• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1266-1266
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• 2001

Pasture composition, an important attribute determining sward condition and value, is normally assessed by hand separation, drying and measuring weight contribution of each species in the mixture. This is a tedious, time and labour consuming procedure. NIRS has demonstrated the potential for predicting botanical composition of swards, but most of the work has been carried out on dry samples. The aim of this work was to evaluate the feasibility of developing NIR models for predicting the white clover and ryegrass content in fresh or dry mixtures artificially prepared from pure samples of both species. Mixtures from pure stands of white clover(Trifolium repens) and perennial ryegrass (Lolium perenne) were prepared with different proportions (0 to 100%) of each species (fresh weight). A total of 55 samples were made (11 mixtures,5 cuts). Spectra (400 to 2500 nm) were taken from fresh chopped (rectangular cuvettes, transport sample module) samples, in a NIR Systems 6500 scanning monochromator controlled by the software NIRS 3 (Infrasoft International), which was also utilized for calibration development. Different math treatments (derivative order, subtraction gap and smooth segment) and a scatter correction treatment of the spectra (SNV and Detrend) were tested. Equations were developed by modified partial least squares. Prediction accuracy evaluated by cross-validation, showed that percentage of clover or ryegrass, as contribution in dry weight, can be successfully percentage of clover or ryegrass, as contribution in dry weight, can be successfully predicted either on fresh or dried samples, with equations developed by different math treatments. Best equations for fresh samples were developed including a first, second, or third derivative, whereas for dry samples best equations included a second or third derivative. Standard errors of ross validation were about 6% for fresh and 3.6% for dry samples, Coefficient of determination of cross validation (1-VR) were over 0.95 times the value of SECV for fresh samples and over 8 times the value of SECV for dry samples. Scatter correction (SNV and Detrend) in general improved prediction accuracy. It is concluded more precise on dried and ground samples, it can be used with an acceptable error level and less time and labour, on fresh samples.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1247-1247
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• 2001

Constituents of animal biofluids such as milk, blood and urine contain information specifically related to metabolic and health status of the ruminant animals. Some changes in composition of biofluids can be attributed to disease response of the animals. Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and reducing milk quality. The purpose of this study was to investigate potential of NIRS combined with multivariate analysis for cow's mastitis diagnosis based on NIR spectra of milk, blood and urine. A total of 112 bulk milk, urine and blood samples from 4 Holstein cows were analyzed. The milk samples were collected from morning milking. The urine samples were collected before morning milking and stored at -35$^{\circ}C$ until spectral analysis. The blood samples were collected before morning milking using a catheter inserted into the carotid vein. Heparin was added to blood samples to prevent coagulation. All milk samples were analyzed for somatic cell count (SCC). The SCC content in milk was used as indicator of mastitis and as quantitative parameter for respective urine and blood samples collected at same time. NIR spectra of blood and milk samples were obtained by InfraAlyzer 500 spectrophotometer, using a transflectance mode. NIR spectra of urine samples were obtained by NIR System 6500 spectrophotometer, using 1 mm sample thickness. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. SIMCA was implemented to create models of the respective classes based on NIR spectra of milk, blood or urine. For the calibration set of samples, SIMCA models (model for samples from healthy cows and model for samples from mastitic cows), correctly classified from 97.33 to 98.67% of milk samples, from 97.33 to 98.61% of urine samples and from 96.00 to 94.67% of blood samples. From samples in the test set, the percent of correctly classified samples varied from 70.27 to 89.19, depending mainly on spectral data pretreatment. The best results for all data sets were obtained when first derivative spectral data pretreatment was used. The incorrect classified samples were 5 from milk samples,5 and 4 from urine and blood samples, respectively. The analysis of changes in the loading of first PC factor for group of samples from healthy cows and group of samples from mastitic cows showed, that separation between classes was indirect and based on influence of mastitis on the milk, blood and urine components. Results from the present investigation showed that the changes that occur when a cow gets mastitis influence her milk, urine and blood spectra in a specific way. SIMCA allowed extraction of available spectral information from the milk, urine and blood spectra connected with mastitis. The obtained results could be used for development of a new method for mastitis detection.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1283-1283
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• 2001

NIR spectroscopy has been used extensively to investigate the structure of water, alcohol and other self-associate molecules because the frequencies of NIR bands due to OH and NH groups strength of hydrogen bonds. We have studied the structure of water -methanol mixtures by use of NIR spectroscopy. Strong features in the 7200-6300 $cm^{-1}$ / region consist of a number of overlapped bands due to the combination of OH antisymmetric and symmetric stretching modes of water and the first overtone of the OH stretching modes of free and hydrogen bonded methanol, while weak fratures in the 6000-5800 cm-1 region are ascribed to the first overtones of $CH_3$ stretching modes of methanol. We will focus the discussion on the $CH_3$ stretching bands. They seem to show a significant shift is not clear from the spectra shown in figure 1(a). Figure 1(b) depicts the second derivative in the 6000-5700 $cm^{-1}$ / region. Now, it is clear from the second derivative that there are two major bands near 5950 and 5900 $cm^{-1}$ / and that they do show a shift be about 30 $cm^{-1}$ / Why do the $CH_3$ bands show the shift with increasing concentration of methanol\ulcorner Probably, the CH, group interacts directly with OH groups of water. The results in figure 1(b) demonstrate the usefulness of the second derivative in resolution enhancement as well as the potential of NIR spectroscopy in the studies of molecular interactions.(Figure omitted).

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.

• Journal of the Korean Wood Science and Technology
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• v.43 no.6
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• pp.701-713
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• 2015

A model was designed to identify wood species between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. using the near-infrared (NIR) spectroscopy in combination with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). In the PCA using all of the spectra, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could not be classified. In the PCA using the spectrum that has been measured in sapwood, however, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could be identified. In particular, it was clearly classified by sapwood in radial section. And more, these two species could be perfectly identified using PLS-DA prediction model. The best performance in species identification was obtained when the second derivative spectra was used; the prediction accuracy was 100%. For prediction model, the $R_p{^2}$ value was 0.86 and the RMSEP was 0.38 in second derivative spectra. It was verified that the model designed by NIR spectroscopy with PLS-DA is suitable for species identification between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc.

• Smart Media Journal
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• v.2 no.2
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• pp.33-38
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• 2013

In this paper, we investigated baseline estimation methods for remove background noise using Raman spectra from acute alcohol liver injury and acute ethanol-induced chronic liver fibrosis. Far the baseline estimation, we applied first derivative, linear programming and rolling ball method. Optimal input parameter of each method were determined by the training rate of MAP (maximum a posteriori probability) classifier. According to the experimental results, classification results baseline estimation with the rolling ball algorithm gave about 89.4%, which is very promising results for classification of acute alcohol liver injury and acute ethanol-induced chronic liver fibrosis. From these results, to determined the appropriate methods and parameters of baseline estimation impact on classification performance was confirmed.

• Near Infrared Analysis
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• v.1 no.1
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• pp.31-35
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• 2000

Near-infrared reflectance spectroscopy(NIRS) was used to determine the humic acids in soil samples from the fields of different crops and land-use over Youngnam and Honam regions in Korea. An InfraAlyzer 500 scanning spectrophotometer was obtained near infrared relectance spectra of soil at 2-nm intervals from 1100 to 2500nm. Multiple linear regression(MLR) or partial least square regression (PLSR) was used to evaluate a NIRS method for the rapid and nondestructive determination of humic acid, fulvic acid and its total contents in soils. The raw spectral data(log 1/R) can be used for estimating humic acid, fulvic acid and its total contents in soil by MLR procedure between the content of a given constituent and the spectral response of several bands. In which the predicted results for fulvic acid is the best in the constituents. The new spectral data are converted from the raw spectra by PLSR method such as the first derivative of each spectrum can also be used to predict humic acid and fulvic acid of the soil samples. A low SEC, SEP and a high coefficient of correlation in the calibration and validation stages enable selection of the best manipulation. But a simple calibration and prediction method for determining humic acid and fulvic acid should be selected under similar accuracy and precision of prediction. NIRS technique may be an effective method for rapid and nondestructive determination for humic acid, fulvic acid and its total contents in soils.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.893-897
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• 2006

Structures with additional frictional damping system have strong nonlinearity that the dynamic behavior is highly affected. by the relative magnitude between frictional force and excitation load. In this study, normalized response spectra of the structures with non-dimensional friction force are obtained through nonlinear time history analyses of the mass-normalized single degree of freedom systems using 20 ground motion data recorded on rock site. The variation of the control performance of frictional damping system is investigated in terms of the dynamic load and the structural natural period, of which effects were not considered in the previous studies. Least square curve fitting equations are presented for describing those normalized response spectrum and optimal non-dimensional friction forces are obtained for controlling the peak displacement and absolute acceleration of the structure based on the derivative of the curve fitted design spectrum.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.1 s.118
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• pp.88-94
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• 2007

Structures with additional frictional damping system have strong nonlinearity that the dynamic behavior is highly affected by the relative magnitude between frictional force and excitation load. In this study, normalized response spectra of the structures with non-dimensional friction force are obtained through nonlinear time history analyses of the mass-normalized single degree of freedom systems using 20 ground motion data recorded on rock site. The variation of the control performance of frictional damping system is investigated in terms of the dynamic load and the structural natural period, of which effects were not considered in the previous studies. Least square curve fitting equations are presented for describing those normalized response spectrum and optimal non-dimensional friction forces are obtained for controlling the peak displacement and absolute acceleration of the structure based on the derivative of the curve-fitted design spectrum.

• Journal of the Korean Chemical Society
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• v.21 no.2
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• pp.94-101
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• 1977

The NMR spectra of 2,4-disubstituted or 2,4,6-trisubstituted anisole derivative were examined to study the chemical shift of the ring proton signal. It was found that the chemical shift of the ring proton nearest to the 2-substituted group was influenced by the deshielding effect of the neighboring substituted groups in order of the Van der Waals radii of those groups. These observations were interpreted as a steric influence of 2-substituted group on the reactivity of the neighboring ring proton itself. The spectroscopical data were presented and the results were discussed with views of the above conceptions.