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Liang J, Wang Y, Shi Y, Huang X, Li Z, Zhang X, Zou X, Shi J. Non-destructive discrimination of homochromatic foreign materials in cut tobacco based on VIS-NIR hyperspectral imaging. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:4545-4552. [PMID: 36840508 DOI: 10.1002/jsfa.12528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 02/20/2023] [Accepted: 02/25/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND The presence of foreign materials (FM) not only reduces the commercial value of tobacco and the quality of cigarette products, but also affects the aroma and flavor of cigarettes. Existing tobacco deblending equipment has received little study with respect to homochromatic FM. In the present study, visible-near infrared (VIS-NIR) hyperspectral imaging technique combined with chemometrics were used to identify and visualize the homochromatic FM on the surface of thining tobacco. A comparison with conventional vision method was made to analyze the feasibility of the method. The importance of detecting FM in cut tobacco was further demonstrated by first studying the volatile organic compounds produced in cigarette mixed FM smoke and their effects on human health before conducting hyperspectral experiments. RESULTS The results indicated that solid-phase microextraction and gas chromatography mass spectrometry could detect volatile organic compounds in mainstream cigarette smoke that were not cigarette components and affected consumer health. Then, spectral features of the samples were extracted from hyperspectral images for building identification models to distinguish FM from cut tobacco. The visual RGB values of cut tobacco and FM were also used for the analysis of the recognition models. The results showed that the accuracy, precision and recall reached 100.00% using the back propagation artificial neural network classification model based on the principal component analysis raw wavelengths. The visualization results based on the optimal model produced clearer localization than conventional computer vision method. CONCLUSION The present study revealed that the VIS-NIR hyperspectral imaging technology had advantage in the detection and localization of FM on the surface of thinning tobacco, which provided a foundation for improving the quality and safety of cut tobacco production. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Jing Liang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Yueying Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yu Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Xiaowei Huang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Zhihua Li
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Xinai Zhang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Xiaobo Zou
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Jiyong Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
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Shi Y, Wang Y, Hu X, Li Z, Huang X, Liang J, Zhang X, Zheng K, Zou X, Shi J. Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging. Food Chem 2023; 411:135431. [PMID: 36681022 DOI: 10.1016/j.foodchem.2023.135431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/02/2023] [Accepted: 01/05/2023] [Indexed: 01/09/2023]
Abstract
Analogous density foreign matter (ADFM) embedded in soy protein meat semi-finished (SFSPM) is hidden by SFSPM and has similar acoustic impedance features to SFSPM, which makes non-destructive testing techniques such as computer vision (CV), reflectance spectroscopy and ultrasound imaging inappropriate for ADFM, which not only seriously affects the quality of soy protein meat (SPM) products but also increases the safety risk to consumers. In this study, to locate and separate ADFM by using transmission hyperspectral imaging (T-HSI) technique which is sensitive to chemical composition and highlight internal contours. The optimal discrimination model SVM + PCA + MSC + SPA was constructed using transmission spectral information with an accuracy of 95.00 %. The visualization results based on the optimal model showed clearer localization results than CV and ultrasound imaging. The study demonstrated that the advantages of T-HSI technology in detecting and locating ADFM inside SFSPM, which provides a basis for improving the production quality and safety of SPM.
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Affiliation(s)
- Yu Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Yueying Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xuetao Hu
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Zhihua Li
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xiaowei Huang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Jing Liang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xinai Zhang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Kaiyi Zheng
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xiaobo Zou
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Jiyong Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China.
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Li Y, Hu X, Shi J, Qiu B, Xiao J. Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology. EFOOD 2022. [DOI: 10.53365/efood.k/143830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Hyperspectral imaging technology with chemometrics was used for identifying and counting each species in microbial community during mixed fermentation. Hyperspectral images of microbial community of <i>Enterobacter</i> sp, <i>Acetobacter pasteurianus</i>, and <i>Lactobacillus paracasei</i> colonies were obtained and the spectra of strain colonies were extracted. Identification models were developed using linear discriminant analysis (LDA) and least-squares support vector machine (LS-SVM) by using 23 variables selected by genetic algorithm. The optimal LS-SVM model with identification rate of 96.67 % was used to identify colonies and prepare colony distribution maps in color for strains counting. The counting results by hyperspectral imaging technology agree with that of the manual counting method with average relative error of 3.70 %. The developed counting method has been successfully used to identify and count the specific strain from the mixed strains simultaneously. The hyperspectral imaging technology has a great potential to monitor changes in the microbial community structure.
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