• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.177-183
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• 2012

Purpose: In near infrared spectroscopy, interactance configuration of a light source and a spectrometer probe can provide more information regarding fruit internal attributes, compared to reflectance and transmittance configuration. However, there is no through study on the parameters of interactance measurement setup. The objective of this study was to investigate the effect of the parameters on the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from greenhouses at three different harvesting seasons. The prediction models were developed at three distances of 2, 5, and 8 cm between the light source and the spectrometer probe, three measurement points of 2, 3, and 6 evenly distributed on each sample, and different number of fruit samples for calibration models. The performance of the models was compared. Results: In the test at the three distances, the best results were found at a 5 cm distance. The coefficient of determination ($R_{cv}{^2}$) values of the cross-validation were 0.717 (standard error of prediction, SEP=$1.16^{\circ}Brix$) and 0.504 (SEP=4.31 N) for the estimation of SSC and firmness, respectively. The minimum measurement point required to fully represent the spectral characteristics of each fruit sample was 3. The highest $R_{cv}{^2}$ values were 0.736 (SEP=$0.87^{\circ}Brix$) and 0.644 (SEP=4.16 N) for the estimation of SSC and firmness, respectively. The performance of the models began to be saturated when 60 fruit samples were used for developing calibration models. The highest $R_{cv}{^2}$ of 0.713 (SEP=$0.88^{\circ}Brix$) and 0.750 (SEP=3.30 N) for the estimation of SSC and firmness, respectively, were achieved. Conclusions: The performance of the prediction models was quite different according to the condition of interactance measurement setup. In designing a fruit grading machine with interactance configuration, the parameters for interactance measurement setup should be chosen carefully.

• Food Science and Preservation
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• v.19 no.1
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• pp.95-103
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• 2012

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels ($5^{\circ}C$, $20^{\circ}C$, and $40^{\circ}C$). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at $40^{\circ}C$ and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.968-974
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• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.70-70
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• 2017

This study was conducted to characterize the amylose and protein contents of 4,948 rice landrace germplasm using the NIRS model developed in the previous study. The amylose contents estimated by NIRS in the standard rice were Sinseonchal (6.881%) 4.994%, Chucheong (19.731%) 18.633%, Goami (23.246%) 20.548%. Protein contents were Sinseonchal (6.890%) 6.824%, Chucheong (6.350%) 6.869%, Goami (6.777%) 7.839%. The NIRS analysis showed that 1.1-2.7%point lower in amylose and 0.4-0.6%point higher in protein than standard contents. The average amylose content of the germplasm was 20.39% with a range of 3.97-37.13%. The average protein content was 8.17% with a range of 5.20-17.45%. Amylose contents with a range of 20.06-27.02% represented 62.20% of the germplasm. Protein contents with a range of 6.78-9.75% represented 81.60% of the germplasm. Korean landrace comprised 24.9% among the 4,948 germplasm collected from 41 countries. A specific range of amylose contents showed in Korea 16.58-20.06%, in Japan 20.06-23.25%, in North Korea 23.25-27.02% and in China 27.02-37.13%. Protein contents exhibited 5.20-17.45% evenly in the whole landraces, whereas Chinese landrace particularly observed with 6.78-8.27% and 9.75-17.45%. Fifty resources were selected with low and high amylose ranging from 3.97-6.66% to 30.41-37.13% respectively. Similarly fifty resources were selected with low and high protein ranging from 5.20-6.09% to 13.21-17.45% respectively. Landraces with higher protein should be adapted to practical utilization of food sources.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.1
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• pp.15-20
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• 2008

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to measure anthocyanin contents in black colored soybean by using NIRS system. Total 300 seed coat of black colored soybean samples previously analyzed by HPLC were scanned by NIRS and over 250 samples were selected for calibration and validation equation. A calibration equation calculated by MPLS(modified partial least squares) regression technique was developed in which the coefficient of determination for anthocyanin pigment C3G, D3G and Pt3G content was 0.952, 0.936, and 0.833, respectively. Each calibration equation was applied to validation set that was performed with the remaining samples not included in the calibration set, which showed high positive correlation both in C3G and D3G content file. In case Pt3G, the prediction model was needed more accuracy because of low $R^2$ value in validation set. This results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of C3G and D3G contents in black colored soybean.

• Korean Journal of Plant Resources
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• v.23 no.4
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• pp.386-392
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• 2010

This study was done to measure the color and catechins contents in processed teas using the whole bands (400~2500 nm) with near-infrared spectroscopy(NIRS). The powder colors of 109 processed teas were measured with a colorimeter. The a/b ratios in Hunter color scale in processed teas accounted for about 98.9% of the variation in the fermentation degree(FD), indicating that the a/b ratio was a very useful trait for assessing fermentation degree. Also tea powders were scanned in the visible bands used with NIRSystem. The calibration equations for powder colors were developed using the regression method of modified partial least squares(MPLS) with the internal cross validation. The equations had low SECV (standard errors of cross-validation), and high $R^2$ (coefficient of determination in calibration) values with 0.996~1.00, indicating that the visible bands(400~700 nm) with NIRS could be used to rapidly measure the variables related to powder color and fermentation degree. Also another powders of 137 processed teas were scanned at 780~2500 nm bands in the reflectance mode. The calibration equations were developed using the regression method of MPLS with the internal cross validation. The equations had low SECV, and high $R^2$ (0.896~0.983) values, showing that NIRS could be used to rapidly discriminate the contents of EGC($R^2$=0.919), EC(0.896), EGCg(0.978), ECg(0.905) and total catechins(0.983) in processed teas with high precision and ease.

• Korean Journal of Plant Resources
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• v.27 no.4
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• pp.286-292
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• 2014

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to measure baicalin, baicalein, and wogonin contents in Scutellaria baicalensis by using NIRS system. Total 63 samples previously were analyzed by HPLC, which showed baicalin, baicalein, and wogonin contents ranging 4.56 to 13.59%, 0.28 to 5.54%, and 0.50 to 1.63% with an average of 9.66%, 2.09% and 0.52%, respectively. Each sample was scanned by NIRS and calculated for calibration and validation equation. A calibration equation calculated by modified partial least squares(MPLS) regression technique was developed in which the coefficient of determination for baicalin, baicalein, and wogonin content was 0.958, 0.944, and 0.709, respectively. Each calibration equation was applied to validation set that was performed with the remaining samples not included in the calibration set, which showed high positive correlation both in baicalin and baicalein content file. In case of wogonin, the prediction model was needed more accuracy because of low $R^2$ value in validation set. These results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of baicalin and baicalein contents in Scutellaria baicalensis.

• Polymer(Korea)
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• v.37 no.1
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• pp.5-14
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• 2013

In this paper the effect of UV (ultraviolet) exposure on HDPE (high density polyethylene)-smooth and f-PP (flexible polypropylene) geomembranes is evaluated under UVB-313 (ultraviolet wavelength 290-315 nm) exposure. Tensile property, melt flow index (MFI), oxidation induction time (OIT), both standard-OIT and high pressure-OIT and Fourier transform infrared spectroscopy/attenuated total reflectance (FTIR/ATR) results are discussed. Although tensile properties of the exposed geomembrane samples remained unchanged, the depletion of antioxidants was found higher for f-PP than for HDPE geomembrane. Arrhenius model by extrapolation was used on the data to predict the antioxidant lifetime to a typical site temperature of $20^{\circ}C$. There was no significant difference between the MFI value of the virgin and UV exposed HDPE geomembrane samples but a decrease in MFI was found in f-PP geomembrane that signifies that crosslinking has occurred. From FTIR spectra, the small peak (near $1750\;cm^{-1}$) observed in the spectrum of UV exposed sample corresponds to a carbonyl (C=O) linkage, which suggests that oxidation has occurred in the polymer structure, and another new band for f-PP between 3100 and $3500\;cm^{-1}$ is attributed to a hydroxyl bond and/or hydroperoxide bond.

• Korean Journal of Heritage: History & Science
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• v.56 no.3
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• pp.158-171
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• 2023

Pottery filled with organic materials was excavated from the G-2 building site of Yongjangseong Fortress, Jingo, a relic of the Goryeo Dynasty. In this study, the characteristics of organic material were confirmed by a scientific analysis of organic material in pottery found at the palace in Yongjangseong, Jindo. In addition, it was intended to review the analysis method to identify the natural resin and to secure characteristic components(biomarkers) for each natural resin and use them as basic data in the future. The organic materials in the pottery were analyzed using attenuated total reflectance Fourier-transformed infrared spectroscopy(ATR-FTIR) and gas chromatography mass spectrometry(GC-MS). The infrared spectral characteristics were estimated to be natural resin, and biomarkers of organic materials were identified as sesquiterpene-based compounds(C₁₅H₂₄, MW 204) and derivatives. The lacquer(T.vemicifluum) is composed mainly of alkenes, alkanes, and catechol. Pine resin(P.densiflora), on the other hand, is primarily composed of diterpenoid(abietic acid, pimaric acid) and Whangchil(yellow lacquer) is identified to have sesquiterpenes(such as selinene, muurolene, calamenene) as its main components. So, the organic material in the pottery can be identified as Whangchil by comparing their compounds with modern resin materials from Dendropanax. morbifera that correspond with the results. Whangchil, which is exuded from the Dendropanax. morbifera, has been used as a natural coating materials since ancient times, and it has been confirmed that the characteristic components are well preserved even 700 years later. It can be assumed that the interior Whangchil was stored not for use as a coating, but rather for ritual purposes when the building was constructed, because the pottery was found near the cornerstone. Furthermore, based on simplified sample preparation using pyrolysis-gas chromatography mass spectrometry(Py-GC-MS), the thermal decomposition products were found to be similar to the characteristic components, suggesting that this method can be applied to the identification of natural resins used in historic artifacts.