Estimation of wood stiffness of increment cores by near-infrared spectroscopy

2002 ◽  
Vol 32 (1) ◽  
pp. 129-135 ◽  
Author(s):  
L R Schimleck ◽  
R Evans ◽  
J Ilic ◽  
A C Matheson
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Radiata Pine ◽  
Radial Variation ◽  
Wood Stiffness ◽  
Test Sets ◽  
Longitudinal Modulus ◽  
J 51 ◽  
Eucalyptus Delegatensis ◽  
Good Agreement

The use of calibrated near-infrared (NIR) spectroscopy for predicting the radial variation of the longitudinal modulus of elasticity (EL) of increment cores is described. Sets of Eucalyptus delegatensis R.T. Baker (alpine ash) and Pinus radiata D. Don (radiata pine) samples were characterized in terms of EL(SS) (estimated stiffness based on a combination of SilviScan-2 diffractometric data and measured density (R. Evans and J. Ilic. 2001. For. Prod. J. 51(3): 53–57)). NIR spectra, obtained from the radial–longitudinal face of each sample, were used to develop EL(SS) calibrations for the E. delegatensis and P. radiata sample sets and the two sets combined. The relationships between laboratory-determined EL(SS) and NIR-fitted EL(SS) were good in all cases. EL(SS) was estimated in separate test sets and found to correlate well with measured EL. NIR spectra were obtained in 15-mm sections from the radial–longitudinal face of two intact P. radiata increment cores. EL(SS) of each section was estimated using the P. radiata and the combined P. radiata and E. delegatensis calibrations. NIR estimates of EL(SS) were in good agreement with SilviScan-2 determined stiffness indicating that NIR spectroscopy can be successfully used to estimate radial variation in wood stiffness of increment cores.

Estimation of Microfibril Angle and Stiffness by near infrared Spectroscopy using sample sets having Limited wood Density Variation

IAWA Journal ◽  
2005 ◽  
Vol 26 (2) ◽  
pp. 175-187 ◽  
Author(s):  
Laurence R. Schimleck ◽  
Robert Evans ◽  
P. David Jones ◽  
Richard F. Daniels ◽  
Gary F. Peter ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Pinus Taeda ◽  
Loblolly Pine ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Nir Spectroscopy ◽  
Radiata Pine ◽  
Density Variation ◽  
Density Range ◽  
Wood Stiffness

Near infrared (NIR) spectroscopy offers a rapid method for the estimation of microfibril angle (MFA) and SilviScan-estimated wood stiffness (EL(SS)). The success of these NIR calibrations may be related to airdry density, because density varies in wood simultaneously with MFA and stiffness. The importance of density variation was investigated by developing calibrations for MFA and EL(SS) using Pinus radiata D. Don (radiata pine) and Pinus taeda L. (loblolly pine) sample sets where the density range was small and the relationships between density and MFA and density and EL(SS) were poor. Excellent calibrations for MFA and EL(SS) were obtained, particularly when sets had densities greater than 500 kg/m3, can provide strong relationships for MFA and stiffness even when density variation is limited. Examination of loading plots from the MFA and EL(SS) calibrations indicates that variation in wood components such as cellulose, lignin and possibly hemicellulose is important.

ESTIMATION OF WOOD STIFFNESS OF INCREMENT CORES BY NEAR INFRARED SPECTROSCOPY: THE DEVELOPMENT AND APPLICATION OF CALIBRATIONS BASED ON SELECTED CORES

IAWA Journal ◽  
2002 ◽  
Vol 23 (3) ◽  
pp. 217-224 ◽  
Author(s):  
Laurence R. Schimleck ◽  
Robert Evans
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Standard Error ◽  
Near Infrared ◽  
Radiata Pine ◽  
Coefficient Of Determination ◽  
Increment Core ◽  
Wood Stiffness ◽  
Longitudinal Modulus ◽  
Data Calibration

Eight Pinus radiata D. Don (Radiata pine) increment core samples representative of a total of thirty-two increment cores were selected for the development of an EL(SS) (longitudinal modulus of elasticity calculated from SilviScan-2 data) calibration based on NIR spectra obtained from the radial–longitudinal face of each sample in 10-mm increments. The primary aim of the work was to investigate if an EL(SS) calibration developed using a subsample of cores representative of a larger set provided better predictions of EL(SS) than those reported in Schimleck et al. (2002a). The EL(SS) calibration was developed using eight factors giving an excellent relationship between SilviScan-2 determined EL(SS) and NIR fitted EL(SS) (coefficient of determination (R2) = 0.97) and a low standard error of calibration (SEC) (0.91 GPa).To test the EL(SS) calibration, NIR spectra were obtained in 10-mm sections from the radial–longitudinal face of two intact P. radiata increment cores and EL(SS) of each section predicted. NIR estimates of EL(SS) were in excellent agreement with EL(SS) determined using SilviScan-2 data, with R2 of 0.99 (core A) and 0.98 (core B). Standard error of predictions (SEP) of 1.6 GPa (core A) and 1.2 GPa (core B) were obtained. Both sets of predictions closely followed the patterns of EL(SS) radial variation determined experimentally. EL(SS) calibration based on NIR spectra obtained from a set of representative cores can provide excellent predictions of EL(SS). The predictions were superior to those reported in Schimleck et al. (2002a).

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

2022 ◽  
pp. 096703352110572
Author(s):  
Nicholas T Anderson ◽  
Kerry B Walsh
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Short Wave ◽  
Least Squares Regression ◽  
Machine Learning Methods ◽  
Transmission Geometry ◽  
Modelling Techniques ◽  
Test Sets ◽  
Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

Estimation of Eucalyptus delegatensis wood properties by near infrared spectroscopy

2001 ◽  
Vol 31 (10) ◽  
pp. 1671-1675 ◽  
Author(s):  
L R Schimleck ◽  
R Evans ◽  
J Ilic
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Raw Material ◽  
Modulus Of Rupture ◽  
Single Type ◽  
Wide Range ◽  
Eucalyptus Delegatensis

The use of calibrated near infrared (NIR) spectroscopy for the prediction of a range solid wood properties is described. The methods developed are applicable to large-scale nondestructive forest resource assessment and to tree breeding and silvicultural programs. A series of Eucalyptus delegatensis R.T. Baker (alpine ash) samples were characterized in terms of density, longitudinal modulus of elasticity (EL), microfibril angle (MFA), and modulus of rupture (MOR). NIR spectra were obtained from the radial–longitudinal face of each sample and used to generate calibrations for the measured physical properties. The relationships were good in all cases, with coefficients of determination ranging from 0.77 for MOR through 0.90 for EL to 0.93 for stick density. In view of the rapidly expanding range of applications for this technique, it is concluded that appropriately calibrated NIR spectroscopy could form the basis of a "universal" testing instrument capable of predicting a wide range of product properties from a single type of spectrum obtained from the product or from the raw material.

Estimation of Wood Stiffness and Strength Properties of Hybrid Larch by Near-Infrared Spectroscopy

2007 ◽  
Vol 61 (8) ◽  
pp. 882-888 ◽  
Author(s):  
Takaaki Fujimoto ◽  
Hiroyuki Yamamoto ◽  
Satoru Tsuchikawa
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Strength Properties ◽  
Density Variation ◽  
Modulus Of Rupture ◽  
Cellulose Content ◽  
Hybrid Larch ◽  
Absorption Bands ◽  
Wood Stiffness ◽  
Predicted Values

This work was undertaken to investigate the feasibility of near-infrared (NIR) spectroscopy for estimating wood mechanical properties, i.e., modulus of elasticity (MOE) and modulus of rupture (MOR) in bending tests. Two sample sets having large and limited density variation were prepared to examine the effects of wood density on estimation of MOE and MOR by the NIR technique. Partial least squares (PLS) analysis was employed and it was found that the relationships between laboratory-measured and NIR-predicted values were good in the case of sample sets having large density variation. MOE could be estimated even when density variation in the sample set was limited. It was concluded that absorption bands due to the OH group in the semi-crystalline or crystalline regions of cellulose strongly influenced the calibrations for bending stiffness of hybrid larch. This was also suggested from the result that both α-cellulose content and cellulose crystallinity showed moderate positive correlation to wood stiffness.

scholarly journals Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ting Wu ◽  
Nan Zhong ◽  
Ling Yang
Keyword(s):  
Neural Network ◽  
Cold Storage ◽  
Storage Time ◽  
Near Infrared ◽  
High Efficiency ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Back Propagation Neural Network ◽  
Least Squares Regression ◽  
Test Sets

The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets (R2test) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R2test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products.

APPLICATION OF NEAR INFRARED SPECTROSCOPY TO THE EXTRACTED WOOD OF A DIVERSE RANGE OF SPECIES

IAWA Journal ◽  
2003 ◽  
Vol 24 (4) ◽  
pp. 429-438 ◽  
Author(s):  
Laurence Schimleck ◽  
Robert Evans ◽  
Jugo Ilic
Keyword(s):  
Infrared Spectroscopy ◽  
Physical Properties ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Nir Spectroscopy ◽  
X Ray Diffraction ◽  
Wood Chemistry ◽  
Diverse Range ◽  
X Ray ◽  
Longitudinal Modulus

Near infrared (NIR) spectroscopy was applied to fifty-four species (59 samples in total) representing a diverse array of taxonomic affiliations, wood chemistry and physical properties. Acetone and ethanol were used to remove extractives from the wood samples used in this study. The extracted samples were characterized in terms of collapse-free density, microfibril angle and longitudinal modulus of elasticity (estimated using the collapse-free density and X-ray diffraction data obtained from Silvi- Scan-2). NIR spectra were obtained from the radial longitudinal face of each sample and used to generate calibrations for the measured physical properties. Extraction was found to improve the calibration statistics for all properties.

Stiffness Prediction of Radiata Pine Clearwood Test Pieces Using near Infrared Spectroscopy

2001 ◽  
Vol 9 (2) ◽  
pp. 117-122 ◽  
Author(s):  
Armin Thumm ◽  
Roger Meder
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Pinus Radiata ◽  
Modulus Of Elasticity ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Radiata Pine ◽  
Model Based ◽  
Test Pieces ◽  
The Mean

Near infrared (NIR) spectroscopy has been used to predict the modulus of elasticity (stiffness) of samples taken from knot-free sapwood specimens of radiata pine ( Pinus radiata D. Don). The method shows the potential of using NIR spectroscopy for assessment of lumber stiffness. A model based on NIR spectra taken on the radial face of 404 samples of radiata pine clearwood was established to predict stiffness. Samples were moved past the detector at a rate of 900 mm min−1. This model then was used to predict the stiffness of a further 80 samples and the results show an error in prediction of 14% of the mean measured value.

Application of near Infrared Spectroscopy to the Measurement of Cell Mass in Solid Cultures of Mushroom

1998 ◽  
Vol 6 (1) ◽  
pp. 273-277 ◽  
Author(s):  
Ken-Ichiro Suehara ◽  
Yasuhisa Nakano ◽  
Takuo Yano
Keyword(s):  
Growth Rate ◽  
Cell Wall ◽  
Conventional Method ◽  
Ganoderma Lucidum ◽  
Near Infrared ◽  
Cell Mass ◽  
Nir Spectroscopy ◽  
Culture Cell ◽  
Solid Culture ◽  
Good Agreement

Near infrared (NIR) spectroscopy was applied to the prediction of the growth rate of mushroom, Ganoderma lucidum, in a solid culture. Cell mass is conventionally measured by analysing the concentration of glucosamine which is a component of the cell wall. The correlation between the concentration of glucosamine obtained by the conventional method and that obtained by NIR spectroscopy was examined by multiple regression analysis. The value predicted by NIR spectroscopy was in fairly good agreement with that obtained by the conventional method. This result suggests that NIR spectroscopy is applicable to the prediction of growth rate in the solid culture and gives a useful method for the control of mushroom production.

scholarly journals A NIR-Spectroscopy-Based Approach for Detection of Fluids in Rectangular Glass Micro-Capillaries

2020 ◽  
Vol 2 (1) ◽  
pp. 43
Author(s):  
Valentina Bello ◽  
Elisabetta Bodo
Keyword(s):  
Light Propagation ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Infrared Region ◽  
Broadband Radiation ◽  
Fluid Detection ◽  
Aluminum Mirror ◽  
Tungsten Lamp ◽  
Good Agreement ◽  
Rectangular Glass

In this work, we present a micro-opto-fluidic platform to distinguish water and alcohol samples flowing in rectangular glass micro-capillaries laid onto a bulk Aluminum mirror illuminated by the broadband radiation emitted by a Tungsten lamp. The fluid detection is based on the spectral analysis of the light reflected by the micro-structure in the near-infrared region from 1.0 μm to 1.7 μm. A theoretical model was implemented to study light propagation in the channel, taking into account absorption effects, and the results of the simulation are in good agreement with the experimental spectra obtained by testing water, ethanol, isopropanol and ethylene glycol.

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