scholarly journals Quantitative prediction of moisture content distribution in acetylated wood using near-infrared hyperspectral imaging

2022 ◽  
Author(s):  
Muhammad Awais ◽  
Michael Altgen ◽  
Mikko Mäkelä ◽  
Tiina Belt ◽  
Lauri Rautkari
Keyword(s):  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Infrared Image ◽  
Moisture Distribution ◽  
Quantitative Prediction ◽  
Chemical Information ◽  
Least Squares Regression ◽  
Dynamic Vapor Sorption ◽  
Wood Surfaces

AbstractThe uptake of moisture severely affects the properties of wood in service applications. Even local moisture content variations may be critical, but such variations are typically not detected by traditional methods to quantify the moisture content of the wood. In this study, we used near-infrared hyperspectral imaging to predict the moisture distribution on wood surfaces at the macroscale. A broad range of wood moisture contents were generated by controlling the acetylation degree of wood and the relative humidity during sample conditioning. Near-infrared image spectra were then measured from the surfaces of the conditioned wood samples, and a principal component analysis was applied to separate the useful chemical information from the spectral data. Moreover, a partial least squares regression model was developed to predict moisture content on the wood surfaces. The results show that hyperspectral near-infrared image regression can accurately predict the variations in moisture content across wood surfaces. In addition to sample-to-sample variation in moisture content, our results also revealed differences in the moisture content between earlywood and latewood in acetylated wood. This was in line with our recent studies where we found that thin-walled earlywood cells are acetylated faster than the thicker latewood cells, which decreases the moisture uptake during the conditioning. Dynamic vapor sorption isotherms validated the differences in moisture content within earlywood and latewood cells. Overall, our results demonstrate the capabilities of hyperspectral imaging for process analytics in the modern wood industry. Graphical abstract

A survey of moisture distribution in two sets of Scots pine logs by NIR-spectroscopy

Holzforschung ◽  
2008 ◽  
Vol 62 (4) ◽  
Author(s):  
Kyösti Karttunen ◽  
Asta Leinonen ◽  
Matti-Paavo Sarén
Keyword(s):  
Moisture Content ◽  
Scots Pine ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Optical Probe ◽  
Moisture Distribution ◽  
Least Squares Regression ◽  
Step Procedure ◽  
Surface Drying ◽  
Automated Scanning

Abstract Moisture content distributions of Scots pine logs in the green state were measured by a novel multi-step procedure. After sample preparation, the transverse sections of the wood surfaces were scanned by an automated scanning device with a fiber optical probe connected to a Fourier transform near-infrared spectroscope. In the course of the measurement sequences, several issues were addressed, such as surface drying, measurement geometry, ease of automation and interconnected data handling. The near-infrared (NIR) data were first modeled separately for heartwood and sapwood by means of multivariate partial least squares regression. The models for moisture content were evaluated by root mean square error of prediction, the result being 0.8% for heartwood and 10% for sapwood. The two models were then applied to the NIR data collected from sets of disks cut from nine logs. The results of the calculated moisture contents were evaluated by methods of descriptive statistics, and they indicated clear differences and trends in the distribution of moisture content in transverse or longitudinal regions of a log. Additionally, inter-tree variation in moisture content was detected.

Applicability of Vis-NIR hyperspectral imaging for monitoring wood moisture content (MC)

Holzforschung ◽  
2013 ◽  
Vol 67 (3) ◽  
pp. 307-314 ◽  
Author(s):  
Hikaru Kobori ◽  
Nathalie Gorretta ◽  
Gilles Rabatel ◽  
Véronique Bellon-Maurel ◽  
Gilles Chaix ◽  
...  
Keyword(s):  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Water Distribution ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Wood Moisture ◽  
Standard Normal Variate ◽  
Average Reflectance ◽  
Standard Normal ◽  
High Prediction

Abstract Visible-near-infrared hyperspectral imaging was tested for its suitability for monitoring the moisture content (MC) of wood samples during natural drying. Partial least-squares regression (PLSR) prediction of MC was performed on the basis of average reflectance spectra obtained from hyperspectral images. The validation showed high prediction accuracy. The results were compared concerning the PLSR prediction of MC mapping from raw spectra and standard normal variate (SNV) treatment. SNV pretreatment leads to the best results for visualizing the MC distribution in wood. Hyperspectral imaging has a high potential for monitoring the water distribution of wood.

scholarly journals Chemometric Tools for NIRS and NIR Hyperspectral Imaging

2012 ◽  
Vol 69 (1) ◽  
Author(s):  
Laura M. DALE ◽  
André THEWIS ◽  
Ioan ROTAR ◽  
Juan A. FERNANDEZ PIERNA ◽  
Christelle BOUDRY ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Spatial Information ◽  
Imaging System ◽  
Agricultural Products ◽  
Chemical Information ◽  
Special Focus ◽  
Large Area ◽  
Analytical Tools

Nowadays in agriculture, new analytical tools based on spectroscopic technologies are developed. Near Infrared Spectroscopy (NIRS) is a well known technology in the agricultural sector allowing the acquisition of chemical information from the samples with a large number of advantages, such as: easy to use tool, fast and simultaneous analysis of several components, non-polluting, noninvasive and non destructive technology, and possibility of online or field implementation. Recently, NIRS system was combined with imaging technologies creating the Near Infrared Hyperspectral Imaging system (NIR-HSI). This technology provides simultaneously spectral and spatial information from an object. The main differences between NIR-HSI and NIRS is that many spectra can be recorded simultaneously from a large area of an object with the former while with NIRS only one spectrum was recorded for analysis on a small area. In this work, both technologies are presented with special focus on the main spectrum and images analysis methods. Several qualitative and quantitative applications of NIRS and NIR-HSI in agricultural products are listed. Developments of NIRS and NIR-HSI will enhance progress in the field of agriculture by providing high quality and safe agricultural products, better plant and grain selection techniques or compound feed industry’s productivity among others.

scholarly journals Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

2019 ◽  
Vol 9 (18) ◽  
pp. 3926 ◽  
Author(s):  
Yue Zhang ◽  
Hongzhe Jiang ◽  
Wei Wang
Keyword(s):  
Hyperspectral Imaging ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Chicken Meat ◽  
Regions Of Interest ◽  
Regression Coefficients ◽  
Least Squares Regression ◽  
Distribution Maps ◽  
Plsr Model ◽  
Volume Range

The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.

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