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.

识别植物物种需要大量知识，如果没有完整的标本，可能会很困难。傅里叶变换近红外光谱 (FT-NIR) 是区分植物物种，尤其是被子植物的有效技术。然而，它的功效从未在蕨类植物上进行过测试。在这里，我们测试了 FT-NIR 在区分Microgramma属物种方面的准确性. 我们从属于 13 个物种的 100 个标本的正面和背面获得了每个个体的 16 个光谱读数。分析包括所有 1557 个光谱变量。我们通过构建判别模型（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）。无论使用何种模型或交叉验证，所有分析都恢复了对所有数据集的样本识别正确预测的总体高百分比 (> 90%)。平均而言，使用偏最小二乘判别分析和交叉验证时的准确率 > 95%。我们的结果表明，当使用干燥植物标本的叶子时，FT-NIR 在正确区分蕨类植物方面具有很高的预测能力。该技术足够敏感，可以反映物种划界问题和可能的杂交，并且有可能帮助更好地划定和识别蕨类植物。