- Volume 41 Issue 3
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Net Analyte Signal-based Quantitative Determination of Fusel Oil in Korean Alcoholic Beverage Using FT-NIR Spectroscopy
- Lohumi, Santosh (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
- Kandpal, Lalit Mohan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
- Seo, Young Wook (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
- Cho, Byoung Kwan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University)
- Received : 2016.06.27
- Accepted : 2016.07.27
- Published : 2016.09.01
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 (
Supported by : Rural Development Administration
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