Dataset Information

Rapid identification of species, sex and maturity by mass spectrometric analysis of animal faeces.

ABSTRACT: BACKGROUND:We describe a new approach to the recovery of information from faecal samples, based on the analysis of the molecular signature generated by rapid evaporative ionisation mass spectrometry (REIMS). RESULTS:Faecal pellets from five different rodent species were analysed by REIMS, and complex mass spectra were acquired rapidly (typically a few seconds per sample). The uninterpreted mass spectra (signatures) were then used to seed linear discriminant analysis and classification models based on random forests. It was possible to classify each species of origin with a high rate of accuracy, whether faeces were from animals maintained under standard laboratory conditions or wild-caught. REIMS signatures were stable to prior storage of the faecal material under a range of different conditions and were not altered rapidly or radically by changes in diet. Further, within species, REIMS signatures could be used to discriminate faeces from adult versus juvenile mice, male versus female mice and those from three different laboratory strains. CONCLUSIONS:REIMS offers a completely novel method for the rapid analysis of faecal samples, extending faecal analysis (previously focused on DNA) to an assessment of phenotype, and has considerable potential as a new tool in the armamentarium of the field biologist.

SUBMITTER: Davidson NB

PROVIDER: S-EPMC6693146 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Rapid identification of species, sex and maturity by mass spectrometric analysis of animal faeces.

Davidson Nicola B NB Koch Natalie I NI Sarsby Joscelyn J Jones Emrys E Hurst Jane L JL Beynon Robert J RJ

BMC biology 20190814 1

<h4>Background</h4>We describe a new approach to the recovery of information from faecal samples, based on the analysis of the molecular signature generated by rapid evaporative ionisation mass spectrometry (REIMS).<h4>Results</h4>Faecal pellets from five different rodent species were analysed by REIMS, and complex mass spectra were acquired rapidly (typically a few seconds per sample). The uninterpreted mass spectra (signatures) were then used to seed linear discriminant analysis and classifica ...[more]

PMID: 31412863

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Project description:The United Nations Office on Drugs and Crime has designated several "legal highs" as "plants of concern" because of the dangers associated with their increasing recreational abuse. Routine identification of these products is hampered by the difficulty in distinguishing them from innocuous plant materials such as foods, herbs, and spices. It is demonstrated here that several of these products have unique but consistent headspace chemical profiles and that multivariate statistical analysis processing of their chemical signatures can be used to accurately identify the species of plants from which the materials are derived. For this study, the headspace volatiles of several species were analyzed by direct analysis in real-time high-resolution mass spectrometry (DART-HRMS). These species include Althaea officinalis, Calea zacatechichi, Cannabis indica, Cannabis sativa, Echinopsis pachanoi, Lactuca virosa, Leonotis leonurus, Mimosa hositlis, Mitragyna speciosa, Ocimum basilicum, Origanum vulgare, Piper methysticum, Salvia divinorum, Turnera diffusa, and Voacanga africana. The results of the DART-HRMS analysis revealed intraspecies similarities and interspecies differences. Exploratory statistical analysis of the data using principal component analysis and global t-distributed stochastic neighbor embedding showed clustering of like species and separation of different species. This led to the use of supervised random forest (RF), which resulted in a model with 99% accuracy. A conformal predictor based on the RF classifier was created and proved to be valid for a significance level of 8% with an efficiency of 0.1, an observed fuzziness of 0, and an error rate of 0. The variables used for the statistical analysis processing were ranked in terms of the ability to enable clustering and discrimination between species using principal component analysis-variable importance of projection scores and RF variable importance indices. The variables that ranked the highest were then identified as m/z values consistent with molecules previously identified in plant material. This technique therefore shows proof-of-concept for the creation of a database for the detection and identification of plant-based legal highs through headspace analysis.

Dataset Information

Rapid identification of species, sex and maturity by mass spectrometric analysis of animal faeces.

Publications

Rapid identification of species, sex and maturity by mass spectrometric analysis of animal faeces.

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