Research Progress on Methods for Improving the Stability of Non-Destructive Testing of Agricultural Product Quality
Gorde:
| Argitaratua izan da: | Foods vol. 13, no. 23 (2024), p. 3917 |
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| Egile nagusia: | |
| Beste egile batzuk: | , , |
| Argitaratua: |
MDPI AG
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| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/foods13233917 |2 doi | |
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| 045 | 2 | |b d20240101 |b d20241231 | |
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| 100 | 1 | |a Xu, Sai |u Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; <email>liangxin@gdaas.cn</email>; School of Life Sciences, South China Normal University, Guangzhou 510631, China; <email>hantingw316@outlook.com</email> | |
| 245 | 1 | |a Research Progress on Methods for Improving the Stability of Non-Destructive Testing of Agricultural Product Quality | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Non-destructive testing (NDT) technology is pivotal in the quality assessment of agricultural products. In contrast to traditional manual testing, which is fraught with subjectivity, inefficiency, and the potential for sample damage, NDT technology has gained widespread application due to its advantages of objectivity, speed, and accuracy, and it has injected significant momentum into the intelligent development of the food industry and agriculture. Over the years, technological advancements have led to the development of NDT systems predicated on machine vision, spectral analysis, and bionic sensors. However, during practical application, these systems can be compromised by external environmental factors, the test samples themselves, or by the degradation and noise interference inherent in the testing equipment, leading to instability in the detection process. This instability severely impacts the accuracy and efficiency of the testing. Consequently, refining the detection methods and enhancing system stability have emerged as key focal points for research endeavors. This manuscript presents an overview of various prevalent non-destructive testing methodologies, summarizes how sample properties, external environments, and instrumentation factors affect the stability of testing in practical applications, organizes and analyzes solutions to enhance the stability of non-destructive testing of agricultural product quality based on current research, and offers recommendations for future investigations into the non-destructive testing technology of agricultural products. | |
| 653 | |a Test equipment | ||
| 653 | |a Volatile organic compounds--VOCs | ||
| 653 | |a Deep learning | ||
| 653 | |a Agricultural production | ||
| 653 | |a Nondestructive testing | ||
| 653 | |a Vision systems | ||
| 653 | |a Agricultural technology | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Agriculture | ||
| 653 | |a Data analysis | ||
| 653 | |a Instrumentation | ||
| 653 | |a Efficiency | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Food industry | ||
| 653 | |a Agricultural products | ||
| 653 | |a Quality assessment | ||
| 653 | |a Spectral analysis | ||
| 653 | |a Agricultural commodities | ||
| 653 | |a Product quality | ||
| 653 | |a Machine vision | ||
| 653 | |a Technology assessment | ||
| 653 | |a Bionics | ||
| 653 | |a Computer vision | ||
| 653 | |a Quality control | ||
| 653 | |a Neural networks | ||
| 653 | |a Sensors | ||
| 653 | |a Biodegradation | ||
| 653 | |a Information processing | ||
| 653 | |a Chemical bonds | ||
| 653 | |a Industrial development | ||
| 653 | |a Algorithms | ||
| 653 | |a Environmental factors | ||
| 653 | |a Light | ||
| 653 | |a Infrared radiation | ||
| 653 | |a Systems stability | ||
| 700 | 1 | |a Wang, Hanting |u School of Life Sciences, South China Normal University, Guangzhou 510631, China; <email>hantingw316@outlook.com</email> | |
| 700 | 1 | |a Liang, Xin |u Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; <email>liangxin@gdaas.cn</email> | |
| 700 | 1 | |a Lu, Huazhong |u Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China | |
| 773 | 0 | |t Foods |g vol. 13, no. 23 (2024), p. 3917 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3144052505/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3144052505/fulltextwithgraphics/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3144052505/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |