Paiva Darlem Nikerlly Amaral, Perdiz Ricardo de Oliveira, Almeida Thaís Elias
Universidade Federal do Oeste do Pará, Programa de Pós-graduação em Biodiversidade, Rua Vera Paz, s/n (Unidade Tapajós) Bairro Salé, Santarém, PA, 68040-255, Brazil.
Instituto Nacional de Pesquisas da Amazônia, Programa de Pós-graduação em Ciências Biológicas, Avenida André Araújo, Manaus, AM, 293669060-001, Brazil.
J Plant Res. 2021 May;134(3):509-520. doi: 10.1007/s10265-021-01265-9. Epub 2021 Apr 7.
Identifying plant species requires considerable knowledge and can be difficult without complete specimens. Fourier-transform near-infrared spectroscopy (FT-NIR) is an effective technique for discriminating plant species, especially angiosperms. However, its efficacy has never been tested on ferns. Here we tested the accuracy of FT-NIR at discriminating species of the genus Microgramma. We obtained 16 spectral readings per individual from the adaxial and abaxial surfaces of 100 specimens belonging to 13 species. The analyses included all 1557 spectral variables. We tested different datasets (adaxial + abaxial, adaxial, and abaxial) to compare the correct identification of species through the construction of discriminant models (Linear discriminant analysis and partial least squares discriminant analysis) and cross-validation techniques (leave-one-out, K-fold). All analyses recovered an overall high percentage (> 90%) of correct predictions of specimen identifications for all datasets, regardless of the model or cross-validation used. On average, there was > 95% accuracy when using partial least squares discriminant analysis and both cross-validations. Our results show the high predictive power of FT-NIR at correctly discriminating fern species when using leaves of dried herbarium specimens. The technique is sensitive enough to reflect species delimitation problems and possible hybridization, and it has the potential of helping better delimit and identify fern species.
识别植物物种需要相当多的知识，而且如果没有完整的标本会很困难。傅里叶变换近红外光谱（FT-NIR）是一种鉴别植物物种，尤其是被子植物的有效技术。然而，其在蕨类植物上的有效性从未得到过测试。在此，我们测试了FT-NIR鉴别Microgramma属物种的准确性。我们从属于13个物种的100个标本的正面和背面获取了每个个体16个光谱读数。分析包括所有1557个光谱变量。我们测试了不同的数据集（正面+背面、正面和背面），通过构建判别模型（线性判别分析和偏最小二乘判别分析）和交叉验证技术（留一法、K折法）来比较物种的正确识别情况。所有分析中，无论使用何种模型或交叉验证方法，所有数据集对标本识别的正确预测总体比例都很高（>90%）。平均而言，使用偏最小二乘判别分析和两种交叉验证方法时，准确率>95%。我们的结果表明，当使用干燥标本馆标本的叶子时，FT-NIR在正确鉴别蕨类植物物种方面具有很高的预测能力。该技术足够灵敏，能够反映物种界定问题和可能的杂交情况，并且有潜力帮助更好地界定和识别蕨类植物物种。
