• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1254-1254
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• 2001

Near infrared spectroscopy (NIRS) was applied to determination of the lipid content of compost during compost fermentation of tofu(soybean-curd) refuse. The reflected rays in the wavelength range between 800 and 2500 nm were measured at 2 nm intervals. The absorption of lipid observed at 4 wavelengths, 1208, 1712, 2312 and 2352 nm on the second derivative spectra. To formulate a calibration equation, a multiple linear regression analysis was carried out between the near-infrared spectral data and on the lipid content in the calibration sample set (sample number, n=60) obtained using a Soxhlet extraction method. The calibration equation for prediction of lipid, the value of the multiple correlation coefficient (R) was 0.975 when using the wavelengths of 1208 and 1712nm. To validate the calibration equation obtained, the lipid content in the validation sample set (n=35) not used for formulating the calibration equation were calculated using the calibration equations, and compared with the values obtained using the Soxhlet extraction method. Good agreement were observed between the results of the Soxhlet extraction method and those values of the NIRS method. The simple correlation coefficient (r) and standard error of prediction (SEP) were 0.964 and 0.815 %, respectively. Then, the NIRS method was applied to a compost fermentation in which the time course the lipid content were measured and good results were obtained. The study indicates that NIRS is a useful method for process management of the compost fermentation of tofu refuse.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1624-1624
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• 2001

Near-infrared spectroscopy (NIRS) was used to investigate the possibility for application in identification of apple cultivars. Three apple cultivars ‘Kamhong, Hwahong, and Fuji’ produced in Korea were scanned over the range of 1100-2500nm using NIRS (Infra Alzer 500). Two types of samples were used for scanning; one was apple with skin and the other was apple without skin. For cultivar identification, the NIR absorbance spectrums were analyzed by qualitative calibration in “Sesame” analysis program, and the various influence properties such as sugar contents, acidity, color, firmness, and micro-structure were compared in scanned samples. The ‘Kamhong’ cultivar could be identified from ‘Hwahong’ and ‘Fuji’ cultivars using the cluster model analysis. The test samples in calibration between ‘Kamhong’ and ‘Fuji’ cultivars could be completely identified. The test samples in calibration between ‘Kamhong’ and ‘Hwahong’ cultivars could be identified most of all. But, ‘Hwahong’ and ‘Fuji’ cultivars could not be quite classified each other. The apple skin influenced the identification process of apple cultivars. The samples without skin were more difficult to classify in calibration than the samples with skin. The physicochemical properties of apple cultivars showed like the result of identification in calibration using NIRS. Some physicochemical properties of ‘Kamhong’ cultivar were different from those of the other cultivars. Those of ‘Hwahong’ and ‘Fuji’ cultivars showed. similar to each other. The sucrose contents of ‘Kamhong’ cultivar were higher and the fructose contents and firmness of skin and flesh were lower than those of the others. The hypodermis layer of skin in ‘Kamhong’ cultivar was thinner than those of the others. In this studies, the identification of all apple cultivars by NIRS was not quite accurate because of the physicochemical properties which were different in the same cultivar, and inconsistent patterns by culivars in some properties. To solve these problems in NIRS application for apple cultivar identification, further study should be focused on the use of peculiar properties among the apple cultivars.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.5
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• pp.643-648
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• 2005

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal grains and forages. If samples could be analyzed without drying and grinding, then sample preparation time and costs may be reduced. This study was conducted to develop robust NIRS equations to predict fermentation quality of corn (Zea mays) silage and to select acceptable sample preparation methods for prediction of fermentation products in corn silage by NIRS. Prior to analysis, samples (n = 112) were either oven-dried and ground (OD), frozen in liquid nitrogen and ground (LN) and intact fresh (IF). Samples were scanned from 400 to 2,500 nm with an NIRS 6,500 monochromator. The samples were divided into calibration and validation sets. The spectral data were regressed on a range of dry matter (DM), pH and short chain organic acids using modified multivariate partial least squares (MPLS) analysis that used first and second order derivatives. All chemical analyses were conducted with fresh samples. From these treatments, calibration equations were developed successfully for concentrations of all constituents except butyric acid. Prediction accuracy, represented by standard error of prediction (SEP) and $R^2_{v}$ (variance accounted for in validation set), was slightly better with the LN treatment ($R^2$ 0.75-0.90) than for OD ($R^2$ 0.43-0.81) or IF ($R^2$ 0.62-0.79) treatments. Fermentation characteristics could be successfully predicted by NIRS analysis either with dry or fresh silage. Although statistical results for the OD and IF treatments were the lower than those of LN treatment, intact fresh (IF) treatment may be acceptable when processing is costly or when possible component alterations are expected.

• Journal of Crop Science and Biotechnology
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• v.10 no.3
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• pp.185-192
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• 2007

Near-infrared spectroscopy(NIRS) was used to develop a rapid and efficient method to determine lignan glucosides in intact seeds of sesame(Sesamum indicum L.) germplasm accessions in Korea. A total of 93 samples(about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for lignan glucosides contents were measured by high performance liquid chromatography. Calibration equations for sesaminol triglucoside, sesaminol($1{\rightarrow}2$) diglucoside, sesamolinol diglucoside, sesaminol($1{\rightarrow}6$) diglucoside, and total amount of lignan glucosides were developed using modified partial least square regression with internal cross validation(n=63), which exhibited lower SECV(standard errors of cross-validation), higher $R^2$(coefficient of determination in calibration), and higher 1-VR(ratio of unexplained variance divided by variance) values. Prediction of an external validation set(n=30) showed a 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, as factors used to evaluate the accuracy of equations. The models for each glucoside content had relatively higher values of SD/SEP(C) and $r^2$(more than 2.0 and 0.80, respectively), thereby characterizing those equations as having good quantitative information, while those of sesaminol($1{\rightarrow}2$) diglucoside showing a minor quantity had the lowest SD/SEP(C) and $r^2$ values(1.7 and 0.74, respectively), indicating a poor correlation between reference values and NIRS estimated values. The results indicated that NIRS could be used to rapidly determine lignan glucosides content in sesame seeds in the breeding programs for high quality sesame varieties.

• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.3
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• pp.209-213
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• 2014

This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical and fermentation parameters of whole crop winter rye silages. A representative population of 216 fresh winter rye silages was used as database for studying the possibilities of NIRS to predict chemical composition and fermentation parameters. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh winter rye silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH as well as lactic acid content with correlation coefficients of cross-validation ($R^2cv$) of 0.96, 0.86, 0.79, 0.85, 0.82 and 0.78 respectively and standard error of cross-validation (SECV) of 1.89, 2.02, 2.79, 1.14, 1.47 and 0.46 % DM respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical parameters of winter rye silages as routine analysis method in feeding value evaluation and for farmer advice.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.61 no.1
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• pp.64-69
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• 2016

This study was conducted to develop a fast and efficient screening method to determine the quantity of fatty acid in peanut oil for high oleate breeding program. A total of 329 peanut samples were used in this study, 227 of which were considered in the calibration equation development and 102 were utilized for validation, using near infrared reflectance spectroscopy (NIRS). The NIRS equations for all the seven fatty acids had low standard error of calibration (SEC) values, while high R2 values of 0.983 and 0.991 were obtained for oleic and linoleic acids, respectively in the calibration equation. Furthermore, the predicted means of the two main fatty acids in the calibration equation were very similar to the means based on gas chromatography (GC) analysis, ranging from 36.7 to 77.1% for oleic acid and 7.1 to 42.7% for linoleic acid. Based on the standard error of prediction (SEP), bias values, and $R^2$ statistics, the NIRS fatty acid equations were accurately predicted the concentrations of oleic and linoleic acids of the validation sample set. These results suggest that NIRS equations of oleic and linoleic acid can be used as a rapid mass screening method for fatty acid content analysis in peanut breeding program.

• Current Optics and Photonics
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• v.6 no.5
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• pp.489-496
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• 2022

Our previous study on monitoring cerebral oxygenation with a variation of isoflurane concentration in a rat model showed that near-infrared spectroscopy (NIRS) signals have potential as a new depth of anesthesia (DOA) index. However, that study obtained results from the brain in a completely invasive way, which is inappropriate for clinical application. Therefore, in this follow-up study, it was investigated whether the NIRS signals measured in a minimally invasive model including the skull and cerebrospinal fluid layer (CSFL) are similar to the previous study used as a gold standard. The experimental method was the same as the previous study, and only the subject model was different. We continuously collected NIRS signals before, during, and after isoflurane anesthesia. The isoflurane concentration started at 2.5% (v/v) and decreased to 1.0% by 0.5% every 5 min. The results showed a positive linear correlation between isoflurane concentration and ratio of reflectance intensity (RRI) increase, which is based on NIRS signals. This indicates that the quality of NIRS signals passed through the skull and CSFL in the minimally invasive model is as good as the signal obtained directly from the brain. Therefore, we believe that the results of this study can be easily applied to clinics as a potential indicator to monitor DOA.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.353-365
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• 2019

A near-infrared reflectance spectroscopy (NIRS) prediction model was set to establish a rapid analysis system of wheat germplasm and provide statistical information on the characteristics of protein contents. The variability index value (VIV) of calibration resources was 0.80, the average protein content was 13.2%, and the content range was from 7.0% to 13.2%. After measuring the near-infrared spectra of calibration resources, the NIRS prediction model was developed through a regression analysis between protein content and spectra data, and then optimized by excluding outliers. The standard error of calibration, R², and the slope of the optimized model were 0.132, 0.997, and 1.000 respectively, and those of external validation results were 0.994, 0.191, and 1.013, respectively. Based on these results, a developed NIRS model could be applied to the rapid analysis of protein in wheat. The distribution of NIRS protein content of 6,794 resources were analyzed using a normal distribution analysis. The VIV was 0.79, the average protein was 12.1%, and the content range of resources accounting for 42.1% and 68% of the total accessions were 10-13% and 9.5-14.6%, respectively. The composition of total resources was classified into breeding line (3,128), landrace (2,705), and variety (961). The VIV in breeding line was 0.80, the protein average was 11.8%, and the contents of 68% of total resources ranged from 9.2% to 14.5%. The VIV in landrace was 0.76, the protein average was 12.1%, and the content range of resources of 68% of total accessions was 9.8-14.4%. The VIV in variety was 0.80, the protein average was 12.8%, and the accessions representing 68% of total resources ranged from 10.2% to 15.4%. These results should be helpful to the related experts of wheat breeding.

• Journal of Animal Environmental Science
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• v.18 no.sup
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• pp.81-90
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• 2012

This study was evaluated high end research grade Near infrared spectrophotometer (NIRS) to low end popular field grade multiple Near infrared spectrophotometer (NIRS) for rapid analysis at forage quality at sight with 241 samples of Italian ryegrass silage during 3 years collected whole country for evaluate accuracy and precision between instruments. Firstly collected and build database high end research grade NIRS using with Unity Scientific Model 2500X (650 nm~2,500 nm) then trim and fit to low end popular field grade NIRS with Unity Scientific Model 1400 (1,400 nm~2,400 nm) then build and create calibration, transfer calibration with special transfer algorithm. The result between instruments was 0.000%~0.343% differences, rapidly analysis for chemical constituents, NDF, ADF, and crude protein, crude ash and fermentation parameter such as moisture, pH and lactic acid, finally forage quality parameter, TDN, DMI, RFV within 5 minutes at sight and the result equivalent with laboratory data. Nevertheless during 3 years collected samples for build calibration was organic samples that make differentiate by local or yearly bases etc. This strongly suggest population evaluation technique needed and constantly update calibration and maintenance calibration to proper handling database accumulation and spread out by knowledgable control laboratory analysis and reflect calibration update such as powerful control center needed for long lasting usage of forage analysis with NIRS at sight. Especially the agriculture products such as forage will continuously changes that made easily find out the changes and update routinely, if not near future NIRS was worthless due to those changes. Many research related NIRS was shortly study not long term study that made not well using NIRS, so the system needed check simple and instantly using with local language supported signal methods Global Distance (GD) and Neighbour Distance (ND) algorithm. Finally the multiple popular field grades instruments should be the same results not only between research grade instruments but also between multiple popular field grade instruments that needed easily transfer calibration and maintenance between instruments via internet networking techniques.