Through-packaging analysis of butter adulteration using line-scan spatially offset Raman spectroscopy

Lohumi, Santosh,Lee, Hoonsoo,Kim, Moon S.,Qin, Jianwei,Cho, Byoung-Kwan Springer-Verlag 2018 Analytical and Bioanalytical Chemistry Vol.410 No.22

Raman hyperspectral imaging and spectral similarity analysis for quantitative detection of multiple adulterants in wheat flour

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>

Erratum to: Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique

Lohumi, Santosh,Wakholi, Collins,Baek, Jong Ho,Kim, Byeoung Do,Kang, Se Joo,Kim, Hak Sung,Yun, Yeong Kwon,Lee, Wang Yeol,Yoon, Sung Ho,Cho, Byoung-Kwan Korean Society for Food Science of Animal Resource 2019 한국축산식품학회지 Vol.39 No.3

Nondestructive Evaluation for the Viability of Watermelon (Citrullus lanatus) Seeds Using Fourier Transform Near Infrared Spectroscopy

Lohumi, Santosh,Mo, Changyeun,Kang, Jum-Soon,Hong, Soon-Jung,Cho, Byoung-Kwan Korean Society for Agricultural Machinery 2013 바이오시스템공학 Vol.38 No.4

Purpose: Conventional methods used to evaluate seeds viability are destructive, time consuming, and require the use of chemicals, which are not feasible to implement to process plant in seed industry. In this study, the effectiveness of Fourier transform near infrared (FT-NIR) spectroscopy to differentiate between viable and nonviable watermelon seeds was investigated. Methods: FT-NIR reflectance spectra of both viable and non-viable (aging) seeds were collected in the range of 4,000 - 10,000 $cm^{-1}$ (1,000 - 2,500 nm). To differentiate between viable and non-viable seeds, a multivariate classification model was developed with partial least square discrimination analysis (PLS-DA). Results: The calibration and validation set derived from the PLS-DA model classified viable and non-viable seeds with 100% accuracy. The beta coefficient of PLS-DA, which represented spectral difference between viable and non-viable seeds, showed that change in the chemical component of the seed membrane (such as lipids and proteins) might be responsible for the germination ability of the seeds. Conclusions: The results demonstrate the possibility of using FT-NIR spectroscopy to separate seeds based on viability, which could be used in the development of an online sorting technique.

Quantitative analysis of Sudan dye adulteration in paprika powder using FTIR spectroscopy

Lohumi, Santosh,Joshi, Ritu,Kandpal, Lalit Mohan,Lee, Hoonsoo,Kim, Moon S.,Cho, Hyunjeong,Mo, Changyeun,Seo, Young-Wook,Rahman, Anisur,Cho, Byoung-Kwan Taylor Francis 2017 Food additives & contaminants. Part A. Chemistry, Vol.34 No.5

Raman imaging from microscopy to macroscopy: Quality and safety control of biological materials

Lohumi, S.,Kim, M.S.,Qin, J.,Cho, B.K. Elsevier Scientific Pub. Co 2017 Trends in analytical chemistry Vol.93 No.-

<P>Raman imaging can analyze biological materials by generating detailed chemical images. Over the past decade, significant advancements in Raman imaging and data analysis techniques have overcome problems such as long data acquisition and analysis times and poor sensitivity. In this review article, Raman spectroscopy and imaging are introduced and the corresponding computational methods for image data analysis are discussed. We provide an overview of the applications of this method in areas such as food, pharmaceutical, and biomedical sectors, with emphasis on recent developments that have helped industrialize its applications in various sectors. Finally, the current limitations and trends for future Raman imaging are outlined and discussed with a view toward new research practices for applying this technique more efficiently and adaptably in numerous sectors. (C) 2017 Elsevier B.V. All rights reserved.</P>

Net Analyte Signal-based Quantitative Determination of Fusel Oil in Korean Alcoholic Beverage Using FT-NIR Spectroscopy

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.

Net Analyte Signal-based Quantitative Determination of Fusel Oil in Korean Alcoholic Beverage Using FT-NIR Spectroscopy

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.

Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique

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_v²) 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_v² values≥0.8 (0.73 for loin parts) with low RMSEV values. However, lower accuracy (R_v² =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.

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.