• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.353-365
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• 2019

A near-infrared reflectance spectroscopy (NIRS) prediction model was set to establish a rapid analysis system of wheat germplasm and provide statistical information on the characteristics of protein contents. The variability index value (VIV) of calibration resources was 0.80, the average protein content was 13.2%, and the content range was from 7.0% to 13.2%. After measuring the near-infrared spectra of calibration resources, the NIRS prediction model was developed through a regression analysis between protein content and spectra data, and then optimized by excluding outliers. The standard error of calibration, R², and the slope of the optimized model were 0.132, 0.997, and 1.000 respectively, and those of external validation results were 0.994, 0.191, and 1.013, respectively. Based on these results, a developed NIRS model could be applied to the rapid analysis of protein in wheat. The distribution of NIRS protein content of 6,794 resources were analyzed using a normal distribution analysis. The VIV was 0.79, the average protein was 12.1%, and the content range of resources accounting for 42.1% and 68% of the total accessions were 10-13% and 9.5-14.6%, respectively. The composition of total resources was classified into breeding line (3,128), landrace (2,705), and variety (961). The VIV in breeding line was 0.80, the protein average was 11.8%, and the contents of 68% of total resources ranged from 9.2% to 14.5%. The VIV in landrace was 0.76, the protein average was 12.1%, and the content range of resources of 68% of total accessions was 9.8-14.4%. The VIV in variety was 0.80, the protein average was 12.8%, and the accessions representing 68% of total resources ranged from 10.2% to 15.4%. These results should be helpful to the related experts of wheat breeding.

• Journal of Animal Environmental Science
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• v.13 no.2
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• pp.113-120
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• 2007

This study was conducted to measure Nitrogen(N), Phosphate($P_2O_5$), Potassium ($K_2O$), Organic matter(OM) and Moisture content of liquid pig manure by Near Infrared Spectroscopy(NIRS) and to develop an alternative and analytical instrument which are used for measurement of N, $P_2O_5$, $K_2O$, OM, and Moisture contents for liquid pig manure. The liquid pig manure sample's transmittance spectra were measured with a NIRS in the wavelength range of 400 to 2,500 nm. Multiple linear regression and partial least square regression were used for calibrations. The correlation coefficient(RSQ) and standard error of calibration(SEC) obtained for nitrogen were 0.9190 and 2.1649, respectively. The RSQ for phosphate, potassium, organic matter and moisture contents were 0.9749, 0.5046, 0.9883 and 0.9777, and the SEC were 0.5019, 1.9252, 0.1180 and 0.0789, respectively. These results are indications of the rapid determination of components of liquid pig manure through the NIR analysis. The simple analytical instrument for liquid pig manure consisted of a tungsten halogen lamp for light source, a sample holder, a quartz cell, a SM 301 spectrometer for spectrum analyzer, a power supply, an electronics, a computer and a software. Results showed that the simple analytical instrument that was developed can approximately predict the phosphate, organic matter and moisture content of the liquid pig manure when compared to the analysis taken by NIRS. The low predictability value of potassium however, needs further investigation. Generally, the experiment proved that the simple analytical instrument was reliable, feasible and practical for analyzing liquid pig manure.

• Korean Journal of Optics and Photonics
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• v.13 no.4
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• pp.363-369
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• 2002

We have designed conductive transparent filters using a low-emissivity coating such as [dielectric|Ag|dielectric] for display applications. The design is the repetition of [$TiO_{2}$|Ti|Ag |$TiO_{2}$] to increase the transmittance in the visible and decrease the transmittance in the near IR. The conductive transparent filters are deposited by a radio frequency(RF) magnetron sputtering system. The optical, structural and electrical properties of the filters were investigated and the optical spectra are compared with simulated spectra. The thickness of the deposited Ag films is above 13 ㎚ to increase the conductivity and that of $TiO_{2}$ films is 24 ㎚ to increase the transmittance in the visible range. Ti blockers are employed to prevent the Ag films from being oxidized by an oxygen gas during the reactive sputtering process. Also, it is shown that the thicker Ti film is necessary as the period increases. Finally, a filter with repetition of the basic structure three times shows the better cut-off near infrared(NIR) and the sheet resistance as low as 2Ω/□ which is enough to shield an unnecessary electromagnetic waves for a display panel.

• Journal of the Korean institute of surface engineering
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• v.51 no.2
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• pp.126-132
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• 2018

SnS thin films with different substrate temperatures ($150 {\sim}300^{\circ}C$) as process parameters were grown on soda-lime glass substrates by RF magnetron sputtering. The effects of substrate temperature on the structural and optical properties of SnS thin films were investigated by X-ray diffraction (XRD), Raman spectroscopy (Raman), field-emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDS), and Ultraviolet-visible-near infrared spectrophotometer (UV-Vis-NIR). All of the SnS thin films prepared at various substrate temperatures were polycrystalline orthorhombic structures with (111) planes preferentially oriented. The diffraction intensity of the (111) plane and the crystallite size were improved with increasing substrate temperature. The three major peaks (189, 222, $289cm^{-1}$) identified in Raman were exactly the same as the Raman spectra of monocrystalline SnS. From the XRD and Raman results, it was confirmed that all of the SnS thin films were formed into a single SnS phase without impurity phases such as $SnS_2$ and $Sn_2S_3$. In the optical transmittance spectrum, the critical wavelength of the absorption edge shifted to the long wavelength region as the substrate temperature increased. The optical bandgap was 1.67 eV at the substrate temperature of $150^{\circ}C$, 1.57 eV at $200^{\circ}C$, 1.50 eV at $250^{\circ}C$, and 1.44 eV at $300^{\circ}C$.

• Food Science and Preservation
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• v.19 no.1
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• pp.95-103
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• 2012

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels ($5^{\circ}C$, $20^{\circ}C$, and $40^{\circ}C$). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at $40^{\circ}C$ and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.

• Food Engineering Progress
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• v.15 no.1
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• pp.48-55
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• 2011

In this research, the near-infrared absorption from 1,100-2,300 nm was used to measure the content of capsaicinoids in the red-pepper powder by using the Acousto-optic tunable filters (AOTF) spectrometer with sample plate and sample rotating unit. Non-spicy red-pepper samples from one location (Younggwang-gun. Korea) were mixed with spicy one (var. Chungyang) to make samples separated by particle size (below 0.425 mm, 0.425-0.71 mm, and 0.71- 1.4 mm). The Partial Least Squares Regression (PLSR) model to predict the capsaicinoid content on particle sizes was developed with measured spectra by AOTF spectrometer and used to analyze the amount of capsaicinoids by HPLC. The PLSR Model of red-pepper powder of below 0.425 mm, 0.425-0.71 mm, and 0.71-1.4 mm with cross validation had ${R_V}^2$ = 0.948-0.979 and Standard Error of Prediction (SEP) = 6.56-7.94 mg%. The prediction error of smaller particle size of red-pepper powder was low. The best PLSR model was found in pretreatment of Range Normalization, Standard Normal Variate, and 1st Derivatives of red-pepper powder of below 1.4 mm with cross validation, having ${R_V}^2$ = 0.959 and SEP = 8.82 mg%.