DOI: 10.5061/dryad.nvx0k6drv
title: Evaluating the use of Fourier transform Raman spectroscopy for pollen chemical characterization

description: 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.
description: # Evaluating the use of Fourier transform Raman spectroscopy for pollen
 chemical characterization ## Dataset overview Description and explanation
 of methodology that were followed are included in the published article:
 Muthreich F, Tafintseva V, Zimmermann B, Kohler A, Vila-Viçosa CM, Seddon
 AWR. Evaluating the Use of Fourier Transform Raman Spectroscopy for Pollen
 Chemical Characterization. Applied Spectroscopy. 2025;0(0).
 doi:10.1177/00037028251334405 ## Dependencies Analysis and article were
 written in R version 4.3.1. Packages needed for data processing, analysis,
 and plotting are listed in 'R/packages.R'. Alternatively, a renv
 snapshot (R package renv) is provided, which can be loaded with
 'renv::restore()'. To knit to PDF, the manuscript was built with
 bookdown. A version of pandoc and latex is required with rmarkdown and
 bookdown to ensure successful knitting, these differ depending on OS used.
 The latest knitted versions of the manuscript and this file as
 '.pdf' are provided. ## Data folder and file structure Main
 folder that contains the data is: 'AppSpec_Muthreich_Data.zip'
 The data has two main folders: One for input files and one for output
 files generated during analyses. ``` data |-- input | |--
 data_metainfo.csv | |-- node_1.csv | |-- node_1_ftir.csv | |--
 node_1_raman.csv | |-- node_2.csv | |-- node_2_ftir.csv | |-- node_3.csv |
 |-- node_3_raman.csv | |-- Quercus_ATR_RAW.csv | |-- Raman_Portugal.csv |
 |-- scores.csv |-- output ``` ## File descriptions: **data_metainfo.csv**
 Columns contained in this file. * ID: ID code * SID: machine sample id *
 Sample.Name: Sample name from analysis machine * SRep: Sample replicate;
 same number indicates same sample * MRep: Measurement replicate * Section:
 Taxonomic Quercus section * Genus: Genus * Species: Species * Sub_Spec:
 taxonomic subspecies * Latitude: Sampled tree location latitude *
 Longitude: Sampled tree location longitude * Group: subgroup sample
 location * Location: Sample location description * Date: date tree was
 sampled **node_1.csv** File contains the classification results for the
 different preprocessing parameters at the first classification node, to
 Quercus section. Classification metric is recall. * First 4 rows are the
 FTIR only models trained with spectra processing parameters corresponding
 to the column names * second 4 rows are the Raman only models * Rest of
 the table are the different permutations of Multiblock were both spectral
 datasets were used * column 1-4 columns describe what preprocessing
 parameters were used in the FTIR dataset for the corresponding row model *
 column 5-8 describe what preprocessing parameters were used in the
 FT-Raman dataset for the corresponding row model Explanation of column
 names: * short: short spectral range 700-1900 cm^-1 * long: extended
 spectral range 600-3200 cm^-1 * 11: window size parameter of Savitzky
 Golay processing method * 29: window size parameter of Savitzky Golay
 processing method * Cerris: Quercus Section Cerris recall results * Ilex:
 Quercus Section Ilex recall results * Quercus: Quercus Section Quercus
 recall results * Accuracy: weighted Accuracy of all Quercus Section recall
 results * Ave_Acc: average Accuracy of all Quercus Section recall results
 **node_1_ftir.csv** File contains the loadings of the FTIR model for the
 first classification node for the entire spectral region 700-3200 cm^-1.
 First Column is Quercus section Rest of the columns are the wavenumbers in
 cm^-1 **node_1_ftir_2.csv** File contains the loadings of the FTIR model
 for the first classification node for the reduced/short spectral region
 700-1900 cm^-1. First Column is Quercus section Rest of the columns are
 the wavenumbers in cm^-1 **node_1_raman.csv** File contains the loadings
 of the Raman model for the first classification node for the entire
 spectral region 700-3200 cm^-1. First Column is Quercus section Rest of
 the columns are the wavenumbers in cm^-1 **node_1_raman_2.csv** File
 contains the loadings of the Raman model for the first classification node
 for the reduced/short spectral region 700-1900 cm^-1. First Column is
 Quercus section Rest of the columns are the wavenumbers in cm^-1
 **node_2.csv** File contains the classification results for the different
 preprocessing parameters at the second classification node, to Section
 Ilex Quercus species. Classification metric is recall per species and
 overall accuracy. * First 4 rows are the FTIR only models * second 4 rows
 are the Raman only models * Rest of the table are the different
 permutations of Multiblock were both spectral datasets were used * column
 1-4 columns describe what preprocessing parameters were used in the FTIR
 dataset for corresponding row model * column 5-8 describe what
 preprocessing parameters were used in the FT-Raman dataset for
 corresponding row model Explanation of column names: * short: short
 spectral range 700-1900 cm^-1 * long: extended spectral range 600-3200
 cm^-1 * 11: window size parameter of Savitzky Golay processing method *
 29: window size parameter of Savitzky Golay processing method * coc:
 Quercus species Quercus coccifera recall results * rot: Quercus species
 Quercus rotundifolia recall results * Accuracy: weighted Accuracy of both
 Quercus species recall results * Ave_Acc: average Accuracy of both Quercus
 species recall results **node_2_ftir.csv** File contains the loadings of
 the FTIR model for the second classification node. First Column is Quercus
 species name Rest of the columns are the wavenumbers in cm^-1
 **node_3.csv** File contains the classification results for the different
 preprocessing parameters at the third classification node, to Section
 Cerris Quercus species. Classification metric is recall per species and
 overall accuracy. * First 4 rows are the FTIR only models * second 4 rows
 are the Raman only models * Rest of the table shows the different
 permutations of Multiblock were both spectral datasets were used * column
 1-4 columns describe what preprocessing parameters were used in the FTIR
 dataset for the corresponding row model * column 5-8 describe what
 preprocessing parameters were used in the FT-Raman dataset for
 corresponding row model Explanation of column names: * short: short
 spectral range 700-1900 cm^-1 * long: extended spectral range 600-3200
 cm^-1 * 11: window size parameter of Savitzky Golay processing method *
 29: window size parameter of Savitzky Golay processing method * fag:
 Quercus species Quercus faginea recall results * rob: Quercus species
 Quercus robur recall results * Accuracy: weighted Accuracy of both Quercus
 species recall results * Ave_Acc: average Accuracy of both Quercus species
 recall results **node_3_raman.csv** File contains the loadings of the
 Raman model for the third classification node. First Column is Quercus
 species name Rest of the columns are the wavenumbers in cm^-1
 **Quercus_ATR_RAW.csv** File with the raw FTIR spectra dataset. Columns
 are wavenumbers and rows are samples. **Raman_Portugal.csv** File with the
 raw Raman spectra dataset. Columns are wavenumbers and rows are samples.
 **scores.csv** File contains the latent variables or component scores for
 each sample used in the figures in the paper. Contains 4 columns: * sample
 name * LV1: First latent variable * LV2: Second latent variable * scores:
 type of score, either * global: global model latent variable score * fitr:
 ftir block model latent variable score * raman: Raman block model latent
 variable score