• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1620-1620
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• 2001

Forages, both grazed and conserved, provide the basis of ruminant production systems throughout the world. More than 90 per cent of the feed energy consumed by herbivorous animals world - wide were provided by forages. With such world - wide dependence on forages, the economic and nutritional necessity of been able to characterize them in a meaningful way is vital. The characterization of forages for productive animals is becoming important for several reasons. Relative to conventional laboratory procedures, Near Infrared Reflectance Spectroscopy (NIRS) offers advantages of simplicity, speed, reduced chemical waste, and more cost-effective prediction of product functionality. NIR spectroscopy represents a radical departure from conventional analytical methods, in that entire sample of forage is characterized in terms of its absorption properties in the near infrared region, rather than separate subsamples being treated with various chemicals to isolate specific components. This forces the analyst to abandon his/her traditional narrow focus on the sample (one analyte at a time) and to take a broader view of the relationship between components within the sample and between the sample and the population from which it comes. forage is usually analysed by NIRS in dry and ground presentation. Initial success of NIRS analysis of coarse forages suggest a need to better understand the potential for analysis of minimally processed samples. Preparation costs and possible compositional alterations could be reduced by samples presented to the instrument in undried and unground conditions. NIRS has gained widespread acceptance for the analysis of forage quality constituents on dry material, however little attention has been given to the use of NIRS for chemical determinations on undried and unground forages. Relatively few works reported the use of NIRS to determine quality parameters on undried materials, most of them on both grass and corn silage. Only two works have been found on the determination of quality parameters on fresh forages. The objectives of this paper were (1) to evaluate the use of NIRS for determination of nitrogen and moisture on undried and unground forage samples and (2) to explore two mathematical treatments and two NIR regions to predict chemical parameters on fresh forage. Four hundred forage samples (n: 400) were analysed in a NIRS 6500 instrument (NIR Systems, PA, USA) in reflectance mode. Two mathematical treatments were applied: 1,4,4,1 and 2,5,5,2. Predictive equations were developed using modified partial least squares (MPLS) with internal cross - validation. Coefficient of determination in calibration (${R^2}_{CAL}$) and standard error in cross-validation (SECV) for moisture were 0.92 (12.4) and 0.92 (12.4) for 1,4,4,1 and 2,5,5,2 respectively, on g $kg^{-1}$ dry weight. For crude protein NIRS calibration statistics yield a (${R^2}_{CAL}$) and (SECV) of 0.85 (19.8) and 0.85 (19.6) for 1,4,4,1 and 2,5,5,2 respectively, on a dry weight. It was concluded that NIRS is a suitable method to predict moisture and nitrogen on fresh forage without samples preparation.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1517-1517
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• 2001

Cultural conditions during production of compost, using wheat straw and chicken litter as raw materials, will affect the microbial and biochemical characteristics, leading to a wide variation in mushroom productivity. Over the past 10 years, chemical and instrumental methods, suitable for assessing compost quality have been studied in Northern Ireland. In addition, the use of near subject of investigation over the past 4 years. Previous studies have shown that NIRS can be used fer assessing quality of dried and milled composts. The aim of the current investigation is to develop NIR calibrations for key quality parameters such as dry matter, pH, nitrogen, carbon, ash, microbial population and fibre factions during the two stages of production using spectra of fresh composts. Near infrared reflectance measurements of fresh composts prepared by 6 producers were made during a two-year period. Although the spectra of fresh composts were dominated by two moisture peaks at 1450 nm and 1940 nm, good calibrations for determining moisture content, conductivity, pH, nitrogen, carbon and fibre fractions were developed. The results of quality assessment during commercial production using the calibrations will be presented and discussed.

• Proceedings of the Korean Society of Crop Science Conference
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• 1993.10a
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• pp.16-17
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• 1993

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.2
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• pp.106-111
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• 2001

The applicability of near infrared reflectance spectroscopy(NIRS) was tested to determine the protein and oil contents in ground soybean [Glycine max (L.) Merr.] seeds. A total of 189 soybean calibration samples and 103 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of protein, the most accurate equation was obtained at 2, 8, 6, 1(2nd derivative, 8 nm gap, 6 points smoothing and 1 point second smoothing) math treatment condition with SNV-D (Standard Normal Variate and Detrend) scatter correction method and entire spectrum by using MPLS (Modified Partial Least Squares) regression. In the case of oil, the best equation was obtained at 1, 4, 4, 1 condition with SNV-D scatter correction method and near infrared (1100-2500nm) region by using MPLS regression. Validation of these NIRS equations showed very low bias (protein:-0.016%, oil : -0.011 %) and standard error of prediction (SEP, protein: 0.437%, oil: 0.377%) and very high coefficient of determination ($R^2$, protein: 0.985, oil : 0.965). Therefore, these NIRS equation seems reliable for determining the protein and oil content, and NIRS method could be used as a mass screening method of soybean seed.

• Journal of Biosystems Engineering
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• v.22 no.2
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• pp.256-265
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• 1997

The objectives of this study were to examine the ability to predict soluble solid and firmness in intact apples based on the visible/near-infrared spectroscopic technique. Two cultivars of apples, Delicious and Gala, were handled, tested and analyzed separately. Reflectance spectra, Magness-Tayor (MT) firmness, and soluble solids in apples were measured sequentially. Maximum and minimum diameters, height, and weight of apples were recorded before the MT firmness tests. A spectrophotometer was used to collect reflectance spectra of intact apples over a wavelength range of 400 to 2, 498 nm. The W firmness tests were conducted using a standard 11.1mm (7/16 in.) MT probe mounted in an Instron universal testing machine. A digital refractormeter was used to measure soluble solid contents in the apples. Apple samples were divided into a calibration set and a prediction set. The calibration set was used during model development, and the prediction set was used to predict soluble solids and firmness from unknown spectra. The method of partial least square (PLS) analysis was used. An unique set of PLS loading vectors (factors) was developed for soluble solid content and firmness. The PLS model showed good correlations between predicted and measured soluble solids of intact apples in 860～1078 nm of the wavelengths. However, the PLS analysis was not good enough to predict the apple firmness.

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.400.2-400.2
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• 2002

Near-infrared(NIR) reflectance spectroscopy was employed to qualify various ploymorphs. We collected 8 potential polymorphs forms of Medicine T for this study. Near-infared spectra of the powder samples contained in glass vials were obtained over the wavelength region of 1100-1750nm. There were the peak around 1560nm in the 6 spectra among 8 spectra. Principal component analysis(PCA) has been performed to examine the qualitative difference of 8 polymorphs PC space. (omitted)

• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.4
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• pp.277-282
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• 2014

Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination ($R^2$) and the root mean squared error of prediction (RMSEP). The results showed the moisture content ($R^2_{val}=0.97$, RMSEP=0.109), crude protein content ($R^2_{val}=0.94$, RMSEP=0.212), neutral detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.763), acid detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.142), gross energy ($R^2_{val}=0.82$, RMSEP=23.249), in vitro dry matter digestibility ($R^2_{val}=0.68$, RMSEP=1.69), and metabolizable energy (approximately $R^2_{val}$ >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.

• Korean Journal of Food Science and Technology
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• v.26 no.6
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• pp.718-725
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• 1994

The applicability of near infrared reflectance spectroscopy (NIRS) to determine moisture, protein, fat and amylose content of domestic rice was studied. The standard error of prediction (SEP) of moisture, protein, fat and amylose in polished rice was 0.014, 0.196, 0.098 and 1.427%, and those SEP of brown rice was 0.12, 1.226, 0.153 and 1.923%, respectively. It is concluded that the NIRS method allowed to detect the content of moisture and protein in rice samples with fair precision comparing conventional analysis, but the accuracy for determining amylose and fat was not acceptable.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.1
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• pp.116-123
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• 2003

Forages are the most important feed resource for ruminants worldwide, whether fed as pastures, forage crops or conserved hay, silage or haylage. There is large variability in the quality of forages so measurement and prediction of feeding value and nutritive value are essential for high levels of production. Within a commercial animal production system, methods of prediction must be inexpensive and rapid. At least 50% of the variation in feeding value of forages is due to variation in voluntary feed intake. Identification of the factors that constrain voluntary feed intake allows these differences to be managed and exploited in forage selection. Constraints to intake have been predicted using combinations of metabolic and physical factors within the animal while simple measurements such as the energy required to shear the plant material are related to constraints to intake with some plant material. Animals respond to both pre- and post-ingestive feedback signals from forages. Pre-ingestive signals may play a role in intake with signals including taste, odour and texture together with learned aversions to nutrients or toxins (post-ingestive feedback signals). The challenge to forage evaluation is identification of the factors which are most important contributors to these feedback signals. Empirical models incorporating chemical composition are also widely used. The models tend to be useful within the ranges of the datasets used in their development but none can claim to have universal application. Mechanistic models are becoming increasingly complex and sophisticated and incorporate both feed characteristics and use of biochemical pathways within the animal. Improvement in utilisation through the deliberate selection of pasture plants for high feeding value appears to have potential and has been poorly exploited. Use of Near Infrared Reflectance Spectroscopy is a simple method that offers significant potential for the preliminary screening of plants with genetic differences in feeding value. Near Infrared Reflectance Spectroscopy will only be as reliable as the calibration sets from which the equations are generated.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.5
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• pp.643-648
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• 2005

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal grains and forages. If samples could be analyzed without drying and grinding, then sample preparation time and costs may be reduced. This study was conducted to develop robust NIRS equations to predict fermentation quality of corn (Zea mays) silage and to select acceptable sample preparation methods for prediction of fermentation products in corn silage by NIRS. Prior to analysis, samples (n = 112) were either oven-dried and ground (OD), frozen in liquid nitrogen and ground (LN) and intact fresh (IF). Samples were scanned from 400 to 2,500 nm with an NIRS 6,500 monochromator. The samples were divided into calibration and validation sets. The spectral data were regressed on a range of dry matter (DM), pH and short chain organic acids using modified multivariate partial least squares (MPLS) analysis that used first and second order derivatives. All chemical analyses were conducted with fresh samples. From these treatments, calibration equations were developed successfully for concentrations of all constituents except butyric acid. Prediction accuracy, represented by standard error of prediction (SEP) and $R^2_{v}$ (variance accounted for in validation set), was slightly better with the LN treatment ($R^2$ 0.75-0.90) than for OD ($R^2$ 0.43-0.81) or IF ($R^2$ 0.62-0.79) treatments. Fermentation characteristics could be successfully predicted by NIRS analysis either with dry or fresh silage. Although statistical results for the OD and IF treatments were the lower than those of LN treatment, intact fresh (IF) treatment may be acceptable when processing is costly or when possible component alterations are expected.