• Horticultural Science & Technology
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• v.32 no.1
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• pp.105-114
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• 2014

We established a high throughput screening system of African yam tuber lines which contain high contents of total carotenoids, flavonoids, and phenolic compounds using ultraviolet-visible (UV-VIS) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The total carotenoids contents from 62 African yam tubers varied from 0.01 to $0.91{\mu}g{\cdot}g^{-1}$ dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to $229{\mu}g{\cdot}g^{-1}$ and from 0.29 to $5.2mg{\cdot}g^{-1}$dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and $1,100-950cm^{-1}$, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins ($1,700-1,500cm^{-1}$), phosphodiester groups from nucleic acid and phospholipid ($1,500-1,300cm^{-1}$) and carbohydrate compounds ($1,100-950cm^{-1}$). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate the 62 African yam tuber lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds from African yam tuber lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients ($R^2$) between predicted values and estimated values of total carotenoids, flavonoids and phenolic compounds were 0.83, 0.86, and 0.72, respectively. These results showed that quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from FT-IR spectra of African yam tuber lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding African yam lines.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.10
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• pp.1388-1398
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• 2013

The present study investigated the contribution of carcass traits on carcass prices of Holstein steers in Korea. Phenotypic data consisted of 76,814 slaughtered Holsteins (1 to 6 yrs) from all over Korea. The means for live body weight at slaughter (BWT), chilled carcass weight (CWT), dressing percentage (DP), quantity grade index (QGI), eye muscle area (EMA), backfat thickness (BF) and marbling score (MS), carcass unit price (CUP), and carcass sell prices (CSP) were 729.0 kg, 414.2 kg, 56.79%, 64.42, $75.26cm^2$, 5.77 mm, 1.98, 8,952.80 Korean won/kg and 3,722.80 Thousand Korean won/head. Least squares means were significantly different by various age groups, season of slaughter, marbling scores and yield grades. Pearson's correlation coefficients of CUP with carcass traits ranged from 0.12 to 0.62. Besides, the relationships of carcass traits with CSP were relatively stronger than those with CUP. The multiple regression models for CUP and CSP with carcass traits accounted 39 to 63% of the total variation, respectively. Marbling score had maximum economic effects (partial coefficients) on both prices. In addition, the highest standardized partial coefficients (relative economic weights) for CUP and CSP were calculated to be on MS and CWT by 0.608 and 0.520, respectively. Path analyses showed that MS (0.376) and CWT (0.336) had maximum total effects on CUP and CSP, respectively; whereas BF contributed negatively. Further sub-group (age and season of slaughter) analyses also confirmed the overall outcomes. However, the relative economic weights and total path contributions also varied among the animal sub-groups. This study suggested the significant influences of carcass traits on carcass prices; especially MS and CWT were found to govern the carcass prices of Holstein steers in Korea.

• Journal of Biosystems Engineering
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• v.34 no.3
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• pp.197-203
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• 2009

This study was performed to develop a nondestructive determination technology for sugar content in sweet persimmons, and the main research results included the following. In order to determine sugar content in sweet persimmons, a dual side reflex was adopted, and the study was to measure sugar content using a reflectance spectrum for 2 parts because it was difficult to determine representative sugar content due to a great deviation in sugar content according to the part of sweet persimmons. To predict sugar contents of sweet persimmon, PLSR and PCR models were compared with a few preprocess methods. As a result, PLSR had $R^2$=0.67, SEP=0.42 brix, LV=11, and PCR had $R^2$=0.65, SEP=0.41 brix, PC=16. SNV method was the best among preprocess methods for predicting sugar contents.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4102-4102
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• 2001

In multivariate analysis, absorbance spectrum is measured over a band of wavelengths. One does not often pay attention to the size of this wavelength band. However, it is desirable that spectrum is measured at only necessary wavelengths as long as the acceptable accuracy of prediction can be met. In this paper, the method of selecting an optimal band of wavelengths based on the loading vector analysis was proposed and applied for determining total protein in human serum using near-infrared transmission spectroscopy and PLSR. Loading vectors in the full spectrum PLSR were used as reference in selecting wavelengths, but only the first loading vector was used since it explains the spectrum best. Absorbance spectra of sera from 97 outpatients were measured at 1530∼1850 nm with an interval of 2 nm. Total protein concentrations of sera were ranged from 5.1 to 7.7 g/㎗. Spectra were measured by Cary 5E spectrophotometer (Varian, Australia). Serum in the 5 mm-pathlength cuvette was put in the sample beam and air in the reference beam. Full spectrum PLSR was applied to determine total protein from sera. Next, the wavelength region of 1672∼1754 nm was selected based on the first loading vector analysis. Standard Error of Cross Validation (SECV) of full spectrum (1530∼l850 nm) PLSR and selected wavelength PLSR (1672∼1754 nm) was respectively 0.28 and 0.27 g/㎗. The prediction accuracy between the two bands was equal. Wavelength selection based on loading vector in PLSR seemed to be simple and robust in comparison to other methods based on correlation plot, regression vector and genetic algorithm. As a reference of wavelength selection for PLSR, the loading vector has the advantage over the correlation plot since the former is based on multivariate model whereas the latter, on univariate model. Wavelength selection by the first loading vector analysis requires shorter computation time than that by genetic algorithm and needs not smoothing.

• 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 CROP SCIENCE
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• v.54 no.1
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• pp.29-35
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• 2009

The aim of the present study was to update the calibration that is used for the measurement of the total nitrogen content in the rice plant samples by using the visible and near infrared spectrum. Before the equation merge, correlation coefficient of calibration equation for nitrogen content on each rice parts was 0.945 (Leaf), 0.928 (Stem), and 0.864 (Whole plant), respectively. In the calibration models created by each part in the rice plant under the various regression method, the calibration model for the leaf was recorded with relatively high accuracy. Among of those, the calibration equation developed by Partial least squares (PLS) method was more accurate than the Multiple linear regression (MLR) method. The calibration equation was sensitive based on variety and location variations. However, we have merged and enlarged various of the samples that made not only to measure the nitrogen content more accurately, but also later sampling populations became more diversified. After merging, $R^2$ value becomes more accurate and significantly to 0.950 (L.), 0.974 (S.), 0.940 (W.). Also, after removal of outlier, R2 values increased into 0.998, 0.995, and 0.997. In view of the results so far achieved, Standard error of prediction (SEP) and SEP (C) were reduced in the stem and whole plant. Biases were reduced in the leaf, stem as well as whole plant. Slopes were high in the stem. Standard deviation reduced in the stem but $R^2$ was high in the stem and whole plant. Result was indicated that calibration equation make update, and updating robust calibration equation from merge function and multi-variate calibration.

• Analytical Science and Technology
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• v.11 no.1
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• pp.47-53
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• 1998

This study is to improve the diagnosis of diabetes mellitus and the self-monitoring of blood glucose in people with diabetes by providing a non-invasive method of monitoring blood glucose. A near-infrared (NIR) spectrophotometer was used to measure absorption spectra of 80 glucose samples ranges from 1 mg/dL to 200 mg/dL, and shows the standard error of prediction 1.8 mg/dL. Also, to investigate the effect of interference in blood, NaCl and sand were added in glucose and found the standard error of prediction of 2.8 mg/dL and 3.8 mg/dL, respectively. A new and more accurate calibration system for the spectrophotometer was developed from systematic study of light scattering, which cause nonlinear spectrophotometer response.

• The Korean Journal of Food And Nutrition
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• v.30 no.4
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• pp.719-727
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• 2017

The objective of this study was to investigate the effect of alcohol content in Makgeolli made with Chinese matrimony vine (M-CMV) on the sensory profile and consumer acceptability. The M-CMVs were prepared with 6, 7, 8, and 9% alcohol content. Descriptive analysis of M-CMV was performed with six trained panelists. Thirteen attributes were generated and their intensities were alcohol content dependent. The consumer acceptance test was conducted with 57 consumers. M-CMV samples with 7% alcohol had the highest acceptance rate (5.8) followed by 6% M-CMV (5.6). Commercial rice Makgeolli (CRM) had the lowest consumer acceptance. Consumers were divided into two groups by clustering analysis. The majority of consumers (n=38) preferred M-CMV and did not like the commercial sample. Only 19 consumers indicated high acceptance ratings for CRM. However, these consumers also preferred 6 and 7% M-CMV. Partial least-squares regression analysis revealed moderate attribute intensities were related to greater consumer acceptability. The optimal alcohol content for the greatest consumer acceptance predicted by linear regression was 6.7%.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.675-696
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• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.4
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• pp.99-112
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• 2019

The landscape of the education market is changing. As part of efforts to deal with the decrease of a school-age population in Korea, Korean universities and colleges are endeavoring to attract foreign students. Chinese students, the largest share of foreign university students in Korea, are becoming an important element at Korean universities and colleges. Chinese students face various kinds of difficulties while trying to cope with new environments in the country. This study aims to analyze the impact of academic factors and psychological factors of Chinese students on the level of adaptation to university life and their satisfaction with it. Data on 128 Chinese students attending D University located in Busan were collected and a path analysis was conducted using partial least squares (PLS) regression. Research results are as follows. First, professors as an academic factor and the level of interest of university staff have significant impact on the level of adaptation to university life while Korean language proficiency does not have significant impact on the level of adaptation to university life. Second, homesickness as a psychological factor is correlated to the level of adaptation to university life with significance while acculturative stress is not correlated to it. Third, the level of adaptation to university life is correlated to the level of satisfaction with university life. Based on these findings, the significance, limitations and future directions of this study are discussed.