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Issue 20, 2013
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Identification of Dactylopius cochineal species with high-performance liquid chromatography and multivariate data analysis

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Abstract

Identification of American cochineal species (Dactylopius genus) can provide important information for the study of historical works of art, entomology, cosmetics, pharmaceuticals and foods. In this study, validated species of Dactylopius, including the domesticated cochineal D. coccus, were analysed by high-performance liquid chromatography with a diode array detector (HPLC-DAD) and submitted to multivariate data analysis, in order to discriminate the species and hence construct a reference library for a wide range of applications. Principal components analysis (PCA) and partial least squares discriminant analysis (PLSDA) models successfully provided accurate species classifications. This library was then applied to the identification of 72 historical insect specimens of unidentified species, mostly dating from the 19th century, and belonging to the Economic Botany Collection, Royal Botanic Gardens, Kew, England. With this approach it was possible to identify anomalies in how insects were labelled historically, as several of them were revealed not to be cochineal. Nevertheless, more than 85% of the collection was determined to be species of Dactylopius and the majority of the specimens were identified as D. coccus. These results have shown that HPLC-DAD, in combination with suitable chemometric methods, is a powerful approach for discriminating related cochineal species.

Graphical abstract: Identification of Dactylopius cochineal species with high-performance liquid chromatography and multivariate data analysis

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Publication details

The article was received on 07 Jan 2013, accepted on 30 Jul 2013 and first published on 31 Jul 2013


Article type: Paper
DOI: 10.1039/C3AN00052D
Citation: Analyst, 2013,138, 6081-6090
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    Identification of Dactylopius cochineal species with high-performance liquid chromatography and multivariate data analysis

    A. Serrano, M. Sousa, J. Hallett, M. S. J. Simmonds, M. Nesbitt and J. A. Lopes, Analyst, 2013, 138, 6081
    DOI: 10.1039/C3AN00052D

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