• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.395-413
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• 2023

This research studied the effect of customer experience characteristics in offline home appliance stores on perceived value and revisit intention. Among the offline distribution of home appliances with more than 100 stores nationwide, two home appliance retailers (HiMart, E-Land), three hypermarkets (E-Mart, Homeplus, Lotte Hi-Mart), and two home appliance stores (LG Best Shop, Samsung Digital Plaza) were selected, and a survey was conducted on men and women in their 20s or older in Seoul, Gyeonggi, and Incheon who had visited and purchased the home appliance store within the last 6 months. As a result of the survey, a statistical analysis was conducted on a total of 330 samples using the PLS (Partial Least Squares) structural equation model and SPSS statistical package. Through this study, the following research results can be obtained. First, educational experience, deviant experience, and aesthetic experience had a positive (+) effect on the functional value. However, entertainment experience did not affect functional value. Second, educational experience, deviant experience, and aesthetic experience all had a positive (+) effect on emotional value. Third, both functional and sensory values had a positive (+) effect on the revisit intention. Fourth, it was confirmed that brand loyalty had no moderating effect between functional value and sensory value revisit intention. The results of this study show the structural relationship between customer experience characteristics, perceived value (functional value, sensory value), and revisit intention. This result provides guidelines on what activities home appliance offline stores should do at a time when online channels threaten the survival of offline channels.

• Knowledge Management Research
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• v.25 no.2
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• pp.193-218
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• 2024

The current study investigated the factors influencing the buyer's repurchase intention for second-hand products. This study first identified perceived risk and purchase value as the two primary influencing variables. Additionally, some exogenous variables influencing these two variables were examined. Statistical analysis using Partial Least Squares (PLS) revealed that product uncertainty, seller uncertainty, and site trust had statistically significant relationships with perceived transaction risk. However, while economic benefit showed a significant impact on purchase value, product scarcity and resale value did not exhibit a significant relationship with purchase value. Perceived transaction risk was found to have an insignificant relationship with repurchase intention, but indirectly influenced repurchase intention through purchase value. Purchase value was identified as having a significant influence on repurchase intention. Therefore, it was concluded that purchase value is the most important factor influencing repurchase intention in the purchase of second-hand products, while transaction risk indirectly influences repurchase intention through purchase value. The study indicates that product uncertainty and economic benefit are the most significant exogenous factors influencing transaction risk and purchase value, respectively.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4112-4112
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• 2001

The feasibility of NIR spectroscopy as a quick and nondestructive method for quality control of uniformity of coating thickness of pharmaceutical tablets was investigated. Near infrared spectra of a set of pharmaceutical tablets with varying coating thickness were measured with a diffuse reflectance fiber optic probe connected to a Broker IFS 28/N FT-NIR spectrometer. The challenging issues encountered in this study included: 1. The similarity of the formulation of the core and coating materials, 2. The lack of sufficient calibration samples and 3. The non-linear relationship between the NIR spectral intensity and coating: thickness. A peak at 7184 $cm^{-1}$ was identified that differed for the coating material and the core material when M spectra were collected at 2 $cm^{-1}$ resolution (0.4 nm at 7184 $cm^{-1}$). The study showed that the coating thickness can be analyzed by polynomial fitting of the peak area of the selected peak, while least squares calibration of the same data failed due to the lack of availability of sufficient calibration samples. Samples of coal powder and solid pieces of coal were analyzed by FT-NIR diffuse reflectance spectroscopy with the goal of predicting their ash content, percentage of volatile components, and energy content. The measurements were performed on a Broker Vector 22N spectrometer with a fiber optic probe. A partial least squares model was constructed for each of the parameters of interest for solid and powdered sample forms separately. Calibration models varied in size from 4 to 10 PLS ranks. Correlation coefficients for these models ranged from 86.6 to 95.0%, with root-mean-square errors of cross validation comparable to the corresponding reference measurement methods. The use of FT-NIR diffuse reflectance measurement techniques was found to be a significant improvement over existing measurement methodologies in terms of speed and ease of use, while maintaining the desired accuracy for all parameters and sample forms.(Figure Omitted).

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1245-1245
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145-154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4111-4111
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• 2001

When a drug is prepared in a tablet, the active component represents only a small portion of the dosage form. The other components of the formulation include materials to assist in the dissolution, antioxidants, coloring agents and bulk fillers. The tablets are tested using approved testing methods usually involving separation and subsequent quantification of the active component. Tablets may also be tested by near-Infrared Reflectance spectrometry (NIRS). In the present study, based on NIRS and multivariate calibration methods, a novel and precise method is developed for direct determination of ascorbic acid in vitamin C tablet. Two different tablet formulations were powdered in three different sizes, 63-125 ${\mu}{\textrm}{m}$, and examined. Spectral region of 4750-4950 $cm^{-1}$ / was used and optimized for quantitative operations. Partial least squares (PLS) and multiple linear regression (MLR) methods were performed for this spectral region. The results of optimized PLS and MLR methods showed that reproducibility increase with decreasing grain size and standard error of calibration (SEP) of less than 1% w/w of ascorbic acid and a correlation coefficient of 0.998 can be achieved. The PLS method showed better results than MLR. Seven overdose and underdose samples (prepared in the laboratory to match marketed products) were tested by proposed and iodometric standard methods. A correlation between NIRS predicted ascorbic acid values and iodomet.ic values was calculated ($R^2$=0.9950). Finally, the direct analysis of individual intact tablets in their unit-dose packages (Blistering in aluminum and PVC foils) obtained from market were also carried out and a correlation coefficient of 0.9989 and SEP of 0.931% w/w of ascorbic acid were achieved.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1152-1152
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145 154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• Food Science and Preservation
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• v.18 no.2
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• pp.156-164
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• 2011

This study was conducted to develop a portable quality evaluation system of bee-honey by near infrared spectroscopic technique. Two kinds of bee-honeys from acacia and polyflower sources were tested in this study. The system consists of power supply, tungsten-halogen lamp, detector, and optical fiber probe. Performance of the system was analyzed by comparing the prediction accuracy of the laboratory spectrophotometer. Total of 346 spectra was divided into a calibration set and a prediction set. The PLS (Partial Least Squares) models were developed to predict the quality parameters of bee-honeys. Reflectance spectra, moisture contents, ash, invert sugar, sucrose, F/G ratio, HMF(hydroxy methyl furfural), and $C^{12}/C^{13}$ ratio of honeys were measured. The PLS models of the laboratory spectrophotometer showed good relationships between predicted and measured quality parameters of honeys in the wavelength range of 1.100~2.200 nm. The PLS analysis of the portable quality evaluation system showed good relationships between predicted and measured quality parameters of honeys in the wavelength range of 1.100~1.300 nm and 1.400~1.700 nm. The results showed the feasibility of the portable quality evaluation system to determine the quality parameters of bee-honey in the field during harvesting.

• Mass Spectrometry Letters
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• v.4 no.4
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• pp.59-66
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• 2013

A metabolomics study was conducted to identify urinary biomarkers for breast cancer, using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), analyzed by principal components analysis (PCA) as well as a partial least squares-discriminant analysis (PLS-DA) for a metabolic pattern analysis. To find potential biomarkers, urine samples were collected from before- and after-mastectomy of breast cancer patients and healthy controls. Androgens, corticoids, estrogens, nucleosides, and polyols were quantitatively measured and urinary metabolic profiles were constructed through PCA and PLS-DA. The possible biomarkers were discriminated from quantified targeted metabolites with a metabolic pattern analysis and subsequent screening. We identified two biomarkers for breast cancer in urine, ${\beta}$-cortol and 5-methyl-2-deoxycytidine, which were categorized at significant levels in a student t-test (p-value < 0.05). The concentrations of these metabolites in breast cancer patients significantly increased relative to those of controls and patients after mastectomy. Biomarkers identified in this study were highly related to metabolites causing oxidative DNA damage in the endogenous metabolism. These biomarkers are not only useful for diagnostics and patient stratification but can be mapped on a biochemical chart to identify the corresponding enzyme for target identification via metabolomics.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.633-645
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• 2017

This study proposes a statistical procedure for analyzing container ship operation data that can help determine fuel consumption patterns. We first investigate the features that affect fuel consumption and develop the prediction model to find current fuel consumption. The ship data can be divided into two-type data. One set of operation data includes sea route, voyage information, longitudinal water speed, longitudinal ground speed, and wind, the other includes machinery data such as engine power, rpm, fuel consumption, temperature, and pressure. In this study, we separate the effects of external force on ships according to Beaufort Scale and apply a partial least squares regression to develop a prediction model.

• Korean Journal of Medicinal Crop Science
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• v.22 no.1
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• pp.17-22
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• 2014

Ginsenosides of roots in Panax ginseng were analyzed by metabolic-targeting HPLC using the partial least squares discriminant analysis (PLS-DA) and compared depending on sowing methods between direct seeding and transplanting method. Score plots derived from PLS-DA could identify the sowing method between the direct seeding and transplanting method in P. ginseng roots. The ginsenoside compounds were assigned as Rg1, Re, Rf, Rg2, Rb1, Rc, Rb2, Rb3, and Rd. Contents of Re, Rf, Rg2, Rb1, Rc, Rb3, and Rd of main roots produced from the transplanting method were relatively higher than those of samples produced from direct seeding method. Also, contents of Rg1, Re, Rf, Rg2, Rb1, Rc, Rb2, Rb3, and Rd of lateral roots from the transplanted samples were relatively higher than those of samples produced from direct seeding method. Therefore, HPLC with PLS-DA analysis can be a straightforward tool for identification of ginsenosides in main or lateral roots of P. ginseng obtained from two different seeding methods between direct and transplanting methods.