Additionally, we examined which strategy among our implementations, one-vs-all, i.e., one species compared with the pooled set of the remaining species, or binary-decision strategies, worked best with our data to reduce a multi-class system to a two-class system, as is necessary for PLS. We opted for a 100% classification certainty, i.e., a residual risk of misidentification of zero within the available data, at the cost of excluding specimens from identification. Furthermore, we evaluated which of our implementations of the three analysis approaches, partial least squares regression (PLS), artificial neural networks (ANN), and random forests (RF), is most efficient in species identification with our data set. B, all four co-occurring above 1,300 m above sea level in the Alps, can be identified unambiguously using NIRS. Hence, we tested if the four morphologically highly similar, but genetically distinct ant species Tetramorium alpestre, T. Despite its efficiency, NIRS has never been tested on a group of more than two cryptic species, and a working routine is still missing. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of species. Species identification-of importance for most biological disciplines-is not always straightforward as cryptic species hamper traditional identification.
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