• Journal of Crop Science and Biotechnology
• /
• v.10 no.3
• /
• pp.185-192
• /
• 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 Tobacco Science
• /
• v.20 no.2
• /
• pp.191-197
• /
• 1998

Discriminant analysis using near infrared spectra derived from Korean Flue-cured(KF) and American Flue-cured(AF), and also Korean Burley(KB) and American Burley(AB) tobacco was done to classify flue-cured and burley tobacco as either grown in Korea or grown in the USA. Samples were scanned in the wavelength of 400 ～ 2500 nm by near infrared analyzer(NIRSystem Co., model 6500). The discrimination equations for flue-cured and burley tobacco were developed using partial least square 2 method in Infrasoft International NIRS 3 software package. KF samples used for the development of the discrimination equations were higher contents of total sugar, crude ash and chlorine, and higher value of leaf density and brightness, but lower contents of nicotine, total nitrogen and ether extracts, and higher value of redness than those of AF samples. KB samples were higher contents of nicotine, crude ash and chlorine, but lower contents of ether extracts and value of brightness than those of AB samples. On 3 dimensional graph drawn with 3 principal component scores calculated with 3 principal component from KF and KB sample spectra, KF sample spectra were significantly different from AF, and also KB sample spectra were significantly different from AB. The discrimination equations of flue-cured and burley were developed with 3 principal component, respectively. The discrimination equations for flue-cured and burley had a standard error of 0.03 and 0.04, and a R2 of 0.88 and 0.84, respectively. The tobacco samples used for the development of discrimination equation were perfectly classified as KF and AF by flue-cured discrimination equation, and also perfectly classified KB and AB by burley discrimination equation, respectively. The correct classification rates of KF and AF samples not used for the development of discrimination equations were 9S % (828 out of 869 samples) and 98 % (98 out of 100 samples) by flue-cured discrimination equations, and KB and AB samples were 94%(345 out of 368 samples) and 100%(42 out of 42 samples) by burley discrimination equations, respectively.

• Journal of Animal Science and Technology
• /
• v.62 no.3
• /
• pp.321-333
• /
• 2020

Beef quality is influenced by the fattening period. Therefore, meat metabolomics profiles from the different fattening periods (e.g., short-term vs. long-term) were analyzed for identify potential indicators using nuclear magnetic resonance. Additionally, blood, free fatty acid, sensory, and mineral compositions in Korean steers were determined. Blood, free fatty acid, and mineral concentrations showed significant differences between short-term and long-term groups that were fed different diets. However, there were no sensory differences in the two fattening groups. Additionally, the metabolic profiles of meats were clearly separated based on multivariate orthogonal partial least square discriminant analysis. Six metabolites of variable importance in the projection plot were identified and showed high sensitivity as candidate markers for meat characteristics. In particular, lactate, carnosine, and creatine could be directly linked to scientific indicators of the fattening stage (31 vs. 28 mo) of meat. Our findings suggest that the metabolomics approach could be a powerful method for the detection of novel signatures underlying the managing period of beef.

• Journal of Biosystems Engineering
• /
• v.37 no.6
• /
• pp.411-419
• /
• 2012

Purpose: Nondestructive evaluation of seed viability is a highly demanded technique in the seed industry. In this study, hyperspectral imaging system was used for discrimination of viable and non-viable radish seeds. Method: The spectral data with the range from 400 to 1000 nm measured by hyperspectral reflectance imaging system were used. A calibration and a test models were developed by partial least square discrimination analysis (PLS-DA) for classification of viable and non-viable radish seeds. Either each data set of visible (400~750 nm) and NIR (750~1000 nm) spectra and the spectra of the combined spectral ranges were used for developing models. Results: The discrimination accuracy of calibration was 84% for visible range and 76.3% for NIR range. The discrimination accuracy of test was 84.2% for visible range and 75.8% for NIR range. The discrimination accuracies of calibration and test with full range were 92.2% and 92.5%, respectively. The resultant images based on the optimal PLS-DA model showed high performance for the discrimination of the nonviable seeds from the viable seeds with the accuracy of 95%. Conclusions: The results showed that hyperspectral reflectance imaging has good potential for discriminating nonviable radish seeds from massive amounts of viable seeds.

• Food Science of Animal Resources
• /
• v.40 no.6
• /
• pp.924-937
• /
• 2020

The purpose of this study was to investigate the meat metabolite profiles related to differences in beef quality attributes (i.e., high-marbled and low-marbled groups) using nuclear magnetic resonance (NMR) spectroscopy. The beef of different marbling scores showed significant differences in water content and fat content. High-marbled meat had mainly higher taste compounds than low-marbled meat. Metabolite analysis showed differences between two marbling groups based on partial least square discriminant analysis (PLS-DA). Metabolites identified by PLS-DA, such as N,N-dimethylglycine, creatine, lactate, carnosine, carnitine, sn-glycero-3-phosphocholine, betaine, glycine, glucose, alanine, tryptophan, methionine, taurine, tyrosine, could be directly linked to marbling groups. Metabolites from variable importance in projection plots were identified and estimated high sensitivity as candidate markers for beef quality attributes. These potential markers were involved in beef taste-related pathways including carbohydrate and amino acid metabolism. Among these metabolites, carnosine, creatine, glucose, and lactate had significantly higher in high-marbled meat compared to low-marbled meat (p<0.05). Therefore, these results will provide an important understanding of the roles of taste-related metabolites in beef quality attributes. Our findings suggest that metabolomics analysis of taste compounds and meat quality may be a powerful method for the discovery of novel biomarkers underlying the quality of beef products.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.3
• /
• pp.87-94
• /
• 2012

This study investigates the relationship among locus of control, organizational justice, and job satisfaction. First, this study examines the conceptual structures of organizational justice through field study. Second, organizational justice influences job satisfaction. Third, locus of control influences organizational justice. Literature review identified four factors of organizational justice - distributive, procedural, interpersonal, and informational justice. These identified determinants are expected to have significant relationships with locus of control and job satisfaction. Questionnaires were distributed to 500 members of a manufacturing companies. Among returned questionnaires, 357 samples were used for empirical study. Structural equation analyses based on PLS (partial least square) method were employed to test the hypotheses and the model. The overall adequacy of fit was found to be acceptable. Confirmatory factor analyses supported a 4-factor structure of organizational justice. Distributive, procedural, interpersonal, and informational justice had significant positive effects on job satisfaction. In addition, the effects of locus of control had significant positive effects on organizational justice factors. The results of this study suggest that interpersonal and informational justice should be regarded as important factors of organizational justice to analyze the relationships between locus of control and job satisfaction as well as the relationships among the justice constructs.

• Journal of Digital Convergence
• /
• v.13 no.7
• /
• pp.213-221
• /
• 2015

The aim of this study is to find the factors affecting attitude on u-health system using Pender's Health Promotion Model (HPM) and also, analyze the moderating effect of gender variable in research model. To assess the proposed research model based on HPM, this study adopted Partial Least Square (PLS) method using Smartpls 2.0 version for the evaluation of research model. Thus, this study used survey questionnaire in order to collect useful data to potential users of u-health system. As a result of analysis, the examined variables explain 29% of variance on attitude to use of the u-health system. According to the PLS analysis, self efficacy and perceived benefits showed significantly positive relationship on attitude to use of u-health system. In addition, on the moderating effect of gender variable, female had more interest on self efficacy for positive attitude on use of u-health system.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.11
• /
• pp.469-477
• /
• 2020

The main objective of this paper is to examine the applicability of Linan and Chen's entrepreneurial intention model (EIM) in predicting the entrepreneurial intention. EIM is an adaptation of the Theory of Planned Behavior that focuses on entrepreneurial intention and hypothesizing slightly different patterns of relationship with regards to subjective norms. The model also includes human capital and demographic factors. Snowball sampling method was used to collect data using the entrepreneurial intention questionnaire (EIQ) through several social media platforms. The survey indicates that the overall entrepreneurial intention of Saudi students is high (mean = 5.41). Eight out of the seventeen hypothesized relationships were found to be significant. Among the demographic variables, gender-personal attitude was significant whereas self employment experience and years of business education were found to be significantly related with perceived behavioral control. The statistical analysis using partial least square structural equation modelling validated the model. All the three antecedents of entrepreneurial intention were significantly related with entrepreneurial intention. The results of this study will help policy makers to get deep understanding into the phenomenon of entrepreneurship among Saudi university students and thereby develop a conducive environment. This study also validates the entrepreneurial intention model in a different cultural context.

• Korean Journal of Food Science and Technology
• /
• v.38 no.2
• /
• pp.165-168
• /
• 2006

Fourier transform-near infrared spectroscopy (FT-NIRS) was evaluated for determination of total dietary fiber (TDF) content of brown rice. Enzymatic-gravimetric method was suitable to obtain reference values for calibration of NIR at 1,000-2,500 nm range. Standard error of laboratory procedure ranged 0.17 to 0.72%. Partial least square (PLS) regression was used to develop the calibration equations. Regression was performed automatically using NIRCal chemometric software. Accuracy of prediction model for TDF content was certified for regression coefficient (r), standard error of estimation (SEE) and standard error of prediction (SEP), showing 0.9780, 0.0636, and 0.0642, respectively. This prediction model can be used for determination of TDF in brown rice and would be useful for real-time analysis in food industry.

• KSBB Journal
• /
• v.21 no.1 s.96
• /
• pp.59-67
• /
• 2006

2D spectrofluorometer produces many spectral data during fermentation processes. The fluorescence spectra are analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). Analysis of the spectral data by PCA results in scores and loadings that are visualized in score-loading plots and used to monitor a few fermentation processes by S. cerevisae and recombinant E. coli. Two chemometric models were established to analyze the correlation between fluorescence spectra and process variables using PCR and PLS, and PLS was found to show slightly better calibration and prediction performance than PCR.