Detection of color dye contamination in spice powder using 1064 nm Raman chemical imaging system

被引:0
|
作者
Dhakal, Sagar [1 ]
Chao, Kuanglin [1 ]
Kim, Moon [1 ]
Qin, Jianwei [1 ]
Bae, Abigail [2 ]
机构
[1] USDA ARS, Environm Microbial & Food Safety Lab, Beltsville Agr Res Ctr East, Bldg 303,10300 Baltimore Ave, Beltsville, MD 20705 USA
[2] River Hill High Sch, Clarksville, MD 21029 USA
来源
SENSING FOR AGRICULTURE AND FOOD QUALITY AND SAFETY X | 2018年 / 10665卷
关键词
1064 nm Raman system; metanil yellow; Sudan-I; turmeric; curry powder; food safety; METANIL-YELLOW; SUDAN-I; QUALITY; CHILI;
D O I
10.1117/12.2303832
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Spice powders are used as food additives for flavor and color. Economically motivated adulteration of spice powders by color dyes is hazardous to human health. This study explored the potential of a 1064 nm Raman chemical imaging system for identification of azo color contamination in spice powders. Metanil yellow and Sudan-I, both azo compounds, were mixed separately with store-bought turmeric and curry powder at the concentration ranging from 1 % to 10 % (w/w). Each mixture sample was packed in a shallow nickel-plated sample container (25 mm x 25 mm x 1 mm). One Raman chemical image of each sample was acquired across the 25 mm x 25 mm surface area using a 0.25 mm step size. A threshold value was applied to the spectral images of metanil yellow mixtures (at 1147 cm(-1)) and Sudan-I mixtures (at 1593 cm(-1)) to obtain binary detection images by converting adulterant pixels into white pixels and spice powder pixels into the black (background) pixels. The detected number of pixels of each contaminant is linearly correlated with sample's concentration (R-2 = 0.99). This study demonstrates the 1064 nm Raman chemical imaging system as a potential tool for food safety and quality evaluation.
引用
收藏
页数:7
相关论文
共 43 条
  • [21] Detection of biopsy needle position in a live body using color Doppler imaging system
    Yamagata Univ, Yamagata, Japan
    Proc IEEE Ultrason Symp, (1697-1702):
  • [22] Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection
    Doherty, Trevor
    McKeever, Susan
    Al-Attar, Nebras
    Murphy, Tiarnan
    Aura, Claudia
    Rahman, Arman
    O'Neill, Amanda
    Finn, Stephen P.
    Kay, Elaine
    Gallagher, William M.
    Watson, R. William G.
    Gowen, Aoife
    Jackman, Patrick
    ANALYST, 2021, 146 (13) : 4195 - 4211
  • [23] Simultaneous detection of multiple adulterants in dry milk using macro-scale Raman chemical imaging
    Qin, Jianwei
    Chao, Kuanglin
    Kim, Moon S.
    FOOD CHEMISTRY, 2013, 138 (2-3) : 998 - 1007
  • [24] Rapid Detection of Nonprotein Nitrogen Adulterants in Milk Powder Using Point-Scan Raman Hyperspectral Imaging Technology
    Yang, Qiaoling
    Niu, Bing
    Gu, Shuqing
    Ma, Jinge
    Zhao, Chaomin
    Chen, Qin
    Guo, Dehua
    Deng, Xiaojun
    Yu, Yongai
    Zhang, Feng
    ACS OMEGA, 2022, 7 (02): : 2064 - 2073
  • [25] 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
    APPLIED SPECTROSCOPY, 2017, 71 (11) : 2469 - 2476
  • [26] Sentinel lymph nodes detection with an imaging system using Patent Blue V dye as fluorescent tracer
    Tellier, F.
    Steibel, J.
    Chabrier, R.
    Rodier, J. F.
    Pourroy, G.
    Poulet, P.
    ADVANCED BIOMEDICAL AND CLINICAL DIAGNOSTIC SYSTEMS XI, 2013, 8572
  • [27] A Nondestructive Detection Method for Mixed Veterinary Drugs in Pork Using Line-Scan Raman Chemical Imaging Technology
    Wenxiu Wang
    Chen Zhai
    Yankun Peng
    Kuanglin Chao
    Food Analytical Methods, 2019, 12 : 658 - 667
  • [28] A Nondestructive Detection Method for Mixed Veterinary Drugs in Pork Using Line-Scan Raman Chemical Imaging Technology
    Wang, Wenxiu
    Zhai, Chen
    Peng, Yankun
    Chao, Kuanglin
    FOOD ANALYTICAL METHODS, 2019, 12 (03) : 658 - 667
  • [29] A High-Speed Raman Imaging Method for the Detection of Adulteration in Milk Powder Using Self-encode Shrinkage Neural Network
    Xia Q.
    Huang Z.
    Bao L.
    Bu H.
    Chen D.
    Shipin Kexue/Food Science, 2022, 43 (24): : 296 - 302
  • [30] A Raman Imaging Methodology for Non-targeted Detection of Milk Powder Authenticity Using Flow-based Discrimination Neural Network
    Xia Q.
    He T.
    Huang Z.
    Chen D.
    Shipin Kexue/Food Science, 2023, 44 (12): : 315 - 321