# Tracking flax dew retting by infrared vibrational spectroscopy combined with a powerful multivariate statistical analysis

Suvajit Mukherjee, Estelle Goulas, Isabelle de Waele, Anne Creach, Simon Hawkins, Sebastien Grec, Anne-Sophie Blervacq

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Suvajit Mukherjee, Estelle Goulas, Isabelle de Waele, Anne Creach, Simon Hawkins, et al.. Tracking flax dew retting by infrared vibrational spectroscopy combined with a powerful multivariate statistical analysis. Industrial Crops and Products, 2025, Industrial Crops and Products, 227, pp.120798. 10.1016/j.indcrop.2025.120798. hal-05020428

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# Tracking flax dew retting by infrared vibrational spectroscopy combined with a powerful multivariate statistical analysis

Suvajit Mukherjee<sup>a, ID</sup>, Estelle Goulas<sup>a</sup>, Isabelle De Waele<sup>b</sup>, Anne Créach<sup>a</sup>, Simon Hawkins<sup>a</sup>, Sébastien Grec<sup>a</sup>, Anne-Sophie Blervacq<sup>a,* ID</sup>

## ARTICLE INFO

### Keywords:

- Cell wall degradation

- Flax

- Dew retting

- Spectral signature

- FT-IR spectroscopy, mixOmics

## ABSTRACT

Natural fibers are of great interest for the industrial sector, particularly those from flax (Linum usitatissimum L.). Before the industrial process, flax stems have to undergo 'dew retting'. Under-retting and over-retting can result in fibers with lower industrial value and this process is currently empirical. In this study, we aimed to accurately monitor the flax dew retting by determining FT-IR spectra of uprooted stems after 1-, 6-, 20- and 25 days. As FTIR was usually performed after retting, it is original. With both one-way anova ('a priori' approach') and a multi-variate analysis ('without a priori' approach), we pinpoint significant markers of the retting progression. But the 'without a priori' approach reveals more significant markers. The early retting step was suported by peaks corresponding to esterified and alkyl-esterified pectins (1734 cm⁻¹), other pectin (1335 cm⁻¹) and aromatics (1508 cm⁻¹). Degradation of the epidermis and the middle lamella from cortical parenchyma are taking place early. This is a prerequisite for fungus hyphae or bacteria colonization. The final retting step showed significant contributions from cellulose/hemicellulose peak (1317; 1427 cm⁻¹), crystalline cellulose (1371 cm⁻¹) and, OH-(3270 cm⁻¹). This suggests almost complete outer tissue disorganization as well as the beginning of the release of bast fibers from bundles. Identifying specific peaks related to the end of the retting will help to build a predictive model. This will constitute a valuable tool to parameter a handheld device for helping farmers to determine the optimal retting point to harvest flax stems ensuring maximum fiber quality.

## 1. Introduction

Whatever the use, flax fibers are extracted from raw flax stem material with various processing stages. Considering the outer tissues anatomy of the flax stems, the fiber bundles should be detached from other plant tissues (epidermis, parenchyma, secondary xylem and phloem). Such a release requisites a pre-extraction procedure, known as retting, which involves the activity of enzymes produced by plants then by ground microorganisms (Hodges et al., 2004; Mukherjee et al., 2024; Valenzuela et al., 2017). As in field retting in which uprooted flax stems are left on the ground for several weeks, or under damp weather conditions where plants are flooded with water, the flax stems become colonized by various fungi species, producing a spectrum of polysaccharide-degrading enzymes, primarily pectinolytic. Sequenced degradation of pectins represent the first step of retting and might promote the microbial colonization of the stem (Datta et al., 2024; Datta et al., 2020, Mukherjee et al., 2024). Moreover, these enzymes play a

pivotal role in macerating and disrupting the middle lamella of the cortical cells surrounding fiber bundles (Akin, 2013; Brown and Sharma, 1984; Meijer et al., 1995). Compared to chemical retting, the dew retting process seems eco-friendly and less cost-effective. It is controlled both by abiotic factors (e.g., climate, soil properties) and by biotic ones (micro-organisms cohort from the soil and from the phyllosphere) (Bou Orm et al., 2024; Chabbert et al., 2020; Djemiel et al., 2017; Djemiel et al., 2020; Mukherjee et al., 2024). Notably, field retting's efficacy is weather-dependent, ceasing in dry weather and causing under-retted straw. Continuous wet conditions lead to cellulase production, induce non-enzymic degradation and fiber decay (Bratt et al., 1988; Brown and Sharma, 1984), both resulting in unfavorable evolutions of fiber characteristics. Many factors should also be considered such as: the flax cultivar, soil nutrients availability, crop management, soil loading/microbial diversity. The few studies on controlling field retting suggested that a more sensitive method is needed to determine flax's initial rettability and monitor the degree of retting during field retting (Meijer

<footnote><sup>a</sup> Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille F-59000, France</footnote>

<footnote><sup>b</sup> Univ. Lille, CNRS, UMR 8516 - LASIRE - Laboratoire de Spectroscopie pour les Interactions, la Réactivité et l'Environnement, Lille F-59000, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: anne-sophie.blervacq@univ-lille.fr (A.-S. Blervacq).</footnote>
et al., 1995). The retting process of fiber plant such as flax or hemp, is a multifactorial and complex phenomenon, and its endpoint is difficult to appreciate with conventional methods commonly used such as stem color (Bleuze et al., 2018), Fried's test (visual scoring of the fiber-release degree after mechanical agitation in hot water treatment), texture and/or mechanical properties (Archibald et al., 1998). Flax retting monitoring is still based on a highly empirical approach that requires the expertise of the flax farmer, and to our knowledge no tools are currently available to assess in a reproducible and standardized way the status of stalks during retting. The estimation of the degree of retting (under-, over- vs optimal retting) appears challenging, as it could affect the quality of fibers. As the textile and materials industry requires more and more batches of fibers of standardized quality, it is crucial to avoid both under-retting and over-retting. One way to overcome this challenge is to provide dedicated tools to perform precise monitoring of retting progression within the field.

Spectroscopic approaches represent convenient methods to assess the composition of plant fibers and several previous studies recently aimed to give information on fiber-plants retting with experimental designs usually based on stem cross sections grounded into powder, in order to produce KBr or BaF₂ pellets, eventually including treatment with various enzyme cocktails (Archibald et al., 1998; Himmelsbach et al., 2002). This has been related in other natural plant fibers such as *Ficus macrocarpa* (Tengsuthiwat et al., 2024) in which cellulose fibers were treated with alkali treatments. The well-known RAMAN spectroscopy is an interesting non-destructive method that is not sensitive to water presence. In contrast, the high-fluorescence compounds that progressively appear during retting let spectra acquisition impossible. Such fluorescence can be due to color pigments (as the stems change color during the retting process) (Bleuze et al., 2018), residual chlorophyll from the stem cortical parenchyma (Gamble et al., 2000), and/or the presence of microorganisms' cell wall residues. Moreover, the phenolic constituents in or around bast fibers (Day et al., 2005; Huis et al., 2012) can be subjected to oxidation during the retting process. Mukherjee et al. (2024) also showed that CAD-7 and CAD-8 proteins (involved in monolignol/lignin metabolism) are one of the differentially abundant proteins that was expressed in the very early stage of retting. Consequently, to bypass such fluorescence, the use of FT-IR (Fourier Transform Infra-Red) spectroscopy instead of RAMAN has to be considered in the context of this study. Indeed, for many decades, FT-IR spectroscopy has been often used in industry, for example to evaluate the bonding strength between flax fiber and resins for composite material (Cao et al., 2012). FT-IR has successfully been applied on indian flax and sisal (*Agave sisalana* L.) in order to validate different mechanical extraction methods in order to obtain more uniform fibers (Nayak et al., 2021), or in the context of natural biodegradation of indian flax (Mohapatra and Malik, 2015). FT-IR profiling has also been previously performed in flax, where researchers monitored spectral changes via the production of KBr or BaF₂ pellets of stem coarse powder (Archibald et al., 1998; Himmelsbach et al., 2002). The authors successfully exposed cross sections to different buffers (with or without chelator, with or without oxalic acid, with enzyme mixture), usually at high temperature (50°C) and for a short period (6h-37h). At last, another recent technical study successfully describes an Attenuated Total Reflectance (ATR)-FT-IR spectroscopy approach on flax and hemp (*Cannabis sativa* L.), through comparisons of various retting methods (degumming, water- or dew-retting) (Rozanska et al., 2023; Vongsvivut et al., 2019).

Moreover, even if FT-IR has been demonstrated to be a convenient method to monitor chemical modifications of plant material (either fiber cell wall during retting, or industrial treatments of scutched fibers), there is still a lack of information since, to our knowledge, none of them established a complete and well-detailed statistical pipeline able to determine the significance of these modifications all along the process (Pandey and Pitman, 2003; Erukhimovitch et al., 2010; Chow and Ting, 2019). As an example, authors do not perform time-course statistics to

prove that the difference in the maximum intensity of some peaks, as retting progressed, was significant between their sampling times. This is why in this paper, an original statistical analysis pipeline based on comparison between 'classical' and multivariate statistical analyses was designed to precisely discriminate which are the reliable spectrometric biomarkers allover flax dew retting. For multivariate analysis, Partial Least Square-Discriminant Analysis (PLS-DA) was performed since such a method has to be chosen when a large number of variables constitute the dataset, as it is the case when using FT-IR. It helps to describe variability and to obtain which are the significant peaks (later assigned to plant cell wall polymers), as their relative importance (i.e. 'weight'), that explains this variability between retting times. Such approach has been recently successively undertaken in some studies dealing with biomarker identification of grain filling, polyphenol degradation in red radish or biomarker identification of flax fiber quality (Bancel et al., 2019; Li et al., 2020; Chabi et al., 2023). It was well demonstrated that the mixOmics R-package is suitable for such analyses since it provides a wide range of linear multivariate methods for data exploration, integration and dimension reduction of large biological data sets, and is also regularly updated (Le Cao et al., 2016).

In this study, we aim to establish a FT-IR-based standardization of flax dew retting that can be instrumental in optimizing its application for biofilm, biopolymer, and biocomposite production, leveraging supply of constant fiber properties for sustainable material development. We propose for the first time FT-IR monitor the chemical transformation of the flax outer tissues that occurs all along the natural retting processes, in order to provide markers that could be further used to identify the optimal end of the retting. Since recent work established that major changes in flax stem composition only occurred mainly during the early stages and late stages of the retting (Mukherjee et al., 2024), we decided to focus our attention on the differences that characterize the starting point and the end-point of the retting process [R0/day1 vs R7/day25], with a focus on the starting phase [R0/day1 vs R2/day6], and the later phase [R6/day20 vs R7/day25]. By combining FT-IR spectroscopy, classic and multivariate statistical analyses (with or without a priori approach, respectively) this study aims to identify more markers (i.e. chemical bonds = peaks, related to cell wall polymers) that are specific to these different retting stages, and that could be referred to determine the optimal end-point retting time. We are confident that such highlighting of biological markers of retting will enable the rationalization and better control of the variability induced through retting and particularly the empirical monitoring used up to date to evaluate its progress.

## 2. Material and Methods

### 2.1. Plant Material

Seeds of fiber flax (*Linum usitatissimum* L., var. Evasion, Terre de Lin) were grown in a field located at Bavinchove (France, Hauts-de-France region) with these coordinates (50.7864583,2.4500290) under the supervision of Van Robaeys Frères® (Killem, France). Seeds were sown the 31st of March 2021, and plants were up-rooted after 116 days of cultivation and laid on the ground. The further 25 days correspond to the dew retting itself and its monitoring was performed by peculiar samplings of stems after 1 day (R0), 6 days (R2), 20 days (R6) and 25 days (R7) after the up-rooting since such time points were recently highlighted as key points of the retting process (Mukherjee et al., 2024). Supplementary Fig. 1 shows the global chart flow that was followed in this study.

### 2.2. Preparation of sample for FT-IR spectroscopy

Ten stem fragments (i.e. 10 individuals) of 30–40 cm in length were cut from the middle section of each individual (between fruit and root), avoiding kick points made by the uprooting machine (see
Supplementary Fig. 1). They were collected, at three collecting places (i. e. biological replicates, T1, T2, T3), and for each time of the dew retting kinetic (R0, R2, R6, R7). By the way, a total of 12 samples, combining [T, R: 3 places in the field x 4 retting times] are to be analyzed. Fragments were peeled in order to enrich samples in fiber bundles as well as epidermis and cortical parenchyma cell types, this corresponds to the “outer tissues” fraction. Outer tissues were then dried at 45°C in a hot air oven, for two days (48 h) until weight is stabilized. The inner tissue mainly corresponding to the wood (secondary lignified xylem) was not further considered for analysis. Each sample (= a mix of 10 stem fragments) was reduced in coarse powder with a Retsch Mixer Moll MM 400 in a 50 mL grinding jar and Ø25 mm metal balls, for 25 Hz, 2 min. Coarse powder was placed between the two faces of a diamond compression cell (SpectraTec) and pressed to obtain powder fine enough. Each face of this diamond cell was used to acquire spectra. Two to three mg.sample⁻¹ were used for FT-IR spectra acquisition (Supplementary Fig. 1).

## 2.3. FT-IR spectroscopy

A minimum of 15 spectra (i.e. technical replicates) was acquired for each of the 12 samples prepared (as previously described) by the use of a Bruker FT-IR microscope Hyperion 3000 in transmission mode. The equipment was coupled to a Bruker Vertex 70 spectrometer (Advanced Characterization Platform for Vibrational Spectroscopy, Institut ME Chevreul, CNRS FR2638, U-Lille, France). The microscope was equipped with a liquid nitrogen cooled MCT detector. 256 scans were recorded at 4 cm⁻¹ spectral resolution in the spectral range from 650 cm⁻¹ to 4000 cm⁻¹ using a 36x Cassegrainian objective (NA=0.5). The area of interest was selected by adjusting knife edge aperture so that only the area of interest is exposed for analysis. Absorption bands maxima in the middle infra-red region at 3400–800 cm⁻¹ were used to identify the main plant polymers and other residues. The spectra were recorded from 3400 to 800 cm⁻¹ spectral ranges corresponding to the plant cell wall fingerprint (800–1900 cm⁻¹), total CH₂, CH₃ chemical bonds (2800–3000 cm⁻¹), and finally, N-H, OH-inter and intramolecular bonds from cellulose I and II, water (3000–3400 cm⁻¹) (see Supplementary Table 1).

## 2.4. Spectra treatments for average spectra illustrations

Baselines were corrected using Totint using R package (https://cran.r-project.org/web/packages/ChemoSpec/index.html; https://www.r-project.org/, 1st July 2024 accession) (Blervacq et al., 2022; Blervacq et al., 2023). Three biological replicates (T1, T2, T3) corresponding to three locations within the field, were harvested at each retting time (R0, R2, R6 and R7). A minimum of 15 individual spectra was acquired for each biological replicate and average spectra for each biological replicate was calculated by cumulating the minimum 15 individual spectra (Supplementary Figure 1).

Five distinct main zones in the spectral signature were determined by expert curation in our retted flax datasets: Zone A (1190–800 cm⁻¹), Zone B (1500–1191 cm⁻¹), Zone C (1785–1501 cm⁻¹), Zone D (3000–2600 cm⁻¹) and Zone E (3800–3000 cm⁻¹) and further considered for the statistical analyses.

## 2.5. Statistical analyses

All spectra provided after spectra treatments described above (see §2.4 above) were baseline corrected then normalized regarding total surface under the spectrum by using the TotInt method of the Chemo-spec R package (Blervacq et al., 2022; Blervacq et al., 2023). The y-data of a spectra object is normalized by dividing each y-value by the sum of the y-values in a given spectrum. Thus, each spectrum sums to 1. This method assumes that the total concentration of all substances giving peaks does not vary across samples. The normality of spectral data was further assessed using the Shapiro-Wilk test, and equal variances among

groups were verified with Levene's test. Outliers identified through the interquartile range (IQR) were removed, and any missing values were replaced with the group mean.

All further statistical analyses were done independently on the five zones identified in the whole spectrum (§2.4). Once done, the normalization occurred within one given zone.

### 2.5.1. Multiple pairwise comparisons by one way-ANOVA analysis ('a priori' approach)

To evaluate the effect of the retting degree (R) on the modification of spectral profiles of outer-tissue powder, a one-way analysis of variance (ANOVA) was conducted, followed by Tukey's HSD (Honestly Significant Difference) test. All analyses were performed with R software and Agricolate package (https://www.r-project.org/, 1st July 2024 accession).

### 2.5.2. Multivariate statistical analyses ('without a priori' approach)

We first performed unsupervised exploratory analyses by doing Principal Component Analysis (PCA) on normalized datasets of the dew retting kinetic R0 (1 day), R2 (6 days), R6 (20 days) and R7 (25 days) with the help of the RStudio software (2022.04.02 build 764) and the R-package mixOmics (v. 6.1.2) (Le Cao et al., 2016), for up to the 5 spectral zones previously defined by expert curation (i.e. zone A to zone E) (Supplementary Fig. 2).

In order to select variables most explanatory of retting discrepancy within our spectroscopic peaks datasets, we then performed supervised exploratory analysis by doing partial Least-Squares Discriminant Analysis (PLS-DA) within each zone of interest. This allows searching for the highest covariance between variates, which are a linear combination of initial variables, taking into account all variables in the input matrices. Our PLS-DA analyses were designed according to the number of principal components explaining at least 70 % of the total variability. Then sparse Partial Least-Squares Discriminant Analyses (sPLS-DA) were conducted since it combines this approach with a Lasso penalization to enable the selection of variables the most explanatory of varieties discrepancy. As an approach performed in Chabi et al. (2023), a 10 % cut-off of the most significant wavenumbers from each X-component considered were taken into account for the sPLS analysis.

# 3. Results

## 3.1. Evolution of the global spectral signature during dew retting

Whole average spectra were compared for the 4 selected retting points (R0, R2, R6, R7) (Fig. 1). In recent FT-IR studies dealing with plant cell wall (Bhagia et al., 2022), animal cells infection (Vongsvivut et al., 2019), or pollen taxonomy (Kendel and Zimmermann, 2020), an approach by zonation of the spectral signature was shown to lead to the identification of relevant candidates. We decided to apply such a zonation on the average spectra, in zones where peaks are noticed. As a consequence, the silent zone [1800–2600 cm⁻¹] was not considered in our analyses.

Zone A localized close to the detection limit (below 900 cm⁻¹), a zone corresponding to 750–970 cm⁻¹ could be assigned to (1,4) (1,6)-α-D-glucans, (1,3)-α-D-glucans and β-1,4 glycosidic bonds (Szymanska-Chargot and Zdunek, 2013; Rozanska et al., 2023). (1,6) and (1,4)-β-D-glucans are more specific fungi cell walls (chitin) and storage (glycogen). In this zone, only the band at 899 cm⁻¹ was considered since it was associated with amorphous cellulose that is present in all plant primary cell walls (epidermis, cortical parenchyma) as well as in flax bast fibers (Blervacq et al., 2022; Blervacq et al., 2023). In zone A above 900 cm⁻¹, several peaks were assigned to probable plant cell wall polymers: Cellulose (1105; 1032; 899 cm⁻¹), Hemicellulose (xylan, xyloglucan) (1157 cm⁻¹), and Pectin (1105 cm⁻¹). Other peaks such as Mannan (1055; 1032 cm⁻¹) and β-1,3 D Glucan (1105 cm⁻¹) are also detected.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Average spectra of each retting time (R0/day1 in blue; R2/day6 in black; R6/day20 in red and R7/day25 in orange) including the 5 expert curated zones 800–1190 cm⁻¹ (zone A), 1191–1500 cm⁻¹ (zone B), 1501–1785 cm⁻¹ (zone C), 600–3000 cm⁻¹ (zone D) and 3000–3800 cm⁻¹ (zone E). Assignments were done according to the *Supplementary Table 1*. Each peak corresponds to a chemical bond between two/three atoms present in polymers *Cellulose; *Hemicellulose-Xylan-XYloglucan; * β-D Glucan; *Mannan; *Pectin*. A minimum of 15 individual spectrum (covering all T) was used to calculate the average spectra for each time of dew retting. Normalization was done according to the TotInt process (R software, mixOmics package).</figcaption></figure>

Zone B covers many peaks corresponding to those from the plant cell wall. They are assigned to Cellulose (1427; 1371; 1317; 1260; 1201 cm⁻¹), Pectin (1427; 1335 cm⁻¹) and Hemicelluloses (1317; 1281; 1201 cm⁻¹).

Shift towards increasing wave numbers is observed in zone C, and is detected as water is removed during retting. The maximum intensity of this peak is expected to be aligned at 1650 cm⁻¹ that could be assigned to pectin, but also to the chemical bond [Amide C-N] in protein. Shoulder could also be slightly present around 1633 cm⁻¹ (assigned to COO- carboxylate ion stretching in pectin). Zone C is assigned to pectin major peaks (1734; 1650–1633 cm⁻¹). The first well- determined peak (1734 cm⁻¹) reflects esterified uronic acid, and modification of carbonyl group in esters, but could also be associated with cutin polyester (Mazurek et al., 2017).

Great modifications were detected in zone D, that corresponds to [CH₂; CH₃; CH₂-OH] chemical bonds (2918; 2899; 2852 cm⁻¹). It probably reflects the whole polysaccharides (i.e. carbohydrates) cell wall reorganization during retting, and epidermis disorganization, particularly the begin of cutin degradation (Mazurek et al., 2017).

Zone E corresponds to intra/intermolecular hydrogen bonds from cellulose I or II (Schwanninger et al., 2004; Oh et al., 2005a, 2005b; Fan et al., 2012; Abidi et al., 2014; Kruer-Zerhusen et al., 2018).

## 3.2. Analysis of retting spectral profiles by zones: an 'a priori' approach

An 'a priori' approach was applied in order to explore the major peaks assigned to plant cell wall polymers. By expert curation, we firstly defined a list of peaks (without considering that other ones could also be significant, and without considering the relative loading weight or 'importance' of one selected peak, compared to the other ones). To assess the dynamic of selecting peaks as significance changes during the retting, point to point comparison were conducted and presented in the following Figures (Figs. 2–6).

The zone A (Fig. 2) contains 5 selected peaks related to the cell wall compounds covering cellulose and hemicellulose. Three of them did not

show any significant variability along the retting process (1032; 1055; 1105 cm⁻¹). The two others (899; 1157 cm⁻¹) exhibited significance that are fluctuating according to the retting times. The zone B (Fig. 3) contains 3 selected peaks related to the cell wall compounds also covering cellulose and hemicellulose. All of them exhibited significant variability especially for the early step [R0 vs R2]. Nevertheless, no significant variability characterized the late step [R6 vs R7]. The zone C (Fig. 4), corresponds to chemical bonds related to pectin, esterified pectins and cutin polyester. Only the peak (1734 cm⁻¹) shows significant variability especially at early step [R0 vs R2]. In the zone D (Fig. 5), corresponding to [CH₂; CH₃; CH₂-OH] showed no significant difference whatever the peak or the kinetic. Finally, the zone E (Fig. 6) that corresponds to global OH in cellulose I and II, and putative remnant water, does not exhibit any significant variability during retting.

All these results can be gathered in Table 1.

With this 'a priori' approach (i.e. chosen peaks, in each zone) some significant peaks are observed (Table 1). The early step [R0/ day 1 vs R2/ day 6] is characterized by amorphous cellulose (899 cm⁻¹), xyloglucan and O-H or C-O-C cellulose bonds (1201 cm⁻¹), crystalline cellulose (1371 cm⁻¹) and pectin (notably esterified or alkyl-esterified, 1734 cm⁻¹). No specific peak is identified for the late step [R6/ day 20 vs R7/ day 25]. Overall process [R0 vs R7] led us to identify two major peaks cellulose (899 cm⁻¹) and esterified pectin/cutin (1734 cm⁻¹).

## 3.3. Characterization of a suitable workflow for multivariate analysis: a 'without a priori' approach

All individual spectra (rep₁ to rep₁₅) of each biological replicate (T1, T2, T3) for each retting point (R0, R2, R6 and R7) were considered for the 'without a priori' analyses. Spectra treatment was at first achieved in order to focus exactly on the same wavenumber window [3400–800 cm⁻¹] for each individual spectrum, followed by baseline correction. Then were considered separately the five zones (A to E) highlighted above to process with normalization, as described in M&M
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** "With a priori" comparative analysis of zone A (800–1190 cm⁻¹) between retting time R0/day1 (blue), R2/day6 (black), R6/day20 (red) and R7/day25 (orange). A: spectral profile extract. Assignments were done according to the **Supplementary Table 1**. B: Box-plots resulting from 1-way ANOVA. The box-and-whiskers plots represent values of nR0= 15, nR2= 15, nR6= 10, nR7= 17 individual spectra respectively covering all T. Within one time point R, all imax of the technical repetitions were chosen for each of these peaks. Statistical significance with Anova (Tukey's HSD) test between retting points are shown with letter marking where p-Value < 0.05. *Cellulose; \*Hemicellulose-Xylan-Xyloglucan; \* β-D Glucan; \*Mannan; \*Pectin.</figcaption></figure>

(§ 2.5.1).

By doing such zonation into this ‘without a priori’ approach, our aim was to limit the loading weight (importance) of peak contribution exhibiting very high intensity in the whole spectrum. Indeed, global analysis of the whole spectra could generate a bias as peaks exhibiting high i<sub>max</sub> could crush those that showed lower i<sub>max</sub> (i.e reduce the signal to noise ratio). Such a choice to analyze by spectral desired region was previously successfully proposed in flax enzymatic retting (Archibald et al., 1998), fungal infestation of potato (Erukhimovitch et al., 2010) or developing cotton fibers (Abidi et al., 2014) studies. The workflow for ‘without a priori’ statistical analyses is summarized in **Supplementary Fig. 3**.

**3.4. Multivariate analysis ('without a priori' approach)**

Figs. 7–9 show for each spectral zone of interest, PCA scree plots (Fig. 7 A), PLS-DA (Fig. 7B), sPLS-DA projections (Figs. 7–9) and sPLS-DA plotLoadings (Fig. 7D; Fig. 8B; Fig. 9B) that were performed while

three sets of comparisons: i) extreme points [R0/ day 1 vs R7/ day 25] (Fig. 7), ii) early step [R0/ day 1 vs R2/ day 6] (Fig. 8) and final step [R6/ day 20 vs R7/ day 25] (Fig. 9), within different spectral zones (A: 1190–800 cm⁻¹; B: 1500–1191 cm⁻¹; C: 1785–1501 cm⁻¹; D: 3000–2600 cm⁻¹; E: 3800–3000 cm⁻¹). All correlation circles can be seen in **Supplementary Fig. 4**.

The [R0 vs R7] comparison corresponding to the comparison between the [Start vs End] points of the dew retting process is illustrated in Fig. 7 and Table 2. PCA was calculated for each sub-datasets and three zones appeared interesting for this comparison: zone A, zone B and zone C (see Fig. 1 for zonation) in which clear separation between groups were observed (Supplementary Fig. 5). PCA screeplots of A, B and C zones (Fig. 7A) showed that PC1 explained 69 %, 48 %, and 53 % of the variability, respectively. And that PC2 explained 14 % of the variability in zone A, and 26 % in both B and C zones, respectively. Therefore, only components PC1 and PC2 were considered for further regression analysis, as they explain respectively a total of 83 % (zone A), 74 % (zone B), and 79 % (zone C) of the variability. The PLS-DA method (Fig. 7B)
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** "With a priori" comparative analysis of zone B (1191–1500 cm⁻¹) between retting time R0/day 1 (blue), R2/day 6 (black), R6/day 20 (red) and R7/day 25 (orange). A: spectral profile extract. Assignments were done according to the Supplementary Table 1. B: Box-plots resulting from 1-way ANOVA. The box-and-whiskers plots represent values of nR0= 15, nR2= 15, nR6= 10, nR7= 17 individual spectra respectively covering all T. Within one time point R, all lmax of the technical repetitions were chosen for each of these peaks. Statistical significance with Anova (Tukey's HSD) test between retting points are shown with letter marking where p-Value < 0.05. *Cellulose; \*Hemicellulose-Xylan-Xyloglucan.</figcaption></figure>

applied on all wavenumbers present in each component of zones A, B and C confirmed that Principal Component 1 and 2 from each of these three zones were enough to discriminate the starting point (R0) from the endpoint (R7) of the dew retting, notably along the X1-component axis (negatively in zone B: 44 % X1-comp; positively in zones A and C: 68 % and 53 % for X1-comp). Positive or negative contributions (Supplementary Table 2) are related to the fact that correlation circle plots are representations of the variables in a space spanned by the components, where each variable coordinate is defined as the correlation between the original variable value and each component (Supplementary Fig. 4). Therefore, groups of wavenumbers/chemical compounds are grouped at the same zone, and their projection on the axe lead to 'positive' or 'negative' values (González et al., 2012). The retting stages are rather well discriminated in the mean subspaces resulting from sPLS-DA (Fig. 7C). In the resulting subspaces, it was especially the x-axis which discriminates the retting time points, whatever the zones considered. In zone A, the two opposite retting timepoints (R0/day 1 and R7/day 25) were at extremities of this axis, which therefore could be considered as the time point-axis. The early retting stage (R0/day 1) has mostly negative X-coordinates unlike the late

retting stage (R7/day 25). No clear discrimination could be observed on the y-axis. When undergoing classification, plotLoadings (Fig. 7D) color each variable's bar according to whether the median is higher (or lower) in a given group of interest. Greater absolute values in this loading vector meaning that a given variable has a greater importance (i.e. weight). In the A zone, the loading weights indicated a limited set of wavelength numbers strongly contributing to the first two components (Fig. 7D-a; Supplementary Table 2). For the first component, 20 variables were highlighted, 16 with negative contributions and 4 with positive ones, among which 8 showed an absolute weight > 0.2 (Table 3). These wavenumbers corresponded to hemicellulose, either rich in xylan (1157 cm⁻¹) or mannose (800 cm⁻¹), with negative and positive contributions, respectively and were classified as R0/day 1 and R7/day 25, respectively. For the second component, 21 variables were highlighted, 15 with negative contributions and 6 with positive ones, among which 8 showed an absolute weight > 0.2. These wavenumbers corresponded to hemicellulose, rich in xylan (1157 cm⁻¹) (negative contributions), and cellulose/mannans (1055 cm⁻¹) (positive contributions), both classified as R0/day 1. (Supplementary Fig. 4).

In zone B, for the first component, 33 variables were highlighted, 20 with negative contributions and 13 with positive ones (Fig. 7D-b), among which 12 showed an absolute weight > 0.2 (Table 3). These wavenumbers are related to pectin (1335 cm⁻¹) and hemicellulose/cellulose (1317 cm⁻¹) with negative contributions, and crystalline cellulose (1371 cm⁻¹) with positive ones, and were classified as R7/day 25 and R0/day 1, respectively (Supplementary Table 2). For the second component, 34 variables were highlighted, 9 with negative contributions and 25 with positive ones (Fig. 7D-b), among which 12 showed an absolute weight > 0.2 (Table 3). These wavenumbers were assigned to chemical bonds related to cellulose (1281 cm⁻¹) (R7; negative contributions) and cellulose 1462 cm⁻¹ (R0/day 1), and pectin/cellulose for (1427 cm⁻¹) (R7/day 25) both with positive contributions.

At last, in zone C, for the first component, 113 variables were highlighted, 63 with negative contributions and 50 with positive ones (Fig. 7D-c), among which none showed an absolute weight > 0.2 (Table 3). For the second component, 115 variables were highlighted, 60 with negative contributions and 55 with positive ones, among which 4 showed a positive weight > 0.2 corresponding to aromatic compounds (1508 cm⁻¹) (R7/day 25).

The [R0 vs R2] statistical comparison corresponding to the early events during dew retting process was performed similarly as above, and is illustrated by respective average spectra in Supplementary Fig. 2, Table 2 and Table 3. For the sPLS-DA analysis of condition [R0 vs R2] (Fig. 8A-a-b, Table 2), in zone B, for the first component, 29 variables were highlighted, 8 with negative contributions and 21 with positive ones (Fig. 8B-a), among which 8 showed an absolute weight > 0.2 (Table 3). These wavenumbers corresponded to crystalline cellulose (1371 cm⁻¹) and pectin (1335 cm⁻¹). For the second component, 51 variables were highlighted, 30 with negative contributions and 21 with positive ones, among which 13 showed a positive weight > 0.2 corresponding to cellulose (1462 cm⁻¹) and pectin (1335 cm⁻¹), with negative and positive contributions, respectively (Fig. 8B-a; Table 3). In zone C, for the first component, 14 variables were highlighted, all with positive ones, among which none showed an absolute weight > 0.2 (Fig. 8B-b; Table 3). For the second component, 15 variables were highlighted, all with positive ones, among which 8 showed a positive weight > 0.2 corresponding to aromatics (1508 cm⁻¹) and esterified pectin/cutin (1734 cm⁻¹), with positive contributions (Fig. 8B-b; Table 3).

The late events during the dew retting process [R6 vs R7] comparison was illustrated by respective average spectra in Supplementary Fig. 2, Table 2 and Table 3. For the sPLS-DA analysis of condition [R6 vs R7] (Fig. 9A-a-b; Table 2), the zone B, for the first component, 75 variables were highlighted, 31 with negative contributions and 44 with positive ones, among which 3 showed an absolute weight > 0.2 (Fig. 9B-a; Table 3). These wavenumbers corresponded to cellulose (1281 cm⁻¹) and crystalline cellulose (1371 cm⁻¹), with negative and positive
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** "With *a priori*" comparative analysis of zone C (1501–1785 cm⁻¹) between retting time R0/day1 (blue), R2/day6 (black), R6/day20 (red) and R7/day25 (orange). A: spectral profile extract. Assignments were done according to the **Supplementary Table 1**. B: Box-plots resulting from 1-way ANOVA. The box-and-whiskers plots represent values of nR0= 15, nR2= 15, nR6= 10, nR7= 17 individual spectra respectively covering all T. Within one time point R, all iₘₐₓ of the technical repetitions were chosen for each of these peaks. Statistical significance with Anova (Tukey's HSD) test between retting points are shown with letter marking where p-Value < 0.05. *Pectin.</figcaption></figure>

contributions, respectively. For the second component, 76 variables were highlighted, 28 with negative contributions and 48 with positive ones, among which 5 showed a positive weight > 0.2 corresponding to aromatics (1508 cm⁻¹) and pectin cellulose (1427 cm⁻¹), with negative and positive contributions, respectively (Table 3). In zone E, for the first component, 66 variables were highlighted, 39 with negative contributions and 27 with positive ones, among which 5 showed an absolute weight > 0.2 (Fig. 9B-b; Table 3). These wavenumbers corresponded to O(3)H...O(5)-cellulose I (3270–3350 cm⁻¹) with negative contributions only. For the second component, 67 variables were highlighted, 9 with negative contributions and 58 with positive ones, among which 6 showed a positive weight > 0.2 corresponding to CH and OH-Cellulose (3270–3350 cm⁻¹).

### 3.5. Comparison between 'with a priori' and 'without a priori' approaches

Table 4 discriminates specific peaks (and their related polymers) that characterize one retting time compared to another one, and common peaks between both retting points, that could also be of interest. Both 'with *a priori*' and 'without *a priori*' approaches highlighted a certain amount of variables corresponding to groups of significant wavenumbers. Numbers greatly differ according to the approach developed, 5 individual candidates for the "with *a priori*" approach and 17 for the "without *a priori*" one. It has to be noticed that 60 % (i.e. 3 out of the 5) 'with *a priori*' wavenumbers (1335 cm⁻¹, pectin; 1371 cm⁻¹, crystalline cellulose; 1734 cm⁻¹, esterified pect, cutin) were also highlighted as of interest within the 17 individual wavenumbers revealed by the 'without *a priori*' approach: 800 cm⁻¹ mannan-hemicellulose, 1055 cm⁻¹, mannan; 1105 cm⁻¹ βGlucan, cellulose; 1157 cm⁻¹, hemicellulose, xylan; 1281 cm⁻¹, cellulose; 1317 cm⁻¹, hemicellulose, cellulose;
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** "With a priori" comparative analysis of zone D (600–3000 cm⁻¹) between retting time R0/day1 (blue), R2/day6 (black), R6/day20 (red) and R7/day25 (orange). A: spectral profile extract. Assignments were done according to the Supplementary Table 1 B: Box-plots resulting from 1-way ANOVA. The box-and-whiskers plots represent values of nR0= 15, nR2= 15, nR6= 10, nR7= 17 individual spectra respectively covering all T. Within one time point R, all lᵢₘₐₓ of the technical repetitions were chosen for each of these peaks. Statistical significance with Anova (Tukey's HSD) test between retting points are shown with letter marking where p-Value < 0.05.</figcaption></figure>

1317 cm⁻¹, hemicellulose, cellulose; 1335 cm⁻¹, pectin; 1371 cm⁻¹, crystalline cellulose; 1427 cm⁻¹, pectin, cellulose; 1462 cm⁻¹, cellulose; 1508 cm⁻¹, aromatics; 1633 cm⁻¹, pectin; 1650 cm⁻¹ pectin, amide I; 1734 cm⁻¹, esterified pect, cutin; 3270–3350 cm⁻¹, OH bonds from cellulose; 3500–55 cm⁻¹, intramolecular hydrogen bond to O from cellulose, and 3725–70 cm⁻¹, free O-H bonds.

The [Start vs End] points process [R0 vs R7] is globally characterized by specific R0 mainly negative contributions of chemical bonds related to hemicellulose (1055–1157 cm⁻¹), cellulose (1105 cm⁻¹), pectin (1633–1734 cm⁻¹), amide I C-N protein (1650 cm⁻¹). In this comparison, the final step R7 is specifically characterized by mainly positive contributions related to Hemicellulose (800 cm⁻¹), pectin (1427 cm⁻¹), protein (1650 cm⁻¹), and esterified pectin/cutin (1734 cm⁻¹). Major cell wall rearrangements in outer tissues, in order to release bast fibers, are therefore occurring during this process. Early step [R0 vs R2] that show very few significant peaks. R2 is only specifically characterized by positive contributions of aromatics (1508 cm⁻¹) and pectin (1633–1734 cm⁻¹). In contrast, the starting step R0 exhibits significant peaks with positive contributions assigned to hemicellulose (1317 cm⁻¹) and pectin (1335 cm⁻¹). Early step of retting impacts the

middle lamella of cell walls, and the hemicellulose part. At last, late step [R6 vs R7] explains the retting progression toward the final step. Under retting (R6) is characterized by amorphous cellulose (1281 cm⁻¹) with positive contribution, as well as pectin (1335 cm⁻¹). Progression to the final step R7 is specifically characterized by a negative contribution of hemicellulose (1317 cm⁻¹), pectin (1427 cm⁻¹), whereas a positive contribution is observed for crystalline cellulose (1371 cm⁻¹). It has to be noticed that for each conditions tested, led us to identify specific peaks either for the first retting point, or for the second retting point, except these peaks assigned to aromatics (1508 cm⁻¹) and esterified pectin/cutin (1734 cm⁻¹) for the condition [R0 vs R7].

# 4. Discussion

## 4.1. Identification of major peaks

Retting induced chemical and structural reorganization of the flax stem tissues while micro-organisms (bacteria and fungi) infiltrate and invade the stem. To degrade them, such organisms should digest different tissue components (epidermis, cortical parenchyma), until they reach fiber bundles. In these bundles, elementary fibers (i.e. very long single cells) were glued together with pectins. (Chow and Ting, 2019) mentioned that (1,4) (1,6)-α-D-glucans and (1,3)-α-D-glucans are usually detected in fungi as peaks in the window 750–970 cm⁻¹ (in which, the specific 899 cm⁻¹ corresponds to plant amorphous cellulose, Zone A). If we focus on the global spectra (i.e. whole dataset, 3400–800 cm⁻¹), three major peaks could be suggested to belong to such fungi cell wall: 1105; 1055 and 1032 cm⁻¹, respectively related to β-1,3-D Glucan/chitin and to the mannan residues on proteins (N- and O-glycosylated proteins) (Galichet et al., 2001). Their contributions may be added or subtracted from those originated from the plant (and which could be assigned to another cell wall polymer). As hyphae are present as dew retting was progressing (Chabbert et al., 2020), the chemical environment could progressively be changed, and chemical bonds could vibrate differently. Degradation of the epidermis and cortical parenchyma by cellulases is essential for accessing fiber bundles (Placet et al., 2017). Endo glucanases (GH5, GH17), beta glucanases (GH3) and cellobiohydrolases (GH7) have been identified from the start of the retting process (Mukherjee et al., 2024), with an increasing and dominant abundance at the end of the retting process. Such enzymes degrade β-1, 4-D glucosidic bonds in the cellulose polymer, and attack amorphous cellulose before the crystalline one (Lee et al., 2020) leading eventually to mechanical properties alteration of fiber, and therefore their quality (Fila et al., 2001; Placet et al., 2017). The technical properties of the elementary flax fiber are due to the high content of crystalline cellulose (Müller et al., 1998). To obtain high quality fiber, the fiber-cell wall integrity should not be compromised whereas outer surrounding tissues should be degraded. During flax retting, (Bourmaud et al., 2019) established with XRD and NMR (Nuclear Magnetic Resonance) that the proportion of cellulose crystallinity increased of 8 %. The authors suggested that it was due to the disappearance of amorphous material that was also mentioned by (Bleuze et al., 2018; Chabbert et al., 2020). In our FT-IR datasets, the 899 and 1201 cm⁻¹ peaks were assigned to amorphous cellulose, and we also showed they were greatly modified during the retting process.

Chow and Ting (2019) identified a 1377 cm⁻¹ peak specific from Ganoderma boninense (fungi) (as Erukhimovitch et al., 2010). Such a 1377 cm⁻¹ peak was not significantly detected in our multivariate FT-IR analysis as a well-defined band. Nevertheless, as our peak 1371 cm⁻¹ was relatively broad, we can suggest that such contribution could also be taken into account.

Mohapatra and Malik (2015) identified the 1637 cm⁻¹ peak as related to water adsorption during flax dew retting. In this study, we also identify a peak at 1633 cm⁻¹ that is probably related to pectin (Chatjigakis et al., 1998; Szymanska-Chargot and Zdunek, 2013). During retting, dehydration also progressively occurred in up-rooted stems
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** "With a priori" comparative analysis of zone E (3000–3800 cm⁻¹) between retting time R0/day1 (blue), R2/day6 (black), R6/day20 (red) and R7/day25 (orange). A: spectral profile extract. Assignments were done according to the **Supplementary Table 1**. B: Box-plots resulting from 1-way ANOVA. The box-and-whiskers plots represent values of nRO= 15, nR2= 15, nR6= 10, nR7= 17 individual spectra respectively covering all T. Within one time point R, all iₘₐₓ of the technical repetitions were chosen for each of these peaks. Statistical significance with Anova (Tukey's HSD) test between retting points are shown with letter marking where p-Value < 0.05.</figcaption></figure>

(Supplementary Fig. 6). Archibald et al. (1998) considered that hydration of raw material greatly varied between their samples as it was the case in our study. Our observations led us to notice a clear horizontal shift in zone C (1734; 1650; 1633 cm⁻¹, containing pectin contributions). We firstly attributed a part of this shift to water. Indeed, this shift could be related to the non-esterified carboxylic groups, i.e. free acid group COOH (related peak at 1734 cm⁻¹) and the release of carboxylate (COO⁻) during retting. The (de)complexation with Calcium (1633 cm⁻¹) could also be taken into account (Tajmir-Riahi, 1983; Chatjigakis et al., 1998; Synytsya et al., 2003; Naraet et al., 2013). In the literature, shifts could be due to pectinates, methyl pectin and C-H stretching, depending on the global level of the methyl-ester groups in pectin (Synytsya et al., 2003). But the vibration at 1633 cm⁻¹ is also assigned to O-H bending of adsorbed water in pectin (Abidi et al., 2014). Indeed, drying in an oven removes free water, but some water molecules remain tightly bound to plant polymers (e.g., cellulose, hemicellulose, pectin). These contribute to O-H stretching bands in the 3000-3500 cm⁻¹ range. So, even after drying, bound water and intrinsic -OH group remains (Thybring et al., 2017; Pharmawati and Wrasiati, 2020; Dai et al., 2023). Such a phenomenon should also be considered for estimating the retting optimal end-point. Indeed, environmental hygrometry (due to rains for example)

can also deeply modify macromolecular structure at the plant cell level (Chen et al., 2018). Plant fiber swelling in a wet environment could be a consequence of internal structural changes that induced sorption/desorption/swelling (Peyrache et al., 2024). Other environmental factors such as the need to use different fields every year and the meteorological parameters that could differ each year of campaigns, could also modify flax dew retting progression (Mukherjee et al., 2024, see also Supplementary Figure 6). In addition to these environmental abiotic factors, the soil microbiome could also play an important role (Djemiel et al., 2017, 2020). Only the global consideration of all of these (a)biotic factors could help farmers identify the optimal end-point of retting whatever the campaign, and not only empirical aspects.

Cutin from the cuticle was expected to be partially or totally degraded during the early step of retting. Indeed, fiber and yarn samples graded low in quality had more wax and cutin residues (Morrison and Archibald, 1998; Morrison et al., 2000; Akin et al., 2001). Additionally, the cutin marker 8,16-dihydroxyhex-adecanoic acid is established as characteristic of such a poor quality (Morrison et al., 1999; Akin et al., 2001). We can hypothesize that the residual peak (1734–1750 cm⁻¹) that could still persist, is related to esters of alipathic compounds potentially cutin (Mazurek et al., 2017). This can be used as a marker of
<caption>**Table 1**</caption>

One way ANOVA of $i_{max}$ value from spectra for [R0 vs R2], [R6 vs R7] and [R0 vs R7] comparisons, in zones A, B, C, D and E previously defined (see Fig.1). The couple of letters (a, b, c) correspond to the significant groups with the one-way ANOVA (Tukey's HSD) based on the whole datasets od R0/day 1, R2/ day 6, R6/ day 20 and R7/ day 25 (see Fig. 2 to Fig. 6, and Supplementary Table 1 for peak assignment). Significant differences between the groups are noticed in the gray boxes. Annotation: AcGM: actyl glucomannan; XG: xyloglucan.

<table><thead><tr><th rowspan="2">Zone</th><th rowspan="2">Peak in cm<sup>-1</sup></th><th rowspan="2">Related Cell Wall polymer</th><th colspan="3">Comment according to the retting process</th></tr><tr><th>[R0 vs R2]</th><th>[R6 vs R7]</th><th>[R0 vs R7]</th></tr></thead><tbody><tr><td rowspan="5">Zone A</td><td>899</td><td>Cellulose</td><td>b,a</td><td>ab, a</td><td>b, a</td></tr><tr><td>1032</td><td>Cellulose and AcGM</td><td>a,a</td><td>a,a</td><td>a,a</td></tr><tr><td>1055</td><td>Cellulose<br>Mannans</td><td>a,a</td><td>a,a</td><td>a,a</td></tr><tr><td>1105</td><td>Cellulose, Pectin<br>β-D-glucans</td><td>a,a</td><td>a,a</td><td>a,a</td></tr><tr><td>1157</td><td>Hemicellulose<br>XG</td><td>ab,b</td><td>ab,ab</td><td>ab,a</td></tr><tr><td rowspan="3">Zone B</td><td>1201</td><td>Cellulose, XG</td><td>a,b</td><td>ab,ab</td><td>a,ab</td></tr><tr><td>1335</td><td>Pectin</td><td>b,a</td><td>ab,ab</td><td>b,ab</td></tr><tr><td>1371</td><td>Crystalline cellulose</td><td>b,a</td><td>ab,ab</td><td>b,ab</td></tr><tr><td rowspan="3">Zone C</td><td>1650</td><td>Pectin, Proteins</td><td>a</td><td>a</td><td>a</td></tr><tr><td>1734</td><td>Pectin (ester)<br>Cutin polyester</td><td>a,b</td><td>bc,c</td><td>a,c</td></tr><tr><td></td><td>CH<sub>3</sub></td><td>a</td><td>a</td><td>a</td></tr><tr><td rowspan="3">Zone D</td><td>2852</td><td>CH<sub>2</sub></td><td>a</td><td>a</td><td>a</td></tr><tr><td>2899</td><td>CH<sub>3</sub></td><td>a</td><td>a</td><td>a</td></tr><tr><td>2918</td><td>OH from cellulose I<br>and II</td><td>a</td><td>a</td><td>a</td></tr><tr><td>Zone E</td><td>3270</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>3770</td><td></td><td></td><td></td><td></td></tr></tbody></table>

the future quality of the flax fiber obtained from this specific field, for the considered year of campaign. Indeed, the presence of cutin residues alter the enzyme accession to cortical tissues (Akin et al., 2001). The relative absorbance of CH₂ chemical bond (2899 cm⁻¹) (zone D: 3000–2600 cm⁻¹) could be associated with proteins, cellulose and pectin (Séné et al. 1994). Two similar bands (2850; 2918 cm⁻¹) were also identified as related to C-H in methyl and methylene groups by (Mohapatra and Malik, 2015) during flax stem degradation onto the fields. The zone E (3800–3000 cm⁻¹) corresponds to intra-/intermolecular hydrogen bonding to O from cellulose (Schwanninger et al., 2004; Oh et al., 2005a, 2005b; Abidi et al., 2014).

## 4.2. Peak intensity evolutions during retting

In the literature, the hemicellulose degradation by fungi xylanases was necessary to attack the plant primary cell wall (Hatsch et al., 2006; Kikot et al., 2009). Putative fungi related peaks (1104 cm⁻¹ for β-glucan-chitin, and 1056; 1030 cm⁻¹ related to N- and O- glycosylated proteins) (Galichet et al., 2001), could also be associated to the progressive invasion of fungi hyphae within these outer tissues. β-mannanases have been identified from *Bacillus licheniformis* after flax water-retting (Ge et al., 2016). Recently Mukherjee et al. (2024), have mentioned the dynamics of hemicellulose-degrading CAZymes (GH17, GH55, GH35, GH10) including mannosidases (GH2, GH26) in flax dew retting. In our FT-IR results, a negative contribution was observed for β-D-Glucan (1105 cm⁻¹), as well as for other hemicellulose peaks (800; 1055; 1157; 1317 cm⁻¹). The sPLS-DA also strengthened their involvement as they exhibited a weight loading value > |0.2|. It means that the chemical context related to hemicellulose polymers was therefore greatly modified during the retting process as previously mentioned by Tian et al. (2016). Mukherjee et al., 2024; describe a sequential activity of these cell wall degrading enzymes, where Polygalacturonase (PG) plays a critical role early in retting [R0 vs R2] (expected to catalyze the breakdown of pectins in the middle lamella), followed by a rise in hemicellulose-degrading enzymes like xylosidase and galactosidase, and later by cellulases such as glucosidase and cellobiohydrolase, which is

known to degrade the cellulose matrix. As retting was initiated and progresses from day 6 to day 25, the degradation of xyloglucan and amorphous cellulose intensifies, with increased activity of cellulases (GH7, GH5, GH94) and hemicellulases (GH2) observed at later stages [R6-R7] (Mukherjee et al., 2024). Monosaccharide analysis reveals a marked decrease in xylose, galactose, and fucose (Mukherjee et al., 2024). The FT-IR analysis supports these findings by showing a reduction in the intensities of peaks corresponding to amorphous cellulose (1104; 1281; 1427; 1462 cm⁻¹), crystalline cellulose (1371 cm⁻¹) during the retting process. In our study, crystalline cellulose peak (1371 cm⁻¹) showed significant difference with the 'a priori' approach for [R0 vs R2], and a common significant presence identified with the sPLS-DA between the two retting points tested for each condition tested. Its common contribution was positive [R0 vs R7] and [R6 vs R7], or negative [R0 vs R2]. This is consistent with NMR results from Bourmaud et al. (2019), as well as (Chabbert et al., 2020; Mukherjee et al., 2024) which established a modest increase in crystallinity. Mohapantra and Malik (2015) also noticed with FT-IR such cellulose changes (1372; 1336; 1313; 1280; 1160; 1105 cm⁻¹) during flax biodegradation. Unfortunately, their findings were not supported by an appropriate statistical analysis. Such a decrease in the ratio of crystalline cellulose / amorphous components was considered as the first indication of cell wall degradation during hemp retting (Placet et al., 2017). In our context, there are also epidermis and cortical parenchyma that contribute to the initial decrease in cellulose [R0 vs R2], coming from their primary cell walls. Numerous of these enzymes are produced by microorganisms during flax retting. For example, early-acting PGs are found to be cell wall-related CAZymes such as GH35 (β-galactosidase), CE8 (pectin methylesterase) from flax, GH28 (polygalacturonase), and the pectate lyases PL1 and PL3 from microbes (Mukherjee et al., 2024, Djemiel et al., 2017). These structural changes in pectin and hemicellulose supposedly indicate degradation of the middle lamella, a key step that facilitates fiber separation in retting. At the tissue level, such enzymes could firstly reach parenchyma cells that surround fiber bundles, and secondly, the pectin-rich middle lamella (Chabbert et al., 2020). Pectin-esterases (family CE8) catalyze the de-esterification of pectins into pectate and methanol. Moreover, Pectin-Methyl-Esterases are particularly involved in esterified pectins parts and may be supported by the 1734 cm⁻¹ peak (zone C of [R0 vs R2] condition). The action of pectinases leads to further increased accessibility to other cell wall components by other degrading enzymes (Kikot et al., 2009). Aromatics could be detected due to putative cutin residues as well as low lignin that could be present around bast fibers. In the zone D could also be related to cutin as it was observed in *Arabidopsis* petal cuticle (2851 and 2917 cm⁻¹ for the *Arabidopsis* wild type) (Mazurek et al., 2017). Since up-rooting of stems, the epidermis was attacked to facilitate hyphae penetration, then outer tissues were later disorganized. Cutin from the cuticle was therefore degraded during the early step of retting. It is consistent with a cutin hydrolase enzyme (CE5) found during flax dew retting (Mukherjee et al., 2024). Nevertheless, this zone D is not significant whatever the condition tested in this study. At last, hydrolytic and oxidase activities are also noticed since the flax up-rooting R0 (Chabbert et al., 2020, Mukherjee et al., 2024). These combined actions will release cell wall constituents used as nutrients for microorganisms.

## 4.3. Comparison between the 'a priori' and the 'without a priori' statistical approaches

In this study, two approaches for statistical analyses on the same dataset were developed. The 'a priori' approach considered all selected retting times, and the $i_{max}$ evolution of a limited number of peaks (i.e. wavenumbers) previously chosen by expert curation regarding the literature. This can be considered as a classical approach since such an approach in vibrational spectroscopies (FT-IR or Raman) has been already applied (de Sousa Marques et al., 2014; Li et al., 2019; Sanchez et al., 2019; Morey et al., 2020). With such an approach, we were able to
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** “Without a priori” multivariate analyses applied to the FT-IR data for the Start vs End retting process [R0 vs R7] comparison in zone A (800–1190 cm⁻¹), zone B (1191–1500 cm⁻¹) and zone C (1501–1785 cm⁻¹). A: screeplots from the PCA showing the amount of explained variance for each principal component. B: sample plots after PLS-DA where samples are projected into the space spanned by the first two principal components, and are colored according to the time of retting. C: sample plots after sPLS-DA. D: loading plots from the sPLS-DA. Colors indicate the retting time point in which the median is maximum for each peak. Bars lengths are proportional to the values that they represent. Variables with absolute weight > 0.2 are presented in Table 3. The detail of all variable contributions can be seen in Supplementary Table 2.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** "Without *a priori*" multivariate analyses applied to the FT-IR data for the early retting process [R0 vs R2] in (a) zone B 1191–1500 cm⁻¹, (b) zone C 1501–1785 cm⁻¹. A: sample plots after sPLS-DA where samples are projected into the space spanned by the first two principal components, and are colored according to the time of retting. B: loading plots from the sPLS-DA. Colors indicate the retting time in which the median is maximum for each peak. Bars lengths are proportional to the values that they represent. Variables with absolute weight > 0.2 are presented in Table 3. The detail of all variable contributions can be seen in Supplementary Table 2.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** "Without *a priori*" multivariate analyses applied to the FT-IR data for late retting process [R6 vs R7] in (a) zone B 1191–1500 cm⁻¹ and (b) zone E 3000–3800 cm⁻¹. A: sample plots after sPLS-DA where samples are projected into the space spanned by the first two principal components, and are colored according to the time of retting. B: loading plots from the sPLS-DA. Colors indicate the retting time in which the median is maximum for each peak. Bars lengths are proportional to the values that they represent. Variables with absolute weight > 0.2 are presented in Table 3. The detail of all variable contributions can be seen in Supplementary Table 2.</figcaption></figure>

identify 2 specific chemical bounds in the start vs end retting points comparison, 5 in the early events comparison, and unfortunately none in the late events comparison (Table 4). The early event [R0 vs R2] is characterized by modifications of amorphous cellulose and pectins. The initiation of retting is well undergoing. The end of retting could be based on a few measurements of the esterified pectin/cutin (1335–1734 cm⁻¹) and amorphous/crystalline cellulose (1201 vs 1371 cm⁻¹). The ‘without *a priori*’ approach considered the whole spectrum evolution while series of comparisons of two retting points of the retting process. In this ‘without *a priori*’ approach, our purpose was to limit the loading weight

(importance) of peak contributions exhibiting very high intensity in the whole spectrum, since global analysis of the whole spectra could generate a bias as peaks exhibiting high $i_{max}$ could crush those that showed lower $i_{max}$. Therefore, we chose to proceed with PCA at first since it is a dimensionality reduction technique that helps to represent a multivariate dataset as a smaller set of variables. Indeed, the power of PCA relies on its ability to represent correlated information from hundreds of spectral channels into a few orthogonal PCs, which provides a convenient way for image visualization and feature extraction (Zhang et al., 2005). However, trends, clusters and outliers can be visualized, or
<caption>**Table 2**</caption>

Identification of peaks specific for periods in the retting process. The different percentages of explained variabilities are given according to the different steps of the multivariate analysis (PCA, PLS-DA, sPLS-DA). "First retting point" and "Second retting point" correspond to the 2 conditions that are compared (i.e. for [R0 vs R7], the first retting point is related to R0, and the second retting point is assigned to R7). The total percentage of the explained variability is given (Total Variability). The % of the explained variability is given for each component X-comp1 and X-comp2. Police in regular light case corresponds to positive contribution, in *italics* to negative contribution, and in **bold** to contribution with an absolute value > |0.2|. The specific peaks corresponding to the variables are indicated according to Supplementary Table 1. Annotations: aCel: cellulose; cCel: crystalline cellulose; Cut: cutin polyester; ePect: esterified pectin; GlcMan: glucomannan; Hem: hemicellulose; Man: mannan; Pect: pectin; Xyl: xylan; XG: xyloglucan.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">[R0 vs R7]<br>day1 vs day 25</th><th colspan="2">[R0 vs R2]<br>day1 vs day 6</th><th colspan="2">[R6 vs R7]<br>day20 vs day 25</th></tr><tr><th>Zone A</th><th>Zone B</th><th>Zone C</th><th>Zone B</th><th>Zone C</th><th>Zone B</th><th>Zone E</th></tr></thead><tbody><tr><td rowspan="5">PCA</td><td>PC1</td><td>69 %</td><td>48 %</td><td>53 %</td><td>49 %</td><td>45 %</td><td>41 %</td><td>64 %</td></tr><tr><td>PC2</td><td>14 %</td><td>26 %</td><td>26 %</td><td>22 %</td><td>23 %</td><td>31 %</td><td>19 %</td></tr><tr><td>Total variability</td><td>83 %</td><td>74 %</td><td>79 %</td><td>71 %</td><td>68 %</td><td>72 %</td><td>83 %</td></tr><tr><td>X1-comp</td><td>68 %</td><td>44 %</td><td>53 %</td><td>48 %</td><td>44 %</td><td>30 %</td><td>57 %</td></tr><tr><td>X2-comp</td><td>15 %</td><td>29 %</td><td>13 %</td><td>21 %</td><td>18 %</td><td>18 %</td><td>19 %</td></tr><tr><td rowspan="5">PLS-DA</td><td>Nb Var</td><td>4</td><td>13</td><td>50</td><td>21</td><td>14</td><td>44</td><td>27</td></tr><tr><td>X1-comp</td><td>Nb Var > |0.2|</td><td>16</td><td>20</td><td>63</td><td>8</td><td>0</td><td>31</td><td>30</td></tr><tr><td>First retting point</td><td>8</td><td>12</td><td>0</td><td>8</td><td>0</td><td>3</td><td>5</td></tr><tr><td>Second retting point</td><td>Man Hem 800</td><td>cCel 1371</td><td>Hem Cel 1317</td><td>cCel 1371<br>Pect 1335</td><td></td><td>Cel 1281<br>cCel 1371</td><td>OH-Cel, 3725</td></tr><tr><td>X2-comp</td><td>Nb Var</td><td>6</td><td>25</td><td>55</td><td>21</td><td>15</td><td>48</td><td>58</td></tr><tr><td rowspan="5">sPLS-DA</td><td>Nb Var > |0.2|</td><td>8</td><td>12</td><td>4</td><td>13</td><td>8</td><td>5</td><td>6</td></tr><tr><td>First retting point</td><td>Cel Man 1055</td><td>Cel 1462</td><td></td><td></td><td>Aromatics 1508<br>ePect, Cut 1734</td><td>Aromatics 1508<br>Pect Cel 1427</td><td>OH-Cel<br>3350</td></tr><tr><td>Second retting point</td><td></td><td>Cel 1281<br>Pect Cel 1427</td><td>Aromatics 1508</td><td>Pect 1335<br>Cel 1462</td><td></td><td></td><td></td></tr></tbody></table>

not. This is why we further proceed with PLS method searches in order to highlight the highest covariance between our retting variates, which are a linear combination of initial variables, taking into account all variables in the input matrices. Indeed, the goal of PLS regression is to provide a dimension reduction strategy in a situation where one wants to relate a set of response variables Y (i.e. spectrometric data) to a set of predictor variables X (retting time). The particular case of PLS-DA is a PLS regression where Y is a set of binary variables describing the categories of a categorical variable on X. sPLS-DA is a variant of PLS-DA that makes a sparsity assumption, i.e., that only a small number of features are responsible for driving a biological event or effect. Using lasso penalization, these methods add penalties to better guide the feature selection and model fitting process and achieve improved classification by allowing to select a subset of the covariates instead of using all of them (Ruiz-Perez et al., 2020). Thus, using PCA followed by sPLS-DA, we were able to identify roughly 3.5 times more specific chemical bounds in start vs end points, early events and late events comparisons than with the 'a priori' approach (Table 4). Since three peaks R0_esterified pectin/cutin (1734 cm⁻¹), R2_pectin (1335 cm⁻¹) and R2_crystalline cellulose (1371 cm⁻¹), whose putative involvement in accessibility to other cell wall components is discussed above, were observed in both methods, their presence in the larger set of candidates highlighted by means of the 'without a priori' approach supports the hypothesis that the other candidates in such approach are very reliable. At last, the other huge advantage of the 'a priori' method is to be able to pinpoint which of the highlighted variable has a greater "importance" (loading weight) within a very large set of putative candidates, as was for example recently used in a study of the effect of hot air drying on the degradation of polyphenols in red radish (Li et al., 2020). In the Table 4, we could even decipher what are the peaks whose contribution is significatively specific to this or that retting point of our 25d kinetic, as well as if it is via a positive or a negative contribution [R0 vs R7, with 9 vs 7 contributions respectively], [R0 vs R2, with 4 and 3 contributions] and [R6 vs R7, with 3 and 5 contributions]. Such original analysis could be undertaken thanks to advances in biostatistics tools, as was recently deployed in flax and allowed to highlight relevant candidates that will be used as effective predictors of flax fiber quality parameters (Chabi et al., 2023).

## 5. Conclusions

In this study, we demonstrate that FT-IR vibrational spectroscopy is a valuable method to characterize to monitor the retting process that until now is still monitored on the field with manual empirical evaluation. Indeed FT-IR allows to discriminate subtle differences in chemical bonds, and in their related cell wall polymers that seem progressively altered. Specifically, the early step of cell wall disorganization and the later step that finally allows elementary fiber releasing can be deciphered. Cellulose (1371; 1462 cm⁻¹, from outer tissues) and pectin (1335 cm⁻¹), notably the esterified pectin/cutin (1734 cm⁻¹), are markers of the early step [R0 vs R2]. The final step [R6 vs R7] in which the optimal end-point could be identified, is characterized with the intensity variation of peaks cellulose (1281; 1371 cm⁻¹), pectin/cellulose (1427 cm⁻¹), aromatics (1508 cm⁻¹), free OH-chemical bonds (3725 cm⁻¹), and OH-cellulose I and II bonds (3350 cm⁻¹). The amorphous cellulose/pectin peaks (1281–1427 cm⁻¹) greatly decreased during retting.

We finally argue for the development of machine learning approaches based on the use of large spectroscopic datasets combined with appropriate powerful statistical analyses. There's no doubt that the increasingly powerful informatic tools and biostatistics will benefit the development of such approaches. The choice of 'without a priori' method is at first glance more time consuming since it needs to develop a relevant statistical pipeline to train the model. But it gives a larger number of relevant candidates and deeper results as the whole spectrum is used in the analysis. By combining FT-IR technology and relevant multivariate analyses, we therefore succeeded to highlight a reasonable number of relevant wavenumbers for retting stage monitoring, which will be technically easy to use in handheld devices as recently developed in disease diagnosis (de Sousa Marques et al., 2014; Sanchez et al., 2019) or for identification (Li et al., 2019; Morey et al., 2020) or for food quality control purposes (Cebi et al., 2023).

## Ethical approval

We confirm that all the research meets ethical guidelines and adheres to the legal requirements of the study country. The research does not
<caption>**Table 3**</caption>

Loading plot values from the sPLS-DA applied the retting data to discriminate retting times by means of [R0 vs R2], [R6 vs R7] and [R0 vs R7] comparisons, in zones A, B, C and E previously defined (see Fig. 1). Police in regular light cases corresponds to positive contribution, in italics to negative contribution. Only wavenumber (i.e. peaks or variables) with absolute weight > 0.2 are presented. The detail of all contributions can be seen in Supplementary Table 2.

<table><thead><tr><th></th><th></th><th>PC1</th><th>PC2</th><th></th></tr></thead><tbody><tr><td rowspan="10">[R0 vs R7]<br>day 1 vs<br>day 25</td><td>Zone A</td><td>1137.85303<br>1135.92446<br>1139.78159<br>1133.9959<br>1141.71016<br>1132.06733</td><td>800.35425<br>802.28281<br>1164.85293<br>1162.92437<br>1166.78149<br>1160.9958<br>1168.71006<br>1159.06724<br>1170.63862</td><td>1014.4249</td></tr><tr><td>Zone B</td><td>1309.49526<br>1311.42383<br>1307.5667<br>1313.35239</td><td>1396.28066<br>1394.3521<br>1398.20923<br>1392.42354<br>1390.49497<br>1388.56641<br>1400.13779<br>1386.63784</td><td>1423.28057<br>1425.20913<br>1427.1377<br>1429.06626<br>1430.99482<br>1434.85195<br>1432.92339<br>1421.352<br>1486.92319<br>1436.78052<br>1488.85176</td></tr><tr><td>Zone C</td><td></td><td></td><td>1506.20884<br>1540.923<br>1508.1374<br>1542.85156</td></tr><tr><td rowspan="10">[R0 vs R2]<br>day 1 vs<br>day 6</td><td>Zone B</td><td>1394.3521<br>1392.42354<br>1396.28066<br>1390.49497<br>1398.20923</td><td>1344.20942<br>1342.28086<br>1346.13799<br>1467.63755<br>1465.70898<br>1469.56611<br>1463.78042<br>1461.85186<br>1459.92329<br>1457.99473<br>1471.49468<br>1456.06616<br>1473.42324</td><td>1344.20942<br>1342.28086<br>1346.13799</td></tr><tr><td>Zone C</td><td></td><td></td><td>1502.35171<br>1504.28027<br>1749.20796<br>1747.27939<br>1751.13652<br>1753.06509<br>1745.35083<br>1754.99365</td></tr><tr><td rowspan="10">[R6 vs R7]<br>day20 vs<br>day25</td><td>Zone B</td><td>1282.49536<br>1284.42393</td><td>1384.70928</td><td>1498.49458<br>1496.56602</td></tr><tr><td>Zone E</td><td>3758.77212<br>3770.34351<br>3756.84355<br>3768.41494<br>3754.91499</td><td></td><td>1419.42344<br>1417.49487<br>1421.352<br>3274.70244<br>3276.63101<br>3272.77388<br>3278.55957<br>3270.84531<br>3280.48813</td></tr></tbody></table>

involve any human or animal welfare related issues

## Fundings

This work was performed within the framework of the PEARL Flax-Tronic project co-funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement N°847568, and the I-SITE ULNE foundation (ANR).

## CRediT authorship contribution statement

**Blervacq Anne-Sophie:** Writing – review & editing, Writing – original draft, Validation, Methodology, Formal analysis, Data curation, Conceptualization.
**Grec Sébatien:** Writing – review & editing, Project administration, Funding acquisition, Conceptualization.
**Mukherjee Suvajit:** Writing – review & editing, Formal analysis.
**Hawkins Simon:**

<caption>**Table 4**</caption>

Summarize the candidates revealed by the 'a priori' and 'without a priori' approaches. The first one considers only a single expert-curated chosen peak (one-way ANOVA, Tukey's HSD). The second one is based on the whole spectrum (PCA, sPLS-DA). Police in light case corresponds to positive contribution, in italics to negative contribution, and in **bold**, to contribution with an absolute value > |0.2|. Common candidates are underlined. Annotations: aCel: cellulose; cCel: crystalline cellulose; ePect: esterified pectin; GlcMan: glucomannan; Hem: hemicellulose; Man: mannan; Pect: pectin; Xyl: xylan; XG: xyloglucan.

<table><thead><tr><th></th><th>[R0 vs R7]</th><th>[R0 vs R2]</th><th>[R6 vs R7]</th></tr></thead><tbody><tr><td>'a priori',</td><td>899 cm<sup>-1</sup>, aCel<br>1734 cm<sup>-1</sup>,<br>ePect, Cut</td><td>899 cm<sup>-1</sup>, aCel<br>1201 cm<sup>-1</sup>; XG,<br>aCel<br>1371 cm<sup>-1</sup>, cCel<br>1335 cm<sup>-1</sup>, Pect<br>1734 cm<sup>-1</sup>,<br>ePect, Cut</td><td>None</td></tr><tr><td>'without a priori'<br>positive<br>contribution</td><td>specific for R0:<br><b>1055 cm<sup>-1</sup></b>,<br><b>Man</b></td><td>specific for R0:<br><b>1371 cm<sup>-1</sup>, cCel</b><br><b>1508 cm<sup>-1</sup></b>,<br><b>Aromatics</b></td><td>specific for R6:<br>1281 cm<sup>-1</sup>, Cel<br>1335 cm<sup>-1</sup>, Pect<br>3500–55 cm<sup>-1</sup>, O-H</td></tr><tr><td>negative<br>contribution</td><td>1105 cm<sup>-1</sup> βGlc,<br>Cel</td><td>1633 cm<sup>-1</sup>, Pect</td><td></td></tr><tr><td>absolute value<br>> |0.2|</td><td><b>1157 cm<sup>-1</sup></b>,<br><b>Hem, Xyl</b><br>1371 cm<sup>-1</sup>, cCel<br>1462 cm<sup>-1</sup>, Cel<br>1508 cm<sup>-1</sup>,<br>Aromatics<br>1633 cm<sup>-1</sup>, Pect<br>1650 cm<sup>-1</sup> Pect,<br><b>Amide I</b><br>1734 cm<sup>-1</sup>,<br>ePect, Cut</td><td><b>1734 cm<sup>-1</sup></b>,<br><b>ePect, Cut</b></td><td></td></tr><tr><td>specific for R7:<br><b>800 cm<sup>-1</sup></b>,<br><b>Man, Hem</b><br><b>1281 cm<sup>-1</sup>, Cel</b><br><b>1317 cm<sup>-1</sup></b>,<br><b>HemCel</b><br>1335 cm<sup>-1</sup>, Pect<br><b>1427 cm<sup>-1</sup></b>,<br><b>PectCel</b><br><b>1508 cm<sup>-1</sup></b>,<br><b>Aromatics</b><br>1650 cm<sup>-1</sup> Pect,<br><b>Amide I</b><br>1734 cm<sup>-1</sup>,<br>ePect, Cut</td><td>specific for R2:<br>1317 cm<sup>-1</sup>, HemCel<br>1427 cm<sup>-1</sup>, PectCel<br>1335 cm<sup>-1</sup>, Pect<br><b>1462 cm<sup>-1</sup>, Cel</b></td><td>specific for R7:<br><b>1317 cm<sup>-1</sup>, Hem, Cel</b><br><b>1427 cm<sup>-1</sup>, PectCel</b><br>1371 cm<sup>-1</sup>, cCel<br><b>3725–70 cm<sup>-1</sup></b>, O-H<br><b>3270–3350 cm<sup>-1</sup></b>, O-H</td><td></td></tr></tbody></table>

Writing – review & editing. **Créach Anne:** Writing – review & editing.
**de Waele Isabelle:** Writing – review & editing, Validation, Data curation.
**Goulas Estelle:** Writing – review & editing, Writing – original draft, Validation, Methodology, Formal analysis, Data curation, Conceptualization.

## Declaration of Generative AI and AI-assisted technologies in the writing process

Authors did not use generative AI and AI-assisted technologies in the writing process of this manuscript.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Suvajit MUKHERJEE reports financial support was provided by European Union. Suvajit MUKHERJEE reports financial support was provided by I-SITE ULNE foundation (Agence Nationale de la Recherche, France). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## Acknowledgements

Suvajit Mukherjee thanks the I-Site ULNE foundation for funding and M. Alexis Boulet for coordinating the PEARL (Programme for Early-stage Researchers in Lille) program. The authors thank Dr. Ali Reda from IEMN (UMR 8520) for its valuable help in collecting samples. Our industrial partner Van Raubaes Frères, F-59122 Killem, is thanked for making the flax field available to us and for carrying out the industrial characterization of flax fibers. Dr F. Guyomarch (Univ. Lille, ULR 2697, France) is thanked for valuable discussion according to the multivariate analyses and PC components. Acquisition and baseline correction of FT-IR spectra, and Min-Max normalization for illustrations, were carried out on the vibrational spectroscopy facility of the Advanced Characterization Platform of the Chevreul Institute (FR2638, France).

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.indcrop.2025.120798.

## Data Availability

Data will be made available on request.

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