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

• Journal of Biosystems Engineering
• /
• v.42 no.4
• /
• pp.301-313
• /
• 2017

Purpose: This study was conducted to discriminate between normal hulled barley and Fusarium (Fusarium asiaticum and Fusarium graminearum) infected hulled barley by using the near-infrared spectroscopy (NIRS) technique. Methods: Fusarium asiaticum and Fusarium graminearum were artificially inoculated in hulled barley and the reflectance spectrum of the barley spike was obtained by using a near-infrared spectral sensor with wavelength band in the range 1,175-2,170 nm. After obtaining the spectrum of the specimen, the hulled barley was cultivated in a greenhouse and visually inspected for infections. Results: From a partial least squares discriminant analysis (PLS-DA) prediction model developed from the raw spectrum data of the hulled barley, the discrimination accuracy for the normal and infected hulled barley was 99.82% (563/564) and 100% (672/672), respectively. Conclusions: NIRS is effective as a quick and nondestructive method to detect whether hulled barley has been infected with Fusarium. Further, it expected that NIRS will be able to detect Fusarium infections in other grains as well.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.54 no.1
• /
• pp.29-35
• /
• 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.

• Journal of Crop Science and Biotechnology
• /
• v.10 no.1
• /
• pp.13-18
• /
• 2007

Near-infrared spectroscopy(NIRS) was used as a rapid and nondestructive method to determine the fatty acid composition in intact seed samples of rapeseed(Brassica napus L.). A total of 349 samples(about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for fatty acid composition were measured by gas-liquid chromatography. Calibration equations for individual fatty acids were developed using the regression method of modified partial least-squares with internal cross validation(n=249). The equations had low SECV(standard errors of cross-validation), and high $R^2$(coefficient of determination in calibration) values(>0.8) except for palmitic and eicosenoic acid. Prediction of an external validation set(n=100) showed 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. The models developed in this study had relatively higher values(> 3.0 and 0.9, respectively) of SD/SEP(C) and $r^2$ for oleic, linoleic, and erucic acid, characterizing those equations as having good quantitative information. The results indicated that NIRS could be used to rapidly determine the fatty acid composition in rapeseed seeds in the breeding programs for high quality rapeseed oil.

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

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.33 no.3
• /
• pp.177-184
• /
• 2013

This work was conducted to assess the use of Near-infrared reflectance spectroscopy (NIRS) as a technique to analyze nutritional constituents of Distillers dried grain with solubles (DDGS) and corn quickly and accurately, and to apply an NIRS-based indium gallium arsenide array detector, rather than a NIRS-based scanning system, to collect spectra and induce and analyze calibration equations using equipment which is better suited to field application. As a technique to induce calibration equations, Partial Least Squares (PLS) was used, and for better accuracy, various mathematical transformations were applied. A multivariate outlier detection method was applied to induce calibration equations, and, as a result, the way of structuring a calibration set significantly affected prediction accuracy. The prediction of nutritional constituents of distillers dried grains with solubles resulted in the following: moisture ($R^2$=0.80), crude protein ($R^2$=0.71), crude fat ($R^2$=0.80), crude fiber ($R^2$=0.32), and crude ash ($R^2$=0.72). All constituents except crude fiber showed good results. The prediction of nutritional constituents of corn resulted in the following: moisture ($R^2$=0.79), crude protein ($R^2$=0.61), crude fat ($R^2$=0.79), crude fiber ($R^2$=0.63), and crude ash ($R^2$=0.75). Therefore, all constituents except for crude fat and crude fiber were predicted for their chemical composition of DDGS and corn through Near-infrared reflectance spectroscopy.

• Journal of Biosystems Engineering
• /
• v.40 no.2
• /
• pp.153-158
• /
• 2015

Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data from line-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models were developed to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals were line-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region of Interest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) were selected for chemometric analysis. The partial least squares regression (PLSR) and the principal component analysis (PCA) methods of the chemometric analysis were applied to the model development. The purpose of the models was to correctly classify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showed that the success classification rates were 97.9% for beef samples and 99.4% for fish samples by the PLSR model, and 85.1% for beef samples and 88.2% for fish samples by the PCA model. Conclusion: The chemometric analysis-based PLSR and PCA models for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.

• Journal of Biosystems Engineering
• /
• v.37 no.6
• /
• pp.404-410
• /
• 2012

Purpose: The objective of this research was to select high quality cucumber (cucumis sativus) seed by classifying into viable or non-viable one using Raman spectroscopy. Method: Both transmission and back-scattering Raman spectra of viable and non-viable seeds in the range from $150cm^{-1}$ to $1890cm^{-1}$ were collected with a laser illumination. Results: The Raman spectra of cucumber seed showed Raman peaks with features of polyunsaturated fatty acids. The partial least squares-discriminant analysis (PLS-DA) to predict viable seeds was developed with measured transmission and backscattering spectra with Raman spectroscopy and germination test results. Various types of spectra pretreatment were investigated to develop the classification models. The results of developed PLS-DA models using the transmission spectra with mean normalization or range normalization, and back-scattering spectra with mean normalization treatment or baseline correction showed 100% discrimination accuracy. Conclusions: These results showed that Raman spectroscopy technologies can be used to select the high quality cucumber seeds.

• Journal of the Korean Society of Food Culture
• /
• v.32 no.2
• /
• pp.150-161
• /
• 2017

This study was conducted to identify sensory characteristics of soy-meat samples by trained panels and to observe the relationship between these sensory characteristics and consumer acceptability of the samples. Descriptive analysis was performed on eight samples; four types of patty style soy-meat samples (Soy-meat Patty; SP) made with a Ddukgalbi recipe (YSP, VSP, LSP, and SSP) and four types of Bulgogi style soy-meat samples (Soy-meat Bulgogi; SB) made with a Bulgogi recipe (YSB, VSB, LSB, and SSB). Seven panelists were trained, and they evaluated the appearance, odor/aroma, flavor/taste, texture/mouth feel, and after taste attributes of these samples. Forty attributes were generated by panelists, and 37 attributes were significantly different across products (p<0.05). The SB group was characterized by beef, leek, and garlic flavor as well a sweetness, denseness, slipperiness, chewiness, and pepper after taste. The SP group was characterized by roughness, particle size, rancid oil flavor, raw bean flavor, astringent, sourness, and adhesiveness. Consumer test (n=125) showed that the VSB sample had the highest scores for acceptability of appearance, flavor, texture, and overall liking. The PLSR results show that the attributes that were more positively associated with acceptance of soy-meat samples were beef taste, wetness, and chewiness, whereas the raw bean smell and rancid oil flavor attributes were negative.

• Journal of Animal Environmental Science
• /
• v.20 no.3
• /
• pp.117-124
• /
• 2014

The objectives were to assess the use of near-infrared reflectance spectroscopy (NIRS) as a tool for estimating nutrient compositions of corn kernel, and to apply an NIRS-based indium gallium arsenide array detector to the system for collecting spectra and analyzing calibration equations using equipments designed for field application. Partial Least Squares Regression (PLSR) was employed to develop calibration equations based on representative data sets. The kennard-stone algorithm was applied to induce a calibration set and a validation set. As a result, the method for structuring a calibration set significantly affected prediction accuracy. The prediction of chemical composition of corn kernel resulted in the following (kennard-stone algorithm: relative) moisture ($R^2=0.82$, RMSEP=0.183), crude protein ($R^2=0.80$, RMSEP=0.142), crude fat ($R^2=0.84$, RMSEP=0.098), crude fiber ($R^2=0.74$, RMSEP=0.098), and crude ash ($R^2=0.81$, RMSEP=0.048). Result of this experiment showed the potential of NIRS to predict the chemical composition of corn kernel.