• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.2
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• pp.114-120
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• 2019

This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation($R^2$) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The $R^2$ and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of winter annual forage for routine analysis method to evaluate the feed value.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.53-62
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• 2006

Near infrared reflectance spectroscopy (NIRS) has been increasingly used as a rapid, accurate method of evaluating some chemical compositions in forages. Analysis of forage quality by NIRS usually involves dry ground 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 and spectral math treatments on prediction ability of chemical composition for corn silage by NIRS. A population of 112 corn silage representing a wide range in chemical parameters were used in this investigation. Samples of com silage were scanned at 2nm intervals over the wavelength range 400-2500nm and the optical data recorded as log l/Reflectance(log l/R) and scanned in overt-dried grinding(ODG), liquid nitrogen grinding(LNG) or intact fresh(IF) condition. Samples were analysed for neutral detergent fiber(NDF), acid detergent fiber(ADF), acid detergent lignin(ADL), crude protein(CP) and crude ash content were expressed on a dry-matter(DM) basis. The spectral data were regressed against a range of chemical parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with four spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation(SECV). The results of this study show that NIRS predicted the chemical parameters with very high degree of accuracy(the correlation coefficient of cross validation$(R^2cv)$ range from $0.70{\sim}0.95$) in ODG. The optimum equations were selected on the basis of minimizing the standard error of prediction(SEP). The Optimum sample preparation methods and spectral math treatment were for ADF, the ODG method using 2,10,5 math treatment(SEP = 0.99, $R^2v=0.93$), and for CP, the ODG method using 1,4,4 math treatment(SEP = 0.29. $R^2v=0.91$).

• 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.

• 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 CROP SCIENCE
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• v.53 no.1
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• pp.15-20
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• 2008

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to measure anthocyanin contents in black colored soybean by using NIRS system. Total 300 seed coat of black colored soybean samples previously analyzed by HPLC were scanned by NIRS and over 250 samples were selected for calibration and validation equation. A calibration equation calculated by MPLS(modified partial least squares) regression technique was developed in which the coefficient of determination for anthocyanin pigment C3G, D3G and Pt3G content was 0.952, 0.936, and 0.833, respectively. Each calibration equation was applied to validation set that was performed with the remaining samples not included in the calibration set, which showed high positive correlation both in C3G and D3G content file. In case Pt3G, the prediction model was needed more accuracy because of low $R^2$ value in validation set. This results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of C3G and D3G contents in black colored soybean.

• Korean Journal of Plant Resources
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• v.27 no.4
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• pp.286-292
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• 2014

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to measure baicalin, baicalein, and wogonin contents in Scutellaria baicalensis by using NIRS system. Total 63 samples previously were analyzed by HPLC, which showed baicalin, baicalein, and wogonin contents ranging 4.56 to 13.59%, 0.28 to 5.54%, and 0.50 to 1.63% with an average of 9.66%, 2.09% and 0.52%, respectively. Each sample was scanned by NIRS and calculated for calibration and validation equation. A calibration equation calculated by modified partial least squares(MPLS) regression technique was developed in which the coefficient of determination for baicalin, baicalein, and wogonin content was 0.958, 0.944, and 0.709, respectively. Each calibration equation was applied to validation set that was performed with the remaining samples not included in the calibration set, which showed high positive correlation both in baicalin and baicalein content file. In case of wogonin, the prediction model was needed more accuracy because of low $R^2$ value in validation set. These results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of baicalin and baicalein contents in Scutellaria baicalensis.

• Analytical Science and Technology
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• v.16 no.4
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• pp.277-282
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• 2003

The amount of water for the cultivation of citrus is different based on the growing period. The effect of water stress induces to enhance of sugar accumulation in citrus. The water content in the leaves of citrus can be a index for watering during cultivation. The purpose of this study is to determine the water content of citrus leaves non-destructively by using near infrared spectroscopy (NIRS). Citrus leaves were prepared from 'Okitsu' Satusuma mandarin leaves (Citrus unshiu Marc.) ranging from 20.80 to 69.98% of water content by loss on drying method, and NIR reflectance spectra of citrus leaves were acquired by using a fiber optic probe. It was found that the variation of absorbance band 1450 nm from OH vibration of water depending on the water content change. Partial least squares regression (PLSR) was applied to develop a calibration model over the spectral range 1100-1700 nm. The calibration model predicted the water content for the validation set with a standard errors of prediction (SEP) of 0.97%. In order to validate the developed calibration model, routine analyses were performed using independently prepared citrus leaves. The NIR routine analyses showed good results with those of loss on drying method with a SEP of 0.81%. The rapid and non-destructive determination of the water content in citrus leaves was successfully performed by portable NIR system.

• 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.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1615-1615
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• 2001

During the past ten years, Near Infrared Spectroscopy has been successfully applied to the analysis of a great variety of agriculture products. Previous works (Morra et al., 1991; Salgo et al., 1998) have shown the potential of this technology for soil analysis, estimating different parameters just with one single scan. The main advantages of NIR applications in soils are the speed of response, allowing the increase of the number of samples analysed to define a particular soil, and the instantaneous elaboration of recommendations for fertilization and soil amendment. Another advantage is to avoid the use of chemical reagents at all, being an environmentally safe technique. In this paper, we have studied a set of 129 soil samples selected from representative glasshouse soils from Southern Spain. The samples were dried, milled, and sieved to pass a 2 mm sieve and then analysed for organic carbon, total nitrogen, inorganic nitrogen (nitrate ammonium), hygroscopic humidity, pH and electrical conductivity in the 1:1 extract. NIR spectra of all samples were obtained in reflectance mode using a Foss NIR Systems 6500 spectrophotometer equipped with a spinning module. Calibration equations were developed for seven analytical parameters (ph, Total nitrogen, organic nitrogen, organic carbon, C/N ratio and Electric Conductivity). Preliminary results show good correlation coefficients and standard errors of cross validation in equations obtained for Organic Carbon, Organic Nitrogen, Total Nitrogen and C/N ratio. Calibrations for nitrates and nitrites, ammonia and electric conductivity were not acceptable. Calibration obtained for pH had an acceptable SECV, but the determination coefficient was found very poor probably due to the reduced range in reference values. Since the estimation of Organic Carbon and C/N ratio are acceptable NIIRS could be used as a fast method to assess the necessity of organic amendments in soils from Mediterranean regions where the low level of organic matter in soils constitutes an important agronomic problem. Furthermore, the possibility of a single and fast estimation of Total Nitrogen (tedious determination by modifications of the Kjeldahl procedure) could provide and interesting data to use in the estimation of nitrogen fertilizer rates by means of nitrogen balances.

• Journal of The Korean Society of Grassland and Forage Science
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• v.29 no.3
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• pp.253-262
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• 2009

The present study was conducted to develop a rapid and accurate method of evaluating chemical composition of total mixed ration (TMR) for dairy cows using near infrared reflectance spectroscopy (NIRS). A total of 253 TMR samples were collected from TMR manufacturers and dairy farms in Korea. Prior to NIR analysis, TMR samples were dried at $65^{\circ}C$ for 48 hour and then ground to 2 mm size. The samples were scanned at 2 nm interval over the wavelength range of 400-2500 nm on a FOSS-NIR Systems Model 6500. The values obtained by NIR analysis and conventional chemical methods were compared. Generally, the relationship between chemical analysis and NIR analysis was linear: $R^2$ and standard error of calibration (SEC) were 0.701 (SEC 0.407), 0.965 (SEC 0.315), 0.796 (SEC 0.406), 0.889 (SEC 0.987), 0.894 (SEC 0.311), 0.933 (SEC 0.885) and 0.889 (SEC 1.490) for moisture, crude protein, ether extract, crude fiber, crude ash, acid detergent fiber (ADF) and neutral detergent fiber (NDF), respectively. In addition, the standard error of prediction (SEP) value was 0.371, 0.290, 0.321, 0.380, 0.960, 0.859 and 1.446 for moisture, crude protein, ether extract, crude fiber, crude ash, ADF and NDF, respectively. The results of the present study showed that the NIR analysis for unknown TMR samples would be relatively accurate. Use of the developed NIR calibration curve can obtain fast and reliable data on chemical composition of TMR. Collection and analysis of more TMR samples will increase accuracy and precision of NIR analysis to TMR.