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Li Y, Sun J, Wu X, Chen Q, Lu B, Dai C. Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model. J FOOD PROCESS PRES 2019. [DOI: 10.1111/jfpp.14238] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yating Li
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Bing Lu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Chunxia Dai
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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Seo Y, Lee H, Mo C, Kim MS, Baek I, Lee J, Cho BK. Multispectral Fluorescence Imaging Technique for On-Line Inspection of Fecal Residues on Poultry Carcasses. SENSORS 2019; 19:s19163483. [PMID: 31395841 PMCID: PMC6720503 DOI: 10.3390/s19163483] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 07/19/2019] [Accepted: 07/30/2019] [Indexed: 11/30/2022]
Abstract
Rapid and reliable inspection of food is essential to ensure food safety, particularly in mass production and processing environments. Many studies have focused on spectral imaging for poultry inspection; however, no research has explored the use of multispectral fluorescence imaging (MFI) for on-line poultry inspection. In this study, the feasibility of MFI for on-line detection of fecal matter from the ceca, colon, duodenum, and small intestine of poultry carcasses was investigated for the first time. A multispectral line-scan fluorescence imaging system was integrated with a commercial poultry conveying system, and the images of chicken carcasses with fecal contaminants were scanned at processing line speeds of one, three, and five birds per second. To develop an optimal detection and classification algorithm to distinguish upper and lower feces-contaminated parts from skin, the principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were first performed using the spectral data of the selected regions, and then applied in spatial domain to visualize the feces-contaminated area based on binary images. Our results demonstrated that for the spectral data analysis, both the PCA and PLS-DA can distinguish the high and low feces-contaminated area from normal skin; however, the PCA analysis based on selected band ratio images (F630 nm/F600 nm) exhibited better visualization and discrimination of feces-contaminated area, compared with the PLS-DA-based developed chemical images. A color image analysis using histogram equalization, sharpening, median filter, and threshold value (1) demonstrated 78% accuracy. Thus, the MFI system can be developed utilizing the two band ratios for on-line implementation for the effective detection of fecal contamination on chicken carcasses.
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Affiliation(s)
- Youngwook Seo
- Rural Development Administration, National Institute of Agricultural Sciences, 310 Nonsaengmyeong-ro, Wansan-gu, Jeonju-si, Jeollabuk-do 54875, Korea
| | - Hoonsoo Lee
- Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Korea.
| | - Changyeun Mo
- Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Korea
| | - Moon S Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd. Bldg. 303, BARC-East, Beltsville, MD 20705, USA
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd. Bldg. 303, BARC-East, Beltsville, MD 20705, USA
| | - Jayoung Lee
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea.
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Li Y, Sun J, Wu X, Lu B, Wu M, Dai C. Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms. J Food Sci 2019; 84:2234-2241. [PMID: 31313313 DOI: 10.1111/1750-3841.14706] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 05/18/2019] [Accepted: 05/23/2019] [Indexed: 11/30/2022]
Abstract
In order to rapidly and nondestructively identify tea grades, fluorescence hyperspectral imaging (FHSI) technology was proposed in this paper. A total of 309 Tieguanyin tea samples with three different grades were collected and the fluorescence hyperspectral data was acquired by hyperspectrometer (400 to 1000 nm). The characteristic wavelengths were respectively selected by Bootstrapping Soft Shrinkage (BOSS), Variable Iterative Space Shrinkage Approach (VISSA) and Model Adaptive Space Shrinkage (MASS) algorithms. Then, Support Vector Machine (SVM) was applied to establishing the relationship between the characteristic peaks, the full spectra, three characteristic spectra and the labels of tea grades. The results showed that VISSA-SVM model had the best classification performance, but the model precision can still be improved. Thus, Artificial Bee Colony (ABC) algorithm was introduced to optimize the parameters of SVM model. The accuracy and Kappa coefficient of test set of VISSA-ABC-SVM model were improved to 97.436% and 0.962, respectively. Therefore, the combination of fluorescence hyperspectra with VISSA-ABC-SVM model can accurately identify the grade of Tieguanyin tea. PRACTICAL APPLICATION: The rapid and accurate nondestructive tea grade identification method contributes to the construction of the tea online grade detection system. FHSI technology can solve the shortcomings of the reported methods and improved the identification accuracy of tea grades. It can be applied to the rapid detection of tea quality by tea companies, tea market, tea farmers and other demanders.
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Affiliation(s)
- Yating Li
- School of Electrical and Information Engineering of Jiangsu Univ., Zhenjiang, 212013, China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu Univ., Zhenjiang, 212013, China
| | - Xiaohong Wu
- School of Electrical and Information Engineering of Jiangsu Univ., Zhenjiang, 212013, China
| | - Bing Lu
- School of Electrical and Information Engineering of Jiangsu Univ., Zhenjiang, 212013, China
| | - Minmin Wu
- School of Electrical and Information Engineering of Jiangsu Univ., Zhenjiang, 212013, China
| | - Chunxia Dai
- School of Electrical and Information Engineering of Jiangsu Univ., Zhenjiang, 212013, China
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Optical Parameters for Using Visible-Wavelength Reflectance or Fluorescence Imaging to Detect Bird Excrements in Produce Fields. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9040715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Consumption of produce contaminated with pathogens of fecal origin is the most common source of food borne illnesses. Current practice is to visually survey fields for evidence of fecal contamination, and to exclude problematic areas from harvest. Bird excrement is known to contain human pathogens, and is often not detectable in produce fields using current survey methods. The goal of this project was to identify parameters for optical detection of bird excrements to support development of instruments to be used to supplement existing visual surveys. Under daylight ambient conditions, results suggested that reflectance imaging at around 500–530 nm or 610–640 nm could be used to detect excrements from the three bird species tested. Images were acquired using ad hoc camera parameters; however, normalizing intensities for individual images at 525 nm and using a fixed detection threshold allowed detection of 100% of bird excrements with no false positives against the background that consisted of local soil and fresh romaine and spinach leaves. Similar results were obtained using fluorescence imaging. Fluorescent imaging was accomplished in a darkened room using 405-nm illumination. The largest consistent differences in intensity responses between excrements and the brightest non-excrement object in the background matrix occurred at around 550 nm. Results suggested that using reflectance or fluorescence imaging for detection of bird excrements could be a valuable tool for reducing risks of consuming contaminated produce. One possibility would be to incorporate appropriate reflectance imaging capabilities in drones under the control of the individuals currently conducting field surveys.
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