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RISS 인기검색어
- 검색키워드
- 저자 : ( Santosh Lohumi ) (검색결과 45 건)
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Santosh Lohumi,Lalit Mohan Kandpal,서영욱,조병관 한국농업기계학회 2016 바이오시스템공학 Vol.41 No.3
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 (R2v= 0.95, RMSEV = 100ppm) did not outperform the NAS-PCR model (R2v= 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.
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( Santosh Lohumi ),( Lalit Mohan Kandpal ),( Young Wook Seo ),( Byoung Kwan Cho ) 한국농업기계학회 2016 바이오시스템공학 Vol.41 No.4
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 (R²<sub>v</sub> = 0.95, RMSEV = 100ppm) did not outperform the NAS-PCR model (R²<sub>v</sub>= 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.
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( Santosh Lohumi ),( Byoung-kwan Cho ),( Hoonsoo Lee ),( Moon S. Kim ),( Jianwei Qin ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Growth of the worldwide packaged food market is obvious in coming years due to rising demand from emerging economies. Therefore, considering the consumers’ safety, quality analysis of packaged food has become important. Spectroscopic techniques have been in use and one of the most efficient and advance technique for quality and authenticity analysis of agro-food products. But the conventional spectroscopic techniques are limited to surface inspection and highly affected by the superficial (packaging) layer of food materials. The development of spatially offset Raman spectroscopy (SORS) that collects Raman spectra from a series of surface positions laterally offset from the excitation laser has enabled deep noninvasive chemical characterization of a range of materials. The present research aimed to develop a macro-scale SORS technique for quality analysis of packaged food products or layered food samples. In this study, we developed line-scan Raman hyperspectral imaging system to facilitate SORS analysis using a line-laser illumination to obtain offset Raman spectra of layered food samples. Firstly, the feasibility of the developed SORS system was evaluated by measuring deep into the food samples; including 5 mm starch powder on melamine, and collection of Raman spectra of Salmon fillet through the thick dark skin. As envisaged, the spectral contribution from the subsurface (deep) layer gradually overweighed that of the surface layer as a result of increasing offset distance. The application of line-scan SORS technique was further extended for authenticity analysis of butter samples through the packaging materials. Butter samples were adulterated with margarine (0 - 50 % margarine) and SORS data were collected through the packaging cover. A multivariate calibration model of partial least square regression (PLSR) analysis was executed on the preprocessed data to predict the presence of margarine in butter samples. The results reveal that the conventional backscattering Raman spectroscopy is not able to see effectively through the packaging cover, on the other side, excellent results (R2 > 0.92) for butter adulteration were yield for SORS measurements through the packaging covers of commercial butter samples. Further, to demonstrate the effectiveness of the developed SORS system, a range of commercial butter and margarine samples were measured whilst still in their unopened packaging and successfully discriminated based on the varieties and chemical compositions. This is the first demonstration of the macro-scale line-scan SORS technique, and the developed system can be considered as a high-throughput SORS system because the use of laser line (~ 14 cm wide) allow collection of SORS spectra from a large number of samples in a single scan. We expect that this novel line-scan SORS technique can be used to develop high-throughput and real time analysis techniques for quality and authenticity analysis of various packaged agro- products.
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A Novel Machine Vision Based Seed Quality Sorting System: Toward the Industrialization
( Santosh Lohumi ),( Hee Young Lee ),( Byoung-kwan Cho ) 한국농업기계학회 2020 한국농업기계학회 학술발표논문집 Vol.25 No.1
Seed quality is the main factor affecting the seed germination and thus crop yield. In the course of harvest, seeds normally contain impurities, which are essentially foreign materials, broken seeds, and infected seeds. Including such impurities in seed lot obviously lead to the lowcrop yield. Knowledge of seed lot quality before sowing is important to farmers for yield prediction, and to seed companies for warrant determination. Recently, some laboratory based studies were conducted for rapid detection of quality seeds where few images of limited number of seeds were collected and analyzed with image processing techniques. However, such previous studies majorly sought to development of image processing algorithm for quality seed detection, and only a few presented prototype system for the same purpose. In this work, moving a step forward, a machine vision system has developed for real-time quality screening of tomato and cabbage seed sample. The machine vision system comprises of a color camera, illumination source, conveyor belt, motion controller, a computer unit, and a custom-built software interface for real time image processing and visualization of detected foreign materials and bad seeds. A total of >50 morphological, color, and textural features were firstly extracted from each collected sample (good/bad seed and different foreign materials) images. For the sake of fast computation, optimal features were selected based on random forest and sequential forward selection methods. A multivariate classification method of one-class partial least square (OCPLS) classifier was executed on selected feature to classify imaged samples into two groups as good seed and bad seeds/foreign materials. The developed image processing algorithms and classifiers were incorporated to the software interface for real-time quality seed screening while samples moving through the conveyor unit. The obtained classification accuracy was >95% for tomato seeds and >90% for carrot seeds. The developed machine vision can collect the seed sample images from a 30 cm2 area and the quality screening results can be depicted within few seconds (<3s). Since the conveyor unit was synchronized with camera exposure and image processing, thus can be run gradually for the real-time screening. The proposed machine vision system can be adopted by seed processing companies and large-scale farmers for seed quality screening to improve germination rate of seed lot, crop yield and profitability.
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Lohumi, Santosh,Kandpal, Lalit Mohan,Seo, Young Wook,Cho, Byoung Kwan Korean Society for Agricultural Machinery 2016 바이오시스템공학 Vol.41 No.3
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.
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Lohumi, Santosh,Lee, Hoonsoo,Kim, Moon S.,Qin, Jianwei,Cho, Byoung-Kwan Elsevier 2019 Biosystems engineering Vol.181 No.-
<P>Recent food safety incidents and public health concerns related to food adulteration drive the need for fast, sensitive, and reliable methods for the detection of food hazards and adulteration. Although Raman microscopy imaging has been used for quality and authenticity analysis of food products previously, the application of line-scan Raman imaging has emerged only recently. Here, we assess the applicability of line-scan Raman hyperspectral imaging (RHI) for simultaneous detection of three potential chemical adulterants in wheat flour (0.05–1.5% w/w). RHI of wheat flour samples were collected (0.2-mm step size, 1 s exposure time) in an aluminum sample holder using a 785-nm line laser to generate Raman scattering. Spectral angle mapping (SAM) was applied to the preprocessed data to distinguish adulterants' pixels from the flour background using the pure endmember as input extracted by independent component analysis. SAM images for each adulterant were converted to binary images to effectively visualise and quantitatively detect the adulterant pixels in wheat flour. The pixel-based calculated proportions of adulterants in wheat flour agreed with the concentrations added. The reproducibility of the developed technique was assessed for same samples measured at different times and the results demonstrated that RHI in combination with SAM provided a novel, elegant tool with potential for noninvasive quality and authenticity analyses of powdered foods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Line-scan Raman imaging was used for detection of adulterants in wheat flour. </LI> <LI> Independent component analysis was used for endmember selection. </LI> <LI> SAM modeling of corrected data allows for quantification of adulterants. </LI> <LI> The two major advantages of Raman imaging are good reproducibility and fast. </LI> </UL> </P>
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Santosh Lohumi,Collins Wakholi,Jong Ho Baek,Byeoung Do Kim,Se Joo Kang,Hak Sung Kim,Yeong Kwon Yun,Wang Yeol Lee,Sung Ho Yoon,Byoung-Kwan Cho 한국축산식품학회 2018 한국축산식품학회지 Vol.38 No.5
In this paper, we report the development of a nondestructive prediction model for lean meat percentage (LMP) in Korean pig carcasses and in the major cuts using a machine vision technique. A popular vision system in the meat industry, the VCS2000 was installed in a modern Korean slaughterhouse, and the images of half carcasses were captured using three cameras from 175 selected pork carcasses (86 castrated males and 89 females). The imaged carcasses were divided into calibration (n=135) and validation (n=39) sets and a multilinear regression (MLR) analysis was utilized to develop the prediction equation from the calibration set. The efficiency of the prediction equation was then evaluated by an independent validation set. We found that the prediction equation- developed to estimate LMP in whole carcasses based on six variables-was characterized by a coefficient of determination (R<sub>v</sub><sup>2</sup>) value of 0.77 (root-mean square error [RMSEV] of 2.12%). In addition, the predicted LMP values for the major cuts: ham, belly, and shoulder exhibited R<sub>v</sub><sup>2</sup> values≥0.8 (0.73 for loin parts) with low RMSEV values. However, lower accuracy (R<sub>v</sub><sup>2</sup> =0.67) was achieved for tenderloin cuts. These results indicate that the LMP in Korean pig carcasses and major cuts can be predicted successfully using the VCS2000-based prediction equation developed here. The ultimate advantages of this technique are compatibility and speed, as the VCS2000 imaging system can be installed in any slaughterhouse with minor modifications to facilitate the on-line and real-time prediction of LMP in pig carcasses.
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