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Bai R, Zhou J, Wang S, Zhang Y, Nan T, Yang B, Zhang C, Yang J. Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning. Foods 2024; 13:498. [PMID: 38338633 PMCID: PMC10855119 DOI: 10.3390/foods13030498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model's classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model's accuracy. Upon validating the model's performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.
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Affiliation(s)
- Ruibin Bai
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Junhui Zhou
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Siman Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Yue Zhang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Tiegui Nan
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Bin Yang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Jian Yang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
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Zhong Q, Zhang H, Tang S, Li P, Lin C, Zhang L, Zhong N. Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection. Foods 2023; 12:foods12102089. [PMID: 37238907 DOI: 10.3390/foods12102089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935-1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging.
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Affiliation(s)
- Qiongda Zhong
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Heyuan Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Heyuan 517000, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
| | - Hu Zhang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Heyuan Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Heyuan 517000, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
| | - Shuqi Tang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
| | - Peng Li
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Heyuan Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Heyuan 517000, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
| | - Caixia Lin
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
| | - Ling Zhang
- College of Biology and Food Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Nan Zhong
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Heyuan Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Heyuan 517000, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
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Singh T, Garg NM, Iyengar SRS, Singh V. Near-infrared hyperspectral imaging for determination of protein content in barley samples using convolutional neural network. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01892-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Suratno, Windarsih A, Warmiko HD, Khasanah Y, Indrianingsih AW, Rohman A. Metabolomics and Proteomics Approach Using LC-Orbitrap HRMS for the Detection of Pork in Tuna Meat for Halal Authentication. FOOD ANAL METHOD 2023. [DOI: 10.1007/s12161-023-02472-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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Qiu R, Zhao Y, Kong D, Wu N, He Y. Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121838. [PMID: 36108407 DOI: 10.1016/j.saa.2022.121838] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/26/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.
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Affiliation(s)
- Ruicheng Qiu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yinglei Zhao
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, China
| | - Dandan Kong
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Na Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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Abstract
Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.
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