• 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.

• 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.

• 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.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.68.2-68.2
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• 2010

We present near-infrared (NIR) spectra of 6 young stellar objects (YSOs) around the supernova remnant G54.1+0.3 obtained with TripleSpec, a slit-based NIR cross-dispersion echelle spectrograph on th 5-m Palomar Hale telescope covering the entire NIR atmospheric window of 1-2.4 micron. These YSOs, whose formation was possibly triggered by the progenitor of G54.1+0.3, show significant mid-infrared (MIR) excess and have been proposed to be late O- and early B-type YSOs based on their spectral energy distribution. Our TripleSpec observations reveal the existence of strong H and He I lines, consistent with the previous interpretation of their spectral types, while the absence of Br-gamma emission line indicates that the YSOs do not have a nearby circumstellar disk. We discuss the relation between these YSOs and G54.1+0.3 based on the TripleSpec data and previous photometric data as well.

• 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.

• Korean Journal of Agricultural Science
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• v.39 no.1
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• pp.111-116
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• 2012

Nondestructive evaluation of seed viability is one of the highly demanding technologies for seed production industry. Conventional seed sorting technologies, such as tetrazolium and standard germination test are destructive, time consuming, and labor intensive methods. Near infrared spectroscopy technique has shown good potential for nondestructive quality measurements for food and agricultural products. In this study, FT-NIR spectroscopy was used to classify normal and artificially aged lettuce seeds. The spectra with the range of 1100~2500 nm were scanned for lettuce seeds and analyzed using the principal component analysis(PCA) method. To classify viable seeds from nonviable seeds, a calibration modeling set was developed with a partial least square(PLS) method. The calibration model developed from PLS resulted in 98% classification accuracy with the Savitzky-Golay $1^{st}$ derivative preprocessing method. The prediction accuracy for the test data set was 93% with the MSC(Multiplicative Scatter Correction) preprocessing method. The results show that FT-NIR has good potential for discriminating non-viable lettuce seeds from viable ones.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1253-1253
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• 2001

The ability to accurately assess wine quality is important during the wine making process, particularly when allocating batches of wines to styles determined by consumer requirements. Grape payments are often determined by the quality category of the wine that is produced from them. Wine quality, in terms of sensory characteristics, is normally a subjective measure, performed by experienced winemakers, wine competition judges or winetasting panellists. By nature, such assessments can be biased by individual preferences and may be subject to day-to-day variation. Taste and aroma compounds are often present in concentrations below the detection limit of near infrared (NIR) spectroscopy but the more abundant organic compounds offer potential for objective quality grading by this technique. Samples were drawn from one of Australia's major wine shows and from BRL Hardy's post-vintage wine quality allocation tastings. The samples were scanned in transmission mode with a FOSS NIR Systems 6500, over the wavelength range 400-2500 ㎚. Data analysis was performed with the Vision chemometrics package. With samples from the allocation tastings, the best correlations between NIR spectra and tasting data were obtained with dry red wines. These calibrations used loadings in the wavelengths related to anthocyanins, ethanol and possibly tannins. Anthocyanins are a group of compounds responsible for colour in red wines - restricting the wavelengths to those relating to anthocyanins produced calibrations of similar accuracy to those using the full wavelength range. This was particularly marked with Merlot, a variety that tends to have relatively lower anthocyanin levels than Cabernet Sauvignon and Shiraz. For dry white wines, calibrations appeared to be more dependent on ethanol characteristics of the spectrum, implying that quality correlated with fruit maturity. The correlations between NIR spectra and sensory data obtained using the wine show samples were less significant in general. This may be related to the fact that within most classes in the show, the samples may span vintages, glowing areas and winemaking styles, even though they may be made from only one grape variety. For dry red wines, the best calibrations were obtained with a class of Pinot Noir - a variety that tends to be produced in limited areas in Australia and would represent the least matrix variation. Good correlations were obtained with a tawny port class - these wines are sweet, fortified wines, that are aged for long periods in wooden barrels. During the ageing process Maillard browning compounds are formed and the water is lost through the barrels in preference to ethanol, producing “concentrated” darkly coloured wines with high alcohol content. These calibrations indicated heaviest loadings in the water regions of the spectrum, suggesting that “concentration” of the wines was important, whilst the visible and alcohol regions of the spectrum also featured as important factors. NIR calibrations based on sensory scores will always be difficult to obtain due to variation between individual winetasters. Nevertheless, these results warrant further investigation and may provide valuable Insight into the main parameters affecting wine quality.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1133-1133
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• 2001

Fruit sweetness, as indexed by total soluble solids (TSS), and fruit acidity are key factors in the description of the fruit eating quality. Our group has been using short wave NIR spectroscopy (SW-NIR; 700-1100 nm) in combination with chemometric methods (PLS and MLR) for the non-invasive determination of the fruit eating quality (1,2). In order to further improve calibration performance, we have investigated SW-NIR spectra of sucrose and D-glucose. In previous reports on the band assignment for these sugars in the 1100-2500 nm spectral region (3-7), it has been established that change in concentration, temperature and physical state of sugars reflects on the shape and position of the spectral bands in the whole NIR region(5-7). The effect of change in concentration and temperature of individual sugar solutions and sugar spiked Juice samples was analysed using combined spectroscopic (derivative, difference, 2D spectroscopy) and linear regression chemometric (PLS, MLR) techniques. The results have been compared with the spectral data of a range of fruit types, varying in TSS content and temperature. In the 800-950 nm spectral region, the B-coefficients for apples, peaches and nectarines resemble those generated in a calibration of pure sucrose in water (Fig. 1). As expected, these fruits exhibit better calibration and prediction results than those in which the B-coefficients were poorly related to those for sugar.(Figure omitted).

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.44.4-44.4
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• 2019

The supernova remnant Cassiopeia A (Cas A) provides a unique opportunity to observe the fine details of the explosion of core-collapse supernova (SN). Previous optical and near-infrared (NIR) observations of Cas A have shown that the spatial distribution of the metal-enriched SN ejecta is very complicated, indicating that the SN explosion should have been asymmetric and turbulent, especially near the core. Recently, we obtained a long-exposure (~10 hr) image of Cas A by using the UKIRT 3.6-m telescope with a narrow-band filter centered at [Fe II] 1.644 um emission. This 'deep [Fe II] image' provides an unprecedented panoramic view of Cas A, revealing the distribution of dense SN ejecta over the entire remnant. We have carried out NIR multi-object spectroscopic observations of the dense ejecta knots in the northeastern (NE) and eastern (E) outer regions of the remnant using the MMIRS attached on the MMT 6.5-m telescope. A total of 67 ejecta knots are detected. By analyzing their spectra, we have found that the knots in the NE area show strong [S II]/[S III] and [Fe II] lines but little or no [P II] line, while those in the E outer region show strong [Fe II] lines but weak [S II]/[S III] lines. In this talk, we present the preliminary results of our NIR spectroscopic observations and discuss the implications.

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.405.1-405.1
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• 2002

The amount of water for the cultivation of citrus is different based on the growing period. The water content in the leaves of citrus can be a index for watering during cultivation. The purpose of this study is to determine non-destructively the water content of Citrus leaves by using near infrared spectroscopy (NIRS). Citrus leaves were prepared from satsuma mandarin leaves (Citrus unshiu Marc. var. okitsu) ranging from 62.20 to 69.98% of water content by loss on drying, NIR reflectance spectra of Citrus leaves were acquired by using a fiber optic probe. (omitted)