Most of the important wood properties are highly variable among species and individuals, and even in the same stem. 1 this variation in wood properties is recognised as one of the greatest problems facing the wood industry, where rapid costeffective methods for measuring the properties are required to segregate log and lumber materials for appropriate end products. traditional methods employed to measure wood characteristics are time-consuming, expensive and often destructive. thus, several attempts have been made to quantify the woody materials by non-destructive techniques, such as mechanical, electromagnetic and acoustics, including ultrasonics and vibrational methods. 2 near infrared (nIr) spectroscopy, a fast growing technique for non-destructively evaluating organic materials, has found
Near-infrared spectroscopy (NIRS) is a powerful tool for the nondestructive evaluation of organic materials, and it has found widespread use in a variety of industries. In the food industry, it is important to know the district in which a particular food was produced. Therefore, in this study, we focused on determining the production area (five areas and three districts) of green coffee beans using classification analysis and NIRS. Soft independent modeling of class analogy (SIMCA) was applied as the classification method. Samples of green coffee beans produced in seven locations—Cuba, Ethiopia, Indonesia (Bari, Java, and Sumatra), Tanzania, and Yemen—were analyzed. These regions were selected since green coffee beans from these locations are commonly sold in Japan supermarkets. A good classification result was obtained with SIMCA for the seven green bean samples, although some samples were partly classified into several categories. Then, the model distance values of SIMCA were calculated and compared. A few model distance values were ~10; such small values may be the reason for misclassification. However, over a 73% correct classification rate could be achieved for the different kinds of green coffee beans using NIRS.
Near-infrared (NIR) spectroscopy, coupled with multivariate analysis, has been used to evaluate the wood properties of sawn lumber of Japanese larch (Larix kaempferi), whose diffuse reflection spectra were acquired under static and moving conditions. Prediction models of the dynamic modulus of elasticity (E(fr)), the modulus of elasticity in bending tests (E(b)), the bending strength (F(b)), the wood density (DEN), and the moisture content (MC) were developed using partial least squares (PLS) analysis. For all wood properties, models obtained from data collected under the moving condition as an analogue of on-line measurement were superior to those from the static condition data. The regression coefficients for the PLS models predicting the mechanical properties in both static and moving conditions showed clear peaks at the absorption bands due to the three major polymers of wood, i.e., cellulose, hemicellulose, and lignin. NIR spectroscopy has high potential for the on-line grading of sawn lumber.
The applicability of near-infrared (NIR) spectroscopy to the identification of wood species of archaeologically/historically valuable wooden artifacts in a non-invasive manner was investigated using reference wood samples from the xylarium of the Forestry and Forest Products Research Institute (TWTw) and applied to several wooden statues carved about 1000 years ago. Diffuse-reflectance NIR spectra were obtained from five standard wood samples each of five softwood species (Chamaecyparis obtusa, Cryptomeria japonica, Sciadopitys verticillata, Thujopsis dolabrata, Torreya nucifera) and five hardwood species (Aesculus turbinata, Cercidiphyllum japonicum, Cinnamomum camphora, Prunus jamasakura, Zelkova serrata). A principal component analysis (PCA) model was developed from the second derivative spectra. The score plot of the first two components clearly showed separation of the wood sample data into softwood and hardwood clusters. The developed PCA model was applied to 370 spectra collected from 21 wooden statues preserved in the Nazenji-temple in Shizuoka Prefecture in Japan, including 14 made from Torreya spp. and 7 made from Cinnamomum spp. In the score plot, the statue spectra were also divided into two clusters, corresponding to either softwood (Torreya spp.) or hardwood (Cinnnamomum spp.) species. These results show that NIR spectroscopy combined with PCA is a powerful technique for determining whether archaeologically/historically valuable wooden artifacts are made of softwood or hardwood.
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