• Journal of Biosystems Engineering
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• 제22권3호
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• pp.333-342
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• 1997

The objective of this research was to develop model equations for measuring rice milling ratio by using visible / HIR spectroscopy. Twelve kinds of brown rice(n = 149) were milled to obtain various milling ratio ranged from 86% to 94%. Visible/NIR spectra were collected with a spectrophotometer with sample transport module. The reflectance and transmission spectra were measured in the range of 400~2, 500nm and 600~1, 400nm, respectively, with 2 nm intervals. Multiple linear regression(MLR), Partial least square (PLS), and Artificial neural network(ANN) were used to develop models. Model developed with reflectance spectra showed better prediction results then those with transmission spectra. The MLR model with six-wavelength obtained from first derivative spectra gave to the best results for measuring the rice milling ratio(SEP = 0.535, , $r^2$ = 0.980). The PLS model(SEP = 0.604, $r^2$= 0.976) and ANN model(SEP = 0.566, $r^2$= 0.978) also can be used to determine the rice milling ratio effectively.

• Journal of Biosystems Engineering
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• 제23권1호
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• pp.49-56
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• 1998

The objectives of this study were to develop models to predict the contents of moisture and protein of single kernel of brown rice based on visible/NIR (near-infrared) spectroscopic technique. The reflectance spectra of rice were obtained in the range of the wavelength 400 to 2,500 nm with 2 nm intervals. Multiple linear regression(MLR) and partial least squares (PLS) were used to develop the models. The MLR model using the first derivative spectra(10 nm of gap) with Standard Normal Variate and Detrending (SNV and Drt.) preprocessing showed the best results to predict moisture content of the sin린e kernel brown rice. To predict the protein content of a single kernel of brown ricer the PLS model used the raw spectra with multiplicative scatter correction(MSC) preprocessing over the wavelength of 1,100~1,500 nm.

• 원예과학기술지
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• 제30권6호
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• pp.709-717
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• 2012

To establish the standard of ripe kiwifruit sorting, near infrared (NIR) spectroscopy was performed on kiwifruit sampled from three farms. Destructive measurements of flesh firmness, soluble solids content (SSC), and acidity were performed and compared to measurement using NIR reflectance spectrums from 408 to 2,492 nm. NIR predictions of those quality factors were calculated using the modified partial least square regression method. Flesh firmness was predicted with a standard error of prediction (SEP) of 3.32 N and with a correlation coefficient ($R^2$) of 0.88. SSC was predicted with SEP of $0.49^{\circ}Brix$ and with $R^2$ of 0.98. Acidity was predicted with SEP of 0.28% and with $R^2$ of 0.91. Kiwifruit ripened at $20^{\circ}C$ for 15 days showed uneven qualities with normal distribution. Considering the SEP of each parameter, kiwifruit after ripening treatment could be non-destructively predicted their qualities and sorted by flesh firmness or soluble solids content through NIR prediction.

• Journal of Biosystems Engineering
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• 제39권3호
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• pp.184-193
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• 2014

Purpose: The aim of this study was to develop a technique for the non-destructive and rapid prediction of the moisture content in red pepper powder using near-infrared (NIR) spectroscopy and a partial least squares regression (PLSR) model. Methods: Three red pepper powder products were separated into three groups based on their particle sizes using a standard sieve. Each product was prepared, and the expected moisture content range was divided into six or seven levels from 3 to 21% wb with 3% wb intervals. The NIR reflectance spectra acquired in the wavelength range from 1,100 to 2,300 nm were used for the development of prediction models of the moisture content in red pepper powder. Results: The values of $R{_V}{^2}$, SEP, and RPD for the best PLSR model to predict the moisture content in red pepper powders of varying particle sizes below 1.4 mm were 0.990, ${\pm}0.487%$ wb, and 10.00, respectively. Conclusions: These results demonstrated that NIR spectroscopy and a PLSR model could be useful techniques for measuring rapidly and non-destructively the moisture content in red pepper powder.

• Journal of Biosystems Engineering
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• 제38권4호
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• pp.312-317
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• 2013

Purpose: Conventional methods used to evaluate seeds viability are destructive, time consuming, and require the use of chemicals, which are not feasible to implement to process plant in seed industry. In this study, the effectiveness of Fourier transform near infrared (FT-NIR) spectroscopy to differentiate between viable and nonviable watermelon seeds was investigated. Methods: FT-NIR reflectance spectra of both viable and non-viable (aging) seeds were collected in the range of 4,000 - 10,000 $cm^{-1}$ (1,000 - 2,500 nm). To differentiate between viable and non-viable seeds, a multivariate classification model was developed with partial least square discrimination analysis (PLS-DA). Results: The calibration and validation set derived from the PLS-DA model classified viable and non-viable seeds with 100% accuracy. The beta coefficient of PLS-DA, which represented spectral difference between viable and non-viable seeds, showed that change in the chemical component of the seed membrane (such as lipids and proteins) might be responsible for the germination ability of the seeds. Conclusions: The results demonstrate the possibility of using FT-NIR spectroscopy to separate seeds based on viability, which could be used in the development of an online sorting technique.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1268-1268
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• 2001

The availability of in vivo and in sacco degradability values are limited because those methods require work with fistulated animals and are rather complicated, labour intensive and expensive. That is to say, the dynamics and logistics of the methodology result in considerable work, due to limitations on the amount of samples, number of bags that can be placed in an animal and different time intervals to perform kinetic studies. Therefore, a simpler method is necessary to estimate the degradation characteristics of the feed. In this way, near infrared reflectance spectroscopy has been used to predict degradation characteristics of forages. In other hand, the possibility of achieving successful transfer of spectra and equations between instruments is closely related. The objective of this study was to confirm the potential of NIR to optimize work conditions to avoid duplicated efforts in collaborative trials on animal feeds evaluation between research institutions. For this purpose, one set with forty hays and dehydrated forages samples from SERIDA and ten samples with the same characteristics from SIA, were be used to create a spectral database. A calibration was developed using samples from degradation essays made in SERIDA to predict dry matter and crude protein degradability. With the addition of five samples from SIA in original calibration set, the effect of different origin and location was compensated.

• Asian-Australasian Journal of Animal Sciences
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• 제14권6호
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• pp.858-861
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• 2001

Near Infrared spectroscopy was applied to the samples of processed pork to see the effect of grinding on chemical components analyses. Data from conventional chemical analyses of moisture, fat, protein, NaCl were put into calibration model by NIR of reflectance mode. The other properties observed were pH and color parameters ($L^*,\;a^*,\;b^*$). Spectral ranges of 400~2500 nm and 400~1100 nm were compared for color parameters. Spectral ranges of 400~2500 nm and 1100~2500 nm were compared for chemical components and pH. Different spectral ranges caused little changes in the coefficients of determination or standard errors. $R^{2,}s$ of calibration models for color parameters were in the range of 0.97 to 1.00. $R^{2,}s$ of calibration models of intact sausages for moisture, protein, fat, NaCl and pH were 0.98, 0.89, 0.95, 0.73 and 0.77, respectively using spectra at 1100~2500 nm. $R^{2,}s$ of calibration models of ground sausages for moisture, protein, fat, NaCl and pH were 0.97, 0.91, 0.97, 0.42 and 0.56, respectively using spectra at 1100~2500 nm.

• Journal of the Korean Wood Science and Technology
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• 제43권3호
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• pp.311-319
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• 2015

근적외선 반사율 분광분석법을 이용하여 리기다 소나무, 소나무, 잣나무, 백합나무의 섬유포화점 이하 함수율 예측모델을 개발하였다. 시편들을 다양한 평형함수율 상태로 유도한 후 1000 nm~2400 nm 파장영역의 반사율 스펙트럼을 획득하였다. 최적 함수율 예측 모델을 선정하기 위해 5가지의 수학적 전처리(moving average (smoothing point: 3), baseline, standard normal variate (SNV), mean normalization, Savitzky-Golay $2^{nd}$ derivatives (polynomial order: 3, smoothing point: 11))를 8가지 조합으로 각 시편의 반사율 스펙트럼에 적용하였다. 수학적 전처리 후, 변형된 스펙트럼을 이용하여 PLS 회귀분석을 실시하였다. 그 결과, 최적 함수율 예측 모델을 도출한 전처리 방법은 리기다 소나무와 소나무의 경우 moving average/SNV, 잣나무와 백합나무의 경우 moving average/SNV/Savitzky-Golay $2^{nd}$ derivatives이며, 모든 모델은 3개의 주성분을 포함하고 있었다.

• 아시안잔디학회지
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• 제23권1호
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• pp.77-90
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• 2009

Near Infrared Reflectance Spectroscopy(NIRS)는 짧은 시간 안에 식물의 다양한 영양소를 동시에 정확하고 빠르게 측정할 수 있다. 본 연구는 creeping bentgrass 'CY2' 엽의 여러 가지 기본 요소의 값을 예측하기 위해서 NIRS(근적의선 분광분석기)를 사용하여 측정하였다. 그 결과, 질소와 수분 그리고 탄수화물의 $r^2$은 각각 0.892, 0.925, 0.971이었다. 검량식에 대한 검증에서 $r^2$이 높은 상관관계를 나타냈으므로, 잔디에서 더 많은 연구를 위한 실용화 가능성을 확인 할 수 있었다.

• 한국축산식품학회지
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• 제31권1호
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• pp.115-121
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• 2011

본 연구에서는 유통현장에서 실시간으로 쇠고기 신선도를 측정하기 위해 가시광선-근적외선 반사 스펙트럼을 이용하여 쇠고기 신선도에 영향을 미치는 인자와 설정된 저장기간에 대하여 예측 모델을 개발하고 검증하였다. 쇠고기 시료는 총 216개를 사용하였으며 0-14일의 기간 동안 2일 간격으로 가시광선-근적외선 반사 스펙트럼을 측정한 후, 쇠고기의 신선도에 영향을 미치는 인자인 총균수, pH, VBN, TMA, TBA값을 공인된 방법을 이용하여 측정하였다. 예측모델은 다중회귀분석 방법과 최적 변수 선택이 가능한 stepwise 방법을 이용하여 개발하였으며, 예측모델의 선정은 결정계수, 오차, RPD를 이용하였다. 예측모델의 검증은 미지의 시료를 이용하였으며 그 결과 결정계수, 오차, RPD는 총균수에서 각각 0.74, 0.64, 2.75 Log CFU/$cm^2$, VBN은 각각 0.73, 1.45, 2.00 mg%, TMA는 각각 0.70, 0.19, 2.58 mg%, TBA값은 각각 0.73, 0.13, 2.77 mg MA/kg로 비교적 안정된 예측성능을 보여 주었다. 저장기간에 따른 예측모델의 검증결과는 결정계수, 오차, RPD가 각각 0.77, 1.94일, 2.53으로 실험 시 저장기간이 2일 간격인 점을 고려할 때, 비교적 높은 정밀도를 보이고 있음을 알 수 있다. pH의 예측성능은 결정계수, 오차, RPD가 각각 0.43, 0.10, 1.10로 다른 신선도 인자에 비해 낮은 결과를 보여 주었다. 본 연구에서는 가시광선-근적외선 분광분석법을 이용하여 쇠고기 신선도의 비파괴 평가에 대한 가능성을 제시하였으나 유통현장에서 적용을 위해서는 보다 많은 시료의 확보를 통한 예측모델의 신뢰성 향상과 stepwise방법으로 선정된 파장 영역을 기본으로 하는 부분최소자승법, 인공지능 등의 다양한 알고리즘의 적용을 통한 성능개선이 필요할 것으로 판단된다.