• Korean Journal of Agricultural Science
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• v.41 no.1
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• pp.59-63
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• 2014

Precision farming aims at reduced environmental impacts with increased productivity. Soils are multi-functional media in which air, water and biota occur together and form an essential part of the landscape with a fundamental role in the environment. The requirement for herbicides and fertilizers can vary within a field in response to spatial differences in soil properties. Near infrared (NIR) spectroscopy is widely used today as a nondestructive analytical technique which is capable of determining a number of physio-chemical parameters. The objectives of this study were to develop optimal models to predict chemical properties of paddy soils by visible and NIR reflectance spectra. Total of 60 soil samples were collected in spring from 20 paddy fields within central regions in Korea. Reflectance spectra, moisture contents, pH, total nitrogen (N), organic matter, available phosphate ($P_2O_5$) of soil samples were measured. The reflectance spectra were measured in wavelength ranges of 400-2,500 nm with 2 nm interval. The method of partial least square (PLS) analysis was used to determine the soil properties. The PLS analyses showed good correlation between predicted and measured chemical properties of paddy soils in the wavelength range of 1,800-2,400 nm. Especially, it showed better performance than the previous results which used the entire wavelength range of the spectrophotometer, without considering the optimal wavelength of each soil properties.

• Applied Chemistry for Engineering
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• v.28 no.5
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• pp.587-592
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• 2017

Uniform and optimum sized silver nanoplates were synthesized through the liquid phase reduction method by using silver nitrate solution as a starting chemical, dimethylformmide (DMF) as a reducing solvent, and polyvinylpyrrolidone (PVP) as reducing and surfactant agents. Synthesized and also film samples were characterized by using SEM, TEM, UV-Vis-NIR spectroscopy, particle size analyzer (PSA), and XRD. Triangle nanoplates with the size of 100~200 nm were found from the sample synthesized at $70^{\circ}C$ for 72 h using silver nitrate, DMF and 26 wt% PVP. The sample could reflect near-infrared light because it showed the maximum absorbing peak at about 1,000 nm. When the content or particle size of silver nanoplates increased in coating solutions, the transmittance decreased and the reflectance increased in film samples.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.4
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• pp.278-283
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• 2001

The overall objective of this research was to show a NIR soil analyzer assessing soil fertility by measuring soil properties rapidly. A total of 140 soil samples were used to obtain calibrations and validation estimating soil properties. The soil samples were ground to pass 0.2mm sieve openings. Partial least square regression analysis was used to develop a calibration for soil analysis. The results indicated that NIR soil analyzer could be used as a routine method for quantitatively determining pH, OM, total nitrogen, CEC, extractable Ca, Mg, K, available $SiO_2$ and soil moisture simultaneously within one minute. Therefore, the NIR soil analyzer may be suitable for quick estimation of soil fertility estimation in fertilizer assessments.

• Publications of The Korean Astronomical Society
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• v.27 no.4
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• pp.357-360
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• 2012

This paper presents $H{\alpha}$ emission line detections for four galaxies at z > 3.5 made with AKARI as part of the FUHYU mission program. These are the highest-redshift $H{\alpha}$ detections to date in star-forming galaxies. AKARI's unique near-infrared spectroscopic capability has made these detections possible. For two of these galaxies, this represents the first evidence of their redshifts and confirms their physical association with a companion radio galaxy. The star formation rates (SFRs) estimated from the $H{\alpha}$ lines under-predict the SFRs estimated from their far-infrared luminosities by a factor of ~ 2 - 3. We have also detected broad $H{\alpha}$ components in the two radio galaxies which indicate the presence of quasars.

• Korean Journal of Food Science and Technology
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• v.35 no.5
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• pp.818-824
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• 2003

Capillary electrophoresis (CE) and near-infrared spectroscopy (NIRS) were performed to discriminate astragalus roots (Astragalus membranaceus) according to geographical origin (domestic or foreign). Two-hundred-and-four astragalus roots were extracted with 30% methanol in 0.1 M phosphate buffer (pH 2.5) and separated in a uncoated fused-silica $(50\;{\mu}m{\times}27\;cm)$ capillary. Conditions for optimal analysis included: temperature $-45^{\circ}C$, voltage -14 kV, and pressure injection time -8 sec. The optimal separation buffer was 0.1 M phosphate buffer (pH 2.5) containing 40 mM hexane sulfonic acid with 20% 2-methoxy ethanol. Raw NIR spectra were obtained using NIRS, and modified partial least square regression was used to develop the prediction model. The correlation coefficient and standard error of prediction were 0.915 and 14.3%, respectively. Under the optimal conditions established for CE and NIRS, the geographical origins of the astragalus roots were correctly identified in 80 and 97%, respectively. Astragalus roots that were not discriminated by NIRS were correctly discriminated by CE. Hence, CE and NIRS are potential methods for discriminating the geographical origins of astragalus roots that complement one another.

• Journal of the Korean Wood Science and Technology
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• v.47 no.4
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• pp.385-392
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• 2019

In our previous study, we investigated the use of ensemble models based on LeNet and MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean softwoods (cedar, cypress, Korean pine, Korean red pine, and larch). It had accomplished an average F1 score of more than 98%; the classification performance of the longitudinal surface image was still less than that of the transverse surface image. In this study, ensemble methods of two different convolutional neural network models (LeNet3 for smartphone camera images and NIRNet for NIR spectra) were applied to lumber species classification. Experimentally, the best classification performance was obtained by the averaging ensemble method of LeNet3 and NIRNet. The average F1 scores of the individual LeNet3 model and the individual NIRNet model were 91.98% and 85.94%, respectively. By the averaging ensemble method of LeNet3 and NIRNet, an average F1 score was increased to 95.31%.

• 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.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1519-1519
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• 2001

The aim of this work was to test the feasibility of NIRS in analysing textural characteristics of “Pasta Filata” cheese during the shelf-life. For this purpose, 128 samples of “Pasta Filata” cheese, subdivided into two sets on the basis of the wax used to avoid mechanical damages (paraffin, biodegradable wax), were analysed by using an InfraAlyzer 500 (Bran+Luebbe). Analyses were performed at room temperature. Samples were cut into small cylinders (D=3.2 cm, height = 1 cm), in agreement with literature information. Data were processed by using Sesame Software (Bran+Luebbe). Samples were analysed, during the shelf-life, at 90 and 120 days. In parallel, textural characteristics were detected carrying out a compression method by using an Universal Testing Machine Instron model 4301 (Instron Corporation, Canton, Massachusetts). As compression probe was used a cylinder (D = 5.8 cm, height = 3.7 cm) and a speed rate of 20mm/min was applied. The load at 20 mm of compression was recorded on sample cylinders of 1.7 cm (D) by 2 cm (height). Qualitative analysis of full spectra showed the possibility to gather samples on the basis of the days of shelf-life. The textural characteristics of cheese during the shelf-life was evaluated by comparing NIRS data with rheological results. The best correlation was obtained applying MLR to the first derivative of normalized absorbance values at seven wavelengths. Load values were plotted against the NIR prediction values based on first derivatives. NIRS proved to be an useful tool in classifying samples on the basis of the shelf-life period as well as in predicting their textural characteristics ($R^2$= 0.916, SEC = 0.192, SEP = 0.248, SEV = 0.345).

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.6
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• pp.830-836
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• 2003

The classical energy balance method was used to measure the apparent metabolisable energy (AME) of four batches of broiler starter and finisher diets produced by two commercial feed companies. The results showed there was little variation in protein content between batches, but NDF content varied from 13.3% to 15.5% between batches of diet. The batch variation in chemical composition differed between feed manufacturers. While there was no difference in AME and feed conversion ration (FCR) between batches of starter diets produced by company A, FCR and AME ranged from 1.76-1.94 (p<0.001) and 11.38-11.90 MJ/kg air dry (p<0.05), respectively, for diets produced by company B. Similar results were found in a second experiment. There was no difference in AME, dry matter digestibility (DMD) and FCR between batches for finishing diet produced by company B, but a large variation occurred for the finisher diets from company A (p<0.01), where the ranges of FCR, AME and DMD were 1.95-2.30, 10.5-12.3 (MJ/kg air dry) and 58-68%, respectively. FCR was correlated with AME. AME was negatively related to the content of fibre in the diet, but positively related to DMD. The preliminary results based on 24 samples showed that near infrared spectroscopy (NIR) has the potential to predict FCR, intake, AME and DMD of commercial broiler diets, with $R^2$ being 0.93, 0.89, 0.95 and 0.98, respectively. The standard error of cross validation was below 0.2 for AME and only 0.06 for FCR.