Pinus Taeda L. Wood Property Calibrations Based on Variable Numbers of near Infrared Spectra per Core and Cores per Plantation

2007 ◽  
Vol 15 (4) ◽  
pp. 261-268 ◽  
Author(s):  
Laurence R. Schimleck ◽  
Justin A. Tyson ◽  
P. David Jones ◽  
Gary F. Peter ◽  
Richard F. Daniels ◽  
...  
Keyword(s):  
Near Infrared ◽  
Wood Property ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Single Spectrum ◽  
Near Infrared Spectra ◽  
Pinus Taeda L ◽  
The Cost ◽  
Non Destructive ◽  
Calibration Development

Near infrared (NIR) spectroscopy provides a rapid, non-destructive method for the estimation of several wood properties of increment cores. NIR spectra are collected from adjacent sections of the same core; however, not all spectra are required for calibration purposes as spectra from the same core are autocorrelated. Previously, we showed that wood property calibrations that included a single spectrum per core were almost as successful when used to predict the wood properties of sections of new cores, as calibrations based on multiple, consecutive spectra per core. However, it is not known, for calibration purposes, how many NIR spectra should be collected per core, nor how many cores are required to represent a plantation. In this study, we demonstrate that it is unnecessary to use NIR spectra from every section of a core for calibration development. One spectrum per core adequately represents it, provided that sections from other cores representing juvenile, mature and the juvenile/mature wood transition are included in the calibration set. Calibration and prediction statistics can be slightly improved by increasing the number of spectra per core from one to between three and five, with the addition of further spectra unnecessary. For the plantations examined in this study, a minimum of seven cores per plantation is recommended. Increasing the number of cores per plantation to ten (the maximum) is unnecessary and the small improvement is not worth the cost.

Nondestructive estimation of Pinus taeda L. wood properties for samples from a wide range of sites in Georgia

2005 ◽  
Vol 35 (1) ◽  
pp. 85-92 ◽  
Author(s):  
P D Jones ◽  
L R Schimleck ◽  
G F Peter ◽  
R F Daniels ◽  
A Clark III
Keyword(s):  
Pinus Taeda ◽  
Loblolly Pine ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Small Sample ◽  
Dry Density ◽  
Wide Range ◽  
Pinus Taeda L

Preliminary studies based on small sample sets show that near infrared (NIR) spectroscopy has the potential for rapidly estimating many important wood properties. However, if NIR is to be used operationally, then calibrations using several hundred samples from a wide variety of growing conditions need to be developed and their performance tested on samples from new populations. In this study, 120 Pinus taeda L. (loblolly pine) radial strips (cut from increment cores) representing 15 different sites from three physiographic regions in Georgia (USA) were characterized in terms of air-dry density, microfibril angle (MFA), and stiffness. NIR spectra were collected in 10-mm increments from the radial longitudinal surface of each strip and split into calibration (nine sites, 729 spectra) and prediction sets (six sites, 225 spectra). Calibrations were developed using untreated and mathematically treated (first and second derivative and multiplicative scatter correction) spectra. Strong correlations were obtained for all properties, the strongest R2 values being 0.83 (density), 0.90 (MFA), and 0.93 (stiffness). When applied to the test set, good relationships were obtained (Rp2 ranged from 0.80 to 0.90), but the accuracy of predictions varied depending on math treatment. The addition of a small number of cores from the prediction set (one core per new site) to the calibration set improved the accuracy of predictions and importantly minimized the differences obtained with the various math treatments. These results suggest that density, MFA, and stiffness can be estimated by NIR with sufficient accuracy to be used in operational settings.

scholarly journals High Resolution Scanning of Radial Strips cut from Increment Cores by Near Infrared Spectroscopy

IAWA Journal ◽  
2007 ◽  
Vol 28 (4) ◽  
pp. 473-484 ◽  
Author(s):  
P. David Jenes ◽  
Laurence R. Schimleck ◽  
Chi-Leung So ◽  
Alexander Clark III ◽  
Richard F. Daniels
Keyword(s):  
Infrared Spectroscopy ◽  
High Resolution ◽  
Spatial Resolution ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
High Spatial Resolution ◽  
Microfibril Angle ◽  
Wood Property ◽  
Nir Spectroscopy ◽  
Wood Properties

Near infrared (NIR) spectroscopy provides a rapid method for the determination ofwood properties of radial strips. The spatial resolution of the NIR measurements has generally been limited to sections 10mm wide and as a consequence the estimation of wood properties of individual rings or within rings has not been possible. Many different NIR instruments can be used to collect NIR spectra from the surface of radial strips at relatively high spatial resolution and the purpose of this study was to compare wood property calibrations obtained using NIR spectra collected in 5 mm and 2 mm seetions with several different NIR instruments. We found that calibrations based on spectra collected in 5 mm seetions had good statistics, with those based on the Bruker Vector 22/N spectrometer the strongest. Of the three properties examined (density, microfibril angle and stiffness), density had the weakest statistics. When the spatial resolution was decreased to 2 mm, calibration and prediction statistics were weaker than those at 5 mm. RPDp's were relatively low with the highest being 1.76 for predicted stiffness based on NIR spectra obtained using the ASD Field Spec Pro spectrometer. Based on the low RPDp's, we conclude that none of the instruments examined were suitable for scanning radial strips at a spatial resolution of 2 mm.

Characterization of the wood quality of pernambuco (Caesalpinia echinata Lam) by measurements of density, extractives content, microfibril angle, stiffness, color, and NIR spectroscopy

Holzforschung ◽  
2009 ◽  
Vol 63 (4) ◽  
Author(s):  
Laurence R. Schimleck ◽  
Charles Espey ◽  
Christian R. Mora ◽  
Robert Evans ◽  
Adam Taylor ◽  
...  
Keyword(s):  
Near Infrared ◽  
Wood Quality ◽  
Microfibril Angle ◽  
Wood Property ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Wood Properties ◽  
Average Density ◽  
Dry Density ◽  
Caesalpinia Echinata

Abstract A total of 30 Caesalpinia echinata (pernambuco) sticks were ranked based on their suitability for making high quality bows and were assigned to one of the three following categories: 0=very poor to poor, 1=good to very good, and 2=excellent. From the end of each stick a sample was cut for wood property and near infrared (NIR) spectroscopic analysis. Wood properties measured included air-dry density, extractives content, microfibril angle, stiffness and wood color. NIR spectra were evaluated by principal component analysis (PCA) and on the PC scores. Poor quality samples were discriminated from those of good to very good and excellent quality; however, samples from the two higher quality groups were mixed. Based on relationships observed between PC scores and wood properties, we suggest that, of the measured properties, density and stiffness were the most important in sample discrimination based on quality. Samples ranked in the excellent category had high average density (1119 kg m-3) and stiffness (25.2 GPa) and relatively low extractives content (21.2%) compared to samples in the very poor to poor category (density= 938 kg m-3, stiffness=18.9 GPa and extractives content=24.9%).

scholarly journals Nondestructive Near-Infrared Spectroscopic Analysis of Oils on Wood Surfaces

Forests ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 64 ◽  
Author(s):  
Noboru Ito ◽  
Naoya Okubo ◽  
Yohei Kurata
Keyword(s):  
Near Infrared ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Wood Products ◽  
Added Value ◽  
Environmentally Conscious ◽  
Second Derivatives ◽  
Wood Surfaces ◽  
Non Destructive

The further use of wood resources is expected in an environmentally conscious society. Added-value, such as durability enhancement and preservation by painting, are needed to expand the applicability of wood. Assessment of wood properties such as surface and coat adhesion can be made by studying perviousness to liquid oils, with the aim of developing wood products that deter insects and are weather-resistant; hence, discriminant analysis of oil type is important. Near-infrared (NIR) spectroscopy is a powerful tool for nondestructive characterization of organic materials and has been widely used in many industries. Here, NIR detection of oil on wood surfaces is applied for the distinguishing of three different types of oil (hereafter, “Oil_1”, “Oil_2” and “Oil_3”) via soft independent modeling of class analogy (SIMCA). Oil_1 was antiseptic vehicle or cutting oil. Oil_2 was used as a motor oil for an oil pressure machine. Oil_3 was plant-derived oil. Two types of wood that are commonly used in Japanese construction (Cryptomeria japonica and Chamaecyparis obtuse) were analyzed after applying oil. The NIR spectra measured after the oil was applied were greater in the ranges 1700–1800 nm and 2300–2500 nm than spectra for the bare wood sample. As SIMCA analyses were performed by using spectral data that included the moving average, baseline correction and second derivatives, good results were obtained for Oil_3 for both wood samples. However, the correct classification percentages were low for Oil_1, and the percentage of samples classified within several categories was high. If the components are very different, such as those for Oil_3, NIRS can be a powerful non-destructive method for identifying oil in the context of wood products testing.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy

Food Research ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 382-385
Author(s):  
D. Thumrongchote
Keyword(s):  
Moisture Content ◽  
Cocos Nucifera ◽  
Nir Spectroscopy ◽  
Grocery Stores ◽  
Absorbance Spectrum ◽  
Coconut Tree ◽  
Cocos Nucifera L ◽  
The Cost ◽  
Non Destructive ◽  
Low Glycemic Index

Coconut sugar is a local sugar from the blossoms of a coconut tree. It has been considered a healthy sugar due to its low glycemic index. There is an attempt to add other sugar to it to lower the cost. Thus, this research aimed to identify Thai coconut sugar and to establish models for predicting the moisture content of coconut sugar by using FT-NIR spectroscopy. Thai coconut sugar samples were purchased from local grocery stores in four provinces, online, and the community market. Their moisture contents were varied and equilibrated for 24 hrs prior to the measurements of moisture and FT-NIR spectra. The results showed that FT-NIR spectra of Thai coconut sugar differ from sucrose, glucose and fructose at the absorbance spectrum of 5379-5011 cm-1 . FT-NIR spectroscopy of 54 known moisture samples of Thai coconut sugar was used to obtain a model to predict moisture content. The predicted equation, using the PLS technique with the Spectrum Quant program, was found to give a standard error of prediction (SEP) 0.077% (less than 0.10%), indicating a non-destructive method of accurately and precisely predicting moisture levels in the coconut sugar. The results obtained suggested that FTNIR spectroscopy has the potential to be used as a tool to identify Thai coconut sugar accurately. It can rapidly predict the moisture content in the sample which will be useful in quality control standards.

Foliar Damage Assessment and Selection of Cold Resistant Genotypes Using near Infrared Spectra of Eucalyptus Globulus Leaves

2009 ◽  
Vol 17 (4) ◽  
pp. 223-231 ◽  
Author(s):  
Rosario del P. Castillo ◽  
David Contreras ◽  
Matthias Otto ◽  
Jaime Baeza ◽  
Juanita Freer
Keyword(s):  
Eucalyptus Globulus ◽  
Near Infrared ◽  
Cold Resistance ◽  
External Validation ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Near Infrared Spectra ◽  
Orthogonal Signal Correction ◽  
Foliar Damage ◽  
Reference Parameters

Near infrared (NIR) spectroscopy was used to predict cold resistance in Eucalyptus globulus genotypes after acclimatation treatments at low temperatures. Branches of the genotypes were maintained during 31 days in three cold chambers and the NIR spectra of milled leaves were obtained. The samples were subsequently exposed to artificial freezing (with some branches exposed to −2°C) and the foliar damage was assessed by visually estimating the necrotic area of each leaf. These values were used as reference parameters to evaluate cold resistance in the genotypes. A partial least squares (PLS) method was performed using the foliar damage and the NIR spectra of leaves. Spectra were treated with multiplicative scatter correction (MSC) and orthogonal signal correction (OSC). An excellent model was achieved which predicted foliar damage in the genotypes with a low standard error of prediction (3.5%), a high regression coefficient in cross-validation and external validation ( r > 0.9) and a high percentage of the variance explained by the spectra (95.4%). Furthermore, a pattern recognition method, using a regularised discriminant analysis (RDA) of the scores matrix obtained in PLS, in a denominated PLS/RDA on scores strategy, was applied directly to the spectra to classify each genotype as tolerant or sensitive; 100% of the genotypes were correctly assigned. These results demonstrate the advantages of using the NIR spectra of leaves as a rapid, nondestructive tool to evaluate cold resistance in genotypes.

scholarly journals Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Sylvio Barbon ◽  
Ana Paula Ayub da Costa Barbon ◽  
Rafael Gomes Mantovani ◽  
Douglas Fernandes Barbin
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Chicken Meat ◽  
Poultry Meat ◽  
Spectral Information ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Meat Samples

Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

2016 ◽  
Vol 24 (6) ◽  
pp. 517-528 ◽  
Author(s):  
Susanna Pulkka ◽  
Vincent Segura ◽  
Anni Harju ◽  
Tarja Tapanila ◽  
Johanna Tanner ◽  
...  
Keyword(s):  
Scots Pine ◽  
High Throughput ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tree Breeding ◽  
Decay Resistance ◽  
Increment Core ◽  
Automatic Selection ◽  
Non Destructive ◽  
Selection Of

High-throughput and non-destructive methods for quantifying the content of the stilbene compounds of Scots pine ( Pinus sylvestris L.) heartwood are needed in the breeding for decay resistance of heartwood timber. In this study, near infrared (NIR) spectroscopy calibrations were developed for a large collection of solid heartwood increment core samples in order to predict the amount of the stilbene pinosylvin (PS), its monomethyl ether (PSM) and their sum (STB). The resulting models presented quite accurate predictions in an independent validation set with R2V values ranging between 0.79 and 0.91. The accuracy of the models strongly depended on the chemical being calibrated, with the lowest accuracy for PS, intermediate accuracy for PSM and highest accuracy for STB. The effect of collecting one, two or more (up to five) spectra per sample on the calibration models was studied and it was found that averaging multiple spectra yielded better accuracy as it may account for the heterogeneity of wood along the increment core within and between rings. Several statistical pretreatments of the spectra were tested and an automatic selection of wavenumbers prior to calibration. Without the automatic selection of wavenumbers, a first derivative of normalised spectra yielded the best accuracies, whereas after the automatic selection of wavenumbers, no particular statistical pretreatment appeared to yield better results than any other. Finally, the automatic selection of wavenumbers slightly improved the accuracy of the models for all traits. These results demonstrate the potential of NIR spectroscopy as a high-throughput and non-destructive phenotyping technique in tree breeding for the improvement of decay resistance in heartwood timber.

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