Vibrational spectroscopy is gaining popularity for understanding ecological and evolutionary patterns in plants, particularly in relation to the analysis of pollen grains. So far, Fourier transform infrared spectroscopy (FTIR) has been the main approach used to classify pollen grains based on chemical variations. However, FTIR may be less suitable for detecting differences in the pollen-grain exine, mainly composed of sporopollenin. In contrast, Raman spectroscopy has increased sensitivity for the main chemical components found within sporopollenins. We compare the classification performance and chemical information provided by FTIR and FT-Raman using a large dataset of Quercus L. pollen, comprising 5 species in 3 sections (section Cerris: Q. suber; section Ilex: Q. coccifera, Q. rotundifolia; section Quercus: Q. robur, Q. faginea). We use multiblock sparse partial-least-squares discriminant analyses (MB-SPL-DA) analyses to directly compare the two infrared methods. Both FTIR and FT-Raman successfully classified Quercus pollen to section level (100% accuracy). At the species level our models achieved ~90% accuracy for FT-Rama and FTIR separately and in the combined multiblock model. The multiblock results showed an increased number of sporopollenin peaks observed in FT-Raman spectra as compared to FTIR. These peaks are also of a higher importance for classification. Results also showed differences in the types of vibrations that are of diagnostic value for the two infrared methods. CH2 deformations are more important in FT-Raman, while C-O-C, C-O, and C=O stretches are more important for FTIR-based identification of pollen. These vibrations are indicators of carbohydrates, proteins, and lipids. FT-Raman provides equally successful diagnostic potential to FTIR, but uses more chemical information based on variations in sporopollenin chemistry than FTIR. We suggest that the combined analysis of FTIR and FT-Raman using multiblock analysis has great potential for classification.

Funding provided by: The Research Council of NorwayROR ID: https://ror.org/00epmv149Award Number: PollChem 249844 Funding provided by: L. Meltzers HøyskolefondROR ID: https://ror.org/02rec2820Award Number: 2017/05/LMH