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In this paper, the morphological properties of fiber length (weighted in length) and
of fiber width of unbleached Kraft pulp of Acacia melanoxylon were determined using TECHPAP
Morfir equipment (Techpap SAS, Grenoble, France), and were used in the calibration development
of Near Infrared (NIR) partial least squares regression (PLS-R) models based on the spectral data
obtained for the wood. It is the first time that fiber length and width of pulp were predicted with
NIR spectral data of the initial woodmeal, with high accuracy and precision, and with ratios of
performance to deviation (RPD) fulfilling the requirements for screening in breeding programs.
The selected models for fiber length and fiber width used the second derivative and first derivative
+ multiplicative scatter correction (2ndDer and 1stDer + MSC) pre-processed spectra, respectively,
in the wavenumber ranges from 7506 to 5440 cm 1. The statistical parameters of cross-validation
(RMSECV (root mean square error of cross-validation) of 0.009 mm and 0.39 m) and validation
(RMSEP (root mean square error of prediction) of 0.007 mm and 0.36 m) with RPDTS (ratios of
performance to deviation of test set) values of 3.9 and 3.3, respectively, confirmed that the models
are robust and well qualified for prediction. This modeling approach shows a high potential to be
used for tree breeding and improvement programs, providing a rapid screening for desired fiber
morphological properties of pulp prediction.
In this paper, the morphological properties of fiber length (weighted in length) and of fiber width of unbleached Kraft pulp of Acacia melanoxylon were determined using TECHPAP Morfi® equipment (Techpap SAS, Grenoble, France), and were used in the calibration development of Near Infrared (NIR) partial least squares regression (PLS-R) models based on the spectral data obtained for the wood. It is the first time that fiber length and width of pulp were predicted with NIR spectral data of the initial woodmeal, with high accuracy and precision, and with ratios of performance to deviation (RPD) fulfilling the requirements for screening in breeding programs. The selected models for fiber length and fiber width used the second derivative and first derivative + multiplicative scatter correction (2ndDer and 1stDer + MSC) pre-processed spectra, respectively, in the wavenumber ranges from 7506 to 5440 cm−1. The statistical parameters of cross-validation (RMSECV (root mean square error of cross-validation) of 0.009 mm and 0.39 μm) and validation (RMSEP (root mean square error of prediction) of 0.007 mm and 0.36 μm) with RPDTS (ratios of performance to deviation of test set) values of 3.9 and 3.3, respectively, confirmed that the models are robust and well qualified for prediction. This modeling approach shows a high potential to be used for tree breeding and improvement programs, providing a rapid screening for desired fiber morphological properties of pulp prediction.
Aim of the study: The ability of NIR spectroscopy for predicting the ISO brightness was studied on unbleached Kraft pulps of Acacia melanoxylon R. Br.
Area of study: Sites covering littoral north, mid interior north and centre interior of Portugal.
Materials and methods: The samples were Kraft pulped in standard identical conditions targeted to a kappa number of 15. A Near Infrared (NIR) partial least squares regression (PLSR) model was developed for the ISO brightness prediction using 75 pulp samples with a variation range of 18.9 to 47.9 %.
Main results: Very good correlations between NIR spectra and ISO brightness were obtained. Ten methods were used for PLS analysis (cross validation with 48 samples), and a test set validation was made with 27 samples. The 1stDer pre-processed spectra coupling two wavenumber ranges from 9404 to 7498 cm-1 and 4605 to 4243 cm-1 allowed the best model with a root mean square error of ISO brightness prediction of 0.5 % (RMSEP), a r2 of 99.5 % with a RPD of 14.7.
Research highlights: According to AACC Method 39-00, the present model is sufficiently accurate to be used for process control (RPD ≥ 8).
Pulp yield is an important measure of pulpwood quality, which is used regularly by the pulp and paper industry for which the possibility of using rapid methods to predict pulp yield would be very useful for screening and quality control. This work addresses the prediction of Kraft pulp yield under standard identical conditions and targeted to a kappa number of 15, using near-infrared (NIR) partial least squares regression modelling. A total of 75 pulp samples of Acacia melanoxylon R. Br. (blackwood) with a pulp yield variation range of 47.0–58.2 % were used. Very good correlations between NIR spectra and pulp yield were obtained. Ten methods were used for PLS analysis (cross-validation with 62 samples), and an external validation was made with 13 samples. The 2ndDer pre-processed spectra coupling two wavenumber ranges from 9087 to 5440 and 4605 to 4243 cm−1 allowed the best model with a standard error of prediction of 0.4 %, a r2 of 98.1 %, and the ratios of performance to deviation (RPDTS) of 4.8. According to AACC Method 39-00, the present model is sufficiently accurate to be used in screening programs and in quality control (RPDCV = 6.9).
A total of 120 Acacia melanoxylon R. Br. (Australian blackwood) stem discs, belonging to 20 trees from four sites in Portugal, were used in this study. The samples were kraft pulped under standard identical conditions targeted to a Kappa number of 15. A Near Infrared (NIR) partial least squares regression (PLSR) model was developed for the Kappa number prediction using 75 pulp samples with a narrow Kappa number variation range of 10 to 17. Very good correlations between NIR spectra of A. melanoxylon pulps and Kappa numbers were obtained. Besides the raw spectra, also pre-processed spectra with ten methods were used for PLS analysis (cross validation with 48 samples), and a test set validation was made with 27 samples. The first derivative spectra in the wavenumber range from 6110 to 5440 cm-1 yielded the best model with a root mean square error of prediction of 0.4 units of Kappa number, a coefficient of determination of 92.1%, and two PLS components, with the ratios of performance to deviation (RPD) of 3.6 and zero outliers. The obtained NIR-PLSR model for Kappa number determination is sufficiently accurate to be used in screening programs and in quality control.
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Bee pollen contains almost all nutrients required by the human organism as well as diverse health-promoting substances. However, its composition and nutritional value greatly depend on the botanical origin. As such, it is importante to develop a rapid and non-expensive methodology that allows studying its characteristics, making labelling more objective and easier. The FTIR-ATR technique was used to predict some nutritional parameters in 126 bee pollen samples. FTIR-ATR spectrum obtained in the region between 4000 and 400 cm−1 with PLS Regression models were used to correlate spectral information with the data obtained using reference methods. In this first approach with pollen samples, good correlation models with appropriate accuracy were obtained for the evaluated parameters with r2 varying from 74.8 to 97% and residual prediction deviation between 2.0 and 5.8. These results suggest that FTIR-ATR may be a useful technique for assessing bee pollen’s composition.
The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470:2008), fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions) were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression) and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines) were tested. Classification and regression trees (CART) was the most accurate model for the prediction of pulp ISO brightness (R = 0.85). The other parameters could be predicted with fair results (R = 0.64–0.75) by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and
pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.
The Fourier transform infrared (FTIR) spectroscopic method with attenuated total reflectance (ATR) was used for predicting the alcoholic strength, the methanol, acetaldehyde and fusel alcohols content of grape-derived spirits. FTIR-ATR spectrum in the mid-IR region (4000-400 cm(-1)) was used for the quantitative estimation by applying partial least square (PLS) regression models and the results were correlated with those obtained from reference methods. In the developed method, a cross-validation with 50% of the samples was used for PLS analysis along with a validation test set with 50% of the remaining samples. Good correlation models with a great accuracy were obtained for methanol (r(2)=99.4; RPD=12.8), alcoholic strength (r(2)=97.2; RPD=6.0), acetaldehyde (r(2)=98.2; RPD=7.5) and fusel alcohols (r(2) from 97.4 to 94.1; RPD from 6.2 to 4.1). These results corroborate the hypothesis that FTIR-ATR is a useful technique for the quality control of grape-derived spirits, whose practical application may improve the efficiency and quickness of the current laboratory analysis.
This study aimed to evaluate the potential of FT-Raman spectroscopy in the prediction of the chemical composition of Lavandula spp. monofloral honey. Partial Least Squares (PLS) regression models were performed for the quantitative estimation and the results were correlated with those obtained using reference methods. Good calibration models were obtained for electrical conductivity, ash, total acidity, pH, reducing sugars, hydroxymethylfurfural (HMF), proline, diastase index, apparent sucrose, total flavonoids content and total phenol content. On the other hand, the model was less accurate for pH determination. The calibration models had high r2 (ranging between 92.8% and 99.9%), high residual prediction deviation - RPD (ranging between 4.2 and 26.8) and low root mean square errors. These results confirm the hypothesis that FT-Raman is a useful technique for the quality control and chemical properties' evaluation of Lavandula spp honey. Its application may allow improving the efficiency, speed and cost of the current laboratory analysis.
Stem and wood characterization of Acacia melanoxylon as an introduced species in Europe
info:eu-repo/semantics/publishedVersion
Avaliação da robustez de modelos derivativos PLS-R em espectroscopia usando o número de pontos da suavização espectral.
Wood samples of Cupressus arizonica, C. lusitanica, and C. sempervirens were evaluated for chemical, anatomical, and pulp characteristics as raw material for pulp production. Two 17-year-old trees per species were harvested, and wood samples were taken at a height of 2 m. Wood chips from Pinus pinaster (Portugal) and P. sylvestris (Finland) were used as references. C. arizonica differed from C. lusitanica and C. sempervirens with significantly lower (p < 0.05) tracheid diameter and wall thickness in the earlywood. The total extractives contents were 3.9%, 3.3%, and 2.5% for C. lusitanica, C. sempervirens, and C. arizonica, respectively, lower than the 5.1% for P. pinaster and 4.5% for P. sylvestris. Klason lignin content ranged from 33.0 to 35.6%, higher than the 28.0 to 28.7% for the pinewoods. The kraft pulp yields for C. arizonica, C. lusitanica, and C. sempervirens were 37.7%, 36.7%, and 38.7%, respectively, with kappa numbers of 32.0, 31.6, and 28.7, respectively; the yield values were 40.8% and 42.8%, with kappa numbers of 23.4 and 21.0, for P. pinaster and P. sylvestris, respectively. The cypress species are clearly different from pine in relation to wood pulping behavior. Among the cypress, C. sempervirens provided the best pulping results
Paper properties determine the product application potential and depend on the raw material, pulping conditions,and pulp refining. The aim of this study was to construct mathematical models that predict quantitative relations between the paper density and various mechanical and optical properties of the paper. A dataset of properties of paper handsheets produced with pulps of Acacia dealbata, Acacia melanoxylon, and Eucalyptus globullus beaten at 500, 2500, and 4500 revolutions was used. Unsupervised classification techniques were combined to assess the need to perform separated prediction models for each species, and multivariable regression techniques were used to establish such prediction models. It was possible to develop models with a high goodness of fit using paper density as the independent variable (or predictor) for all variables except tear index and zero-span tensile strength, both dry and wet.
Prediction paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kemel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris) and cypress species (Cupressus lusitanica, C. sempervirens e C. arizonica) beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination) with two variables: the numerical variable density and the categorical variable density.
Paper properties determine the product application potential and depend on the raw material, pulping conditions, and pulp refining. The aim of this study was to construct mathematical models that predict quantitative relations between the paper density and various mechanical and optical properties of the paper. A dataset of properties of paper handsheets produced with pulps of Acacia dealbata, Acacia melanoxylon, and Eucalyptus globulus beaten at 500, 2500, and 4500 revolutions was used. Unsupervised classification techniques were combined to assess the need to perform separated prediction models for each species, and multivariable regression techniques were used to establish such prediction models. It was possible to develop models with a high goodness of fit using paper density as the independent variable (or predictor) for all variables except tear index and zero-span tensile strength, both dry and wet.
