• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1261-1261
• /
• 2001

Mixed feeds and their components are a very diverse matrix compared to other agricultural products worked on with NIRS classically. On a database of mixed feeds and their components (n=2.500) universal PLS calibrations and “local” calibrations were compared. The results from validation (n=600) show the potential of the calibrations and their limitations. Crude protein, crude fiber, crude fat, sugar and starch are predicted with SEPs of 0.6%, 1%, 0.3%, 1% and 1.5%, respectively. Ash content of 15% and more in several mixed feeds or components as well as rare components limit the use of NIRS for routine analyses.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1091-1091
• /
• 2001

Previous reports have shown that Near Infrared Spectroscopy (NIRS) can be used to assess physical and chemical properties of flax fibre and fabric quality. Currently, spinners assess yarn quality mainly based on strength and regularity measurements. There two key characteristics are influenced by quality of raw fibres used, especially the degree of rotting and strength. The aim of this investigation was to evaluate the use of NIRS for assessing quality of weft grade yarn available on the commercial market. In order to develop the NIR calibrations, a range of samples representing poor, medium and good quality weft yarn samples was included in the calibration and validation sample sets. The samples were analysed for physical and chemical parameters including caustic weight loss, fibre fractions, lipid, ash and minerals. A detailed protocol for assessing yarn quality has been developed to maximize the accuracy of the reflectance spectra. The development of partial least squares regression models and validation of the calibration equations using blind samples will be presented and discussed.

• Food Science and Biotechnology
• /
• v.14 no.2
• /
• pp.280-285
• /
• 2005

Applicability of near infrared reflectance spectroscopy (NIRS) was examined for quality control of red pepper powder in milling factories. Prediction of chemical composition was performed using modified partial least square (MPLS) techniques. Analysis of total 51 and 21 red pepper powder samples by conventional methods for calibration and validation, respectively, revealed standard error of prediction (SEP) and correlation coefficient ($R^2$) of moisture content, ASTA color value, capsaicinoid content, and total sugar content were 0.55 and 0.90, 8.58 and 0.96, 31.60 and 0.65, and 1.82 and 0.86, respectively; SEP and $R^2$ were low and high, respectively, except for capsaicinoid content. The results indicate, with slight improvement, on-line quality measurement of red pepper powder with NIRS could be applied in red pepper milling factories.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.24 no.1
• /
• pp.81-90
• /
• 2004

Farmers need timely information on the nutritional status of their animals and the nutritive value of pastures and supplementary feeds if they are to apply successfully this existing nutritional information. Near infrared reflectance(NIR) spectroscopy has been used over the last forty years to analyse accurately protein, fiber, and other organic components in animal foods. NIR spectroscopy is a rapid, non-destructive, and non-polluting technology. When properly calibrated, NIR spectroscopy is used successfully with both concentrate and forage feeds. NIR methods predict in vitro digestibility accurately and precisely, and can predict in vivo digestibility at least as well as conventional "wet chemistry" methods such as in vivo digestion or the pepsin-cellulase method, and much more rapidly. NIR technology has been applied to the routine monitoring (through analysis of feces samples) of the nutritional status of cattle and other grazing animals. This report reviews the use of near infrared reflectance(NIR) spectroscopy to monitor the nutritive value of animal feeds and the nutritional status of grazing animals.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.32 no.3
• /
• pp.99-103
• /
• 2021

The purpose of this review is to examine the clinical use of functional near-infrared spectroscopy (fNIRS) in children and adolescents with psychiatric disorders. Many studies have been conducted using objective evaluation tools for psychiatric evaluation, such as predicting psychiatric symptoms and treatment responses. Compared to other tools, fNIRS has the advantage of being a noninvasive, inexpensive, and portable method and can be used with patients in the awake state. This study mainly focused on its use in patients with attention-deficit/hyperactivity disorder and autism spectrum disorder. We hope that research involving fNIRS will be actively conducted in various diseases in the future.

• Food Science and Industry
• /
• v.55 no.1
• /
• pp.2-22
• /
• 2022

Near-infrared spectroscopy (NIRS) is one of the representative non-destructive and eco-friendly analysis methods used for rapid analysis of various ingredients in the food industry. To develop analysis model with NIRS, Chemometrics are applied after pre-treatment of spectrum. Many studies have been reviewed on the analysis of general and functional components for agricultural and livestock products. In the case of livestock products, some studies have been conducted for on-line analysis. This study investigated results on various samples and component applying near-infrared spectroscopy. Furthermore, the results according to sample condition were compared. It was confirmed that NIRS is applied to various fields in the food industry.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.11a
• /
• pp.407-408
• /
• 2009

fNIRS(functional Near Infrared Spectroscopy)는 비침습형 뇌기능 분석 시스템으로 뇌활성화 시 옥시 헤모글로빈(oxy-hemoglobin)과 디옥시헤모글로빈(deoxy-hemoglobin) 변화량을 측정할 수 있는 장치이다. 본 논문에서는 뇌기능 분석 장치인 fNIRS를 이용하여 피험자가 컴퓨터 게임 중에 어떤 뇌활성화 패턴을 보이는지를 실험하였다. 컴퓨터 게임 주의 및 집중 시 뇌의 전두엽(Frontal Lobe) 영역이 활성화 및 변화되는 것을 실험결과로 확인하였다. 그리고 게임 중 다른 사람이 피험자에게 개입을 하였을 때 전두엽의 활성화 영역이 다른 패턴을 보이는 것을 실험결과로 확인하였다.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.26 no.1
• /
• pp.45-52
• /
• 2006

This study was carried out to explore the accuracy of Near Infrared Reflectance Spectroscopy (NIRS) fer the prediction of digestibility and energy value of corn silages. The spectral data were regressed against a range of digestibility and energy parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with first and second order derivatization, with scatter correction procedure(SNV-Detrend) to reduce the effect of extraneous noise. Calibration models for NIRS measurements gave multivariate correlation coefficients of determination$(R^2)$ and standard errors of cross validation of 0.92(SECV 1.73), 0.91(SECV 1.13) and 0.93(SECV 1.74) for in vitro dry matter digestibility(IVDMD), in vitro true digestibility(IVTD), and cellulase dry matter digestibility(CDMD), respectively. The standard error of prediction(SEP) and the multiple correlation coefficient of validation$(R^2v)$ on the validation set(n=39) was used in comparing the prediction accuracy. The SEP value was 0.30(TDN), 0.01(NEL), and 0.01(ME). The relative ability of NIRS to predict digestibility and energy value was very good for CDMD, total digestible nutrients(TDN), net energy fer lactation(NEL) and metabolizable energy(ME). This paper shows the potential of NIRS to predict the digestibility and energy value of con silage as a routine method in feeding programmes and for giving advice to farmers.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1273-1273
• /
• 2001

The efficiency of the luminal fermentation process influences overall efficiency of luminal production, animal health and reproduction. Ruminant production systems have a significant impact on the global environment, as well. Animal wastes contribute to pollution of the environment as ammonia volatilized to the air and nitrate leached to ground water. Microbial protein synthesis in the rumen satisfies a large proportion of the protein requirements of animals. Quantifying the microbial synthesis is possible by using markers for lumen bacteria and protozoa such as nucleic acids, purine bases, some specific amino acids, or by isotopic $^{15}N,^{32}P,\;and\;^{35}S$ labelled feeds. All those methods require cannulated animals, they are time-consuming and some methods are very expensive as well. Many attempts have been made to find an alternative method for indirect measurement of microbial synthesis in intact animals. The present investigations aimed to assess possibilities of NIRS for prediction of purine nitrogen excretion and ruminal microbial nitrogen synthesis by NIR spectra of urine. Urine samples were collected from 12 growing sheep,6 of them male, and 6- female. The sheep were included in feeding experiment. The ration consisted of sorghum silage and protein supplements -70:30 on dry matter basis. The protein supplements were chosen to differ in protein degradability. The urine samples were collected daily in a vessel containing $60m{\ell}$ 10% sulphuric acid to reduce pH below 3 and diluted with tap water to 4 liters. Samples were stored in plastic bottles and frozen at $-20^{\circ}C$ until chemical and NIRS analysis. The urine samples were analyzed for purine derivates - allantoin, uric acid, xantine and hypoxantine content. Microbial nitrogen synthesis in the lumen was calculated according to Chen and Gomes, 1995. Transmittance urine spectra with sample thickness 1mm were obtained by NIR System 6500 spectrophotometer in the spectral range 1100-2500nm. The calibration was performed using ISI software and PLS regression, respectively. The following statistical results of NIRS calibration for prediction of purine derivatives and microbial protein synthesis were obtained.(Table Omitted). The result of estimation of purine nitrogen excretion and microbial protein synthesis by NIR spectra of urine showed accuracy, adequate for rapid evaluation of microbial protein synthesis for a large number of animals and different diets. The results indicate that the advantages of the NIRS technology can be extended into animal physiological studies. The fast and low cost NIRS analyses could be used with no significant loss of accuracy when microbial protein synthesis in the lumen and the microbial protein flow in the duodenum are to be assessed by NIRS.

• Asian Journal of Turfgrass Science
• /
• v.23 no.1
• /
• pp.77-90
• /
• 2009

Near Infrared Reflectance Spectroscopy(NIRS) is a quick, accurate, and non-destructive method to measure multiple nutrient components in plant leaves. This study was to acquire a liner regression equation by evaluating the nutrient contents of 'CY2' creeping bentgrass rapidly and accurately using NIRS. In particular, nitrogen fertility is a primary element to keep maintaining good quality of turfgrass. Nitrogen, moisture, carbohydrate, and starch were assessed and analyzed from 'CY2' creeping bentgrass clippings. A linear regression equation was obtained from accessing NIRS values from NIR spectrophotometer(NIR system, Model XDS, XM-1100 series, FOSS, Sweden) programmed with WinISI III project manager v1.50e and ISIscan(R) (Infrasoft International) and calibrated with laboratory values via chemical analysis from an authorized institute. The equation was formulated as MPLS(modified partial least squares) analyzing laboratory values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with SEP(standard error of prediction), which indicated as correlation coefficient($r^2$) and prediction error of sample unacquainted, followed by the verification of model equation of real values and these monitoring results. As results of monitoring, $r^2$ of nitrogen, moisture, and carbohydrate in 'CY2' creeping bentgrass was 0.840, 0.904, and 0.944, respectively. SEP was 0.066, 1.868, and 0.601, respectively. After outlier treatment, $r^2$ was 0.892, 0.925, and 0.971, while SEP was 0.052, 1.577, and 0.394, respectively, which totally showed a high correlation. However, $r^2$ of starch was 0.464, which appeared a low correlation. Thereof, the verified equation appearing higher $r^2$ of nitrogen, moisture, and carbohydrate showed its higher accuracy of prediction model, which finally could be put into practical use for turf management system.