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R code and supporting data for the Technical Comment related to the paper by Liu et al. (2023, https://www.science.org/doi/10.1126/sciadv.abo6102). Unzip the data_directory to get the sub-fossilPinus pollen FTIR spectra (from Muthreich 2021). A transcribed version of the sample depth table (SI5 from Liu et al. (2023) is also provided to allow for matching of samples and depths in the FTIR spectra. Go toLiu et al. (2023) and download the data files from thesupplementary materials, and move these to the same data directory. Go toBrużdziak 2019 (Supporting Information 2) and save as an .xlsx file in the data directory to obtain the water vapour spectra. The Renv lock file is also provided. Information about sample collection from Muthreich 2021 The FTIR microspectroscopy transmittance spectra were measured using a Hyperion 3000 iinfrared microscope coupled to the Vertex 70 FTIR spectrometer (both Bruker Optik,Germany), equipped with a globar mid-IR source, a liquid nitrogen-cooled mercury cadmiumtelluride 128 x128 focal plane array detector system, x15 objective, and a computercontrolledx/y/z stage. The samples were deposited onto a 1 mm thick and 25 mm diameterIR transparent ZnSe window. The spectra were recorded with a total of 64 scans in the 3845–900 cm-1 spectral range, with a spectral resolution of 8 cm-1, digital spacing of 3.8522 cm-1,2x2 binning, and Blackman-Harris 3-term apodization function. Background spectra wererecorded by measuring a sample-free area of ZnSe microscope slide. The OPUS software(Bruker Optik GmbH, Germany) was used for data acquisition and instrument control.The Pinus pollen grains were extracted from a sediment core from Dalmutladdo, NorthernNorway (see Bjune et al. 2004 https://doi.org/10.1111/j.1502-3885.2004.tb01142.x) at 20 cmdepth following density separation using sodium polytungstate and then washed three timesin water before being prepared for microscopy. References: P. Brużdziak,Vapor correction of FTIR spectra–a simple automatic least squares approach.Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy.223, 117373 (2019). F. Muthreich,New methods in palaeopalynology: Classification of pollen through pollen chemistry(University of Bergen, 2021;https://bora.uib.no/bora-xmlui/bitstream/handle/11250/2825563/drthesis_2021_muthreich.pdf?sequence=2&isAllowed=y). F. Liu, H. Peng, J. E. A. Marshall, B. H. Lomax, B. Bomfleur, M. S. Kent, W. T. Fraser, P. E. Jardine,Dying in the sun: Direct evidence for elevated UV-b radiation at the end-permian mass extinction.Science Advances.9, eabo6102 (2023). &nbsp

Additional file 1: Table S1. Lipid yield from GC-FID. Figure S1. FTIR spectra of fungal biomass before and after transesterification reactions. Figure S2. FTIR spectra of Mucor circinelloides and Umbelopsis vinacea fungal biomass before and after transesterification reaction. Figure S3. FTIR ATR spectra with ATR correction for IR-beam penetration depth. Table S2. Ratio of normalised measured FAME internal standards. Figure S4. FTIR HTS spectra of fungal biomass after transesterification reaction. Table S3. Fatty acid profiles for Lewis 1 and the optimal Lewis 2 methods.

The analysis of pollen chemical composition is important to many fields, including agriculture, plant physiology, ecology, allergology, and climate studies. Here, the potential of a combination of different spectroscopic and spectrometric methods regarding the characterization of small biochemical differences between pollen samples was evaluated using multivariate statistical approaches. Pollen samples, collected from three populations of the grass Poa alpina, were analyzed using Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, surface enhanced Raman scattering (SERS), and matrix assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS). The variation in the sample set can be described in a hierarchical framework comprising three populations of the same grass species and four different growth conditions of the parent plants for each of the populations. Therefore, the data set can work here as a model system to evaluate the classification and characterization ability of the different spectroscopic and spectrometric methods. ANOVA Simultaneous Component Analysis (ASCA) was applied to achieve a separation of different sources of variance in the complex sample set. Since the chosen methods and sample preparations probe different parts and/or molecular constituents of the pollen grains, complementary information about the chemical composition of the pollen can be obtained. By using consensus principal component analysis (CPCA), data from the different methods are linked together. This enables an investigation of the underlying global information, since complementary chemical data are combined. The molecular information from four spectroscopies was combined with phenotypical information gathered from the parent plants, thereby helping to potentially link pollen chemistry to other biotic and abiotic parameters.