Effectiveness of Fourier transform near‐infrared spectroscopy spectra for species identification of anurans fixed in formaldehyde and conserved in alcohol: A new tool for integrative taxonomy

Torralvo, Kelly; Magnusson, William E.; Durgante, Flávia Machado

doi:10.1111/jzs.12442

Cited by 2 publications

(8 citation statements)

References 43 publications

(75 reference statements)

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“…The overall mean of correct prediction of the models was below that of other studies that tested the method with anurans. 29,30 However, it is proposed that these data do not necessarily reflect the true potential of the approach. The grouping of species by means of the scores of the first principal components (PCA – Figure 4) is a strong indication that the NIR technique with portable equipment is promising for the recognition of live anuran species.…”

Section: Discussionmentioning

confidence: 99%

“…In a previous study using a bench-top Fouriertransform near infrared spectrometer (FT-NIR), a high classification efficiency was found for anurans fixed in formalin and kept in ethyl alcohol from a zoological collection. 30 These results indicated a new field of study that has a high potential to resolve numerous taxonomic problems found in collections and which are sources for reviewing species around the world. 32 The use of spectroscopy in studies and conservation of anurans, especially in situ, would be useful for several reasons.…”

Section: Introductionmentioning

confidence: 91%

“…The frogs were cleaned with water and an absorbent cloth (reusable cloth -Scott Du-ramax®) to remove soil, excreta and urine that could be present after capture. Then, one spectral reading was taken on the dorsal surface and one on the ventral surface of each specimen 30 with the portable equipment in the field. To protect the equipment and avoid possible contamination between the frog readings, the spectral window of the equipment was wrapped with insulfilm® plastic made of PVC polymer.…”

Section: Obtaining Spectra Of Live Anuransmentioning

confidence: 99%

“…22 NIR spectroscopy has been relatively efficient in species identification and recognition tests for plants [23][24][25][26][27] and animals, 19,20,28 including frogs. 29,30 These results are promising, but the technique is not yet as consolidated as others already used in the identification of biodiversity, such as the use of genetic data. Therefore, there is a need for future spectral and taxonomic databases, standardized and available in open-access.…”

Section: Introductionmentioning

confidence: 99%

“…33,34 Difficulties of identifying young specimens or specimens with similar characteristics to another species (cryptic species), can be solved through genetic or molecular tests, which are expensive. NIR spectroscopic techniques can potentially complement 30 or replace 28 the usual methods providing more objective results which can be attained faster and at lower cost. For example, for individuals with similarities in color and spots, the biochemical signature of species obtained in NIR spectra could complement the morphological analysis at the time of identification.…”

Section: Introductionmentioning

confidence: 99%

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Near infrared spectroscopy for the identification of live anurans: Towards rapid and automated identification of species in the field

Torralvo

Durgante

Pasquini

et al. 2023

Journal of Near Infrared Spectroscopy

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In megadiverse regions, such as the Amazon, the identification of species generally requires specialists that are often not available. Therefore, the use of new species-recognition tools is necessary to streamline surveys and avoid errors in species identification that lead to ineffective decision-making. Near infrared spectroscopy is a quick and non-destructive tool that has been widely used in the recognition of biodiversity. In addition to being used as an indicator group, anurans have species with high morphological diversity, which make them the focus of studies and application of new tools that help in the identification and recognition at the species level. In this study, the viability of recognition of species of live Amazonian frogs under field conditions using the near infrared technique and portable equipment was examined. The performance of classification models based on a linear discriminant analysis, built using spectra obtained from the dorsal and ventral surfaces of four pairs of phylogenetically-close and morphologically-similar species was evaluated. It was possible to distinguish the species of live anurans in five of the eight species studied with hit rates above 80% when using only one spectral reading per individual. The overall mean of correct prediction of the models was below that of previous studies that tested the method with anurans, which are likely to be due to particularities in the acquisition of spectra under field conditions and live species. Therefore, suggestions are made to improve the predictive capacity of the techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

Section: Obtaining Spectra Of Live Anuransmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Near infrared spectroscopy for the identification of live anurans: Towards rapid and automated identification of species in the field

Torralvo

Durgante

Pasquini

et al. 2023

Journal of Near Infrared Spectroscopy

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Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians

Chen,

Caprio,

Chen

et al. 2024

PLoS Comput Biol

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Near infrared spectroscopy coupled with predictive modeling is a growing field of study for addressing questions in wildlife science aimed at improving management strategies and conservation outcomes for managed and threatened fauna. To date, the majority of spectroscopic studies in wildlife and fisheries applied chemometrics and predictive modeling with a single-algorithm approach. By contrast, multi-model approaches are used routinely for analyzing spectroscopic datasets across many major industries (e.g., medicine, agriculture) to maximize predictive outcomes for real-world applications. In this study, we conducted a benchmark modeling exercise to compare the performance of several machine learning algorithms in a multi-class problem utilizing a multivariate spectroscopic dataset obtained from live animals. Spectra obtained from live individuals representing eleven amphibian species were classified according to taxonomic designation. Seven modeling techniques were applied to generate prediction models, which varied significantly (p < 0.05) with regard to mean classification accuracy (e.g., support vector machine: 95.8 ± 0.8% vs. K-nearest neighbors: 89.3 ± 1.0%). Through the use of a multi-algorithm approach, candidate algorithms can be identified and applied to more effectively model complex spectroscopic data collected for wildlife sciences. Other key considerations in the predictive modeling workflow that serve to optimize spectroscopic model performance (e.g., variable selection and cross-validation procedures) are also discussed.

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Effectiveness of Fourier transform near‐infrared spectroscopy spectra for species identification of anurans fixed in formaldehyde and conserved in alcohol: A new tool for integrative taxonomy

Cited by 2 publications

References 43 publications

Near infrared spectroscopy for the identification of live anurans: Towards rapid and automated identification of species in the field

Near infrared spectroscopy for the identification of live anurans: Towards rapid and automated identification of species in the field

Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians

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