Mechanical and Physical Properties of Oriented Strand Board Exposed to High Temperature and Relative Humidity and Coupled with Near-Infrared Reflectance Modeling

2018 ◽  
Vol 68 (1) ◽  
pp. 78-85 ◽  
Author(s):  
Qingzheng Cheng ◽  
Chengfeng Zhou ◽  
Wei Jiang ◽  
Xiping Zhao ◽  
Brian K. Via ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Physical Properties ◽  
Near Infrared ◽  
Oriented Strand Board ◽  
Nir Spectroscopy ◽  
Infrared Reflectance ◽  
Wood Chemistry ◽  
Near Infrared Reflectance ◽  
Mechanical And Physical Properties ◽  
Reflectance Modeling

Abstract The mechanism underlying thermal degradation of oriented strand board was tested under a humid environment. Near-infrared reflectance (NIR) spectroscopy coupled with chemometric modeling was utilized to better understand the degradation of functional groups over time. The flexural properties, internal bond (IB), water absorption, and thickness swelling were tested after exposure to various times of 0, 3, 6, and 9 weeks in a climate-controlled laboratory to 76.7°C and 60 percent relative humidity. The largest reduction in all flexural and physical properties occurred during the first 3 weeks of exposure and then leveled off thereafter, while IB decreased significantly through the 9-week period (α = 0.05). Chemometric models built from NIR spectra revealed pertinent chemical changes in wood chemistry and resin components.

Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

1991 ◽  
Vol 31 (2) ◽  
pp. 205 ◽  
Author(s):  
KF Smith ◽  
PC Flinn
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Cost Effective ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

On-line application of visible and near infrared reflectance spectroscopy to predict physical and sensory characteristics of beef quality

2009 ◽  
Vol 2009 ◽  
pp. 135-135
Author(s):  
N Prieto ◽  
D W Ross ◽  
E A Navajas ◽  
G Nute ◽  
R I Richardson ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Sensory Characteristics ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Beef Quality ◽  
Near Infrared Reflectance ◽  
On Line ◽  
Meat Samples

Visible and near infrared reflectance spectroscopy (Vis-NIR) has been widely used by the industry research-base for large-scale meat quality evaluation to predict the chemical composition of meat quickly and accurately. Meat tenderness is measured by means of slow and destructive methods (e.g. Warner-Bratzler shear force). Similarly, sensory analysis, using trained panellists, requires large meat samples and is a complex, expensive and time-consuming technique. Nevertheless, these characteristics are important criteria that affect consumers’ evaluation of beef quality. Vis-NIR technique provides information about the molecular bonds (chemical constituents) and tissue ultra-structure in a scanned sample and thus can indirectly predict physical or sensory parameters of meat samples. Applications of Vis-NIR spectroscopy in an abattoir for prediction of physical and sensory characteristics have been less developed than in other fields. Therefore, the aim of this study was to test the on-line Vis-NIR spectroscopy for the prediction of beef quality characteristics such as colour, instrumental texture, water holding capacity (WHC) and sensory traits, by direct application of a fibre-optic probe to the M. longissimus thoracis with no prior sample treatment.

Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

1995 ◽  
Vol 78 (3) ◽  
pp. 802-806 ◽  
Author(s):  
José Louis Rodriguez-Otero ◽  
Maria Hermida ◽  
Alberto Cepeda
Keyword(s):  
Near Infrared ◽  
Independent Set ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sample Treatment ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

scholarly journals Use of Visible and Near-Infrared Reflectance Spectroscopy Models to Determine Soil Erodibility Factor (K) in an Ecologically Restored Watershed

2020 ◽  
Vol 12 (18) ◽  
pp. 3103
Author(s):  
Qinghu Jiang ◽  
Yiyun Chen ◽  
Jialiang Hu ◽  
Feng Liu
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Erodibility ◽  
Infrared Reflectance ◽  
Cultivated Land ◽  
Near Infrared Reflectance ◽  
Natural Land ◽  
Soil Erodibility Factor

This study aimed to assess the ability of using visible and near-infrared reflectance (Vis–NIR) spectroscopy to quantify soil erodibility factor (K) rapidly in an ecologically restored watershed. To achieve this goal, we explored the performance and transferability of the developed spectral models in multiple land-use types: woodland, shrubland, terrace, and slope farmland (the first two types are natural land and the latter two are cultivated land). Subsequently, we developed an improved approach by combining spectral data with related topographic variables (i.e., elevation, watershed location, slope height, and normalized height) to estimate K. The results indicate that the calibrated spectral model using total samples could estimate K factor effectively (R2CV = 0.71, RMSECV = 0.0030 Mg h Mj−1 mm−1, and RPDCV = 1.84). When predicting K in the new samples, models performed well in natural land soils (R2P = 0.74, RPDP = 1.93) but failed in cultivated land soils (R2P = 0.24, RPDP = 0.99). Furthermore, the developed models showed low transferability between the natural and cultivated land datasets. The results also indicate that the combination of spectral data with topographic variables could slightly increase the accuracies of K estimation in total and natural land datasets but did not work for cultivated land samples. This study demonstrated that the Vis–NIR spectroscopy could be used as an effective method in predicting K. However, the predictability and transferability of the calibrated models were land-use type dependent. Our study also revealed that the coupling of spectrum and environmental variable is an effective improvement of K estimation in natural landscape region.

Application of near infrared reflectance (NIR) spectroscopy to identify potential PSE meat

2015 ◽  
Vol 96 (9) ◽  
pp. 3148-3156 ◽  
Author(s):  
Xiao Li ◽  
Fang Feng ◽  
Runze Gao ◽  
Lu Wang ◽  
Ye Qian ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Pse Meat

scholarly journals The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ming-Zhi Zhu ◽  
Beibei Wen ◽  
Hao Wu ◽  
Juan Li ◽  
Haiyan Lin ◽  
...  
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Control Quality ◽  
Tea Quality ◽  
Instrumental Technique

Tea is known to be one of the most popular beverages enjoyed by two-thirds of the world’s population. Concern of variability in tea quality is increasing among consumers. It is of great significance to control quality for commercialized tea products. As a rapid, noninvasive, and nondestructive instrumental technique with simplicity in sample preparation, near-infrared reflectance (NIR) spectroscopy has been proved to be one of the most advanced and efficient tools for the control quality of tea products in recent years. In this article, we review the most recent advances and applications of NIR spectroscopy and chemometrics for the quality control of tea, including the measurement of chemical compositions, the evaluation of sensory attributes, the identification of categories and varieties, and the discrimination of geographical origins. Besides, challenges and future trends of tea quality control by NIR spectroscopy are also presented.

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