• 우주기술과 응용
• /
• 제3권2호
• /
• pp.154-164
• /
• 2023

우주탐사에서 분광관측은 대상의 구성 성분과 물리적 특성을 이해하는 데 유용한 방법이다. 분광 자료 분석에는 여러 가지 방법이 있으며, 관측 대상과 파장대역에 따라 차이가 있다. 본 논문에서는 달 탐사에서 주로 적용하는 가시광-근적외선(visible & near-infrared, VNIR) 분광 자료 분석 방법에 대해 소개한다. 주요 분석 방법에는 가색상 비율(false color ratio) 영상 처리, 반사도 유형(reflectance pattern) 분석, 통합 대역 깊이(integrated band depth, IBD) 계산이 있으며, 분석 이전의 전처리로는 연속체 제거(continuum removal)가 있다. 이러한 분광 분석 방법들은 가시광-근적외선 영역에서 나타나는 달 표면의 광물 특성을 이해하는데 도움이 되며, 화성과 같은 다른 천체에도 적용할 수 있다.

• Asian-Australasian Journal of Animal Sciences
• /
• 제18권4호
• /
• pp.580-583
• /
• 2005

We have evaluated the application of spectroscopy using an insertion-type fiber-optic probe and a sensor at wavelengths from 400 to 1,100 nm to the measurement of milk fat content on dairy farms. The internal reflectance ratios of 183 milk samples were determined with a fiber-optic spectrophotometer at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$. Partial least squares (PLS) regression was used to develop calibration models for the milk fat. The best accuracy of determination was found for an equation that was obtained using smoothed internal reflectance data and three PLS factors at 20$^{\circ}C$. The correlation coefficients between predicted and reference milk fat at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$ were r=0.753, r=0.796 and r=0.783, respectively. The predictive explained variances ($Q^2$) of the final model, moreover, were more than 0.550 at all temperatures, and the regression coefficients of determination ($R^2$) were more than 0.6 (60%). Our results indicate that milk has different internal reflectance measured in the range of visible and near infrared wavelengths (400 to 1,100 nm), depending on its fat content.

• 한국근적외분광분석학회:학술대회논문집
• /
• 한국근적외분광분석학회 2001년도 NIR-2001
• /
• pp.1267-1267
• /
• 2001

The existing fruit sorter has the method of tilting tray and extracting fruits by the action of solenoid or springs. In peaches, the most sort processing is supported by man because the sorter make fatal damage to peaches. In order to sustain commodity and quality of peach non-destructive, non-contact and real time based sorter was needed. This study was performed to develop peach sorter using near-infrared spectroscopy in real time and nondestructively. The prototype was developed to decrease internal and external damage of peach caused by the sorter, which had a way of extracting tray with it. To decrease positioning error of measuring sugar contents in peaches, fiber optic with two direction diverged was developed and attached to the prototype. The program for sorting and operating the prototype was developed using visual basic 6.0 language to measure several quality index such as chlorophyll, some defect, sugar contents. The all sorting result was saved to return farmers for being index of good quality production. Using the prototype, program and MLR(multiple linear regression) model, it was possible to estimate sugar content of peaches with the determination coefficient of 0.71 and SEC of 0.42bx using 16 wavelengths. The developed MLR model had determination coefficient of 0.69, and SEP of 0.49bx, it was better result than single point measurement of 1999's. The peach sweetness grading system based on NIR reflectance method, which consists of photodiode-array sensor, quartz-halogen lamp and fiber optic diverged two bundles for transmitting the light and detecting the reflected light, was developed and evaluated. It was possible to predict the soluble solid contents of peaches in real time and nondestructively using the system which had the accuracy of 91 percentage and the capacity of 7,200 peaches per an hour for grading 2 classes by sugar contents. Draining is one of important factors for production peaches having good qualities. The reason why one farm's product belows others could be estimated for bad draining, over-much nitrogen fertilizer, soil characteristics, etc. After this, the report saved by the peach grading system will have to be good materials to farmers for production high quality peaches. They could share the result or compare with others and diagnose their cultural practice.

• 한국농업기계학회:학술대회논문집
• /
• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
• /
• pp.513-520
• /
• 2000

An integrated grader which measures soluble solid content, color and weight of fresh apples was developed by NAMRI. The prototype grader consists of the near infrared spectroscopy and machine vision system. Image processing system and an algorithm to evaluate color were developed to speed up the color evaluation of apples. To avoid the light glare and specular reflection, an half-spherical illumination chamber was designed and fabricated to detect the color images of spherical-shaped apples more precisely. A color revision model based on neural network was developed. Near-infrared(NIR) spectroscopy system using NIR reflectance method developed by Lee et al(1998) of NAMRI was used to evaluate soluble solid content. In order to observe the performance of the grader, tests were conducted on conditions that there are 3 classes in weight sorting, 4 classes in combination of color and soluble solid content, and thus 12 classes in combined sorting. The average accuracy in weight, color and soluble solid content is more than about 90 % with the capacity of 3 fruits per second.

• 한국근적외분광분석학회:학술대회논문집
• /
• 한국근적외분광분석학회 2001년도 NIR-2001
• /
• pp.1530-1530
• /
• 2001

The objectives of this study were to develop models to predict quality parameters of Korean bee-honeys by visible and NIR spectroscopic technique. Two kinds of bee-honey fronl acacia and polyflower sources were tested in this study. The honeys were harvested in the spring of 2000 and stored in the storage facility at 20$^{\circ}C$ during experiments. Total of 394 samples of honey were analyzed. Reflectance spectra, moisture contents, ash, invert sugar, sucrose, F/G (fructose/glucose) ratio, HMF (hydroxymethyl furfural), and C12/C13 ratio of honeys were measured. The average values for the tested honeys were 19.9% of moisture contents, 0.12% of ash, 68.4% of invert sugar, 5.7% of sucrose, 1.27 of F/G(fructose/glucose) ratio, 14.4 mg/kg of HMF, and -19.1 of C12/C13 ratio. A spectrophotometer, equipped with a single-beam scanning monochromator (NIR Systems, Model 6500, USA) and a horizontal setup module, was used to collect reflectance data from honey. The reflectance spectra were measured in wavelength ranges of 400∼2,498 nm. with 2 nm of interval. Thirty-two repetitive scans were averaged, transformed to log(1/Reflectance), and then were stored in a microcomputer file, forming one spectrum per measurement. A sample cell and reflectance plate were made to hold honey samples constantly. Spectra of honey samples were divided into a calibration set and a validation set. The calibration set was used during model development, and the validation set was used to predict quality parameters from unknown spectra. The PLS(Partial Least Square) models were developed to predict the quality parameters of honeys. The first and the second derivatives of raw spectra were also used to develop the models with proper smoothing gap. The MSC (multiplicative scatter correction) and the SNV & Dtr.(standard normal variate and detranding) preprocessing were applied to all spectra to minimize sample-to-sample light scatter differences. The PLS models showed good relationships between predicted and measured quality parameters of honeys in the wavelength range of 1100∼2200 nm. However, the PLS analysis was not good enough to predict HMF of honeys.

• 한국근적외분광분석학회:학술대회논문집
• /
• 한국근적외분광분석학회 2001년도 NIR-2001
• /
• pp.1510-1510
• /
• 2001

NIRS uses reflectance signals resulting from bending and stretching vibrations in chemical bonds between carbon, nitrogen, hydrogen, sulfur and oxygen. These reflectance signals are used to measure the concentration of major chemical composition and other descriptors of homogenized and freeze-dried whole broiler carcasses. Six strains of chicken were analyzed and the NIRS model predictions compared to reference data. The results of this comparison indicate that NIRS is a rapid tool for predicting dry matter (DM), fat, crude protein (CP) and ash content in the broiler carcass. Males and females of six commercial strain crosses of broiler chicken (Gallus domesticus) were used in this study (6$\times$2 factorial design). Each strain was grown to 16 weeks of age, and duplicate serial samples were taken for body composition analysis. Each whole carcass was pressure-cooked, homogenized, and a representative sample was freeze-dried. Body composition determined as follows: DM by oven dried method at 105$^{\circ}C$ for 3 hours, fat by Mojonnier diethyl ether extraction, CP by measuring nitrogen content using an auto-analyzer with Kjeldhal digest and ash by combustion in a muffle furnace for 24 hour at 55$0^{\circ}C$. These homogenized and freeze-dried carcass samples were then scanned with a Foss NIR Systems 6500 visible-NIR spectrophotometer (400-2500nm) (Foss NIR Systems, Silver Spring, MD., US) using Infra-Soft-International, ISI, WinISl software (ISI, Port Matilda, US). The NIRS spectra were analyzed using principal component (PC) analysis. This data was corrected for scatter using standard normal “Variate” and “Detrend” technique. The accuracy of the NIRS calibration equations developed using Partial Least Squares (PLS) for predicting major chemical composition and carcass descriptors- such as body mass (BM), bird dry matter and moisture content was tested using cross validation. Discrimination analysis was also used for sex and strain identification. According to Dr John Shenk, the creator of the ISI software, the calibration equations with the correlation coefficient, $R^2$, between reference data and NIRS predicted results of above 0.90 is excellent and between 0.70 to 0.89 is a good quantifying guideline. The excellent calibration equations for DM ($R^2$= 0.99), fat (0.98) and CP (0.92) and a good quantifying guideline equation for ash (0.80) were developed in this study. The results of cross validation statistics for carcass descriptors, body composition using reference methods, inter-correlation between carcass descriptors and NIRS calibration, and the results of discrimination analysis for sex and strain identification will also be presented in the poster. The NIRS predicted daily gain and calculated daily gain from this experiment, and true daily gain (using data from another experiment with closely related broiler chicken from each of the six strains) will also be discussed in the paper.

• Journal of the Korean Wood Science and Technology
• /
• 제43권3호
• /
• pp.311-319
• /
• 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
• /
• 제23권1호
• /
• pp.77-90
• /
• 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.

• 한국작물학회지
• /
• 제67권1호
• /
• pp.61-66
• /
• 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.

• 한국근적외분광분석학회:학술대회논문집
• /
• 한국근적외분광분석학회 2001년도 NIR-2001
• /
• pp.1246-1246
• /
• 2001

The quality of meat is highly variable in many properties. This variability originates from both animal production and meat processing. At the pre-slaughter stage, animal factors such as breed, sex, age contribute to this variability. Environmental factors include feeding, rearing, transport and conditions just before slaughter (Hildrum et al., 1995). Meat can be presented in a variety of forms, each offering different opportunities for adulteration and contamination. This has imposed great pressure on the food manufacturing industry to guarantee the safety of meat. Tissue and muscle speciation of flesh foods, as well as speciation of animal derived by-products fed to all classes of domestic animals, are now perhaps the most important uncertainty which the food industry must resolve to allay consumer concern. Recently, there is a demand for rapid and low cost methods of direct quality measurements in both food and food ingredients (including high performance liquid chromatography (HPLC), thin layer chromatography (TLC), enzymatic and inmunological tests (e.g. ELISA test) and physical tests) to establish their authenticity and hence guarantee the quality of products manufactured for consumers (Holland et al., 1998). The use of Near Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise and non-destructive analysis of a wide range of organic materials has been comprehensively documented (Osborne et at., 1993). Most of the established methods have involved the development of NIRS calibrations for the quantitative prediction of composition in meat (Ben-Gera and Norris, 1968; Lanza, 1983; Clark and Short, 1994). This was a rational strategy to pursue during the initial stages of its application, given the type of equipment available, the state of development of the emerging discipline of chemometrics and the overwhelming commercial interest in solving such problems (Downey, 1994). One of the advantages of NIRS technology is not only to assess chemical structures through the analysis of the molecular bonds in the near infrared spectrum, but also to build an optical model characteristic of the sample which behaves like the “finger print” of the sample. This opens the possibility of using spectra to determine complex attributes of organic structures, which are related to molecular chromophores, organoleptic scores and sensory characteristics (Hildrum et al., 1994, 1995; Park et al., 1998). In addition, the application of statistical packages like principal component or discriminant analysis provides the possibility to understand the optical properties of the sample and make a classification without the chemical information. The objectives of this present work were: (1) to examine two methods of sample presentation to the instrument (intact and minced) and (2) to explore the use of principal component analysis (PCA) and Soft Independent Modelling of class Analogy (SIMCA) to classify muscles by quality attributes. Seventy-eight (n: 78) beef muscles (m. longissimus dorsi) from Hereford breed of cattle were used. The samples were scanned in a NIRS monochromator instrument (NIR Systems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced presentation to the instrument were explored. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.