• Korean Journal of Soil Science and Fertilizer
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• v.47 no.5
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• pp.319-323
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• 2014

This study investigates the prediction of soil chemical properties (organic matter (OM), pH, Ca, Mg, K, Na, total acidity, cation exchange capacity (CEC)) on 688 Korean soil samples using the visible-near infrared reflectance (VIS-NIR) spectroscopy. Reflectance from the visible to near-infrared spectrum (350 to 2500 nm) was acquired using the ASD Field Spec Pro. A total of 688 soil samples from 168 soil profiles were collected from 2009 to 2011. The spectra were resampled to 10 nm spacing and converted to the 1st derivative of absorbance (log (1/R)), which was used for predicting soil chemical properties. Principal components analysis (PCA), partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil chemical properties. The regression rules model (Cubist) showed the best results among these, with lower error on the calibration data. For quantitatively determining OM, total acidity, CEC, a VIS-NIR spectroscopy could be used as a routine method if the estimation quality is more improved.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.333-342
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• 1997

The objective of this research was to develop model equations for measuring rice milling ratio by using visible / HIR spectroscopy. Twelve kinds of brown rice(n = 149) were milled to obtain various milling ratio ranged from 86% to 94%. Visible/NIR spectra were collected with a spectrophotometer with sample transport module. The reflectance and transmission spectra were measured in the range of 400~2, 500nm and 600~1, 400nm, respectively, with 2 nm intervals. Multiple linear regression(MLR), Partial least square (PLS), and Artificial neural network(ANN) were used to develop models. Model developed with reflectance spectra showed better prediction results then those with transmission spectra. The MLR model with six-wavelength obtained from first derivative spectra gave to the best results for measuring the rice milling ratio(SEP = 0.535, , $r^2$ = 0.980). The PLS model(SEP = 0.604, $r^2$= 0.976) and ANN model(SEP = 0.566, $r^2$= 0.978) also can be used to determine the rice milling ratio effectively.

• 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.

• 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 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.

• Near Infrared Analysis
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• v.1 no.1
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• pp.23-26
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• 2000

The objectives of this study were to examine the ability to predict soluble solid and firmness in intact apple based on the visible/near-infrared spectroscopic technique. Two cultivars of apples, Delicious and Gala, were handled, tested and analyzed. Reflectance spectra, Magness-Taylor (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. Apple samples were divided in to a calibration set and a validation set. The method of partial least squares (PLS) analysis was used. a unique set of PLS loading vectors (factors) was development for soluble solid and firmness. The PLS model showed good relationship between predicted and measured soluble solids in intact apples in the wavelength range of 860∼1078 nm. However, the PLS analysis was not good enough to predict the apple firmness.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3105-3105
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• 2001

Ingests and fecal contamination on a poultry carcass is a food safety hazard due to potential microbiological contamination. A visible/near-infrared (NIR) spectrometer was used to discriminate among pure ingesta and fecal material, breast skin contaminated with ingesta or fecal material and uncontaminated breast skin. Birds were fed isocaloric diets formulated with either maize, mile, or wheat and soybean meal for protein requirements. Following completion of the feeding period (14 days), the birds were humanely processed and eviscerated to obtain ingests from the crop or proventriculus and feces from the duodenum, ceca, and colon portion of the digestive tract. Pure feces and ingesta, breast skin, and contaminated breast skin were scanned from 400 to 2500 nm and analyzed from 400 to 900 nm. Principal component analysis (PCA) of reflectance spectra was used to discriminate between contaminates and uncontaminated breast skin. Results indicate that visible (400 to 760 nm) and NIR 760-900 nm spectra can detect contaminates. From PCA analysis, key wavelengths were identified for discrimination of uncontaminated skin from contaminates based the evaluation of loadings weights.

• Food Science of Animal Resources
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• v.31 no.1
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• pp.115-121
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• 2011

The objective of this study was to develop models to predict freshness factors (total viable counts (TVC), pH, volatile basic nitrogen (VBN), trimethylamine (TMA), and thiobarbituric acid (TBA) values) and the storage period in beef using a visible and near-infrared (NIR) spectroscopic technique. A total of 216 beef spectra were collected during the storage period from 0 to 14 d at a $10^{\circ}C$ storage. A spectrophotometer was used to measure reflectance spectra from beef samples, and beef freshness spectra were divided into a calibration set and a validation set. Multi-linear regression (MLR) models using the stepwise method were developed to predict the factors. The MLR results showed that beef freshness had a good correlation between the predicted and measured factors using the selected wavelength. The correlation of determination ($r^2$), standard error of prediction (SEP), and ratio of standard deviation to SEP (RPD) of the prediction set for TVC was 0.74, 0.64, and 2.75 Log CFU/$cm^2$, respectively. The $r^2$, SEP, and RPD values for pH were 0.43, 0.10, and 1.10; those for VBN were 0.73, 1.45, and 2.00 mg%; those for TMA were 0.70, 0.19, and 2.58 mg%; those for TBA values were 0.73, 0.13, and 2.77 mg MA/kg; and those for storage period were 0.77, 1.94, and 2.53 d, respectively. The results indicate that visible and NIR spectroscopy can predict beef freshness during storage.

• Journal of Biosystems Engineering
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• v.23 no.1
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• pp.49-56
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• 1998

The objectives of this study were to develop models to predict the contents of moisture and protein of single kernel of brown rice based on visible/NIR (near-infrared) spectroscopic technique. The reflectance spectra of rice were obtained in the range of the wavelength 400 to 2,500 nm with 2 nm intervals. Multiple linear regression(MLR) and partial least squares (PLS) were used to develop the models. The MLR model using the first derivative spectra(10 nm of gap) with Standard Normal Variate and Detrending (SNV and Drt.) preprocessing showed the best results to predict moisture content of the sin린e kernel brown rice. To predict the protein content of a single kernel of brown ricer the PLS model used the raw spectra with multiplicative scatter correction(MSC) preprocessing over the wavelength of 1,100~1,500 nm.

• Korean Journal of Agricultural Science
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• v.41 no.1
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• pp.59-63
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• 2014

Precision farming aims at reduced environmental impacts with increased productivity. Soils are multi-functional media in which air, water and biota occur together and form an essential part of the landscape with a fundamental role in the environment. The requirement for herbicides and fertilizers can vary within a field in response to spatial differences in soil properties. Near infrared (NIR) spectroscopy is widely used today as a nondestructive analytical technique which is capable of determining a number of physio-chemical parameters. The objectives of this study were to develop optimal models to predict chemical properties of paddy soils by visible and NIR reflectance spectra. Total of 60 soil samples were collected in spring from 20 paddy fields within central regions in Korea. Reflectance spectra, moisture contents, pH, total nitrogen (N), organic matter, available phosphate ($P_2O_5$) of soil samples were measured. The reflectance spectra were measured in wavelength ranges of 400-2,500 nm with 2 nm interval. The method of partial least square (PLS) analysis was used to determine the soil properties. The PLS analyses showed good correlation between predicted and measured chemical properties of paddy soils in the wavelength range of 1,800-2,400 nm. Especially, it showed better performance than the previous results which used the entire wavelength range of the spectrophotometer, without considering the optimal wavelength of each soil properties.