• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1619-1619
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• 2001

Ourwork aims to assess the content of dry matter, protein, cell wall parameters and water soluble carbohydrates in forages without having to handle samples, transport them to a laboratory, dry, grind and chemically analyze them. for this purpose, the concept of fresh forage analysis under field conditions by means of compact integrated NIRS InGaAs-diode array instruments on small plot harvesters is being evaluated for plant breeding trials. This work was performed with the world first commercial experimental forage plot harvester equipped with a NIRS module for the collection, compression, and scanning of forage samples (including automatic referencing and dark current measure ments). It was used for harvesting and analyzing a number of typical forage grass and forage legume plot trials. After NIRS measurements in the field each sample was again analyzed in the laboratory by means of a conventional grating spectrometer equipped with Si-and PbS-detectors. Conventional laboratory analysis of the samples was restricted to dry matter (DM) content by means of oven drying at 105. Routine chemometric procedures were then employed to assess the comparative accuracy and precision of the DM assessments in the spectral range between 950 and 1650nm by the NIRS diode array as well as by the conventional NIRS scanning instrument. The results of this study confirmed that the type of NIRS diode array instrument employed here functioned well even in rugged field operations. further refinements proved to be necessary for optimizing the automatic filling of the sample compartment to adjust for the wide variation in forage material under conditions of extremely low or high harvest yields. The error achieved in calibrating the apparatus for forages of typical DM content proved to be satisfactory (SECV < 1.0). Possibly as a consequence of higher sampling errors, its performance in atypical forages with elevated DM contents was less satisfactory. The error level obtained on the conventional grating NIR spectrometer was similar to that of the diode array instrument for both types of forage.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.5
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• pp.401-406
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• 2001

The applicability of non-destructive near infrared reflectance spectroscopic (NIRS) method was tested to determine the protein and oil contents of intact soybean [Glycine max (L.) Merr.] seeds. A total of 198 soybean calibration samples and 101 validation samples were used for NIRS equation development and validation, respectively. In the developed non-destructive NIRS equation for analysis of protein and oil contents, the most accurate equation was obtained at 2, 8, 6, 1(2nd derivative, 8 nm gap, 6 points smoothing, and 1 point second smoothing) and 2, 1, 20, 10 math treatment conditions with Standard Normal Variate and Detrend (SNVD) scatter correction method and entire spectrum (400-2500 nm) by using Modified Partial Least Squares (MPLS) regression, respectively. Validation of these non-destructive NIRS equations showed very low bias (protein: 0.060%, oil: -0.017%) and standard error of prediction (SEP, protein: 0.568 %, oil : 0.451 %) as well as high coefficient of determination ($R^2$, protein: 0.927, oil: 0.906). Therefore, these non-destructive NIRS equations can be applicable and reliable for determination of protein and oil content of intact soybean seeds, and non-destructive NIRS method could be used as a mass screening technique for selection of high protein and oil soybean in breeding programs.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.56 no.1
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• pp.88-93
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• 2011

This experiment was carried out to find suitable sample type for the more accurate prediction and non-destructive way in the application of near infrared reflectance spectroscopy (NIRS) technique for estimation the protein, total amino acids, and total isoflavone of soybean by comparing three different sample types, single seed, whole seeds, and milled seeds powder. The coefficient of determination in calibration ($R^2$) and coefficient of determination in cross-validation (1-VR) for three components analyzed using NIRS revealed that milled powder sample type yielded the highest, followed by single seed, and the whole seeds as the lowest. The coefficient of determination in calibration for single seed was moderately low($R^2$ 0.70-0.84), while the calibration equation developed with NIRS data scanned with whole seeds showed the lowest accuracy and reliability compared with other sample groups. The scatter plot for NIRS data versus the reference data of whole seeds showed the widest data cloud, in contrary with the milled powder type which showed flatter data cloud. By comparison of NIRS results for total isoflavone, total amino acids, and protein of soybean seeds with three sample types, the powder sample could be estimated for the most accurate prediction. However, based from the results, the use of single bean samples, without grinding the seeds and in consideration with NIRS application for more nondestructive and faster prediction, is proven to be a promising strategy for soybean component estimation using NIRS.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1510-1510
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• 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 Korea Convergence Society
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• v.12 no.12
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• pp.323-330
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• 2021

In this study, we intended to examine the difference in brain activation due to dominant and non-dominant hands using functional near-infrared spectroscopy(fNIRS) in 10 healthy adults. Box & Block Test(BBT) was conducted under two conditions: dominant hand and non-dominant hand. During the experiment, brain activity was measured using fNIRS and signals were analyzed using nirsLAB v2019.04 software after the experiment was completed. As a result, 6 out of 10 people showed activation of the cerebral hemisphere related to the dominant hand, and only 3 out of 10 people showed activation of the cerebral hemisphere related to the non-dominant hand. In other words, both dominant and non-dominant hand cconfirmed that the cerebral hemispheres related to dominant hands were more active. Therefore, it is believed that fNIRS can be used as a fundamental data applicable to children with sensory processing disorders that are difficult to identify dominant hand.

• Journal of the Korean neurological association
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• v.35 no.1
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• pp.8-15
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• 2017

Near-infrared spectroscopy (NIRS), a noninvasive optical method, utilizes the characteristic absorption spectra of hemoglobin in the near-infrared range to provide information on cerebral hemodynamic changes in various clinical situations. NIRS monitoring have been used mainly to detect reduced perfusion of the brain during orthostatic stress for three common forms of orthostatic intolerance (OI); orthostatic hypotension, neurally mediated syncope, and postural orthostatic tachycardia syndrome. Autonomic function testing is an important diagnostic test to assess their autonomic nervous systems for patients with symptom of OI. However, these techniques cannot measure dynamic changes in cerebral blood flow. There are many experimentations about study of NIRS to reveal the pathophysiology of patients with OI. Research using NIRS in other neurologic diseases (stroke, epilepsy and migraine) are ongoing. NIRS have been experimentally used in all stages of stroke and may complement the established diagnostic and monitoring tools. NIRS also provide pathophysiological approach during rehabilitation and secondary prevention of stroke. The hemodynamic response to seizure has long been a topic for discussion in association with the neuronal damage resulting from convulsion. One critical issue when unpredictable events are to be detected is how continuous NIRS data are analyzed. Besides, NIRS studies targeting pathophysiological aspects of migraine may contribute to a deeper understanding of mechanisms relating to aura of migraine. NIRS monitoring may play an important role to trend regional hemodynamic distribution of flow in real time and also highlights the pathophysiology and management of not only patients with OI symptoms but also those with various neurologic diseases.

• Journal of Chest Surgery
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• v.50 no.6
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• pp.448-452
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• 2017

Background: Proper monitoring of cerebral perfusion during carotid artery surgery is crucial for determining if a shunt is needed. We compared the safety and reliability of near-infrared spectroscopy (NIRS) w ith transcranial Doppler (TCD) for cerebral monitoring. Methods: This single-center, retrospective review was conducted on patients who underwent carotid endarterectomy (CEA) using selective shunt-based TCD or NIRS at Daegu Catholic University Medical Center from November 2009 to June 2016. Postoperative complications were the primary outcome, and the distribution of risk factors between the 2 groups was compared. Results: The medical records of 74 patients (45 TCD, 29 NIRS) were reviewed. The demographic characteristics were similar between the 2 groups. One TCD patient died within the 30-day postoperative period. Postoperative stroke (n=4, p=0.15) and neurologic complications (n=10, p=0.005) were only reported in the TCD group. Shunt usage was 44.4% and 10.3% in the TCD and NIRS groups, respectively (p=0.002). Conclusion: NIRS-based selective shunting during CEA seems to be safe and reliable for monitoring cerebral perfusion in terms of postoperative stroke and neurologic symptoms. It also reduces unnecessary shunt usage.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.spc1
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• pp.304-309
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• 2006

Near infrared reflectance spectroscopy (NIRS) was used to develop a rapid and nondestructive method for the determination of fatty acid composition in sesame (Sesamum indicum L.) seed oil. A total of ninety-three samples of intact seeds were scanned in the reflectance mode of a scanning monochromator, and reference values for fatty acid composition were measured by gas-liquid chromatography. Calibration equations were developed using modified partial least square regression with internal cross validation (n=63). The equations obtained had low standard errors of cross-validation and moderate $R^2$ (coefficient of determination in calibration). Prediction of an external validation set (n=30) showed significant correlation between reference values and NIRS estimated values based on the SEP (standard error of prediction), $r^2$ (coefficient of determination in prediction) and the ratio of standard deviation (SD) of reference data to SEP. The models developed in this study had relatively higher values (more than 2.0) of SD/SEP(C) for oleic and linoleic acid, having good correlation between reference and NIRS estimate. The results indicated that NIRS, a nondestructive screening method could be used to rapidly determine fatty acid composition in sesame seeds in the breeding programs for high quality sesame oil.

• Molecules and Cells
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• v.40 no.8
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• pp.523-532
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• 2017

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique that indirectly assesses neuronal activity by measuring changes in oxygenated and deoxygenated hemoglobin in tissues using near-infrared light. fNIRS has been used not only to investigate cortical activity in healthy human subjects and animals but also to reveal abnormalities in brain function in patients suffering from neurological and psychiatric disorders and in animals that exhibit disease conditions. Because of its safety, quietness, resistance to motion artifacts, and portability, fNIRS has become a tool to complement conventional imaging techniques in measuring hemodynamic responses while a subject performs diverse cognitive and behavioral tasks in test settings that are more ecologically relevant and involve social interaction. In this review, we introduce the basic principles of fNIRS and discuss the application of this technique in human and animal studies.