• 한국작물학회지
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• 제50권spc1호
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• pp.1-3
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• 2005

흑자색미의 C3C함량을 신속하게 분석하는데, FT-NIR을 이용한 C3G 함량 분석의 예측값과 HPLC 측정값의 정확도를 비교하였다. 1. FT-NIR을 이용한 C3G 함량 분석에서 사용된 시료는 별도의 전처리 과정없이 현미 상태 측정하여 HPLC 분석방법에 비하여 많은 시간과 비용을 아낄 수 있다. 2. 흑진주벼와 수원425호를 교배한 $F_{10}$ 385 계통을 사용하여 얻은 FT-NIR 검량식은 매우 높은 정상관을 보였다($R^2=0.943$, SEE=0.116). 이 검량식을 검증한 결과도 매우 높은 정상관을 보이고 실험오차도 매우 적어($R^2=0.928$, SEP=0.122) 측정정확도가 높게 평가되었다. 3. 본 연구의 결과, FT-NIR을 이용하여 비파괴적으로 신속하게 흑자색미의 C3G함량을 측정할 수 있게 되었다. 그리고 본 연구의 결과를 C3G함량이 높은 벼를 개발하는데 많은 양의 시료를 빠르게 분석할 수 있는 방법으로 이용할 수 있을 것이다.

• Journal of the Korean Wood Science and Technology
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• 제44권6호
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• pp.852-863
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• 2016

Wood-plastic composite (WPC) is a promising and sustainable material, and refers to a combination of wood and plastic along with some binding (adhesive) materials. In comparison to pure wood material, WPCs are in general have advantages of being cost effective, high durability, moisture resistance, and microbial resistance. The properties of WPCs come directly from the concentration of different components in composite; such as wood flour concentration directly affect mechanical and physical properties of WPCs. In this study, wood powder concentration in WPC was determined by Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy. The reflectance spectra from WPC in both powdered and tableted form with five different concentrations of wood powder were collected and preprocessed to remove noise caused by several factors. To correlate the collected spectra with wood powder concentration, multivariate calibration method of partial least squares (PLS) was applied. During validation with an independent set of samples, good correlations with reference values were demonstrated for both FT-NIR and FT-IR data sets. In addition, high coefficient of determination (${R^2}_p$) and lower standard error of prediction (SEP) was yielded for tableted WPC than powdered WPC. The combination of FT-NIR and FT-IR spectral region was also studied. The results presented here showed that the use of both zones improved the determination accuracy for powdered WPC; however, no improvement in prediction result was achieved for tableted WPCs. The results obtained suggest that these spectroscopic techniques are a useful tool for fast and nondestructive determination of wood concentration in WPCs and have potential to replace conventional methods.

• 농업과학연구
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• 제39권1호
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• pp.111-116
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• 2012

Nondestructive evaluation of seed viability is one of the highly demanding technologies for seed production industry. Conventional seed sorting technologies, such as tetrazolium and standard germination test are destructive, time consuming, and labor intensive methods. Near infrared spectroscopy technique has shown good potential for nondestructive quality measurements for food and agricultural products. In this study, FT-NIR spectroscopy was used to classify normal and artificially aged lettuce seeds. The spectra with the range of 1100~2500 nm were scanned for lettuce seeds and analyzed using the principal component analysis(PCA) method. To classify viable seeds from nonviable seeds, a calibration modeling set was developed with a partial least square(PLS) method. The calibration model developed from PLS resulted in 98% classification accuracy with the Savitzky-Golay $1^{st}$ derivative preprocessing method. The prediction accuracy for the test data set was 93% with the MSC(Multiplicative Scatter Correction) preprocessing method. The results show that FT-NIR has good potential for discriminating non-viable lettuce seeds from viable ones.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.4112-4112
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• 2001

The feasibility of NIR spectroscopy as a quick and nondestructive method for quality control of uniformity of coating thickness of pharmaceutical tablets was investigated. Near infrared spectra of a set of pharmaceutical tablets with varying coating thickness were measured with a diffuse reflectance fiber optic probe connected to a Broker IFS 28/N FT-NIR spectrometer. The challenging issues encountered in this study included: 1. The similarity of the formulation of the core and coating materials, 2. The lack of sufficient calibration samples and 3. The non-linear relationship between the NIR spectral intensity and coating: thickness. A peak at 7184 $cm^{-1}$ was identified that differed for the coating material and the core material when M spectra were collected at 2 $cm^{-1}$ resolution (0.4 nm at 7184 $cm^{-1}$). The study showed that the coating thickness can be analyzed by polynomial fitting of the peak area of the selected peak, while least squares calibration of the same data failed due to the lack of availability of sufficient calibration samples. Samples of coal powder and solid pieces of coal were analyzed by FT-NIR diffuse reflectance spectroscopy with the goal of predicting their ash content, percentage of volatile components, and energy content. The measurements were performed on a Broker Vector 22N spectrometer with a fiber optic probe. A partial least squares model was constructed for each of the parameters of interest for solid and powdered sample forms separately. Calibration models varied in size from 4 to 10 PLS ranks. Correlation coefficients for these models ranged from 86.6 to 95.0%, with root-mean-square errors of cross validation comparable to the corresponding reference measurement methods. The use of FT-NIR diffuse reflectance measurement techniques was found to be a significant improvement over existing measurement methodologies in terms of speed and ease of use, while maintaining the desired accuracy for all parameters and sample forms.(Figure Omitted).

• 한국산림과학회지
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• 제112권2호
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• pp.145-156
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• 2023

본 연구에서는 -18℃ 및 4℃에서 18년간 저장된 소나무 종자 963개에 대해 FT NIR 스펙트럼을 조사하여 7개 머신러닝 방법(XGBoost, Boosted Tree, Bootstrap Forest, Neural Networks, Decision Tree, Support Vector Machine, PLS-DA)을 이용한 종자발아 예측모델을 만들고, 그 성능을 비교하였다. XGBoost 및 Boosted Tree 모델의 예측성능이 가장 우수하였으며, 정확도, 오분류율 및 AUC 값은 각각 0.9722, 0.0278, 0.9735과 0.9653, 0.0347, 0.9647이었다. 2개 모델에서 종자발아 유무를 예측하는 데 있어 상대적 중요도가 높았던 54개 파수 변수들에 대한 파장대는 크게 6개(811~1,088 nm, 1,137~1,273 nm, 1,336~1,453 nm, 1,666~1,671 nm, 1,879~2,045 nm, 2,058~2,409 nm) 그룹으로 나눌 수 있었으며, 방향족 아미노산, 셀룰로스, 리그닌, 전분, 지방산 및 수분과 관련된 것으로 추정되었다. 이상의 결과를 종합할 때, 본 연구에서 얻어진 FT NIR 스펙트럼 데이터과 2개의 머신러닝 모델은 소나무 저장종자의 발아 유무를 정확도 96% 이상으로 예측할 수 있기에 장기저장 종자 유전자원의 비파괴적 활력검정에 유용하게 활용될 수 있을 것으로 생각된다.

• Journal of Preventive Medicine and Public Health
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• 제39권2호
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• pp.165-170
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• 2006

Objectives : The determination of oxalate in urine is required for the diagnosis and treatment of primary hyperoxaluria, idiopathic stone disease and various intestinal diseases. We examined the possibility of using Fourier transform near-infrared (FT-NIR) spectroscopy analysis to quantitate urinary oxalate. The practical advantages of this method include ease of the sample preparation and operation technique, the absence of sample pre-treatments, rapid determination and noninvasiveness. Methods : The range of oxalate concentration in standard urine solutions was $0-221mg/{\ell}$. These 80 different samples were scanned in the region of 780-1,300 nm with a 0.5 nm data interval by a Spectrum One NTS FT-NIR spectrometer. PCR, PLSR and MLR regression models were used to calculate and evaluate the calibration equation. Results : The PCR and PLSR calibration models were obtained from the spectral data and they are exactly same. The standard error of estimation (SEE) and the % variance were $10.34mg/{\ell}$ and 97.86%, respectively. After full cross validation of this model, the standard error of estimation was $5,287mg/{\ell}$, which was much smaller than that of the pre-validation. Furthermore, the MCC (multiple correlation coefficient) was 0.998, which was compatible with the 0.923 or 0.999 obtained from the previous enzymatic methods. Conclusions : These results showed that FT-NIR spectroscopy can be used for rapid determination of the concentration of oxalate in human urine samples.

• 한국식품과학회지
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• 제38권2호
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• pp.165-168
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• 2006

분석이 번거로웠던 현미의 총 식이섬유(TDF) 함량을 신속하면서도 친 환경적인 비파괴 분석방법인 FT-NIRS를 이용하여 예측 모델을 개발하였다. 현미는 국내산으로 전남 지방에서 재배된 47개 품종(516개 시료)에 대해서 AOAC 방법에 준한 효소법에 의해 각 측정 시료별 TDF 함량을 분석하였다. 습식 분석된 TDF 함량의 분석오차범위는 0.17-0.72% 이었다. FT.NIRS로 측정된 스렉트럼의 검량식은 빛의 산란 효과를 최소화하기 위해 수학적 처리를 하였고, 몇 개의 특정 파장이 아닌 전 파장 영역(1,000-2,500nm)에 대해서 PLS법으로 작성하였다 회귀분석과 검량식은 NIRCal chemometric software에 의해 작성되었다. 얻어진 검량식의 정확도는 상관계수(r), SEE 및 SEP로 확인하였다. 현미 중 총 식이섬유 함량에 대한 회귀분석을 행한 결과, 상관계수는 0.9780, SEE는 0.0636, SEP는 0.0642로 측정 정확도가 우수함으로 현장 적용을 위한 실용화도 가능할 것으로 판단된다.

• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2011년도 추계학술발표회 논문집
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• pp.255-269
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• 2011

본 연구에서는 1900년대 이후 근대 한지로 작성된 기록물의 상태를 가능한 빠르게 확인할 수 있는 도구를 연구하고자 비파괴적인 방법으로 분석을 실시하였다. 종이 기록물의 경우 그 자체가 원본임으로 파괴적인 방법으로 분석할 수 없는 한계를 극복하고자 원본을 파괴하지 않고 상태를 확인할 수 있는 방법을 개발하는 것이 본 연구의 목적이다. 본 연구 목적에 적합한 비파괴 분석을 위해 근적외선 분석 장비 중에서 정밀도와 정확도가 좋은 푸리에 변환(Fourier transform) 분석기(spectrometer)를 사용하였다. 또한 측정 대역은 근적외선(NIR) 전체 영역 모두를 측정할 수 있도록 $12,500{\sim}4,000cm^{-1}$ 범위에서 측정하였으며, 분석 대상 한지 기록물을 측정하기 위하여 적분구(integrating sphere)에 접촉하도록 구성 하였다. 한지 기록물의 보존상태를 평가하기 위한 인자로는 화학적 변화 정도를 가장 잘 알 수 있는 산성도(pH)를 사용하였다. 그리고 이들 인자와 근적외선 스펙트럼과 상관관계를 확인하였고, 이때 최적 상관계수를 찾기 위하여 측정한 스펙트럼을 전처리 하였다. 전처리 방법으로는 다산란보정(MSC)과 Savitzky-Golay의 1차 미분을 이용하였다. 상관계수는 부분최소자승법(PLS, Partial least square)으로 확인하였다. 산성도(pH)의 경우에 전처리를 하지 않았을 때의 상관계수($R^2$)는 0.92, 표준예측오차(SEP)는 0.24이었고, 전처리를 하였을 때의 상관계수($R^2$)는 0.98, 표준예측오차(SEP)는 0.19 이었다. 따라서 전처리하였을 때 상관계수와 표준오차가 향상되었음을 알 수 있었다. 또한 1차 미분을 하였을 때의 상관계수($R^2$)는 0.98, 표준예측오차(SEP)는 0.09로써 가장 좋은 표준 예측오차를 얻었다. 따라서 전처리하기 전의 상관계수와 표준오차가 오히려 좋다는 것을 확인하였다. 이러한 결과를 통해 한지 기록물을 적분구와 근적외선 분석기를 이용하여 비파괴적인 방법으로 보다 빠르게 상태를 평가할 수 있을 것으로 판단되었다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2004년도 추계학술발표회
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• pp.86-87
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• 2004

• Journal of Biosystems Engineering
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• 제41권1호
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• pp.51-59
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• 2016

Purpose: This study examined the performance of two spectroscopy methods and multivariate classification methods to discriminate viable pepper seeds from their non-viable counterparts. Methods: A classification model for viable seeds was developed using partial least square discrimination analysis (PLS-DA) with Fourier transform near-infrared (FT-NIR) and Raman spectroscopic data in the range of $9080-4150cm^{-1}$ (1400-2400 nm) and $1800-970cm^{-1}$, respectively. The datasets were divided into 70% to calibration and 30% to validation. To reduce noise from the spectra and compare the classification results, preprocessing methods, such as mean, maximum, and range normalization, multivariate scattering correction, standard normal variate, and $1^{st}$ and $2^{nd}$ derivatives with the Savitzky-Golay algorithm were used. Results: The classification accuracies for calibration using FT-NIR and Raman spectroscopy were both 99% with first derivative, whereas the validation accuracies were 90.5% with both multivariate scattering correction and standard normal variate, and 96.4% with the raw data (non-preprocessed data). Conclusions: These results indicate that FT-NIR and Raman spectroscopy are valuable tools for a feasible classification and evaluation of viable pepper seeds by providing useful information based on PLS-DA and the threshold value.