• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
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• pp.521-524
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• 2000

Nearinfra-red(NIR) reflectance NIR a spectra (400 to 2,100 nm) were collected on 32 beef samples to find feasibility of predicting beef tenderness. The study to predict beef tenderness was accomplished with the stepwise second differential data of the collected NIR spectra. Beef tenderness was measured by Warner-Bratzler(WB) shear force using a Universal Testing Machine(UTM). After modeling the relation between Warner-Bratzler shear force and NIR spectrum of 19 samples among the 32 beef samples, the verification was carried out through predicting the other 13 samples. The SEC and R$^2$ values in the prediction equation were 9.07(N) and 0.6463, respectively. The SEP and R$^2$ were 14.8(N) and 0.7082 (wave length 552 nm, 1988 nm) respectively. The result implied that it was possible to predict the beef tenderness using NIR spectrum and that the tenderness could be predicted non-destructively in real time.

• 반도체디스플레이기술학회지
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• 제21권3호
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• pp.92-97
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• 2022

Plastics are classified into 7 types such as PET (PETE), HDPE, PVC, LDPE, PP, PS, and Other for separation and recycling. Recently, large corporations advocating ESG management are replacing them with bioplastics. Incineration and landfill of disposal of plastic waste are responsible for air pollution and destruction of the ecosystem. Because it is not easy to accurately classify plastic materials with the naked eye, automated system-based screening studies using various sensor technologies and AI-based software technologies have been conducted. In this paper, NIR scanning devices considering the NIR wavelength characteristics that appear differently for each plastic material and a system that can identify the type of plastic by learning the NIR spectrum data collected through it. The accuracy of plastic material identification was evaluated through a decision tree-based SVM model for multiclass classification on NIR spectral datasets for 8 types of plastic samples including biodegradable plastic.

• 한국산학기술학회논문지
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• 제15권1호
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• pp.396-401
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• 2014

본 논문에서는 록소프로펜 제제의 분석 시간을 단축하고 제품 출하 속도를 향상시키기 위하여 근적외부(NIR) 스펙트럼 측정법을 이용하여 기존에 사용하던 HPLC 방법과 상호 비교해 보고자 하였다. 제제학적으로 사용되는 다른 첨가제들과 함께 록소프로펜 혼합시료를 제조하고, NIR 분광광도계와 HPLC로 록소프로펜을 정량하고 평가하였다. 두 시험법의 밸리데이션 항목으로 특이성, 정확성 및 정밀성을 실시하였다. NIR 측정법이 검증되었고, 제약산업 분야의 제조공정 중 품질관리를 위한 적합한 결과를 얻었다. 결론적으로 록소프로펜을 분석하는데 있어서 NIR 측정법을 사용하면 기존의 HPLC 정량법을 NIR의 결과로 대치할 수 있을 것이다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1012-1012
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• 2001

The elegant early experiments of Herschel demonstrated that there is light after the visible spectrum in a region we call the near infrared (NIR). This was followed by the work which showed that the spectrum went further into what we call the mid infrared (MIR). The MIR has been used for many years as a qualitative and quantitative region to measure constituent values. The MIR region contains the fundamental vibrations which can be theoretically calculated from symmetry rules and harmonic oscillator equations. The NIR is not as straight forward because the region from 400-2500 nm does not contain any of the fundamental vibrations only combination bands and overtones. Over the past fifty years efforts to understand the NIR have largely been ignored while the quantitative aspects of the region have been utilized. This presentation will focus on the efforts to define terms for NIR, examine the calculation of combination bands and overtones and ways to interpret the spectra. The interpretation of the NIR has been aided greatly in recent years by the use of two dimensional spectroscopy which allows the correlation of bands in one spectral region with that of the NIR.

• 농약과학회지
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• 제10권2호
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• pp.84-90
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• 2006

농업분야에서 FT-NIR은 또 다른 분석기기를 통하여 정량하고자 하는 대상을 분석하여 작성한 data set을 이용하는 계량분석학적 정량분석기술을 통하여 시료 전처리 없이 농산물의 품질관리에 사용하였다. 농약제품은 고분자 부자재와 다양한 제형의 기기분석적 문제점들 때문에 FT-NIR을 이용하는 기기분석적 검토가 거의 이루어지지 않았다. 본 연구에서는 FT-NIR의 spectrum calculator 모듈로부터 계량분석화학적 기술을 이용하여 농약제품분석의 문제점을 해결할 수 있었다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1999년도 동계 학술대회 논문집
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• pp.340-345
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• 1999

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1210-1210
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• 2001

On farm analysis of protein, moisture and oil in cereals and oil seeds is quickly being adopted by Australian farmers. The benefits of being able to measure protein and oil in grains and oil seeds are several : $\square$ Optimize crop payments $\square$ Monitor effects of fertilization $\square$ Blend on farm to meet market requirements $\square$ Off farm marketing - sell crop with load by load analysis However farmers are not NIR spectroscopists and the process of calibrating instruments has to the duty of the supplier. With the potential number of On Farm analyser being in the thousands, then the task of calibrating each instrument would be impossible, let alone the problems encountered with updating calibrations from season to season. As such, NIR technology Australia has developed a mechanism for \ulcorner\ulcorner\ulcorner their range of Cropscan 2000G NIR analysers so that a single calibration can be transferred from the master instrument to every slave instrument. Whole grain analysis has been developed over the last 10 years using Near Infrared Transmission through a sample of grain with a pathlength varying from 5-30mm. A continuous spectrum from 800-1100nm is the optimal wavelength coverage fro these applications and a grating based spectrophotometer has proven to provide the best means of producing this spectrum. The most important aspect of standardizing NIB instruments is to duplicate the spectral information. The task is to align spectrum from the slave instruments to the master instrument in terms of wavelength positioning and then to adjust the spectral response at each wavelength in order that the slave instruments mimic the master instrument. The Cropscan 2000G and 2000B Whole Grain Analyser use flat field spectrographs to produce a spectrum from 720-1100nm and a silicon photodiode array detector to collect the spectrum at approximately 10nm intervals. The concave holographic gratings used in the flat field spectrographs are produced by a process of photo lithography. As such each grating is an exact replica of the original. To align wavelengths in these instruments, NIR wheat sample scanned on the master and the slave instruments provides three check points in the spectrum to make a more exact alignment. Once the wavelengths are matched then many samples of wheat, approximately 10, exhibiting absorbances from 2 to 4.5 Abu, are scanned on the master and then on each slave. Using a simple linear regression technique, a slope and bias adjustment is made for each pixel of the detector. This process corrects the spectral response at each wavelength so that the slave instruments produce the same spectra as the master instrument. It is important to use as broad a range of absorbances in the samples so that a good slope and bias estimate can be calculated. These Slope and Bias (S'||'&'||'B) factors are then downloaded into the slave instruments. Calibrations developed on the master instrument can then be downloaded onto the slave instruments and perform similarly to the master instrument. The data shown in this paper illustrates the process of calculating these S'||'&'||'B factors and the transfer of calibrations for wheat, barley and sorghum between several instruments.

• Journal of Biosystems Engineering
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• 제29권4호
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• pp.335-340
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• 2004

This study was conducted to evaluate the internal quality of Korean red ginseng using VIS/NIR transmittance spectra. To classify the internal qualities, partial least squares(PLS) regression was conducted. The main results are as follows: To develop the PLS model, several wave bands were divided and incorporated into the model. Among the bands, the wavelength range of 550-1,020nm, excluded noise signal, showed the best evaluation results. Effect of step size on the performance of quality evaluation showed optimal at 15 steps. In order to enhance the accuracy of quality evaluation, the abnormal spectrum shape was considered first and then the PLS model was applied. Among the 150 samples, 12 samples were evaluated by the spectrum shape. In this study, to develop the optimal PLS regression model, among the 150 samples, 138 samples was used with exception of 12 samples which could evaluate the spectrum shape. The result of quality evaluation was promising as SEC and correlation coefficient were 1.09 and 0.967, respectively, and SEP and correlation coefficient were 1.04 and 0.958, respectively.

• Journal of Biosystems Engineering
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• 제22권2호
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• pp.217-226
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• 1997

This study was to develop a machine vision system to detect and to discriminate 5 kinds of apple surface defectbruise, decay. fleck, worm hole and scar. To detect the defects from an image of apple, thresholding technique was applied to images on various frames (R, G, B, H, S and I) of the color machine vision and an image of near infrared (NIR). To discriminate the detected region of defect, various features of the 5 kind defect regions were extracted from the 4 kinds of images selected above. The features were size of area, roundness, axes length ratio, mean and valiance of pixel values, standard deviation of real part of amplitude spectrum in frequency domain obtained by Fourier transform of pixel data and mean and standard deviation of power spectrum obtained by the same transform of pixel data. Routines to discriminate the defects from the features of image were developed and tested to prove their validity. The test resulted that I-frame and NIR images were the most desirable. Accuracies of the two images to discriminate the defects were noted as 76% and 77%, respectively.

• Journal of Biosystems Engineering
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• 제30권5호
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• pp.299-305
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• 2005

This study was developed image processing algorithm for recognition of few teats of a cow in the image using black and white camera attached with infrared filter. Spectroscopic analysis was used for selection of a NIR filter to separate teats from udder skin in the image captured. To verify the performance of image processing algorithm was developed and NIR filter was selected, carried out an experiment with cows. NIR band-pass filter was used to pass the 975nm band of light spectrum. The image processing algorithm was developed could recognize all teats and the process time was 0.9 second to recognize the all teats and to acquire end position of teats.