• Korean Journal of Soil Science and Fertilizer
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• v.34 no.4
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• pp.278-283
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• 2001

The overall objective of this research was to show a NIR soil analyzer assessing soil fertility by measuring soil properties rapidly. A total of 140 soil samples were used to obtain calibrations and validation estimating soil properties. The soil samples were ground to pass 0.2mm sieve openings. Partial least square regression analysis was used to develop a calibration for soil analysis. The results indicated that NIR soil analyzer could be used as a routine method for quantitatively determining pH, OM, total nitrogen, CEC, extractable Ca, Mg, K, available $SiO_2$ and soil moisture simultaneously within one minute. Therefore, the NIR soil analyzer may be suitable for quick estimation of soil fertility estimation in fertilizer assessments.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.1
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• pp.32-38
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• 2005

A long-term study initiated in 1989 at San-born Field, Columbia, Missouri, was designed to evaluate the affect of environmental factors, nitrogen application, and crop rotation on soybean (Glycine max [L.] Merr.) seed composition. Soybeans were grown as part of a four- year rotation which included corn (Zea maize L.), wheat (Triticum aestivum L.), and red clover (Trifolium pratense L.). Results from soil tests made prior to initiation of the study and subsequently every five years, were used to calculate application rates of nitrogen, phosphorus, and potassium necessary for target yield of pursuant crops. In the experimental design, nitrogen was applied to one-half of the plot on which the non-leguminous crop, either corn or wheat was grown. Analysis of soybean seed by near infrared reflectance spectroscopy collected over an 11-year period revealed a linear increase in protein and decrease in oil content. Application of nitrogen fertilizer to non-leguminous crops did not have an apparent effect on total protein or oil content of subsequent soybean crop. Analysis of soybean seed proteins by sodium dodecyl sulfate polyacrylamide gel electrophoresis in conjunction with computer­assisted densitometry revealed subtle changes in the accumulation of seed proteins. Immunoblot analysis using antibodies raised against the $\beta-subunit$ of $\beta-conglycinin$ showed a gradual increase in the accumulation of the 7S components during successive years of the experiment. A linear increase in temperature and decrease in rainfall was observed from the onset of data· collection. Higher temperatures during the growing season have been linked to increased protein and diminished oil content of soybean, thus changes observed in this study are possibly related to climatic conditions. However, crop rotation and subsequent changes in soil ecology may contribute to these observed changes in the seed composition.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2018.04a
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• pp.31-31
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• 2018

The purpose of this study was to statistically analyze amylose and protein content of rice variety resources collected from China (1,542), Japan (1,409), Korea (413), and India (287). The statistical analysis was conducted using ANOVA and DMRT based on the data obtained from NIRS analysis. The average amylose contents were 18.85% in Japanese, 19.99% in Korean, 20.27% in Chinese, and 25.46% in Indian resources. The average protein contents were 7.23% in Korean, 7.73% in Japanese, 8.01% in Chinese, and 8.17% in Indian resources. The amylose and protein content using ANOVA showed significant differences at the level of 0.01. The F-test for amylose content was 158.34, and for protein content was 53.95 compared to critical value 3.78. The amylose and protein content using DMRT (p<0.01) showed significant difference between countries. The value of statistical treatment was divided into three groups such as $China^a$, $Korea^a$, $Japan^b$, $India^c$ in amylose and $China^a$, $India^a$, $Japan^b$, $Korea^c$ in protein. Japanese resources had the lowest level of amylose contents, whereas, the lowest level of protein content was found in Korean resources compared to other origins. Indian resources showed the highest level of amylose and protein contents. It is recommended that these results could be helpful to future breeding experiments.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.spc1
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• pp.283-291
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• 2006

To investigate seed non destructive and fast determination technique utilizing near infrared reflectance spectroscopy (NIRs) for screening ultra high oleic (C18:1) and linoleic (C18:2) fatty acid content sesame varieties among genetic resources and lines of pedigree generations of cross and mutation breeding were carried out in National Institute of Crop Science (NICS). 150 among 378 landraces and introduced cultivars were released to analyse fatty acids by NIRs and gas chromatography (GC). Average content of each fatty acid was 9.64% in palmitic acid (C16:0), 4.73% in stearic acid (C18:0), 42.26% in oleic acid and 43.38% in linoleic acid by GC. The content range of each fatty acid was from 7.29 to 12.27% in palmitic, 6.49% from 2.39 to 8.88% in stearic, 12.59% of wider range compared to that of stearic and palmitic from 37.36 to 49.95% in oleic and of the widest from 30.60 to 47.40% in linoleic acid. Spectrums analyzed by NIRs were distributed from 400 to 2,500 nm wavelengths and varietal distribution of fatty acids were appeared as regular distribution. Varietal differences of oleic acid content good for food processing and human health by NIRs was 14.08% of which 1.49% wider range than that of GC from 38.31 to 52.39%. Varietal differences of linoleic acid content by NIRs was 16.41% of which 0.39% narrower range than that of GC from 30.60 to 47.01%. Varietal differences of oleic and linoleic acid content in NIRs analysis were appeared relatively similar inclination compared with those of GC. Partial least square regression (PLSR) among multiple variant regression (MVR) in NIRs calibration statistics was carried out in spectrum characteristics on the wavelength from 700 to 2,500 nm with oleic and linoleic acids. Correlation coefficient of root square (RSQ) in oleic acid content was 0.724 of which 72.4 percent of sample varieties among all distributed in the range of 0.570 percent of standard error when calibrated (SEC) which were considerably acceptable in statistic confidence significantly for analysis between NIRs and GC. Standard error of cross validation (SECV) of oleic acid was 0.725 of which distributed in the range of 0.725 percent standard error among the samples of mother population between analyzed value by NIRs analysis and analyzed value by GC. RSQ of linoleic acid content was 0.735 of which 73.5 percent of sample varieties among all distributed in the range of 0.643 percent of SEC. SECV of linoleic acid was 0.711 of which distributed in the range of 0.711 percent standard error among the samples of mother population between NIRs analysis and GC analysis. Consequently, adoption NIR analysis for fatty acids of oleic and linoleic instead that of GC was recognized statistically significant between NIRs and GC analysis through not only majority of samples distributed in the range of negligible SEC but also SECV. For enlarging and increasing statistic significance of NIRs analysis, wider range of fatty acids contented sesame germplasm should be kept on releasing additionally for increasing correlation coefficient of RSQ and reducing SEC and SECV in the future.

• Journal of Animal Environmental Science
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• v.18 no.3
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• pp.257-266
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal and dired animal forages. Analysis of forage quality by NIRS usually involves dry grinding samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations on prediction ability of chemical composition and fermentation parameter for Italian ryegrass silages by NIRS. A population of 147 Italian ryegrass silages representing a wide range in chemical parameters were used in this investigation. Samples were scanned at 1nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in oven-dried grinding and fresh ungrinding condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with four spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV) and maximizing the correlation coefficient of cross validation (${R^2}_{CV}$). The results of this study show that NIRS predicted the chemical parameters with high degree of accuracy in oven-dried grinding treatment except for moisture contents. Prediction accuracy of the moisture contents was better for fresh ungrinding treatment (SECV 1.37%, $R^2$ 0.96) than for oven-dried grinding treatments (SECV 4.31%, $R^2$ 0.68). Although the statistical indexes for accuracy of the prediction were the lower in fresh ungrinding treatment, fresh treatment may be acceptable when processing is costly or when some changes in component due to the processing are expected. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation parameter of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Korean Journal of Food Science and Technology
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• v.40 no.6
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• pp.603-610
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• 2008

The principal objective of current study was to evaluate the potential of near infrared reflectance spectroscopy (NIRS) as a non-destructive method for the prediction of the amylose and protein contents of un-hulled and brown rice in broad-based calibration models. The average amylose and protein content of 75 rice accessions were 20.3% and 7.1%, respectively. Additionally, the range of amylose and protein content were 16.6-24.5% and 3.8-9.3%, respectively. In total, 79 rice germplasms representing a wide range of chemical characteristics, variable physical properties, and origins were scanned via NIRS for calibration and validation equations. The un-hulled and brown rice samples evidenced distinctly different patterns in a wavelength range from 1,440 nm to 2,400 nm in the original NIR spectra. The optimal performance calibration model could be obtained by MPLS (modified partial least squares) using the first derivative method (1:4:4:1) for un-hulled rice and the second derivative method (2:4:4:1) for brown rice. The correlation coefficients $(r^2)$ and standard error of calibration (SEC) of protein and amylose contents for the un-hulled rice were 0.86, 2.48, and 0.84, 1.13, respectively. The $r^2$ and SEC of protein and amylose content for brown rice were 0.95, 1.09 and 0.94, 0.42, respectively. The results of this study suggest that the NIRS technique could be utilized as a routine procedure for the quantification of protein and amylose contents in large accessions of un-hulled rice germplasms.

• Korean Journal of Plant Resources
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• v.30 no.1
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• pp.38-49
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• 2017

The objective of this research was to develop Near-Infrared Reflectance Spectroscopy (NIRS) model for amylose and protein contents analysis of large accessions of rice germplasm. A total of 511 accessions of rice germplasm were obtained from National Agrobiodiversity Center to make calibration equation. The accessions were measured by NIRS for both brown and milled brown rice which was additionally assayed by iodine and Kjeldahl method for amylose and crude protein contents. The range of amylose and protein content in milled brown rice were 6.15-32.25% and 4.72-14.81%, respectively. The correlation coefficient ($R^2$), standard error of calibration (SEC) and slope of brown rice were 0.906, 1.741, 0.995 in amylose and 0.941, 0.276, 1.011 in protein, respectively, whereas $R^2$, SEC and slope of milled brown rice values were 0.956, 1.159, 1.001 in amylose and 0.982, 0.164, 1.003 in protein, respectively. Validation results of this NIRS equation showed a high coefficient determination in prediction for amylose (0.962) and protein (0.986), and also low standard error in prediction (SEP) for amylose (2.349) and protein (0.415). These results suggest that NIRS equation model should be practically applied for determination of amylose and crude protein contents in large accessions of rice germplasm.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.70-88
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• 2019

A statistical analysis of 4,380 non-glutinous landrace rice germplasm collected from four East Asian countries namely South Korea (1,032), North Korea (994), Japan (800), and China (528) was conducted using normal distribution, variability index value (VIV), analysis of variation (ANOVA), and Duncan's multiple range test (DMRT) based on a data obtained from Near-Infrared Reflectance Spectroscopy (NIRS) analysis. In normal distribution, the average protein content was 8.2%, and the non-glutinous rice amylose, ranging over 10%, was found to be 22.0%. Protein content in most gremplasm was between 5.4 and 10.9%, and amylose content was between 15.0 and 28.9%. The VIV was 0.50 for protein, and 0.81 for non-glutinous rice amylose content. The average amylose content was 23.34% in Chinese, 21.55% in South Korean, 21.45% in Japanese, and 20.48% in North Korean resources, while the average protein content was found to be 9.02% in Chinese, 8.06% in Japanese, 8.04% in North Korean, and 7.99% in South Korean resources. ANOVA of amylose and protein content showed significant differences at p=0.01. The F-test value for amylose content was 94.92, and for protein content was 81.82 compared to the critical value of 3.79. DMRT of amylose and protein content revealed significant differences (p<0.01). Among the various germplasm obtained from different countries, that from North Korean had the lowest level of amylose content, whereas that from South Korea had the lowest level of protein content than all other resources. Chinese resources had the highest level of amylose and protein content. It is recommended to use these results in breeding fields.

• Korean Journal of Plant Resources
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• v.32 no.2
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• pp.124-143
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• 2019

This study was carried out to build a database system for amylose and protein contents of rice germplasm based on NIRS (Near-Infrared Reflectance Spectroscopy) analysis data. The average waxy type amylose contents was 8.7% in landrace, variety and weed type, whereas 10.3% in breeding line. In common rice, the average amylose contents was 22.3% for landrace, 22.7% for variety, 23.6% for weed type and 24.2% for breeding line. Waxy type resources comprised of 5% of the total germplasm collections, whereas low, intermediate and high amylose content resources share 5.5%, 20.5% and 69.0% of total germplasm collections, respectively. The average percent of protein contents was 8.2 for landrace, 8.0 for variety, and 7.9 for weed type and breeding line. The average Variability Index Value was 0.62 in waxy rice, 0.80 in common rice, and 0.51 in protein contents. The accession ratio in arbitrary ranges of landrace was 0.45 in amylose contents ranging from 6.4 to 8.7%, and 0.26 in protein ranging from 7.3 to 8.2%. In the variety, it was 0.32 in amylose ranging from 20.1 to 22.7%, and 0.51 in protein ranging from 6.1 to 8.3%. And also, weed type was 0.67 in amylose ranging from 6.6 to 9.7%, and 0.33 in protein ranging from 7.0 to 7.9%, whereas, in breeding line it was 0.47 in amylose ranging from 10.0 to 12.0%, and 0.26 in protein ranging from 7.0 to 7.9%. These results could be helpful to build database programming system for germplasm management.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.spc
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• pp.78-83
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• 2008

Soybean [Glycine max (L.)] is a major source of protein for human and animal feed. Inter- and intra-genotype variation of soybean protein has been investigated by soybean researchers. However, limited sample amount of soybean single seed there is no report that investigated intra-plant variation of soybean protein within soybean plant. Recently a non-destructive NIR (near-infrared reflectance) spectroscopy using single seed grain to analyze seed protein was developed. The objectives of this study were to understand variation of seed protein content within plant and to determine the amount of minimum sample size which can represent protein content for a soybean plant. Frequency distribution of protein content within plant showed normal distribution. There was an intra-cultivar variation for protein content in soybean cultivar Seonnogkong. Difference of protein content among single plants of Seonnokong was recognized at 5% level. Seeds in lower position on plant stem tended to accumulate more protein than in higher position. There was significant difference for protein content between sample size 5 seeds and sample size of more than 5 seeds (10, 20, 30, 40, and 50 seeds) at a soybean plant with 57 seeds however no difference was recognized among sample size (5, 10, 20, and 30 seeds) at a soybean plant with 33 seeds. Around 20% seeds of soybean from single plant needed to determine the protein content to represent protein content of single soybean plant. This study is the first one to report evidence of intra-plant variation for proteincontent which detected by non-destructive NIR spectroscopy using single seed grain in soybean.