• Korean Journal of Food Science and Technology
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• v.38 no.5
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• pp.628-634
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• 2006

This study was performed to investigate the quality of pre-processed vegetables used in school and institutional food-services. Pre-processed food materials (carrot, potato, and cabbage) frequently used in food-service were collected from 14 various processing company sources. The sensory and physico-chemical qualities of the pre-processed food materials were determined using sensory and instrumental analysis. For the physico-chemical analysis of the food materials, pH, total acidity, hardness, Hunter colorimeter value, reducing sugar and vitamin C content were determined. For the sensory quality evaluation, 15 panelist were trained and consensus was reached on the quality standards of the preprocessed materials (carrot, potato, and cabbage). Finally, appearance, color, texture, off-odor/taste, and overall quality were determined. In the physico-chemical analysis, there were no significant differences among samples collected from various processing companies. In sensory quality evaluations, the color quality of pre-processed potato was lower than that of other materials. From the coefficient correlations and partial least squares regression analysis between sensory and instrumental data, pH, total acidity, colorimeter values, and hardness were considered important components in assessing the quality of pre-processed vegetables.

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

• Journal of Korea Water Resources Association
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• v.40 no.12
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• pp.957-968
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• 2007

It is essential to obtain accurate and highly reliable streamflow data for quantitative management for water resources. Thereafter such real-time streamflow gauging methods as ultrasonic flowmeter and index-velocity are introduced recently. Since these methods calculate flowrate through entire cross-section by measuring partial velocities of it, rational and theoretical basis are necessary for accurate estimation of discharge. The purpose of the present study lies in analysis on the applicability of Chiu#s(1987, 1988) two dimensional velocity distribution equations by applying them to natural rivers and by comparing simulated velocity distributions with observed ones obtained with ADCP. Maximum and mean velocities are calculated from observed data to estimate entropy parameter M. Such isovel shape parameters as h and $\beta_i$ are estimated by object function based on least squares criterion. In case optimized parameters are applied, Chiu#s velocity distributions fairly well simulate observed ones. By using 14 simulated data sets which have relatively high correlation coefficients, properties of parameters are analyzed and h, $\beta_i$ are estimated for velocity-unknown river sections. When estimated parameters are adopted for verification, simulated velocity distributions well reproduce real ones. Finally, calculated discharges display rough agreement with measured data. The results of the present study mean that if parameters related are properly estimated, Chiu#s velocity distribution is likely to reproduce the real one of natural rivers.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.99-106
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• 2017

The purpose of this study is to analyze the relationship between tourist 's intention to use smartphone and the preceding factors through the questionnaire. For achieving this research purpose, this study surveyed the tourists who use smartphone to investigate the influence of interactivity such as two-way communication and synchronicity as hypotheses on the influence of flow, and the effect of attitude factors on continuous intention to use was analyzed by research model. In this study, a partial least squares based structural equation (PLS-SEM) method was applied to verify the hypothesis of the proposed research model. Finally, the validity of the proposed research model was confirmed through confirmatory factor analysis and hypothesis testing. Therefore, the results of this study show that interactivity and flow are influential for the reuse of users in utilizing information by using smartphone, and it affects the attitude of users in developing smart tourism system in the future. The results of this study can be utilized as basic data related to the acquisition of information by smartphone of tourists in the future development of smart tourism system.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.4
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• pp.292-297
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• 2019

In this study, whole crop rice samples were used to develop near-infrared reflectance (NIR) equations to estimate six forage quality parameters: Moisture, crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), Ash and relative feed value (RFV). A population of 564 whole crop rice representing a wide range in chemical parameters was used in this study. Undried finely chopped whole crop rice samples were scanned at 1 nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R). NIRS calibrations were developed by means of partial least-squares (PLS) regression. The correlation coefficients of cross-validation (R²_cv) and standard error of cross-validation (SECV) for whole crop rice calibration were 0.98 (SECV 1.81%) for moisture, 0.89 (SECV 0.50%) for CP, 0.86 (SECV 1.79%) for NDF, 0.89 (SECV 0.86%) for ash, and 0.84 (SECV 5.21%) for RFV on a dry matter (%), respectively. The NIRS calibration equations developed in this study will be useful in predicting whole crop rice quality for these six quality parameters.

• Agribusiness and Information Management
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• v.3 no.2
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• pp.43-62
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• 2011

The goal of this study is to determine, based on survey results, the underlying factors that affect the intention of the farmers who have not adopted the Hazard Analysis and Critical Control Points (HACCP) system for the rearing phase of pig production to adopt this system in the future. The research model for this study was con structed based on strategic contingency theory, the theory of the diffusion of innovation, and the technology acceptance model (TAM). Using structural equation modeling with partial least squares (PLS), this study analyzes the effects of the intensity of competition, the environmental uncertainty, the innovativeness and self-efficacy of the individual farmers, and the impact of the credibility of the Agricultural Technology Service Center (ATSC), which acts as the principal agent of technology dissemination and as a leader of change, on the perceived usefulness of technology and the farmers' intention to adopt the system. The results of the analysis are as follows. First, with regard to the underlying factors affecting the intention to adopt the new system, the intensity of competition within the industry and the institutional credibility of the ATSC were inferred to underlie the perceived usefulness. Second, institutional credibility has a positive impact on the perceived usefulness of the system, and the perceived usefulness, in turn, has a positive impact on the intention to adopt. The perceived ease of use also has a positive impact on the intention to adopt. Because the factor that has the biggest impact on the intention of a farm to adopt is the credibility of the ATSC, it is crucial for extension organizations, such as the ATSC, to make greater efforts to promote the expansion of the HACCP system. Because farmers feel that the implementation of the HACCP system is an instrumental strategy for coping with the high intensity of competition within the industry, they attempt to gain a competitive edge through the production of safe livestock products.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.257-263
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• 2015

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.4
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• pp.350-357
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• 2017

Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, $R_{cv}^2$, ranged from 0.76 to 0.97); the exception to this result was crude ash ($R_{cv}^2=0.49$ and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy ($R_{cv}^2$ 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.

• Korean Journal of Plant Resources
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• v.23 no.4
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• pp.386-392
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• 2010

This study was done to measure the color and catechins contents in processed teas using the whole bands (400~2500 nm) with near-infrared spectroscopy(NIRS). The powder colors of 109 processed teas were measured with a colorimeter. The a/b ratios in Hunter color scale in processed teas accounted for about 98.9% of the variation in the fermentation degree(FD), indicating that the a/b ratio was a very useful trait for assessing fermentation degree. Also tea powders were scanned in the visible bands used with NIRSystem. The calibration equations for powder colors were developed using the regression method of modified partial least squares(MPLS) with the internal cross validation. The equations had low SECV (standard errors of cross-validation), and high $R^2$ (coefficient of determination in calibration) values with 0.996~1.00, indicating that the visible bands(400~700 nm) with NIRS could be used to rapidly measure the variables related to powder color and fermentation degree. Also another powders of 137 processed teas were scanned at 780~2500 nm bands in the reflectance mode. The calibration equations were developed using the regression method of MPLS with the internal cross validation. The equations had low SECV, and high $R^2$ (0.896~0.983) values, showing that NIRS could be used to rapidly discriminate the contents of EGC($R^2$=0.919), EC(0.896), EGCg(0.978), ECg(0.905) and total catechins(0.983) in processed teas with high precision and ease.

• Korean Journal of Plant Resources
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• v.27 no.4
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• pp.286-292
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• 2014

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to measure baicalin, baicalein, and wogonin contents in Scutellaria baicalensis by using NIRS system. Total 63 samples previously were analyzed by HPLC, which showed baicalin, baicalein, and wogonin contents ranging 4.56 to 13.59%, 0.28 to 5.54%, and 0.50 to 1.63% with an average of 9.66%, 2.09% and 0.52%, respectively. Each sample was scanned by NIRS and calculated for calibration and validation equation. A calibration equation calculated by modified partial least squares(MPLS) regression technique was developed in which the coefficient of determination for baicalin, baicalein, and wogonin content was 0.958, 0.944, and 0.709, respectively. Each calibration equation was applied to validation set that was performed with the remaining samples not included in the calibration set, which showed high positive correlation both in baicalin and baicalein content file. In case of wogonin, the prediction model was needed more accuracy because of low $R^2$ value in validation set. These results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of baicalin and baicalein contents in Scutellaria baicalensis.