the feasibility of using vouchered xylarium specimens to build DNA barcoding reference databases.

Comparison of species classification models of mass spectrometry data: Kernel Discriminant Analysis vs Random Forest; A case study of Afrormosia (Pericopsis elata (Harms) Meeuwen)

RATIONALE

The genus Pericopsis includes four tree species of which only Pericopsis elata (Harms) Meeuwen is of commercial interest. Enforcement officers might have difficulties discerning this CITES-listed species from some other tropical African timber species. Therefore, we tested several methods to separate and identify these species rapidly in order to enable customs officials to uncover illegal trade. In this study, two classification methods using Direct Analysis in Real Time (DART™) ionization coupled with Time-of-Flight Mass Spectrometry (DART-TOFMS) data to discern between several species are presented.

METHODS

Metabolome profiles were collected using DART™ ionization coupled with TOFMS analysis of heartwood specimens of all four Pericopsis species and Haplormosia monophylla (Harms) Harms, Dalbergia melanoxylon Guill. & Perr. Harms, and Milicia excelsa (Welw.) C.C. Berg. In total, 95 specimens were analysed and the spectra evaluated. Kernel Discriminant Analysis (KDA) and Random Forest classification were used to discern the species.

RESULTS

DART-TOFMS spectra obtained from wood slivers and post-processing analysis using KDA and Random Forest classification separated Pericopsis elata from the other Pericopsis taxa and its lookalike timbers Haplormosia monophylla, Milicia excelsa, and Dalbergia melanoxylon. Only 50 ions were needed to achieve the highest accuracy.

CONCLUSIONS

DART-TOFMS spectra of the taxa were reproducible and the results of the chemometric analysis provided comparable accuracy. Haplormosia monophylla was visually distinguished based on the heatmap and was excluded from further analysis. Both classification methods, KDA and Random Forest, were capable of distinguishing Pericopsis elata from the other Pericopsis taxa, Milicia excelsa, and Dalbergia melanoxylon, timbers that are commonly traded.

Source identification of western Oregon Douglas-fir wood cores using mass spectrometry and random forest classification

PREMISE OF THE STUDY

We investigated whether wood metabolite profiles from direct analysis in real time (time-of-flight) mass spectrometry (DART-TOFMS) could be used to determine the geographic origin of Douglas-fir wood cores originating from two regions in western Oregon, USA.

METHODS

Three annual ring mass spectra were obtained from 188 adult Douglas-fir trees, and these were analyzed using random forest models to determine whether samples could be classified to geographic origin, growth year, or growth year and geographic origin. Specific wood molecules that contributed to geographic discrimination were identified.

RESULTS

Douglas-fir mass spectra could be differentiated into two geographic classes with an accuracy between 70% and 76%. Classification models could not accurately classify sample mass spectra based on growth year. Thirty-two molecules were identified as key for classifying western Oregon Douglas-fir wood cores to geographic origin.

DISCUSSION

DART-TOFMS is capable of detecting minute but regionally informative differences in wood molecules over a small geographic scale, and these differences made it possible to predict the geographic origin of Douglas-fir wood with moderate accuracy. Studies involving DART-TOFMS, alone and in combination with other technologies, will be relevant for identifying the geographic origin of illegally harvested wood.

Evaluation of the quality of herbal teas by DART/TOF-MS

The paper focuses on the optimization, settings and validation of direct analysis in real time coupled with time-of-flight detector when used for the evaluation of the quality of selected herbal teas (fennel, chamomile, nettle, linden, peppermint, thyme, lemon balm, marigold, sage, rose hip and St. John's wort). The ionization mode, the optimal ionization temperature and the type of solvent for sample extraction were optimized. The characteristic compounds of the analysed herbal teas (glycosides, flavonoids and phenolic and terpenic substances, such as chamazulene, anethole, menthol, thymol, salviol and hypericin) were detected. The obtained mass spectra were evaluated by multidimensional chemometric methods, such as cluster analysis, linear discriminate analysis and principal component analysis. The chemometric methods showed that the single variety herbal teas were grouped according to their taxonomic affiliation. The developed method is suitable for quick identification of herbs and can be potentially used for assessing the quality and authenticity of herbal teas. Direct analysis in real time/time-of-flight-MS is also suitable for the evaluation of selected substances contained in the mentioned herbs and herbal products. Copyright © 2017 John Wiley & Sons, Ltd.

An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64

In biological mass spectrometry, crude instrumental data need to be converted into meaningful theoretical models. Several data processing and data evaluation steps are required to come to the final results. These operations are often difficult to reproduce, because of too specific computing platforms. This effect, known as 'workflow decay', can be diminished by using a standardized informatic infrastructure. Thus, we compiled an integrated platform, which contains ready-to-use tools and workflows for mass spectrometry data analysis. Apart from general unit operations, such as peak picking and identification of proteins and metabolites, we put a strong emphasis on the statistical validation of results and Data Mining. MASSyPup64 includes e.g., the OpenMS/TOPPAS framework, the Trans-Proteomic-Pipeline programs, the ProteoWizard tools, X!Tandem, Comet and SpiderMass. The statistical computing language R is installed with packages for MS data analyses, such as XCMS/metaXCMS and MetabR. The R package Rattle provides a user-friendly access to multiple Data Mining methods. Further, we added the non-conventional spreadsheet program teapot for editing large data sets and a command line tool for transposing large matrices. Individual programs, console commands and modules can be integrated using the Workflow Management System (WMS) taverna. We explain the useful combination of the tools by practical examples: (1) A workflow for protein identification and validation, with subsequent Association Analysis of peptides, (2) Cluster analysis and Data Mining in targeted Metabolomics, and (3) Raw data processing, Data Mining and identification of metabolites in untargeted Metabolomics. Association Analyses reveal relationships between variables across different sample sets. We present its application for finding co-occurring peptides, which can be used for target proteomics, the discovery of alternative biomarkers and protein-protein interactions. Data Mining derived models displayed a higher robustness and accuracy for classifying sample groups in targeted Metabolomics than cluster analyses. Random Forest models do not only provide predictive models, which can be deployed for new data sets, but also the variable importance. We demonstrate that the later is especially useful for tracking down significant signals and affected pathways in untargeted Metabolomics. Thus, Random Forest modeling supports the unbiased search for relevant biological features in Metabolomics. Our results clearly manifest the importance of Data Mining methods to disclose non-obvious information in biological mass spectrometry . The application of a Workflow Management System and the integration of all required programs and data in a consistent platform makes the presented data analyses strategies reproducible for non-expert users. The simple remastering process and the Open Source licenses of MASSyPup64 (http://www.bioprocess.org/massypup/) enable the continuous improvement of the system.