Potential of NIR spectroscopy for predicting cellulose nanofibril quality in commercial bleached Kraft pulp of Eucalyptus.

Multivariate models were developed to classify cellulose nanofibril (CNF) fibrillation by a quality index from near infrared (NIR) spectra. Commercial pulps of Eucalyptus spp. were used to produce cellulose nanofibrils by means of a fibrillator mill. After each of the five passes through the mill, samples were collected and analyzed for energy consumption and fiber classification. As a standard, pulps were oxidized with TEMPO reagent followed by a single pass through the mill to compare the resulting quality of CNFs produced by each method. NIR spectra of CNFs were associated with quality indices determined by conventional laboratory analyses that included morphology, turbidity, mechanical properties, X-ray diffraction and quality index measurements. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to the spectral and experimental data. Fibrillator milling to obtain CNFs was efficient and resulted in gel formation following the third pass through the mill. NIR spectroscopy combined with PLS-DA was used successfully to create a model to classify quality of CNFs with 96 % certainty in 3 wt% solutions. These findings suggest that NIR spectroscopy holds promise for estimating CNF quality in suspension, particularly in real-time industrial applications where reliable estimates are crucial.

The mechanisms and prediction of non-structural carbohydrates accretion and depletion after mechanical wounding in slash pine (Pinus elliottii) using near-infrared reflectance spectroscopy.

BACKGROUND: The allocation of non-structural carbohydrates (NSCs) plays a critical role in the physiology and metabolism of tree growth and survival defense. However, little is known about the allocation of NSC after continuous mechanical wounding of pine by resin tapping during tree growth. RESULTS: Here, we examine the NSC allocation in plant tissues after 3 year lasting resin tapping, and also investigate the use of near-infrared reflectance (NIR) spectroscopy to quantify the NSC, starch and free sugar (e.g., sucrose, glucose, and fructose) concentrations in different plant tissues of slash pine. Spectral measurements on pine needle, branch, trunk phloem, and root were obtained before starch and free sugar concentrations were measured in the laboratory. The variation of NSC, starch and free sugars in different plant tissues after resin tapping was analyzed. Partial least squares regression was applied to calibrate prediction models, models were simulated 100 times for model performance and error estimation. More NSC, starch and free sugars were stored in winter than summer both in tapped and control trees. The position of resin tapping significantly influenced the NSCs allocation in plant tissues: more NSCs were transformed into free sugars for defensive resin synthesis close to the tapping wound rather than induced distal systemic responses. Models for predicting NSC and free sugars of plant tissues showed promising results for the whole tree for fructose (R2CV = 0.72), glucose (R2CV = 0.67), NSCs (R2CV = 0.66) and starch (R2CV = 0.58) estimates based on NIR models. Models for individual plant tissues also showed reasonable predictive ability: the best model for NSCs and starch prediction was found in root. The significance multivariate correlation algorithm for variable selection significantly reduced the number of variables. Important variables were identified, including features at 1021-1290 nm, 1480, 1748, 1941, 2020, 2123 and 2355 nm, which are highly related to NSC, starch, fructose, glucose and sucrose. CONCLUSIONS: NIR spectroscopy provided a rapid and cost-effective method to monitor NSC, starch and free sugar concentrations after continuous resin tapping. It can be used for studying the trade-off between growth and production of defensive metabolites.

Artificial neural network and partial least square regressions for rapid estimation of cellulose pulp dryness based on near infrared spectroscopic data.

The content of water in fiber suspension and affects pulp refining, bleaching and draining operations. Cellulose pulp dryness estimate through near infrared (NIR) spectroscopy coupled with multivariate regressions or artificial neural network (ANN) techniques are not well explored yet. In this study models were developed to estimate cellulose pulp dryness in pads based on the NIR spectra. Thus, the cellulose pulp pads (4 mm thick) were weighed and their NIR spectra were obtained in several stages during desorption from 13.1 to 98.3% of content of solids. Partial least square regression (PLS-R) was developed from whole NIR spectra (1300 Absorbance values) and six spectral variables (from 1300) were selected for developing the PLS-R (6) and the ANN model. Both trained neural network and regression can predict pulp dryness of unknown cellulose pulp pads from their NIR data with an error of 2.5%. PLS-R models based on whole NIR spectra showed accurate predictions (the R² of lab-determined and estimated values plot was 0.99) while the ANN showed the same predictive performance from only six NIR variables. Predictive models developed from full NIR spectra and those based on only 6 variables were compared. Our findings indicate that NIR spectroscopy coupled with multivariate analysis and Artificial neural networks are a promising tool for monitoring the weight variation due to dewatering of the cellulose pulps in real time.