Cell wall polymer degradation during Rhodonia placenta brown rot decay of thermally modified and unmodified wood

This dataset contains measurement data from the following publication:

Belt T, Awais M, Nousianen P, Rautkari L, Mäkelä M (2025) Cell wall polymer degradation during Rhodonia placenta brown rot decay of thermally modified and unmodified wood. ACS Sustainable Chemistry & Engineering, https://doi.org/10.1021/acssuschemeng.5c08352.

Abstract

Thermal modification produces decay-resistant wood suitable for sustainable applications. Although thermally modified wood remains degradable by fungi, its degradation mechanisms are poorly understood, impacting long-term eco-friendly use. This study investigated thermally modified wood degradation by elucidating chemical changes to wood cell wall polymers during Rhodonia placenta brown rot decay. Modified and unmodified Scots pine samples were exposed to R. placenta in stacked-sample decay tests, generating decay stage progressions. Decayed samples were analysed by near infrared spectroscopy with multivariate analysis to identify key chemical changes. Milled wood lignin was isolated and analysed by two-dimensional nuclear magnetic resonance spectroscopy for further lignin chemistry insight. Results showed that R. placenta degraded thermally modified wood to high mass losses. Chemical changes were characterised by carbohydrate degradation and oxidative lignin modification, typical for brown rot. While most degradative changes were similar between modified and unmodified wood, differences in lignin modification patterns were observed. Interestingly, spectroscopic data revealed different chemical changes in early and late decay stages in modified and unmodified wood. These findings highlight the time-dependent nature of R. placenta degradation and show that thermally modified and unmodified wood are degraded by similar yet different mechanisms, providing new insight into brown rot wood degradation.

Data description

Methodological details can be found in the associated publication.

The "IdentifiersAndMassData.csv" –file gives the sample identifiers and the modification and decay test mass data for all samples. The identifiers “sample type”, “replicate”, and “sample position” are the wood sample type (reference, 200 °C, or 230 °C), the replicate number (1-7), and the position of the sample in the stacked-sample decay test (1-6 from top to bottom). Masses m0, m1, m2, and m3 are the initial unmodified dry mass, the modified dry mass, the decaying wet mass, and the decayed dry mass, respectively. 

The ”NIR_images_raw.mat” –file contains NIR imaging data organised as a structured cell array. Each row represents a sample with six columns: NIR hyperspectral image cube (1245×384×288 double - digital signals), sample ID, white reference correction image (25×384×288 double), sample ID, dark current correction image (25×384×288 double), and sample ID. The dataset contains sample measurements and their corresponding calibration (white reference and dark current), facilitating radiometric correction workflows and multivariate analysis.

The ”NIR_spectra_unprocessed.csv” –file contains unprocessed average NIR spectra extracted from regions of interest (ROI) within the hyperspectral images. The spectral data processing workflow involved: (1) conversion from raw digital signals to reflectance values using white reference and dark current corrections, (2) extraction of average reflectance spectra from predefined ROI, and (3) conversion from reflectance to absorbance units.

The ”NIR_spectra_preprocessed.csv” –file contains preprocessed average NIR spectra extracted from the images. The unprocessed spectra were first converted to second derivative and then mean-centered by subtracting the average spectrum of the whole dataset from each individual spectrum.