• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.208-220
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• 2016

Purpose: Fusel oil is a potent volatile aroma compound found in many alcoholic beverages. At low concentrations, it makes an essential contribution to the flavor and aroma of fermented alcoholic beverages, while at high concentrations, it induced an off-flavor and is thought to cause undesirable side effects. In this work, we introduce Fourier transform near-infrared (FT-NIR) spectroscopy as a rapid and nondestructive technique for the quantitative determination of fusel oil in the Korean alcoholic beverage "soju". Methods: FT-NIR transmittance spectra in the 1000-2500 nm region were collected for 120 soju samples with fusel oil concentrations ranging from 0 to 1400 ppm. The calibration and validation data sets were designed using data from 75 and 45 samples, respectively. The net analyte signal (NAS) was used as a preprocessing method before the application of the partial least-square regression (PLSR) and principal component regression (PCR) methods for predicting fusel oil concentration. A novel variable selection method was adopted to determine the most informative spectral variables to minimize the effect of nonmodeled interferences. Finally, the efficiency of the developed technique was evaluated with two different validation sets. Results: The results revealed that the NAS-PLSR model with selected variables ($R^2_{\upsilon}=0.95$, RMSEV = 100ppm) did not outperform the NAS-PCR model (($R^2_{\upsilon}=0.97$, RMSEV = 7 8.9ppm). In addition, the NAS-PCR shows a better recovery for validation set 2 and a lower relative error for validation set 3 than the NAS-PLSR model. Conclusion: The experimental results indicate that the proposed technique could be an alternative to conventional methods for the quantitative determination of fusel oil in alcoholic beverages and has the potential for use in in-line process control.

• Horticultural Science & Technology
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• v.29 no.3
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• pp.232-239
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• 2011

The genus Acorus is known as an indigenous medicinal plant. Genetic diversity of thirteen accessions of A. calamus and eight of A. gramineus, with an accession of Colocasia antiquorum and two of Iris pseudacorus as outgroups, were evaluated using RAPD markers for cluster analysis and principal coordinate analysis, and NIR spectroscopic profiles for principal component analysis.A total of 371 polymorphic bands were obtained by using the selected 12 random primers. The genetic distances were estimated from 0.03 to 0.31 within A. calamus and from 0.03 to 0.51 within A. gramineus. The dendrogram and three-dimensional plot separated the accessions into four distinct groups (A. calamus, A. gramineus, C. antiquorum, and I. pseudacorus). Moreover, for the diversity among genus Acorus, eleven A. calamus accessions, one A. gramineus accession, and two I. pseudacorus accessions were non-destructively analyzed from their leaves by NIR spectroscopy, which discriminated Acorus accessions like the RAPD analysis. Interestingly, thirteen accessions of A. calamus were clustered into two groups based on RAPD and NIR analyses, which indicates that there are two ecotypes of A. calamus in Korea. An accession (CZ) of A. calamus with yellow stripe on leaves was closely grouped with another (CX) at a genetic distance (GD) of 0.03, which shows that the stripe trait might be generated by chimeric mutation. The genetic distance between A. calamus and A. gramineus was revealed to be farthest from 0.80 to 0.88 GD. In genus Acorus the genetic diversity and genetic variation were identified by using RAPD marker technique and non-destructive NIRs.

• Korean Journal of Food Science and Technology
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• v.38 no.2
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• pp.165-168
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• 2006

Fourier transform-near infrared spectroscopy (FT-NIRS) was evaluated for determination of total dietary fiber (TDF) content of brown rice. Enzymatic-gravimetric method was suitable to obtain reference values for calibration of NIR at 1,000-2,500 nm range. Standard error of laboratory procedure ranged 0.17 to 0.72%. Partial least square (PLS) regression was used to develop the calibration equations. Regression was performed automatically using NIRCal chemometric software. Accuracy of prediction model for TDF content was certified for regression coefficient (r), standard error of estimation (SEE) and standard error of prediction (SEP), showing 0.9780, 0.0636, and 0.0642, respectively. This prediction model can be used for determination of TDF in brown rice and would be useful for real-time analysis in food industry.

• 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.1126-1126
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• 2001

The paper presented here is the initial part of a larger study, in which it was determined which quality parameters in cheese powder could already be predicted by NIR at an early stage in the process and which could only be predicted at the final stages of the process. This initial study was performed in order to establish the levels and nature of variation within and between batches such that the subsequent data collection could be tackled optimally. The perspectives evolved into more than was originally planned and revealed some interesting uses of NIR-technology. Cheese powder production starts as a batch process, where waste cheese from other dairies is melted down in a vat. The process then turns into a continual process as the vat is emptied and the melted cheese is then filtered, homogenized, pasteurized and finally spray dried. Between each batch the powder is to a greater or lesser degree a mixture of 2 batches. This paper is divided into 2 aspects, one regarding the optimization of sampling time and the other is a study of process dynamics. Optimizing sampling time This initial study included 9 powder samples from 9 different batches produced during one day. The raw materials for the batches were chosen with the aim of creating a relatively high level of variation in the data. The total of 81 samples were taken out at regular intervals and spectra were collected on a NIR-systems 6500 instrument. The subsequent reduction of the data by PCA to score values shows the power of NIR as a tool to determine not only when samples are representative of a certain batch, but also which batches are stable enough to include in a further study. Studying process dynamics To take this experiment a step further 1 of the 81 samples were sent to the laboratory for further analyses. The samples were chosen on the criteria that they covered the spectral variation in the dataset. These samples were analysed for 4 chemical components and 5 physical attributes, which are essential for describing the quality of the product. The latent structure of the 7 samples, using the chemical and physical variables, is totally comparable to the latent structure of the NIR spectra. This outcome makes it possible to describe the dynamics of one day's production both chemically and physically with relatively little resources. Additionally it raises the question as to whether reference values are needed, as the latent structure of the NIR-spectra appears to be sufficient in providing information on the quality of the product. To be able to use NIR in this way would require defining quality limits in the principal component space as opposed to each of the reference values. The potential of NIR applied in an explorative fashion with batch processes opens a whole new gateway for the use of this technology. This study explains yet again after so many years in the field “why I'm crazy about NIR!”.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2001.02a
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• pp.365-370
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• 2001

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.spc1
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• pp.250-258
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• 2004

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1157-1157
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• 2001

Yeast, Kurtzurnanomyces sp. I-11, produces biosurfactant, mannosyl erythritol lipid (MEL), from soya oil. The properties of biosurfactant MEL include low-toxicity and high biodegradability. MEL provides new possibilities for a wide range of industrial applications, especially food, cosmetic, pharmaceutical fields and chemicals for biotechnology. In the fermentation process, techniques of measuring and controlling substrates and products are important to obtain high productivity with optimum concentrations of substrate and product in the culture broth. The measurement system for the concentrations of soya oil and MEL in the fermentation process was developed using near-infrared spectroscopy (NIRS). Soya oil and MEL in the culture broth were extracted with ethyl acetate and NIR spectra was carried out between the second derivative NIR spectral data at 1312 and 2040 nm and MEL concentrations obtained using a thin-layer chromatography with a flame-ionization detector (TLC/FID) method. A calibration equation for soya oil was results of the validation of the calibration equation, good agreement was observed between the results of the TLD/FID method and those of the NIRS method for both constituents. NIR method was applied to the measurement of the concentrations of MEL and soya oil in the practical fermentation and good results were obtained. The study indicates that NIRS is a useful method for measurement of the substrate and product in the glycolipid fermentation.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1272-1272
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• 2001

Eutrophication processes of aquatic environment are strictly correlated with the concentration levels of nitrogen, phosphorous, organic matter and biological parameters such as phytoplankton and chlorophylla (Tremel, 1996; Burns et al., 1997; Young et al. 1999; Wei et al.,2000). Accordingly, the monitoring and evaluation of these factors will provide useful information about the health of aquatic ecosystem. However, the traditional types of auqatic chemistry analysis and ecological monitoring of phytoplankton are time-consuming, costly, and further resulting in secondary pollution due to the use of reagents. NIR (near-infrared) spectroscopy, as a rapid, non-destructive, little sample preparation and reagents-free technology (Hildrum et al., 1992), has been extensively applied to the characterization of food (Osborne and Fearn, 1988), pharmaceutical (Morisseau and Rhodes, 1995) and textile materials (Clove et al.,2000). Currently, NIR technology has been used indirectly in inferring lake water chemistry by two approaches, suspended (Malley et al., 1996) or seston (Dabakk et al., 1999), and sediments (Korsman et al., 1992; Malley et al., 1999). In addition, the evaluation of trophic state and the identification of the key factors contributed to the trophication are the key step to restore the damaged aquatic environment. Moreover, an understanding of the factors, which regulate the algal proliferation, is crucial to the successful management of aquatic ecosystem. In the paper, NIR technology will be used to study the environmental factors affecting the algal proliferation in combination with the trophic state index and diversity index. This novel developed system can be applied in monitoring and evaluating allopathic water environment and provide real time information services for the aquatic environment management.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.4
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• pp.242-246
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• 2000

The Near Infrared Reflectance Spectroscopy(NIR) was used to evaluate nutritional diagnosis for rapid leaf analysis method, 177 'Fuji' apple and 130 'Campbell Early' grape leaves were measured by Near Infrared reflectance spectra in the NIR region(1,100~2.500nm). Total nitrogen content was measured by kjelldhal distillation, after salycilic acid-sulfuric acid digestion. An empirical equation to predict total nitrogen content from its spectral signature was developed by adapting the Near Infrared Reflectance Spectroscopy analysis(NIRa) technique and the results were apple-0.965(R). 0.086(SEC), grape-0.926(R), 0.152(SEC). Standard Error of Prediction(SEP) of NIRa for predicting the total nitrogen of apple and grape leaves was 0.360 and 0.210, respectively. It was concluded that Near infrared reflectance spectroscopy analysis is promising method for rapid analysis of apple and grape leaves.