Assessing Cellulose Micro/Nano�bre Morphology Using a High Throughput Fibre Analysis Device to Predict Nanopaper Performance

Characterising cellulose nano�bre (CNF) morphology has been identi�ed as a grand challenge for the nanocellulose research �eld. Direct techniques for CNF morphology characterisation exhibit various di�culties related to the material network structure and equipment cost, while indirect techniques that investigate bre-light interaction, bre-solvent interaction, bre-bre interaction, or speci�c �bre surface area involve relatively facile methods but may be more unreliable. Nanopaper mechanical testing is a prevalent metric for assessing bre-bre interaction, but is an off-line, time-consuming, and destructive methodology. In this study, an optical �bre morphology analyser (MorFi, TechPap) was employed as an on-line, high throughput, fast turnaround tool to assess micro/nano�bre pulp morphology and predict the properties of nanopaper material. Correlation analysis identi�ed �bre content and �bre kink properties as most correlated with nanopaper strength and toughness, while �bre width and coarseness were most inversely correlated with nanopaper performance. Principal component analysis (PCA) was employed to visualise interdependent morphological and mechanical data. Subsequently, two data driven statistical models - multiple linear regression (MLR) and machine learning based support vector regression (SVR) - were established to predict nanopaper properties from �bre morphology data, with SVR generating a more accurate prediction across all nanopaper properties (NRMSE = 0.13-0.33) compared to the MLR model (NRMSE = 0.33-0.51). This study highlights that statistical methods are useful to disentangle and visualise interdependent morphological data from an on-line �bre analysis device, while regression models are also capable of predicting paper mechanical properties from CNF samples even though these devices do not operate at nanoscale resolution.


Importance of Cellulose Nano bre (CNF) Morphology
Characterising CNF morphology has been identi ed as a grand challenge for nanocellulose research (Moon et al. 2011).Across various nanocellulose applications, it is an important parameter for assessment of product quality, quality control, and material safety (Campano et al. 2020).Fibre morphology encompasses the average bre dimensions (length, width), the relative size distribution of bre dimensions throughout the sample, bre aspect ratio, bre surface area, the degree of brillation and branching, bre hydrodynamic volume (rigidity), and bre shape (kink, curl, curvature).Subsequently, nano bre morphology in uences the effective nano bre surface area, bre-bre and bre-solvent interaction, net surface charge, gel point or networking concentration, and therefore the product quality in terms of its mechanical properties, rheological and colloidal behaviour, hydrophilicity, optical properties, electrical conductivity, lm permeability, and material reactivity (Li et al. 2021).Fibre morphology has a well-established impact on the mechanical properties of paper, such that bre length strongly affects paper strength, whereas bre width decreases bre exibility and conformability, which has a negative impact on paper strength (Seth 1995;Larsson et al. 2018).Fine content improves the strength, smoothness, and optical properties of the nal paper (Moral et  Analysing Cellulose Nano bre Morphology Measuring bre morphology has been a di cult task in nanocellulose research and development, primarily due to nanocellulose materials existing over a range of length scales, from poorly brillated millimetric scale cellulose bundles to micron scale cellulose micro bres (CMF), and down to nanoscale brillated cellulose nano bres (Tanaka et al. 2012;Chinga-Carrasco 2013).This length scale range introduces a challenge for capturing a representative, bulk analysis of bre morphology.Other challenges for characterising CNF morphology include the interconnected network structure of the material, and the di culty of observing nano bres in their native, aqueous suspended state (Haapala et al. 2013).
Many tools have been proposed to address the challenge of CNF morphology characterisation, including both direct observational measurement of bres such as SEM, TEM, and AFM (Krishnamachari et  Both direct and indirect bre morphology characterisation exhibit di culties.Direct nano bre characterisation enables high resolution visualization of individual particles or aggregates at multiple magni cation levels, but is limited in its ability to analyse a representative sample of the material in a timely manner and for bres across multiple length scales (Legland and Beaugrand 2013).While the resolution of TEM can analyse bre width down to a few nanometres, SEM is only capable of analysing bre width greater than 100 nm (Kangas et al. 2014).In addition, the three-dimensional structure and bulk morphology of the sample is disrupted during the drying step required for sample preparation (Peng et al. 2012;Silva et al. 2021).Meanwhile, indirect characterisation involves measuring a derived property of the nano bre system, such as the bre-light interaction (DLS, UV-vis transmittance), bre-solvent interaction (rheology, sedimentation behaviour, water retention capacity), bre-bre interaction (nanopaper mechanical properties), or speci c surface area (SANS, SAXS, DSC, BET adsorption, solvent relaxation NMR, conductimetric titration).Typically, calculations or models are employed to infer bre morphology characteristics.These tools generally involve more simple methods of nano bre characterisation but are often unreliable due to potential inaccuracy and lack of generalisation in the models inferring nano bre properties.This issue is emphasised in the nanocellulose eld, where different biomass sources and processing methodologies can produce material ranging widely in terms of bre morphology and mechanical properties.
As nanocellulose materials become increasingly commercial, there is strong incentive to shift from laboratory scale characterisation methods of bre morphology to scalable, fully automated, on-line characterisation systems that are capable of assessing thousands of bre elements over a relatively short time period (Legland and Beaugrand 2013;Balea et al. 2021).

Fibre Analysis Tools in Nanocellulose Literature
Optical bre analysis devices are a potential solution for this characterisation challenge.These tools have the potential to provide high throughput, fast turnaround analysis of micro and nano bre pulp morphology.Commercial bre analysis devices have previously been compared with varying results depending on their bre analysis algorithm (Guay et al. 2005;Turunen et al. 2005;Hirn and Bauer 2006).
One such commercial bre analysis device is the MorFi (Techpap, France), which has been selected as the bre analysis system for this study.Although MorFi is designed to analyse bres in the size range produced within the pulp and paper industry, many studies have been conducted with MorFi as an indirect nano bre characterisation tool, as shown in Table 1.

Biomass Preparation & CNF Production
Biomass wascut into approximately 5 cm lengths, washed three times in distilled water at 80°C for approximately 30 minutes, and subsequently dried in a convection oven at ~55°C for 3 days.Dried biomass was ground using a Retsch SM300 mill (Retsch GmbH, Germany) at 3000 rpm with a 1 mm trapezoidal mesh screen.Oversized material was separated from the ground biomass with a 0.71 mm aperture sieve.Ground biomass was dispersed and stirred overnight at ~350 rpm in deionised water at a solid ratio of 1:20 (20 g of water for every 1 g of ground biomass).Chemical pretreatment was performed using a 2% NaOH solution (w/v) at 80°C for 2 h, stirred at ~350 rpm.NaOH treated (deligni ed) pulp was separated from the waste liquor through a ne mesh sieve (53 µm aperture) and rinsed extensively until the ltrate pH was below 8.The deligni ed pulp suspension was diluted to a setpoint of 0.5% (w/v) using a Mettler Toledo moisture analyser.
Fibre Morphology Analysis 10 g of CNF suspension at 0.5% (w/v) was randomly sampled and added to approximately 1 L of water for bre analysis using the MorFi Compact analyser (Techpap, France) equipped with CCD video camera, a high magni cation optical ow cell and MorFi R.10.07 automatic analysis software.According to the default settings of the MorFi device, bres are classi ed as elements with a length between 100 µm -10 mm and a width between 5 µm -75 µm, while ne elements have a length < 100 µm and a width < 5 µm.Four technical replicates were run for each sample.The output parameters for the MorFi bre analysis are outlined and described in Table 2. Eq. 1 where σ T w is the tensile index or speci c tensile strength per unit weight in Nm. g − 1 , UTS is the ultimate tensile strength in Pa, and ρ nanopaper is the nanopaper density in kg.m − 3 .Toughness was calculated as a numerical approximation of the energy absorbed by the nanopaper strip, according to Equation 2 where U T is the toughness in MJ. m − 3 , 0 is the zero-strain starting point, and ϵ f is the nanopaper failure strain.

Statistical Methodology Correlation Analysis
Following the generation and compilation of bre morphology data, for which the four technical replicates for each sample were calculated into a mean, two Pearson's correlation matrices were built to assess the relationships between bre morphology parameters and nanopaper mechanical properties for all sorghum varieties, sections, and energy levels.The two correlation matrices included: (1) the correlation between each bre morphology parameter, and (2) the correlation between each bre morphology parameter and each of the four nanopaper mechanical properties.

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Principal Component Analysis Principal component analysis (PCA) was performed using the prcomp command of the R statistical software (R Core Team 2021) with RStudio v1.3.1056 in order to reduce the dimensionality of the bre morphology and nanopaper mechanical property data sets.Normal data probability ellipses were tted for the different factors (variety, section, energy level) based on the default normal probability of 68% (Prager et al. 2020).The top two principal components were selected for data visualisation.

Regression Modelling
To predict nanopaper properties, the bre morphology parameters outputs were related to the nanopaper Eq. 4

Model Validation
For model validation, an additional series of CNF suspension was prepared through HPH processing over an extended mechanical energy series.The biomass sample used for this analysis was the Sugargraze variety with all sections combined in equal proportions.CNF pulp was prepared according to the mechanical processing conditions outlined in Table 3. Nanopaper handsheets were fabricated from the prepared CNF pulp and tested in accordance with the previously described experimental methodology.

Fibre Morphology Correlations
Initially, a correlation matrix detailing the relationship between each pair of bre morphology parameters was calculated, as displayed in Figure 2. Using a signi cance threshold of x≥0.7 , a total of 27 of the 210 pairwise correlations were considered signi cantly correlated.Relationships of interest included the positive correlation between bre width ( bre_W), bre coarseness ( bre_coarse), bre curl index ( bre_curl), macro brillation index (MF.index),and broken bre content ( bre_broken), the inverse correlation between these parameters and bre content ( bre_cont), and the inverse correlation between kinked bre content ( bre_kink.cont)and mean ne area ( ne_A) and length ( ne_L).Following the analysis of pairwise correlations in Figure 2, a correlation table was established between each bre morphology parameter and each of the four nanopaper mechanical performance metrics, as outlined in Table 4. Notable positive correlations included bre content ( bre_cont) for tensile index and Young's modulus (0.6 and 0.71, respectively), the ne content ( ne_cont) for Young's modulus (0.67), and all kinkrelated parameters for toughness (0.6 to 0.66).Taking the average correlation across all four nanopaper metrics, kinked bre content ( bre_kink.cont)and kink angle ( bre_kink.ang)have the highest values (0.55 and 0.56, respectively), indicating the strongest overall relationship to nanopaper performance out of all bre morphology parameters.The most indicative bre morphology parameter for a single nanopaper property was the inverse correlation between Young's modulus and mean bre width (-0.8).
Other inverse correlations between bre morphology parameters and nanopaper properties include bre coarseness ( bre_coarse) for all nanopaper metrics besides strain at break (-0.51 to -0.8) and mean ne length ( ne_L) and mean ne area ( ne_A) for Young's modulus (-0.69 and -0.71, respectively).Following the correlation analysis, a series of PCAs were run to visualise the grouping and variance of bre morphology data across the entire sample population in a reduced dimensionality format.Initially, a PCA was conducted on the nanopaper mechanical property data across all biomass samples and energy levels (Figure 3a).The results from the nanopaper PCA demonstrate that the increase in mechanical processing energy from low to high shifted the data along the rst principal component, which explains 61.3% of the overall variance.This shift was closely matched by the shift in nanopaper density and Young's modulus, and to a lesser extent ultimate tensile strength (UTS), tensile index (TI), toughness and strain at break.This con rms the existing notion that energy level has a strong positive correlation with these nanopaper material properties.
Subsequently, a PCA was conducted on the bre morphology data across all biomass samples and energy levels (Figure 3b).The results from the bre morphology PCA demonstrate the bre morphology parameters that are correlated and inversely correlated with the mechanical processing energy (along PC1), which are known to relate to the nanopaper properties from Figure 3a.Fibre kink properties and bre content are positively associated with processing energy, and therefore nanopaper performance, while parameters such as bre width, broken bre content, and bre coarseness are inversely associated with processing energy and nanopaper performance.Fibre morphology parameters that correlate to PC2 include the ne number and the ne content weighted by area, length, and length-weighted length, respectively.The variance in these parameters is more closely associated with different plant sections, speci cally the leaf section, as demonstrated in the Supplementary Material.The two rst principal components for the bre morphology PCA explain 35.6% and 22.8% of the total variance, respectively.

Nanopaper Regression Models
Prediction of nanopaper mechanical properties from bre morphology data was performed using two regression modelling techniques: Multiple Linear Regression (MLR) and Support Vector Regression (SVR).
For both regression models, tting of the bre morphology parameters was assessed for each nanopaper metric based on their R 2 and NRMSE values, as seen in Table 5.Based on the MLR and SVR model outputs, it can be concluded that the given MorFi data best explains nanopaper performance in terms of tensile index (NRMSE of 0.4 and 0.23, respectively) and Young's modulus (NRMSE of 0.33 and 0.13, respectively).For the given dataset, both the MLR and SVR model outputs demonstrate a high level of accuracy for predicting nanopaper properties from bre morphology data across all four nanopaper metrics (R 2 > 0.88).A full overview of the MLR and SVR regression coe cients and parameters are provided in the Supplementary Material.However, as the SVR model demonstrated the higher accuracy for predicting nanopaper properties, it was selected for further investigation in the subsequent analyses.Figure 4 portrays the measured and SVR predicted nanopaper tensile index, which demonstrates the high level of accuracy for this model for all sorghum samples across difference varieties, sections, and energy levels.
Considering the impact of the mechanical energy level on the accuracy of the regression model predictions, Table 6 demonstrates that medium and high energy samples had the lowest NRMSE values across the majority of nanopaper metrics (excluding toughness), indicating a higher bre morphology to nanopaper prediction capability when higher processing energy was applied.For tensile index and Young's modulus, which were the metrics that the SVR model most accurately predicted in the previous section, low energy samples had the highest NRMSE across the energy series, while the high energy samples for these metrics were the most accurately predicted out of all energy levels, nanopaper metrics, and model types.As seen in Table 7, the SVR model is once again more accurate than the MLR model for all nanopaper metrics besides tensile index, with Young's modulus again demonstrated as the most accurate metric for the SVR model.However, the accuracy of the validation series models was signi cantly lower than the original regression models, with NRMSE values ranging from 4.1 to 7.5 times higher across all nanopaper metrics for the validation series.The following section describes possible mechanisms underpinning the correlation results between bre morphology parameters and nanopaper properties.

In uence Kinked Fibres
The relationship between bre kink and paper properties has rarely been described and has not been extensively elucidated in the literature thus far (Leopold and Thorpe 1968; Guangsheng et al. 2012; Sood and Sharma 2021).For tissue paper applications, the presence of kinked bres increases material porosity and surface roughness, but negatively impacts the paper density and inter-bre bond strength (Morais et al. 2021).However, in the case of a cellulose nano bre system, bre bundles that have undergone partial micro brillation through homogenisation may be interpreted as bre kinks due to limitations in optical resolution, which would be related with an increase in nanopaper strength.
Alternatively, bre kinks have previously been described as deformations induced by mechanical stress rather than by chemical pulping (Aguado et al. 2016).Therefore, their association with nanopaper performance could be related to the increasing energy applied to the bre bundles over the HPH processing series, which induced bre deformation.
In uence of Fibre Width Coarseness bre width and coarseness are inversely correlated with all facets of nanopaper performance, such that less brillated materials with higher average bre width yields lower performing nanopaper.Fibres with a larger width decrease the degree of bre collapse during paper formation, impacting paper density and strength, in addition to reducing water retention properties (Morais et al. 2021).In addition, the higher size and rigidity of coarse bres decreases the number of contact points and the bonding strength between bres.Correspondingly, the wet strength of paper has previously been shown to be inversely proportional to the square of the bre coarseness (Seth 1995).

In uence of Fibre Content
In the context of a cellulose nano bre suspension, bre content can be considered a proxy for the degree of brillation, such that the higher the number of distinct bres present within a gram of material infers the disintegration of larger aggregated bre bundles into smaller bre structures.Fibre content only relates to the detectable cellulose micro bres (CMF), as a substantial fraction of the total bre content has been liberated into smaller nano bres through mechanical processing.Nano bres that exist below the detection limit of the MorFi device (length < 10 µm for Fibres) are not accounted for in this parameter.
To take bre content as a reliable proxy for the degree of nano brillation, an assumption must be made that the shift in micron-scale bre content with increased processing energy is mirrored in magnitude by the shift in the nano bre content, which itself is unable to be measured directly with precision and reliability.
Many confounders are present in this parameter -rstly, a signi cant fraction of the nano bre population within the sample is hidden from detection (Morais et al. 2021).In addition, with the increase in mechanical processing, the content of micron-scale CMF will rstly increase in number as cellulose bundles are disrupted and partially brillated, but subsequently decrease once they are su ciently brillated below the MorFi detection limit, creating a non-linear trend with increasing mechanical energy.In summary, it is impossible to know the true bre content (percentage of bres between 100 µm -10 mm in length) or ne content of any sample using the MorFi device.However, bre content still appears to be a promising proxy for the degree of nano brillation.
In a broad sense, this challenge of accurately characterising the true nano bre content of CNF pulp is pervasive across nanocellulose research (Foster et al. 2018).High resolution microscopy is unreliable due to previously discussed drawbacks such as the analysis of representative samples, the greater length scale of CNF material than the observation window, su cient image quality, and the time-consuming post-processing and analysis of images.Fractionation methods such as mechanical screening (Tanaka et al. 2012), gravimetric centrifugation (Ahola et al. 2008), and tube ow fractionation (Haapala et al. 2013) can assess bre size distribution over multiple length scales, but are limited by their minimum size range for analysis and time-consuming operation.Fractionation and ow cytometry analyses are promising techniques for population level analysis of bre size distribution, and have the potential to be used as a process quality monitoring and development tool.However, they are excluded from nano-scale particle analysis due to the limited particle size recognition range (Haapala et al. 2013).The MorFi device ts in a similar category to ow cytometry analysis as a potential on-line, high throughput process quality tool for monitoring micron-scale particles within CMF or CNF.While optical bre analysis does not provide true quantitative information on the bre dimensions and morphology of the material, it may provide valuable insights into the status of the micron scale sub-region of the material at the population level, which can be used to comparatively assess shifts in morphology across different source materials or mechanical processing levels.

In uence of Random Variation
Across facets of nanopaper performance, bre morphology parameters didn't correlate as strongly with nanopaper strain at break as they did with other mechanical properties.A hypothesis to explain this is the impact of non-bre related factors on the strain at break value.A number of microfractures are expected to be imparted to the edges of some nanopaper strips during the sample cutting procedure, which could be a random process or associated with the biochemical composition and rigidity of the nanopaper handsheet itself (Pennells et al. 2021).As such, the number and size of microfractures imparted to the nanopaper strip would disproportionately impact the strain at break result for the tested nanopaper strip.
PCA for Population Level Fibre Analysis PCA has previously been employed to visualise and assess the properties that in uence bre quality (Legland and

Predicting Nanopaper Properties
The overarching goal of this publication is to analyse whether bre morphology generated from an optical bre analysis device can be used to predict the quality of cellulose nano bres in aqueous suspension, without having to fabricate and test nanopaper samples.Achieving this goal would provide substantial bene t for industrial processing of nanocellulose, as this would allow for the adoption of an on-line, fast turnaround quality control tool and save time from the fabrication and testing of nanopaper samples.This goal was addressed by analysing bre morphology and nanopaper mechanical property data using two modelling techniques: Multiple Linear Regression (MLR) and Support Vector Regression (SVR).All bre morphology parameters generated by the MorFi device were included in the MLR model, as no additional effort is required to gather all data outputs when running this bre analysis.However, this approach has the potential to lead to model over tting, which was assessed through model validation.Considering that the NRMSE values were 4.1 to 7.5 times higher for the validation series over the original data series, this indicates that the SVR model was somewhat over t for the original sample population.The inclusion of all bre morphology parameters is a potential explanation for the result, with the exclusion of non-signi cant parameters expected to improve the degree of model over tting.
Subsequent work will investigate the adjusted R 2 of the model as an indicator of su cient parameter inclusion.An additional factor that may reduce model over tting is further optimisation of SVR hyperparameters.

Effect of Energy Level on Nanopaper Predictions
It is well established that the level of energy applied during mechanical processing of biomass into CNF in uences the bre morphology and mechanical properties of the resulting materials, as demonstrated by PCA visualisation in Figure 3. Therefore, it is important to assess the effect of processing energy level on the accuracy of nanopaper predicting models.The results in Table 6 demonstrate that low energy samples had the highest NRMSE values, which indicated that the bre morphology to nanopaper prediction accuracy was lower for these samples.This result was somewhat unexpected, considering that the MorFi device is attuned to analysing micro-scale CMF that are more likely to be present in higher proportions at low energy conditions.The higher the mechanical energy level, the more likely that micro bres are deconstructed to nano-sized bres that are outside the detection limit of the device.On the other hand, the nanopaper mechanical properties had a higher distribution between biomass samples at low energy conditions.Homogenisation at the high energy level led to a more homogenised data distribution between biomass samples, which allowed for a more accurate prediction of nanopaper properties for the regression models.
To visualise the accuracy of SVR model for the validation series dataset and further assess the effect of energy level on nanopaper predictions, measured nanopaper values were compared to the SVR estimated values for the data validation series (Figure 5).Firstly, these results demonstrate that the medium processing energy region is most accurately predicted in the validation series, while the samples at the low and high energy extremes were less accurately predicted.This relationship held for both tensile index and toughness, the latter of which demonstrated a lower prediction accuracy for medium energy samples earlier in Table 6.This suggests that the applicability of the model is lessened when the processing energy conditions are broadened, indicating some degree of over tting to the speci c processing energy conditions of the initial dataset for the SVR model.

Conclusions
Assessing CNF morphology is essential for determining the quality of nanomaterial products.
Considering di culties with direct morphology characterisation methods, indirect or inferred methods are becoming more prevalent.Indirect nano bre morphology using an optical bre analysis device is one such characterisation approach, but has conventionally been limited when assessing nanocellulose morphology due to its micron-scale bre detection limit, with few studies addressing this limitation for use in nanocellulose research.To address this gap in the literature, this study investigated the relationships between bre morphology and nanopaper properties for a broad sample population of sorghum biomass.Important bre morphology parameters elucidated through correlation analysis included the positive correlation between bre content and bre kink with nanopaper properties, and the inverse correlation between bre width and coarseness with nanopaper properties.Regression modelling of the bre morphology to predict nanopaper properties demonstrated superior predictive power for the machine learning based support vector (SVR) model.The SVR model was further validated through the replication data set over an extended processing energy range, yielding a lower prediction accuracy than the original dataset that implied some degree of model over tting.This study constitutes a platform for future investigation targeted at predicting nanopaper mechanical properties from the morphological properties of CNF pulp, with a focus on improving model generalisability.Ultimately, the development of more accurate and generalisable models for the prediction of nanopaper mechanical properties from morphological data will enable scalable and expedient characterisation of CNF products in the future industrial setting.
properties using one of two data modelling options: Multiple linear regression (MLR) or machine learning based support vector regression (SVR) in MatLab v.2021a using the libraries developed by Pedregosa et al. (Pedregosa et al. 2011).The SVR hyperparameters selected included the kernel function of 2nd degree polynomial, the absolute value of the α coe cient equal to the IQR/1.349,and ε equal to IQR/13.49,where IQR is the interquartile range.Each of the SVR models was 5-fold cross validated, with the nal correlation coe cient R 2 corresponding to the average of the ve values calculated for each fold.The accuracy of each model was assessed for each nanopaper metric based on their R 2 and root mean squared error (RMSE) values.RMSE is the square root of the mean of squared errors between the observed and predicted values, as shown in Equation 3 Eq. 3 where O i and P i represent the observed and predicted values for each sample (of size n) (Ritter and Muñoz-Carpena 2013).RMSE was normalised by the mean value of each nanopaper metric to generate normalised RMSE (NRMSE) values, as shown in Equation 4 modulus NRMSE = 0.33, R 2 = 0.93 NRMSE = 0.13, R 2 = 0.99

Figure 2 Fibre morphology parameter correlation matrix Figure 3 Principal
Figure 2

Table 1
Literature publications involving MorFi for CNF characterisation Gharrawi et al. 2021).Less commonly, MorFi been used to conduct detailed characterisation of the nano bre pulp morphology (Lacerda et al. 2013; Rol et al. 2018), and integrating results

Table 2
MorFi output parameters and parameter descriptions automatic Rapid Köthen handsheet former (Xell, Austria) according to the ISO 5269-2:2004 standard operating procedure (International Organization for Standardization 2001).Up to eight rectangular nanopaper strips (L = 150 mm, W = 15 mm) were cut out from each handsheet, and up to two handsheets were prepared for each sample.Tensile properties of each nanopaper strip were measured using an Instron model 5543 universal testing machine (Instron Pty Ltd., Melbourne, Australia) equipped with a 500 N load cell.Tensile index was calculated with Equation1

Table 3
Model validation data set over an extended HPH processing series

Table 4
Pearson correlation values for each bre morphology parameter across the four nanopaper metrics Following the high level of accuracy achieved for the bre morphology to nanopaper SVR model, experimental data for an additional HPH validation series was collected to test the bre morphology to nanopaper model predictions within a new sample population.The HPH validation series extended the processing energy input for CNF production to range from a minimum of one pass at 200 bar, to a maximum of 3 passes at 1100 bar.The validation series was performed on an aggregated biomass sample of the Sugargraze variety with all sections combined in equal proportions.The predicted values for each of the four nanopaper metrics, based on the HPH validation series bre morphology data and the previously established SVR model, was compared to the actual mechanical property results collected from HPH validation series nanopaper samples, to test the degree of over tting of the initial SVR model.

Table 7
MLR and SVR model outputs for validation energy series (Prager et al. 2020and 2013)lo et al. 2016;Desmaisons et al. 2017).In the case of Desmaisons et al., a PCA methodology was employed to reduce the number of relevant parameters required for subsequent multivariate linear regression(Desmaisons et al. 2017).In the case of Legland and Beaugrand, a PCA methodology was employed to identify highly correlated variables and variable clusters within the bre morphology data to eliminate redundant variables.This methodology was extended to identify groups of variables that cluster together to generate a hierarchical clustering dendrogram that delineated bre morphology features based on size, elongation, and tortuosity.This methodology allows for the high resolution morphological characterisation of a diverse bre population(Legland and Beaugrand 2013).Lastly, in the case of García-Gonzalo et al., a PCA methodology was employed to cluster together different paper properties and the effect of different biomass sources on paper properties (García-Gonzalo et al. 2016).In this study, PCA was employed to assess and visualise bre morphology and nanopaper properties from a large population of CNF samples across different biomass types and processing energy levels.Each point represents an individual sample replicate characterised by MorFi bre morphology or nanopaper mechanical performance.The PCA ellipses represent the region de ned by the 68% normal probability(Prager et al. 2020).The normal probability de nition of the ellipse can be adjusted to generate a more or less rigorous ellipse visualisation.Arrows represent the bre morphology parameters or nanopaper properties determined through CNF pulp and nanopaper characterisation, respectively.The arrow direction represents the correlation between the bre morphology parameter/nanopaper properties and the principal component, and the arrow length represents the strength of the relationship between the parameter/property and the principal component.The strength of this methodology is the visualisation of an array of bre morphology data at the population level on a single plot, with the elucidation of biomass and processing factors that are associated with different morphology parameters and nanopaper properties through the grouping with probability ellipses.