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Ma M, Yang X, Ying X, Shi C, Jia Z, Jia B. Applications of Gas Sensing in Food Quality Detection: A Review. Foods 2023; 12:3966. [PMID: 37959084 PMCID: PMC10648483 DOI: 10.3390/foods12213966] [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: 09/11/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 11/15/2023] Open
Abstract
Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.
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Affiliation(s)
- Minzhen Ma
- Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; (M.M.); (X.Y.); (Z.J.); (B.J.)
- College of Food and Pharmacy, Zhejiang Ocean University, Zhoushan 316004, China
| | - Xinting Yang
- Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; (M.M.); (X.Y.); (Z.J.); (B.J.)
- Key Laboratory of Cold Chain Logistics Technology for Agro-Product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
- National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
| | - Xiaoguo Ying
- College of Food and Pharmacy, Zhejiang Ocean University, Zhoushan 316004, China
- Department of Agriculture, Food and Environment (DAFE), Pisa University, Via del Borghetto, 80, 56124 Pisa, Italy
| | - Ce Shi
- Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; (M.M.); (X.Y.); (Z.J.); (B.J.)
- Key Laboratory of Cold Chain Logistics Technology for Agro-Product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
- National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
| | - Zhixin Jia
- Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; (M.M.); (X.Y.); (Z.J.); (B.J.)
- Key Laboratory of Cold Chain Logistics Technology for Agro-Product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
- National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
| | - Boce Jia
- Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; (M.M.); (X.Y.); (Z.J.); (B.J.)
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
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Zhu L, Jia H, Chen Y, Wang Q, Li M, Huang D, Bai Y. A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System. SENSORS 2019; 19:s19153417. [PMID: 31382683 PMCID: PMC6696477 DOI: 10.3390/s19153417] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/01/2019] [Accepted: 08/02/2019] [Indexed: 11/16/2022]
Abstract
Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R2 values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM.
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Affiliation(s)
- Longtu Zhu
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
| | - Honglei Jia
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
| | - Yibing Chen
- Jilin Province Soil and Fertilizer Station, Changchun 130031, China
| | - Qi Wang
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
| | - Mingwei Li
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
| | - Dongyan Huang
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China.
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.
| | - Yunlong Bai
- College of Information, Jilin Agricultural University, Changchun 130118, China
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