Can Raman spectroscopy be used as a high-accuracy method to identify bivalve larvae?


Grant awarded to:

Elizabeth North, Victor Kennedy, Christine Thompson (UMCES)

Scott Gallager, Sheri White (Woods Hole Oceanographic Institution)

Funded by:
The National Science Foundation

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Project Outcomes

Identification of bivalve larvae is notoriously difficult and time consuming; currently no ‘gold standard’ method exists for distinguishing larvae collected in the field. We proposed to determine if Raman spectroscopy could be used to identify species of bivalve larvae. Raman spectroscopy is a non-destructive method that uses a focused laser to produce a spectrum, a graph with peaks that indicate the presence of different molecules like calcium carbonate and organic pigments. Pervious work exploring the use of Raman spectroscopy on shells of bivalve larvae revealed that larval shells of the eastern oyster (Crassostrea virginica) exhibit a unique spectrum at one wavelength which was distinguishable from those of six other bivalve species. The objective of this research was to analyze additional species of larvae and to determine if spectra collected at three different wavelengths could be used to distinguish the larvae.

We collected Raman spectra at three wavelengths from 25 samples of bivalve larvae representing 16 species and four taxonomic Orders. Use of the Raman spectra with three wavelengths enabled classification of larvae into Order/Family groups with accuracies ≥ 92%. Although classifications to Species were more difficult, accuracies above 86% were observed for 7 of 14 species when tested using species groups within Orders/Families. These results indicate that Raman spectroscopy is a promising method for identifying bivalve larvae to Order/Family and potentially to Species, and that using three wavelengths did enhance the ability to identify larvae. Our results also indicate that the utility of the approach likely depends on the composition of species in a specific system and the species of interest. For example, high classification accuracies (85-98%) for distinguishing spectra from eastern oyster larvae were achieved with a set of bivalve larvae occurring in the Choptank River in the Chesapeake Bay, USA, whereas as lower accuracies (70-92%) were found for a set of eastern oyster larvae endemic to the Northeast, USA. This study indicates that continued evaluation of this approach would be a fruitful line of research which could advance knowledge of shellfish biology and ecology.

Overall, we have taken important first steps in developing a new technique which uses Raman spectroscopy to identify bivalve larvae collected from the field. This technique has immediate application to validate and enhance high-throughput image identification methods in the Choptank River, a site of intensive state and federal oyster restoration efforts. It also shows promise for use in other systems. Advancing our ability to distinguish bivalve larvae has important implications for improving knowledge of shellfish biology and ecology. A high-accuracy and non-destructive technique for bivalve larval identification will greatly benefit research efforts to enhance understanding of the early life of commercially and ecological important species including oysters, scallops, hard clams and soft clams. In addition, we have shared the data set of spectra for each bivalve species with the research community and have made the Raman system available to researchers from other institutions. This program also supported the training and career advancement of postdoctoral researcher Christine Thompson.

Publications

Thompson, C.M., E.W. North, S.N. White, and S.M. Gallager. 2014. An analysis of bivalve larval shell pigments using micro-Raman spectroscopy. Journal of Raman Spectroscopy 45(5):349-358.

Thompson, C. M., E. W. North, V. S. Kennedy, and S. N. White. 2015. Classifying bivalve larvae using shell pigments identified by Raman spectra. Analytical and Bioanalytical Chemistry. 407:3591-3604, DOI 10.1007/s00216-015-8575-8


This material is based upon work supported by the National Science Foundation under Grant No. OCE-1240266. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).