• Journal of the Korean Wood Science and Technology
• /
• v.43 no.3
• /
• pp.304-310
• /
• 2015

The moisture content of sawdust must be measured accurately and controlled appropriately during storage and transportation because biological degradation could be caused by improper moisture. In this study, to measure the moisture contents of Larix kaempferi sawdust, the near-infrared reflectance spectra (Wavelength 1000-2400 nm) of sawdust were used as detection parameter. After acquiring the NIR reflection spectrum of specimens which were humidified at each relative humidity condition ($25^{\circ}C$, RH 30~99%), moisture content prediction model was developed using mathematical preprocessings (e.g. smoothing, standard normal variate) and partial least squares (PLS) analysis with the acquired spectrum data. High reliability of the MC regression model with NIR spectroscopy was verified by cross validation test ($R^2$ = 0.94, RMSEP = 1.544). The results of this study show that NIR spectroscopy could be used as a convenient and accurate method for the nondestructive determination of moisture content of sawdust, which could lead to optimize wood utilization.

• Korean Journal of Remote Sensing
• /
• v.37 no.4
• /
• pp.699-717
• /
• 2021

South Korea is pushing for the advancement of crop production technology to achieve food self-sufficiency and meet the demand for safe food. A medium-sized satellite for agriculture is being launched in 2023 with the aim of collecting and providing information on agriculture, not only in Korea but also in neighboring countries. The satellite is to be equipped with various sensors, though reference data for ground information are lacking. Hyperspectral remote sensing combined with 1st derivative is an efficient tool for the identification of agricultural crops. In our study, we develop a system for hyperspectral analysis of the ground-based reflectance spectrum, which is monitored seven times during the cultivation period of three soybean crops using a PSR-2500 hyperspectral sensor. In the reflection spectrum of soybean canopy, wavelength variations correspond with stages of soybean growths. The spectral reflection characteristics of soybeans can be divided according to growth into the vegetative (V)stage and the reproductive (R)stage. As a result of the first derivative analysis of the spectral reflection characteristics, it is possible to identify the characteristics of each wavelength band. Using our developed monitoring system, we observed that the near-infrared (NIR) variation was largest during the vegetative (V1-V3) stage, followed by a similar variation pattern in the order of red-edge and visible. In the reproductive stage (R1-R8), the effect of the shape and color of the soybean leaf was reflected, and the pattern is different from that in the vegetative (V) stage. At the R1 to R6 stages, the variation in NIR was the largest, and red-edge and green showed similar variation patterns, but red showed little change. In particular, the reflectance characteristics of the R1 stage provides information that could help us distinguish between the three varieties of soybean that were studied. In the R7-R8 stage, close to the harvest period, the red-edge and NIR variation patterns and the visible variation patterns changed. These results are interpreted as a result of the large effects of pigments such as chlorophyll for each of the three soybean varieties, as well as from the formation and color of the leaf and stem. The results obtained in this study provide useful information that helps us to determine the wavelength width and range of the optimal band for monitoring and acquiring vegetation information on crops using satellites and unmanned aerial vehicles (UAVs)

• Journal of the Korean Wood Science and Technology
• /
• v.43 no.6
• /
• pp.701-713
• /
• 2015

A model was designed to identify wood species between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. using the near-infrared (NIR) spectroscopy in combination with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). In the PCA using all of the spectra, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could not be classified. In the PCA using the spectrum that has been measured in sapwood, however, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could be identified. In particular, it was clearly classified by sapwood in radial section. And more, these two species could be perfectly identified using PLS-DA prediction model. The best performance in species identification was obtained when the second derivative spectra was used; the prediction accuracy was 100%. For prediction model, the $R_p{^2}$ value was 0.86 and the RMSEP was 0.38 in second derivative spectra. It was verified that the model designed by NIR spectroscopy with PLS-DA is suitable for species identification between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc.

• Analytical Science and Technology
• /
• v.23 no.3
• /
• pp.304-311
• /
• 2010

Near Infrared Spectroscopy (NIRs) has been applied for rapid and nondestructive paper measurement by replacing the current destructive method to the property of paper. Current standard methods for the property of paper were pH, moisture, breaking length, and folding endurance, which data were compared with spectrum of FT-NIR spectrometer. Various paper products such as copy, envelope, white, newspaper, as well as old paper produced around 1960~1980 were used as the sample. The correlation ($R^2$) and standard error of prediction (SEP) results for breaking length, folding endurance, moisture and pH are $R^2$=0.914, SEP=0.508, $R^2$=0.926, SEP=0.281, $R^2$=0.941, SEP=0.931, pH $R^2$=0.949, SEP= -0.0631, respectively. This result show that NIRs can be applied to practical application for nondestructive analysis of paper records materials.

• Food Engineering Progress
• /
• v.13 no.4
• /
• pp.297-301
• /
• 2009

This study was performed to develop a prediction model of sugar content for strawberry. Near-infrared (NIR) spectroscopy has been prevailed for on-line and portable applications for non-invasive quality assessment of intact fruit. This work presents effects of illumination method and coating of reflection surface of light source on prediction result of sugar content. Effect of preprocessing methods was also examined. A low-cost commercially available VIS/NIR spectrometer was used for estimation of total soluble solids content (Brix). To predict sugar contents of strawberry, the best results were obtained with the spectrum data measured under intensive illuminations at three locations induced from the light source with fiber optic bundles. Gold coating of reflection surface of light source lamp gave favorable effect to prediction result. The best results in validation of PLSR model were $r_{SEP}$ = 0.891 and SEP = 0.443 Brix under OSC preprocessing and those of PCR were $r_{SEP}$ = 0.845, SEP $r_{SEP}$= 0.520 Brix, under no preprocessing.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1616-1616
• /
• 2001

To select kernels for breeding that have required constituent content from either naturally distributed samples or artificially mutated ones, it is necessary to process batch samples in a short time. The constituent content of single-kernel grains such as wheat and rice has been determined using conventional bench type NIR instruments; however, it takes a lot of time and effort. Shizuoka Seiki (Fukuroi-city, Japan) and NFRI (National Food Research Institute) of MAFF (Ministry of Agriculture, forestry and Fisheries of Japan) have jointly developed a continuous high-speed single-kernel brown rice sorting machine based on rice protein content. It consists of several sections such as a feeding mechanism, measuring unit, sorting mechanism and controlling PC. The feeding mechanism picks up single-kernel brown rice from the hopper (maximum of 5kg storage capacity) and sends it to the measuring unit. A spectrum of the brown rice is obtained in the measuring unit, which consists of a near-infrared array sensor. The brown rice is then sorted in the sorting mechanism based on its protein content estimated by the controlling PC. In the present study, measuring speed was approximately 500ms for the full spectrum range and overall sorting speed was approximately 2.8s for one kernel. Accuracy of estimation was approximately SEP=0.5% of dry matter protein content for nonglutinous rice.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.14 no.2
• /
• pp.145-151
• /
• 2001

In this study, blue light emiting, soluble PPV copolymers were synthesized by Witting reaction and characterized. ITO/copolymer/Ca and ITO/copolymer/A1 structured light emitting diodes(LED) were fabricated and their I-V characteristics were examined. Copolymers showed $\pi$-$\pi$ transition in UV-Vis./NIR spectra. The PL and abosorption spectrum showed the symmetric vibration modes with mirror images which means that copolymers are highly aligned. By introducing aliphatic hydrocarbon group on polymer main chain, the solubility of copolymers was improved and no significant effects of substituent were observed. The band offset of copolymers are well suited as light emitting material for LED application than monomer or oligomer does. THe band offset of copolymers is ∼3eV in PL spectrum and the threshold voltages of ITO/copolymer/Ca and ITO/copolymer/Al structured LED 3V, 12V respectively. In the case of ITO/copolymer/Ca LED, it is believed that the amount of electrons and holes is well balanced and the recombination of opposite charges occurs easily because the work functions of Ca and Al electrodes are 2.9 and 4.3eV respectively and the difference in barrier height between polymer and electrode was small.

• Journal of Biosystems Engineering
• /
• v.27 no.2
• /
• pp.117-124
• /
• 2002

This research was conducted to develop a method to remove the effect of surface temperature of Shingo pears for sugar content measurement. Sugar content was measured by a near-infrared spectrum analysis technique. Reflected spectrum and sugar content of a pear were used for developing regression models. For the model development, reflected spectrums having wavelengths in the range of 654 to 1,052nm were used. To remove the effect of surface temperature, special sample preparation techniques and partial least square (PLS) regression models were proposed and tested. 71 Shingo pears stored in a cold storage, which had 2$^{\circ}C$ inside temperature, were taken out and left in a room temperature for a while. Temperature and reflected spectrum of each pear was measured. To increase the temperature distribution of samples, temperature and reflected spectrum of each pear was measured four times with one hour twenty minutes interval. During the experiment, temperature of pears increased up to 17 $^{\circ}C$. The total number of measured spectrum was 284. Three groups of spectrum data were formed according to temperature distribution. First group had surface temperature of 14$^{\circ}C$ and total number of 51. Second group consisted of the first and the fourth experiment data which contained the minimum and the maximum temperatures. Third group consisted of 155 data with normal temperature-distribution. The rest data set were used for model evaluation. Results shelved that PLS model I, which was developed by using the first data group, was inadequate for measuring sugar content of pears which had different surface temperatures from 14$^{\circ}C$. After temperature compensation, sugar content predictions became close to the measured values. Since using many data which had wide range of surface temperatures, PLS model II and III were able to predict sugar content of pears without additional temperature compensation. PLS model IV, which included the surface temperatures as an independent variable. showed slightly improved performance(R$^2$=0.73). Performance of the model could be enhanced by using samples with more wide range of temperatures and sugar contents.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.36 no.4
• /
• pp.59-63
• /
• 2013

In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article, a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be compared with a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionality of data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principal components for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research, 3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ash and char are used as response variables. The comparison results of two responses will be shown.

• Agricultural and Biosystems Engineering
• /
• v.1 no.1
• /
• pp.16-21
• /
• 2000

Measurement of spray deposit is necessary for evaluation of a chemical application technology. However it is not easy and time consuming. A simple method for measuring the deposition amount of spray using a tracer and a spectrophotometer was developed. Various materials were tested to determine an adequate tracer. Food dye was selected as a tracer, because it was cheep and easily treatable. Using NIRS(Near Infrared Reflection Spectrophotometer), a regression curves between maximum absorbance of a solution and concentration of the tracer were obtained. Yellow food dye solution showed a peak of spectrum at 452 nm, and absorbance of peak showed a tendency to increase as concentration increased. Green or pink food dye were tested and judged to be good tracers. However, tracer concentration should not exceed certain limits in order to measure maximum absorption. Using spraying liquid with known tracer concentration and known amount of washing liquid, spray deposit amount on real targets on leaves could be estimated at less than 13% error level.