• 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.

• Journal of The Korean Society of Grassland and Forage Science
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• v.33 no.3
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• pp.171-176
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• 2013

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages and feedstuff. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of fresh whole crop barley silages. A representative population of 284 fresh whole crop barley silages was used as a database for studying the possibilities of NIRS to predict chemical composition. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data were recorded as log 1/Reflectance (log 1/R) and were scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh whole crop barley 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, 0.81, 0.79, 0.84, 0.72 and 0.78, respectively, and standard error of cross-validation (SECV) of 1.26, 2.83, 2.18, 1.19, 0.13 and 0.32% DM, respectively. Results of this experiment showed the possibility of the NIRS method to predict the chemical parameters of fresh whole crop barley silages as a routine analysis method in feeding value evaluation and for farmer advice.

• Korean Journal of Food Science and Technology
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• v.28 no.1
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• pp.40-43
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• 1996

Our objective was to evaluate the potential of near-infrared reflectance spectroscopy to determine the salt content of mayonnaise. The calibration equation for salt developed using partial least square regression was compared with conventional method. 100 samples for calibration set and 40 samples for validation set were used. The multiple correlation coefficient was 0.946 and standard error of prediction was 0.017, when calibration equation was applied to validation set. Near-infrared reflectance spectroscopy method for determining salt content of mayonnaise appears quite satisfactory to evaluate nondestructively.

• Korean Journal of Food Science and Technology
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• v.34 no.3
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• pp.356-360
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• 2002

The honey samples harvested in 1996, 1997, and 1998 were used for calibration and validation. NIR spectra were obtained using NIR spectrometer and quartz glass device with gold coating diffuser. Multiple linear regression and partial least square were used for calibrations. The correlation coefficient (RSQ) and standard error of prediction (SEP) obtained for moisture were 0.997 and 0.1%, respectively. The RSQ and SEP for fructose and glucose were 0.926 and 0.951%, and the SEP were 0.54% and 0.52% respectively. The validation results for sucrose, maltose, HMF definition, and acidity of honey were considered to be sufficient for practical use RSQ and SEP for SCIR were 0.950 and $1.08%_{\circ}$, respectively. These results are indications of the rapid determination of purity of the honey through NIR analysis.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.127-134
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• 2021

This study aims to determine the role of innovative capabilities as a mediator in analyzing entrepreneurial marketing's effect on sustainable competitive advantage in food and beverage micro-, small-, and medium- enterprises (MSMEs). Data was obtained from a food and beverage store manager in Tangerang City, comprising 119 samples. Furthermore, the G⁎Power, a tool used to calculate statistical power analysis for various t-tests, F tests, χ2 tests, z tests, and several exact tests, was used to determine the number of research samples, the α error probability of 5%, and 3 variables. The data collection method used questionnaires with Likert Scale 1-5 to indicate strongly disagree to strongly agree. To analyze data, we used Path Analysis supported by SmartPLS statistics software. Path analysis is a form of multiple regression statistical analysis that is used to evaluate causal models by examining the relationships between a dependent variable and two or more independent variables. It aims to provide estimates of the magnitude and significance of hypothesized causal connections between sets of variables. The data processing process took place in two stages, namely the estimation model testing with validity and reliability, and the structural model testing to decide the impact or correlation between variables utilizing the t-test. The result showed a positive and significant effect of entrepreneurial marketing to innovative capability and competitive advantage through the innovative capability of MSMEs.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.257-271
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• 2019

The use of food grade hexane (FGH) for edible oil extraction is responsible for the presence of benzene in the crude oil. Benzene is a Group 1 carcinogen and could pose a serious threat to the health of consumer. However, its detection still depends on classical methods using chromatography which requires a rapid non-destructive detection method. Hence, the aim of this study was to investigate the feasibility of using Fourier transform infrared (FTIR) spectroscopy combined with multivariate analysis to detect and quantify the benzene residue in edible oil (sesame and cottonseed oil). Oil samples were adulterated with varying quantities of benzene, and their FTIR spectra were acquired with an attenuated total reflectance (ATR) method. Optimal variables for a partial least-squares regression (PLSR) model were selected using the variable importance in projection (VIP) and the selectivity ratio (SR) methods. The developed PLS models with whole variables and the VIP- and SR-selected variables were validated against an independent data set which resulted in $R^2$ values of 0.95, 0.96, and 0.95 and standard error of prediction (SEP) values of 38.5, 33.7, and 41.7 mg/L, respectively. The proposed technique of FTIR combined with multivariate analysis and variable selection methods can detect benzene residuals in edible oils with the advantages of being fast and simple and thus, can replace the conventional methods used for the same purpose.

• Journal of agriculture & life science
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• v.53 no.3
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• pp.147-157
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• 2019

In this study, we tried to find out the most appropriate pre-processing method and to verify the feasibility of developing a low-price sensing system for predicting the hardy kiwis sugar content based on VNIRS and subsequent spectral analysis. A total of 495 hardy kiwi samples were collected from three farms in Muju, Jeollabukdo, South Korea. The samples were scanned with a spectrophotometer in the range of 730-2300 nm with 1 nm spectral sampling interval. The measured data were arbitrarily separated into calibration and validation data for sugar content prediction. Partial least squares (PLS) regression was performed using various combinations of pre-processing methods. When the latent variable (LV) was 8 with the pre-processing combination of standard normal variate (SNV) and orthogonal signal correction (OSC), the highest R2 values of calibration and validation were 0.78 and 0.84, respectively. The possibility of predicting the sugar content of hardy kiwi was also examined at spectral sampling intervals of 6 and 10 nm in the narrower spectral range from 730 nm to 1200 nm for a low-price optical sensing system. The prediction performance had promising results with R2 values of 0.84 and 0.80 for 6 and 10 nm, respectively. Future studies will aim to develop a low-price optical sensing system with a combination of optical components such as photodiodes, light-emitting diodes (LEDs) and/or lamps, and to locate a more reliable prediction model by including meteorological data, soil data, and different varieties of hardy kiwi plants.

• Journal of Animal Science and Technology
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• v.64 no.3
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• pp.531-538
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• 2022

In Italy, buffalo mozzarella is a largely sold and consumed dairy product. The fraudulent adulteration of buffalo milk with cheaper and more available milk of other species is very frequent. In the present study, Fourier transform infrared spectroscopy (FTIR), in combination with multivariate analysis by partial least square (PLS) regression, was applied to quantitatively detect the adulteration of buffalo milk with cow milk by using a fully automatic equipment dedicated to the routine analysis of the milk composition. To enhance the heterogeneity, cow and buffalo bulk milk was collected for a period of over three years from different dairy farms. A total of 119 samples were used for the analysis to generate 17 different concentrations of buffalo-cow milk mixtures. This procedure was used to enhance variability and to properly randomize the trials. The obtained calibration model showed an R² ≥ 0.99 (R² cal. = 0.99861; root mean square error of cross-validation [RMSEC] = 2.04; R² val. = 0.99803; root mean square error of prediction [RMSEP] = 2.84; root mean square error of cross-validation [RMSECV] = 2.44) suggesting that this method could be successfully applied in the routine analysis of buffalo milk composition, providing rapid screening for possible adulteration with cow's milk at no additional cost.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.79-91
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• 2022

In this study, the effects of self-efficacy, perceived usefulness, perceived ease of use, and depression on college students' academic persistence in the COVID-19 epidemic and the resulting non-face-to-face education situation were identified as mediating effects on learning satisfaction. In the second semester of 2020, a survey was conducted on students enrolled in a four-year university in Daegu and the data were statistically analyzed. The path coefficient was estimated by the Smart PLS bootstrap method and the significance of the path coefficient was verified. The Sobel Test was conducted to verify the mediating effect of academic continuity intention as a parameter. The research results can be summarized as follows. First, it was found that self-efficacy and perceived usefulness had a significant influence in the relationship with learning satisfaction. Second, the relationship between learning satisfaction and academic continuity intention was found to have a significant influence. Third, depression and ease of use did not show any significant influence in the relationship between learning satisfaction. Finally, a Sobel Test was conducted to verify the mediating effect of academic continuity intention with self-efficacy, usefulness, ease of use, and depression as independent variables and learning satisfaction as parameters. As a result of both regression analyses, it was found that β values decreased, and learning satisfaction had a mediating effect. As a result of this study, it is suggested that research to increase learner satisfaction and develop various contents to increase the effectiveness of education that can increase self-efficacy and perceived usefulness should be conducted in parallel. I think this study can be used as basic data in establishing measures to continue studying for college students in natural disaster situations or psychological crisis situations called COVID-19.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.