Raman Chemical Imaging Technology for Food and Agricultural Applications

( Jianwei Qin ),( Moon S. Kim ),( Kuanglin Chao ),( Byoung-kwan Cho ) 한국농업기계학회 2017 바이오시스템공학 Vol.42 No.3

Purpose: This paper presents Raman chemical imaging technology for inspecting food and agricultural products. Methods The paper puts emphasis on introducing and demonstrating Raman imaging techniques for practical uses in food analysis. Results & Conclusions: The main topics include Raman scattering principles, Raman spectroscopy measurement techniques (e.g., backscattering Raman spectroscopy, transmission Raman spectroscopy, and spatially offset Raman spectroscopy), Raman image acquisition methods (i.e., point-scan, line-scan, and area-scan methods), Raman imaging instruments (e.g., excitation sources, wavelength separation devices, detectors, imaging systems, and calibration methods), and Raman image processing and analysis techniques (e.g., fluorescence correction, mixture analysis, target identification, spatial mapping, and quantitative analysis). Raman chemical imaging applications for food safety and quality evaluation are also reviewed.

Visible to SWIR hyperspectral imaging for produce safety and quality evaluation

Kim, Moon S.,Delwiche, Stephen R.,Chao, Kuanglin,Garrido-Varo, Ana,Pé,rez-Marí,n, Dolores,Lefcourt, Alan M.,Chan, Diane E. Springer-Verlag 2011 Sensing and instrumentation for food quality and s Vol.5 No.5

Raman Chemical Imaging Technology for Food and Agricultural Applications

Qin, Jianwei,Kim, Moon S.,Chao, Kuanglin,Cho, Byoung-Kwan Korean Society for Agricultural Machinery 2017 바이오시스템공학 Vol.42 No.3

Purpose: This paper presents Raman chemical imaging technology for inspecting food and agricultural products. Methods The paper puts emphasis on introducing and demonstrating Raman imaging techniques for practical uses in food analysis. Results & Conclusions: The main topics include Raman scattering principles, Raman spectroscopy measurement techniques (e.g., backscattering Raman spectroscopy, transmission Raman spectroscopy, and spatially offset Raman spectroscopy), Raman image acquisition methods (i.e., point-scan, line-scan, and area-scan methods), Raman imaging instruments (e.g., excitation sources, wavelength separation devices, detectors, imaging systems, and calibration methods), and Raman image processing and analysis techniques (e.g., fluorescence correction, mixture analysis, target identification, spatial mapping, and quantitative analysis). Raman chemical imaging applications for food safety and quality evaluation are also reviewed.

Subsurface inspection of food safety and quality using line-scan spatially offset Raman spectroscopy technique

Qin, Jianwei,Kim, Moon S.,Chao, Kuanglin,Schmidt, Walter F.,Dhakal, Sagar,Cho, Byoung-Kwan,Peng, Yankun,Huang, Min Elsevier 2017 Food Control Vol.75 No.-

<P>Subsurface inspection of food and agricultural products is challenging for optical-based sensing techniques due to complex interactions between light and heterogeneous or layered samples. In this study, a method for subsurface food inspection was presented based on a newly developed line-scan spatially offset Raman spectroscopy (SORS) technique. A 785 nm point laser was used as a Raman excitation source. The line-shape SORS data from the sample was collected in a wavenumber range of 0-2815 cm(-1) using a detection module consisting of an imaging spectrograph and a CCD camera. Two layered samples, one by placing a 1 mm thick plastic sheet cut from original container on top of cane sugar and the other by placing a 5 mm thick carrot slice on top of melamine powder, were created to test the subsurface food inspection method. For each sample, a whole set of SORS data was acquired using one CCD exposure in an offset range of 0-36 mm (two sides of the incident laser point) with a spatial interval of 0.07 mm. Raman spectra from the cane sugar under the plastic sheet and the melamine powder under the carrot slice were successfully resolved using self-modeling mixture analysis (SMA) algorithms, demonstrating the potential of the technique for authenticating foods and ingredients through packaging and evaluating internal food safety and quality attributes. The line-scan SORS measurement technique provides a rapid and nondestructive method for subsurface inspection of food safety and quality. Published by Elsevier Ltd.</P>

Detection and quantification of adulterants in milk powder using a high-throughput Raman chemical imaging technique

Qin, Jianwei,Kim, Moon S.,Chao, Kuanglin,Dhakal, Sagar,Lee, Hoonsoo,Cho, Byoung-Kwan,Mo, Changyeun Informa UK (TaylorFrancis) 2017 Food additives & contaminants. Part A. Chemistry, Vol.34 No.2

<P>Milk is a vulnerable target for economically motivated adulteration. In this study, a line-scan high-throughput Raman imaging system was used to authenticate milk powder. A 5W 785nm line laser (240mm long and 1mm wide) was used as a Raman excitation source. The system was used to acquire hyperspectral Raman images in a wave number range of 103-2881cm(-1) from the skimmed milk powder mixed with two nitrogen-rich adulterants (i.e., melamine and urea) at eight concentrations (w/w) from 50 to 10,000 ppm. The powdered samples were put in sample holders with a surface area of 150x100mm and a depth of 2mm for push-broom image acquisition. Varying fluorescence signals from the milk powder were removed using a correction method based on adaptive iteratively reweighted penalised least squares. Image classifications were conducted using a simple thresholding method applied to single-band fluorescence-corrected images at unique Raman peaks selected for melamine (673cm(-1)) and urea (1009cm(-1)). Chemical images were generated by combining individual binary images of melamine and urea to visualise identification, spatial distribution and morphological features of the two adulterant particles in the milk powder. Limits of detection for both melamine and urea were estimated in the order of 50 ppm. High correlations were found between pixel concentrations (i.e., percentages of the adulterant pixels in the chemical images) and mass concentrations of melamine and urea, demonstrating the potential of the high-throughput Raman chemical imaging method for the detection and quantification of adulterants in the milk powder.</P>

Quantitative Detection of Benzoyl Peroxide in Wheat Flour Using Line-Scan Macroscale Raman Chemical Imaging

Qin, Jianwei,Kim, Moon S.,Chao, Kuanglin,Gonzalez, Maria,Cho, Byoung-Kwan Society for Applied Spectroscopy 2017 Applied spectroscopy Vol.71 No.11

<P>A high-throughput Raman chemical imaging method was developed for direct inspection of benzoyl peroxide (BPO) mixed in wheat flour. A 5W, 785nm line laser (240mm long and 1mm wide) was used as a Raman excitation source in a push-broom Raman imaging system. Hyperspectral Raman images were collected in a wavenumber range of 103-2881cm(-1) from dry wheat flour mixed with BPO at eight concentrations (w/w) from 50 to 6400ppm. A sample holder with a sampling volume of 150x100x2mm(3) was used to present a thin layer (2mm thick) of the powdered sample for line-scan image acquisition with a spatial resolution of 0.2mm. A baseline correction method based on adaptive iteratively reweighted penalized least squares was used to remove the fluctuating fluorescence signals from the wheat flour. To isolate BPO particles from the flour background, a simple thresholding method was applied to the single-band fluorescence-free images at a unique Raman peak wavenumber (i.e., 1001cm(-1)) preselected for the BPO detection. Chemical images were created to detect and map the BPO particles. Limit of detection for the BPO was estimated in the order of 50ppm, which is on the same level with regulatory standards. Pixel concentrations were calculated from the percentages of the BPO pixels in the chemical images. High correlation was found between the pixel concentrations and the mass concentrations of the BPO, indicating that the Raman chemical imaging method can be used for quantitative detection of the BPO mixed in the wheat flour.</P>

Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model

Lim, Jongguk,Kim, Giyoung,Mo, Changyeun,Kim, Moon S.,Chao, Kuanglin,Qin, Jianwei,Fu, Xiaping,Baek, Insuck,Cho, Byoung-Kwan Elsevier 2016 Talanta Vol.151 No.-

<P><B>Abstract</B></P> <P>Illegal use of nitrogen-rich melamine (C<SUB>3</SUB>H<SUB>6</SUB>N<SUB>6</SUB>) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography–mass spectrometry (GC–MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990–1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Melamine particles contained in milk powder were detected by NIR hyperspectral imaging. </LI> <LI> Regression coefficient values were used to reconstruct the PLS images. </LI> <LI> PLS images were used to discriminate the melamine pixels from milk powder pixels. </LI> <LI> Melamine particles at 200ppm in milk powder were confirmed without pretreatment. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Development of multispectral imaging algorithm for detection of frass on mature red tomatoes

Yang, Chun-Chieh,Kim, Moon S.,Millner, Patricia,Chao, Kuanglin,Cho, Byoung-Kwan,Mo, Changyeun,Lee, Hoyoung,Chan, Diane E. Elsevier 2014 Postharvest biology and technology Vol.93 No.-

In this research, a multispectral fluorescence-based imaging algorithm was developed to detect frass contamination on mature Campari tomatoes. Tomato images were acquired using a hyperspectral fluorescence line-scan imaging system with violet LED excitation, then analyzed for wavelength selection. The fluorescence intensities at five wavelengths, 515 nm, 640 nm, 664 nm, 690 nm, and 724 nm, were used to compute three simple ratio functions to detect frass contamination. The contamination spots were created on the tomato surfaces using four low-concentration frass dilutions. The algorithms detected over 99% of the 0.2 kg/L and 0.1 kg/L frass contamination spots and successfully differentiated these spots from tomato skin surfaces, stem scars, and stems. However, differentiation of the 0.05 kg/L and 0.02 kg/L frass contamination spots was more difficult. Adjusting the algorithm to successfully detect 95% of the 0.05 kg/L spots also resulted in false-positive pixel detections occurring on 28% of the tomatoes. This study demonstrates that a simple multispectral fluorescence imaging algorithm based on violet LED excitation could be useful for rapid postharvest detection of frass contamination on tomatoes in processing lines. (C) 2014 Published by Elsevier B.V.

NIR 초분광 영상을 이용한 토마토 크랙 검출

이훈수 ( H. S. Lee ),김문성 ( Moon S. Kim ),정단희 ( Danhee Jeong ),( Kuanglin Chao ),조병관 ( Byoung-kwan Cho ),( Stephen R. Delwiche ) 한국농업기계학회 2011 한국농업기계학회 학술발표논문집 Vol.16 No.2

A Simple Multispectral Imaging Algorithm for Detection of Defects on Red Delicious Apples

Lee, Hoyoung,Yang, Chun-Chieh,Kim, Moon S.,Lim, Jongguk,Cho, Byoung-Kwan,Lefcourt, Alan,Chao, Kuanglin,Everard, Colm D. Korean Society for Agricultural Machinery 2014 바이오시스템공학 Vol.39 No.2

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defective apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.