• 한국작물학회지
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• 제51권spc1호
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• pp.283-291
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• 2006

To investigate seed non destructive and fast determination technique utilizing near infrared reflectance spectroscopy (NIRs) for screening ultra high oleic (C18:1) and linoleic (C18:2) fatty acid content sesame varieties among genetic resources and lines of pedigree generations of cross and mutation breeding were carried out in National Institute of Crop Science (NICS). 150 among 378 landraces and introduced cultivars were released to analyse fatty acids by NIRs and gas chromatography (GC). Average content of each fatty acid was 9.64% in palmitic acid (C16:0), 4.73% in stearic acid (C18:0), 42.26% in oleic acid and 43.38% in linoleic acid by GC. The content range of each fatty acid was from 7.29 to 12.27% in palmitic, 6.49% from 2.39 to 8.88% in stearic, 12.59% of wider range compared to that of stearic and palmitic from 37.36 to 49.95% in oleic and of the widest from 30.60 to 47.40% in linoleic acid. Spectrums analyzed by NIRs were distributed from 400 to 2,500 nm wavelengths and varietal distribution of fatty acids were appeared as regular distribution. Varietal differences of oleic acid content good for food processing and human health by NIRs was 14.08% of which 1.49% wider range than that of GC from 38.31 to 52.39%. Varietal differences of linoleic acid content by NIRs was 16.41% of which 0.39% narrower range than that of GC from 30.60 to 47.01%. Varietal differences of oleic and linoleic acid content in NIRs analysis were appeared relatively similar inclination compared with those of GC. Partial least square regression (PLSR) among multiple variant regression (MVR) in NIRs calibration statistics was carried out in spectrum characteristics on the wavelength from 700 to 2,500 nm with oleic and linoleic acids. Correlation coefficient of root square (RSQ) in oleic acid content was 0.724 of which 72.4 percent of sample varieties among all distributed in the range of 0.570 percent of standard error when calibrated (SEC) which were considerably acceptable in statistic confidence significantly for analysis between NIRs and GC. Standard error of cross validation (SECV) of oleic acid was 0.725 of which distributed in the range of 0.725 percent standard error among the samples of mother population between analyzed value by NIRs analysis and analyzed value by GC. RSQ of linoleic acid content was 0.735 of which 73.5 percent of sample varieties among all distributed in the range of 0.643 percent of SEC. SECV of linoleic acid was 0.711 of which distributed in the range of 0.711 percent standard error among the samples of mother population between NIRs analysis and GC analysis. Consequently, adoption NIR analysis for fatty acids of oleic and linoleic instead that of GC was recognized statistically significant between NIRs and GC analysis through not only majority of samples distributed in the range of negligible SEC but also SECV. For enlarging and increasing statistic significance of NIRs analysis, wider range of fatty acids contented sesame germplasm should be kept on releasing additionally for increasing correlation coefficient of RSQ and reducing SEC and SECV in the future.

• Annals of Clinical Neurophysiology
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• 제20권2호
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• pp.57-65
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• 2018

Near-infrared spectroscopy (NIRS) monitoring has been used mainly to detect reduced perfusion of the brain during orthostatic stress in order to assess orthostatic intolerance (OI). Many studies have investigated the use of NIRS to reveal the pathophysiology of patients with OI. Research using NIRS in other neurological diseases (e.g., stroke, epilepsy, and migraine) is continuing. NIRS may play an important role in monitoring the regional distribution of the hemodynamic flow in real time and thereby reveal the underlying pathophysiology and facilitate the management of not only patients with OI symptoms but also those with various neurological diseases.

• 대한의용생체공학회:의공학회지
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• 제29권2호
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• pp.89-98
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• 2008

NIRS (Near-infrared Spectroscopy) and DOI (Diffuse Optical Imaging) are relatively new, non-invasive, and non-ionizing methods that measure or image optical properties (Scattering and Absorption Coefficient) and physiological properties (Water Fraction, concentration of Oxy-, Deoxy-Hemoglobin, Cytochrome Oxidase, etc) of biological tissues. In this paper, three different types of NIRS systems, mathematical modeling, and reconstruction algorithms are described. Also, recent applications such as functional brain imaging, optical mammography, NIRS based BMI (Brain-Machine Interface), and small animal study are reviewed.

• 한국초지조사료학회지
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• 제33권3호
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• pp.171-176
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• 2013

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages and feedstuff. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of fresh whole crop barley silages. A representative population of 284 fresh whole crop barley silages was used as a database for studying the possibilities of NIRS to predict chemical composition. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data were recorded as log 1/Reflectance (log 1/R) and were scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh whole crop barley silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH, as well as lactic acid content with correlation coefficients of cross-validation ($R^2cv$) of 0.96, 0.81, 0.79, 0.84, 0.72 and 0.78, respectively, and standard error of cross-validation (SECV) of 1.26, 2.83, 2.18, 1.19, 0.13 and 0.32% DM, respectively. Results of this experiment showed the possibility of the NIRS method to predict the chemical parameters of fresh whole crop barley silages as a routine analysis method in feeding value evaluation and for farmer advice.

• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.1-7
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• 2022

인공지능 기술이 발달하면서 뉴로사이언스 마이닝(NSM: NeuroScience Mining)과 AI를 접목하려는 시도가 증가하고 있다. 나아가 NSM은 뉴로사이언스와 비즈니스 애널리틱스의 결합으로 인해 연구범위가 확장되고 있다. 본 연구에서는 fNIRS 실험을 통해 확보한 뉴로 데이터를 분석하여 비즈니스 문제 해결 창의성(BPSC: business problem-solving creativity)을 예측하고 이를 통해 NSM의 잠재력을 조사한다. BPSC는 비즈니스에서 차별성을 가지게 하는 중요한 요소이지만, 인지적 자원의 하나인 BPSC의 측정 및 예측에는 한계가 존재한다. 본 논문에서는 BPSC 예측 성능을 높이는 방안으로 CNN, BiLSTM 그리고 어텐션 네트워크를 결합한 새로운 NSM 기법을 제안한다. 제안된 NSM 기법을 15만 개 이상의 fNIRS 데이터를 활용하여 유효성을 입증하였다. 연구 결과, 본 논문에서 제안하는 NSM 방법이 벤치마킹한 알고리즘(CNN, BiLSTM)에 비하여 우수한 성능을 가지는 것으로 나타났다.

• Animal Bioscience
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• 제37권7호
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• pp.1196-1203
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• 2024

Objective: A study was conducted to quantify the performance differences of the near-infrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. Methods: A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. Results: The performance of the developed NIRS calibration models was evaluated based on R², standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R² (coefficient of determination), standard error, and RPD. Although the R² of calibration models was mainly greater than 0.90 across the feed value indexes, the R² of cross-validations was much lower. The R² of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. Conclusion: Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

• 한국작물학회지
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• 제53권1호
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• pp.15-20
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• 2008

검정콩 종피에 함유된 안토시아닌의 색소별 함량을 비파괴적으로 신속하게 분석하기 위하여 NIRS(근적외선 분광분석기)를 이용한 모델을 개발하였다. 재료는 검정콩 유전자원 300 계통을 사용하였으며, HPLC에서 분석된 종피의 안토시아닌 함량치를 NIRS 스펙트럼에 적용시킨 후 검량식을 작성하였다. NIRS의 검량식을 몇 가지 방법에 의하여 비교 분석한 결과 1차미분된 스펙트럼을 MPLS(Modified Partial Least Squares)를 이용한 회귀식에 이용하는 것이 가장 적합하였다. HPLC를 이용한 유전자원들의 성분 함량과 NIRS에서 도출된 검량식과의 상관계수는 C3G, D3G 및 Pt3G가 각각 0.952, 0.936과 0.833을 나타내었다. 이들 검량식은 validation file에서도 C3G와 D3G는 0.897, 0.849의 높은 상관을 보였으며, 이는 NIRS를 이용하여 검정콩의 안토시아닌 함량을 신속하게 분석할 수 있음을 나타내는 것으로 판단되었다.

• 한국자원식물학회지
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• 제27권4호
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• pp.286-292
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• 2014

황금에 함유된 baicalin, baicalein 및 wogonin 함량을 근적외선분광분석기(NIRS)로 비파괴적으로 신속하게 할 수 있는 방법을 개발하였다. 황금 유전자원 63종을 대상으로 NIRS로 400~2,500 ㎚ 파장범위를 scan한 후 물리적 영향에 의한 바탕선 변화 등의 오차를 줄이고 겹쳐있는 파장을 분리하기 위하여 미분법 등을 이용한 수처리를 하였다. 수처리 후 몇 가지 회귀분석법을 이용하여 검량식을 작성한 결과 MPLS 회귀법이 황금의 유용성분을 분석하기에 적당하였으며, MPLS 회귀분석법에 의한 검량식의 baicalin, baicalein, wogonin 함량은 HPLC로 분석된 황금의 성분함량과의 상관값에서 각각 0.958, 0.944, 0.709로 나타났다. 이렇게 작성된 검량식을 이용하여 validation set에서 상관관계를 분석한 바 baicalin 함량은 0.853, baicalein은 0.895의 상관치를 보여 황금의 baicalin 및 baicalein 함량은 NIRS로 분석이 가능한 것으로 판단되었다. 한편, wogonin 함량에 대한 validation set의 결과는 0.489로 나타나 NIRS 검량식을 wogonin의 정량분석에 바로 이용하기에는 부적당하였다.

• 한국작물학회지
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• 제67권1호
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• pp.61-66
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• 2022

본 연구는 참깨에 함유된 세사민 및 세사몰린의 함량을 비파괴적으로 신속하게 평가하기 위하여 NIRS 분석을 이용해 검량식을 작성하고 검량식의 적용가능성을 검증하였다. 검량식 작성에 사용된 482점 참깨의 HPLC 분석 결과를 NIRS 스펙트럼에 적용시킨 후 검량식을 작성하였다. 세사민 및 세사몰린의 R² 값은 각각 0.936, 0.875로 조사되었으며 이를 cross validation 한 결과에서도 각각 0.899, 0.781로 조사되어 리그난 함량 분석에 적용 가능할 것으로 판단되었다. 작성된 검량식의 적용가능성을 확인하기 위해 2020년에 생산된 참깨 유전자원 90종의 종자를 NIRS를 통해 분석한 결과 세사민 및 세사몰린의 R²값이 각각 0.653, 0.596으로 크게 낮아졌으나 리그난 함량이 높은 상위 30%의 자원을 선발하는데 무리가 없었다. 따라서 본 연구에서 작성된 NIRS 검량식은 육종 초기에 고리그난 함량을 선발하는데 적용 가능할 것으로 판단된다.

• 한국작물학회지
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• 제58권3호
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• pp.301-307
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• 2013

연구용 NIR 장비에서 수집된 벼 생엽의 질소 함량 검량식 및 데이터베이스를 현장용 NIR 장비에 검량식을 이설, 검증함으로서 현장 적용 가능성을 평가하기 위해 수행한 결과는 다음과 같다. 1. 2003년부터 2009년까지 스펙트럼을 수집한 시료 중 선발 된 A 데이터 세트(개체수 454점)의 총 질소범위는 2.041%~4.933%, 2012년 수집된 B 데이터 세트(258점)는 2.180%~3.690%이며 각각의 전체 평균은 3.497%, 2.712%였다. 2. A, B 데이터 세트에서 유도된 검량식 결과 결정계수($R^2$)는 각각 0.845, 0.777,표준오차(SEC)는 0.196, 0.126, SECV는 0.238, 0.150이었다. 3. 연구용 NIR 장비 400 nm~2500 nm 파장에서 얻어진 데이터베이스를 현장용 NIR 장비 1200 nm~2400 nm 파장에 맞게 잘라 이설한 후 2012년 데이터베이스에 업데이트 확장한 후 작성된 검량식 결과 결정계수($R^2$)는 0.880, 표준오차(SEC)는 0.191이었다. 4. 연구용 NIR 장비에 구축된 데이터베이스를 현장용 NIR 장비에 맞춰 데이터베이스를 확장 업데이트하고 검량식을 이설한 결과 연구용 장비와의 표준오차는 0.005%로 거의 동일한 수준의 결과를 얻었다.