• Korean Journal of Food Science and Technology
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• v.28 no.1
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• pp.40-43
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• 1996

Our objective was to evaluate the potential of near-infrared reflectance spectroscopy to determine the salt content of mayonnaise. The calibration equation for salt developed using partial least square regression was compared with conventional method. 100 samples for calibration set and 40 samples for validation set were used. The multiple correlation coefficient was 0.946 and standard error of prediction was 0.017, when calibration equation was applied to validation set. Near-infrared reflectance spectroscopy method for determining salt content of mayonnaise appears quite satisfactory to evaluate nondestructively.

• Food Science and Preservation
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• v.6 no.1
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• pp.29-36
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• 1999

Using Fuji apple fruits cultivated in Kyungpook prefecture, the calibration model for firmness evaluation of fruits by near infrared(NIR) reflectance spectroscopy was developed, and the various influence factors such as instrument variety, measuring method, sample group, apple peel and selection of firmness point were investigated. Spectra of sample were recorded in wavelength range of 1100∼2500nm using NIR spectrometer (InfraAlyzer 500), and data were analyzed by stepwise multiple linear regression of IDAS program. The accuracy of calibration model was the highest when using sample group with wide range, and the firmness mean values obtained in graph by texture analyser(TA) were used as standard data. Chemometrics models were developed using a calibration set of 324 samples and an independent validation set of 216 samples to evaluate the predictive ability of the models. The correlation coefficients and standard error of prediction were 0.84 and 0.094kg, respectively. Using developed calibration model, it was possible to monitor the firmness change of fruits during storage frequently. Time, which was reached to firmness high value in graph by TA, is possible to use as new parameter for freshness of fruit surface during storage.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4112-4112
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• 2001

The feasibility of NIR spectroscopy as a quick and nondestructive method for quality control of uniformity of coating thickness of pharmaceutical tablets was investigated. Near infrared spectra of a set of pharmaceutical tablets with varying coating thickness were measured with a diffuse reflectance fiber optic probe connected to a Broker IFS 28/N FT-NIR spectrometer. The challenging issues encountered in this study included: 1. The similarity of the formulation of the core and coating materials, 2. The lack of sufficient calibration samples and 3. The non-linear relationship between the NIR spectral intensity and coating: thickness. A peak at 7184 $cm^{-1}$ was identified that differed for the coating material and the core material when M spectra were collected at 2 $cm^{-1}$ resolution (0.4 nm at 7184 $cm^{-1}$). The study showed that the coating thickness can be analyzed by polynomial fitting of the peak area of the selected peak, while least squares calibration of the same data failed due to the lack of availability of sufficient calibration samples. Samples of coal powder and solid pieces of coal were analyzed by FT-NIR diffuse reflectance spectroscopy with the goal of predicting their ash content, percentage of volatile components, and energy content. The measurements were performed on a Broker Vector 22N spectrometer with a fiber optic probe. A partial least squares model was constructed for each of the parameters of interest for solid and powdered sample forms separately. Calibration models varied in size from 4 to 10 PLS ranks. Correlation coefficients for these models ranged from 86.6 to 95.0%, with root-mean-square errors of cross validation comparable to the corresponding reference measurement methods. The use of FT-NIR diffuse reflectance measurement techniques was found to be a significant improvement over existing measurement methodologies in terms of speed and ease of use, while maintaining the desired accuracy for all parameters and sample forms.(Figure Omitted).

• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.131-139
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• 2001

Extraction of waterline in tidal flat has been one of the main concerns in the remote sensing of coastal region. This study aimed to define the spectral characteristics of turbid water near the shoreline and to find the appropriate spectrum to delineate the waterline at the inter-tidal flat in the western coast of Korean Peninsula. Spectral reflectance curves were obtained by the field measurements under the diverse condition of water depth and turbidity at the study area in Kyong-gi Bay. Spectroscopy measurements showed that reflectances of the exposed mudflat, shallow turbid water, and normal coastal water were significantly different by wavelength. Shallow water near the waterline showed diverse conditions of turbidity. Spectral reflectance tends to increase as turbidity increases, particularly at the visible and near infrared spectrum. At the middle infrared wavelength, tidal water showed very little reflectance regardless of the turbidity and water depth and was easily disting from the exposed tidal flat. The exact waterline between exposed tidal flat and seawater should be extracted from the image data obtained at the middle infrared wavelength.

• 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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.357-364
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• 2010

Kjeldahl method used in many materials from various plant parts to determine protein contents, is laborious and time-consuming and utilizes hazardous chemicals. Near-infrared (NIR) reflectance spectroscopy, a rapid and environmentally benign technique, was investigated as a potential method for the prediction of protein content. Near-infrared reflectance spectra(1100-2400 nm) of coarse cereal grains(n=100 for each germplasm) were obtained using a dispersive spectrometer as both of grain itself and flour ground, and total protein contents determined according to Kjeldahl method. Using multivariate analysis, a modified partial least-squares model was developed for prediction of protein contents. The model had a multiple coefficient of determination of 0.99, 0.99, 0.99, 0.96 and 0.99 for foxtail millet, sorghum, millet, adzuki bean and mung bean germplasm, respectively. The model was tested with independent validation samples (n=10 for each germplasm). All samples were predicted with the coefficient of determination of 0.99, 0.99, 0.99, 0.91 and 0.99 for foxtail millet, sorghum, millet, adzuki bean and mung bean germplasm, respectively. The results indicate that NIR reflectance spectroscopy is an accurate and efficient tool for determining protein content of diverse coarse cereal germplasm for nutrition labeling of nutritional value. On the other hands appropriate condition of cereal material to predict protein using NIR was flour condition of grains.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1091-1091
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• 2001

Previous reports have shown that Near Infrared Spectroscopy (NIRS) can be used to assess physical and chemical properties of flax fibre and fabric quality. Currently, spinners assess yarn quality mainly based on strength and regularity measurements. There two key characteristics are influenced by quality of raw fibres used, especially the degree of rotting and strength. The aim of this investigation was to evaluate the use of NIRS for assessing quality of weft grade yarn available on the commercial market. In order to develop the NIR calibrations, a range of samples representing poor, medium and good quality weft yarn samples was included in the calibration and validation sample sets. The samples were analysed for physical and chemical parameters including caustic weight loss, fibre fractions, lipid, ash and minerals. A detailed protocol for assessing yarn quality has been developed to maximize the accuracy of the reflectance spectra. The development of partial least squares regression models and validation of the calibration equations using blind samples will be presented and discussed.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.4
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• pp.497-502
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• 2012

This study investigates the prediction of soil OM on Korean soils using the Visible-Near Infrared (Vis-NIR) spectroscopy. The ASD Field Spec Pro was used to acquire the reflectance of soil samples to visible to near-infrared radiation (350 to 2500 nm). A total of 503 soil samples from 61 Korean soil series were scanned using the instrument and OM was measured using the Walkley and Black method. For data analysis, the spectra were resampled from 500-2450 nm with 4 nm spacing and converted to the $1^{st}$ derivative of absorbance (log (1/R)). Partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil OM. Regression rules model estimates the target value by building conditional rules, and each rule contains a linear expression predicting OM from selected absorbance values. The regression rules model was shown to give a better prediction compared to PLSR. Although the prediction for Andisols had a larger error, soil order was not found to be useful in stratifying the prediction model. The stratification used by Cubist was mainly based on absorbance at wavelengths of 850 and 2320 nm, which corresponds to the organic absorption bands. These results showed that there could be more information on soil properties useful to classify or group OM data from Korean soils. In conclusion, this study shows it is possible to develop good prediction model of OM from Korean soils and provide data to reexamine the existing prediction models for more accurate prediction.

• Journal of The Korean Society of Grassland and Forage Science
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• v.33 no.3
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• pp.171-176
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• 2013

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages and feedstuff. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of fresh whole crop barley silages. A representative population of 284 fresh whole crop barley silages was used as a database for studying the possibilities of NIRS to predict chemical composition. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data were recorded as log 1/Reflectance (log 1/R) and were scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh whole crop barley silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH, as well as lactic acid content with correlation coefficients of cross-validation ($R^2cv$) of 0.96, 0.81, 0.79, 0.84, 0.72 and 0.78, respectively, and standard error of cross-validation (SECV) of 1.26, 2.83, 2.18, 1.19, 0.13 and 0.32% DM, respectively. Results of this experiment showed the possibility of the NIRS method to predict the chemical parameters of fresh whole crop barley silages as a routine analysis method in feeding value evaluation and for farmer advice.

• Journal of the Korean Society of Food Science and Nutrition
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• v.27 no.3
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• pp.393-398
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• 1998

The loss of ascorbic acid in dried red pepper leaves prepare with different drying methods of air-, oven-, microwave oven-, and vacuum drying with blanching or without was determined by a HPLC method. Vacuum drying showed the least loss of ascorbic acid than the other drying methods. Additionally, the feasibility of near infrared reflectance spectroscopy(NIRS) to determine the contents of ascorbic acid in the red pepper leaves was studied. NIRS was found to be an efficient way of determining ascorbic acid contents in red pepper leaves, requiring only 30 seconds of an analytical time.