• Archives of Pharmacal Research
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• v.28 no.4
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• pp.458-462
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• 2005

The water content of skin was determined using a FT near infrared (NIR) spectrometer. NIR diffuse reflectance spectra were collected from hairless mouse, in vitro, and from human inner arm, in vivo. It was found that the variation of NIR absorbance band 1450 nm from OH vibration of water and 1940 nm from the combination involving OH stretching and OH deformation, depending on the absolute water content of separated hairless mouse skin, in vitro, using the FT NIR spectrometer. Partial least squares regression (PLSR) was applied to develop a calibration model. The PLS model showed good correlation. For practical use of the evaluation of human skin moisture, the PLS model for human skin moisture was developed in vivo on the basis of the relative water content of stratum corneum from the conventional capacitance method. The PLS model predicted human skin moisture with a standard errors of prediction (SEP) of 3.98 at 1130-1830 nm range. These studies showed the possibility of a rapid and nondestructive skin moisture measurement using FT NIR spectrometer.

• YAKHAK HOEJI
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• v.47 no.3
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• pp.148-153
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• 2003

Skin moisture is an important factor in skin health. Measurement of moisture content can provide diagnostic information on the condition of skin. In this study, a portable near-infrared (NIR) system was newly integrated with a photo diode array detector that has no moving parts, and this system has been successfully applied for the evaluation of human skin moisture. Diffuse reflectance spectra were collected and transformed to absorbance using 1 nm step size over the wavelength range of 1100 nm to 2200 nm. Partial least squares regression (PLSR) was applied to develop a calibration model. For practical use for the evaluation of human skin moisture, the PLS model for human skin moisture was developed in vivo using the portable NIR system on the basis of the relative water content values of stratum corneum from the conventional capacitance method. The PLS model showed a good correlation. The calibration with the use of PLS model predicted human moisture with a standard error of prediction (SEP) of 3.5 at 1120∼1730 nm range. This study showed the possibility of skin moisture measurement using portable NIR system.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.43 no.2
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• pp.101-104
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• 1998

This study was conducted to establish a rapid analysis method for determining protein and moisture contents of pea. Ninety and eighty pea (Pisum sativum L.) lines were analyzed to determine protein and moisture contents, respectively using near-infrared reflectance spectroscopy. Simple correlations (${\gamma}$) of protein content in a ground sample and an intact grain sample by an automatic regression method were 0.978 and 0.910, respectively. Simple correlations by partial least square regression/principal component analysis (PLS/PCA) methods were 0.982 and 0.925, respectively. Standard error of performance (SEP) in protein content was the lowest value, 0.446 in ground sample by PLS/PCA methods. Simple correlation of moisture content was the highest at 0.871 in ground samples. when using a standard regression method. Accuracy for the moisture content was slightly lower than for protein content. It was concluded that the NIRS method would be applicable only for rapid determination of protein content in pea.

• 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.

• Korean Journal of Agricultural Science
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• v.43 no.5
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• pp.715-722
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• 2016

Rapid determination of food quality is important in food distribution. In this study, the chemical properties of oats were analyzed using visible-near infrared (VIS-NIR) spectroscopy. The objective of this study was to develop and validate a predictive model of oat quality by VIS-NIR spectroscopy. A total of 200 oat samples were collected from domestic and import markets. Reflectance spectra, moisture, protein, fat, Fe, and K of oat samples were measured. Reflectance spectra were measured in the wavelength range of 400 - 2,500 nm at 2 nm intervals. The reflectance spectrum of an oat sample was measured after sample cell and reflectance plate spectrum measurement. Preprocessing methods such as normalization and $1^{st}$ and $2^{nd}$ derivations were used to minimize the spectroscopic noise. The partial-least-square (PLS) models were developed to predict chemical properties of oats using a commercial software package, Unscrambler. The PLS models showed the possibility to predict moisture, protein, and fat content of oat samples. The coefficient of determination ($R^2$) of moisture, protein, and fat was greater than 0.89. However, it was hard to predict Fe and K concentrations due to their low concentrations in the oat samples. The coefficient of determinations of Fe and K were 0.57 and 0.77, respectively. In future studies, the stability and practicability of these models should be improved by using a high accuracy spectrophotometer and by performing calibrations with a wider range of oat chemicals.

• Near Infrared Analysis
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• v.1 no.2
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• pp.35-39
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• 2000

The purpose of this research was to find out suitable soil sample preparation and sample holding tools for NIR reflection radiation for estimating soil components. NIR reflectance was scanned at 2nm intervals from 1,100 to 2,500nm with an InfraAlyzer 500(Bran+Luebbe Co.). Coarse(2.0mm) and fine(0.5mm) soil sample and various sample holding tools were used to obtain mean diffuse reflection of the soil for the calibration and validation of the calibration set in estimating moisture, organic matter and total nitrogen of the soils. Multiple linear regression was used to obtain the best correlation of NIR spectroscopy method. Correlation of NIR spectroscopy method. Correlation of NIR spectra for finely and coarsely sized soil did not show much difference. The standard errors of prediction(SE) using different types of sample holding tools for organic matter, total nitrogen and soil moisture were better than 0.765, 0.041 and 0.63% respectively. From the results it can be concluded that NIR spectroscopy with flow type cell could be used as a fast routine testing method in quantitative determination of organic matter, total nitrogen and soil moisture.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.6
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• pp.858-861
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• 2001

Near Infrared spectroscopy was applied to the samples of processed pork to see the effect of grinding on chemical components analyses. Data from conventional chemical analyses of moisture, fat, protein, NaCl were put into calibration model by NIR of reflectance mode. The other properties observed were pH and color parameters ($L^*,\;a^*,\;b^*$). Spectral ranges of 400~2500 nm and 400~1100 nm were compared for color parameters. Spectral ranges of 400~2500 nm and 1100~2500 nm were compared for chemical components and pH. Different spectral ranges caused little changes in the coefficients of determination or standard errors. $R^{2,}s$ of calibration models for color parameters were in the range of 0.97 to 1.00. $R^{2,}s$ of calibration models of intact sausages for moisture, protein, fat, NaCl and pH were 0.98, 0.89, 0.95, 0.73 and 0.77, respectively using spectra at 1100~2500 nm. $R^{2,}s$ of calibration models of ground sausages for moisture, protein, fat, NaCl and pH were 0.97, 0.91, 0.97, 0.42 and 0.56, respectively using spectra at 1100~2500 nm.

• Proceedings of the Optical Society of Korea Conference
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• 2001.08a
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• pp.144-145
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• 2001

No Abstract.See Full-text

• Korean Journal of Food Science and Technology
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• v.26 no.5
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• pp.579-584
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• 1994

Near infrared reflectance(NIR) spectroscopy was used to determine the degree of retrogradation of cooked rice. Cooked rice samples were stored at $4^{\circ}C$ for 120 hours, and the degree of retrogradation was measured at every 6 hour during the storage time. Stored cooked rices were freeze-dried, milled and passed through a 100 mesh sieve. Enzymatic method using glucoamylase was used as reference method for the determination of the degree of retrogradation. Spectral differences due to retrogradation of cooked rice were observed at 1434, 1700, 1928, 2100, 2284 and 2320 nm. 32 samples of which moisture content were below 5% were used for calibration set, and 16 samples were used for validation set. High correlations were achieved between degree of retrogradation determined by conventional enzymatic method and by NIR with multiple correlation coefficient of 0.9753, and a standard error of calibration(SEC) of 3.64%. Comparable results were obtained with 3.91% of standard error of prediction(SEP), when the calibration equation was applied to independent group of samples of which moisture contents were in the range of calibration set. But when the calibration equation was applied to samples of which moisture contents were outer range of calibration set, SEP and bias were increased and correlation coefficient was decreased. The determination of degree of retrogradation was affected by sample moisture content. To determine degree of retrogradation of cooked rice by NIR using this calibration equation, it was suggested that sample moisture content should be controlled to below 5%.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1622-1622
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• 2001

The purpose of this research was to develop a the reflection technique with near infrared (NIR) radiation for estimating physico-chemical properties in compost. The composts (cattle, pig, chicken and waste composts) were air dried and then ground to pass through a 0.5 or 2mm sieve for the physico-chemical properties and spectroscopic determinations. The physico-chemical properties of compost were shown high values ; moisture(30-60%), T-N(0.8-2.9%), organic matter(29-89%), pH(5.89-9.60) K$_2$O(0.27-5.66%), P2O$\sub$5/(0.07-2.62%), CaO(0.03-4.80%), MgO(0.09-1.56%), NaCl(0.01-1.13%), EC(1.41-13.76dS/m). Generally, we should select a simple calibration and prediction method for determining physico-chemical properties in compost under similar accuracy and precision of prediction. It should be remembered that the NIRS approach will never replace the traditional methods. However, NIRS technique may be an effective method for rapid and nondestructive measurements of a large number of compost samples. Near infrared reflectance spectra of composts was obtained by Infra Alyzer 500 scanning spectrophotometer 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 physico-chemical properties and humic acid contents in composts. The standard error of prediction(SEP) for finely sized sample(<0.5mm) and coarsely sized sample(<2mm) did not show much difference. The NIR instrument of filter type showed the same accuracy of the monochromator scanning type to estimate the compost properties. The results summarized that NIR spectroscopy can be used as a routine testing method to determine quantitatively the OM, moisture, T-N, color, pH, cation content in the compost samples nondestructively.