Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies
Grains, as the cornerstone of the global food supply, require quality inspection that is crucial for ensuring food safety and improving production efficiency. Traditional laboratory methods, while accurate, are time-consuming and costly, making them unsuitable for online, real-time monitoring. Near-...
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Academy of National Food and Strategic Reserves Administration
2025-05-01
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| Series: | Liang you shipin ke-ji |
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| author | CUI Chen-hao FAN Chen |
| author_facet | CUI Chen-hao FAN Chen |
| author_sort | CUI Chen-hao |
| collection | DOAJ |
| description | Grains, as the cornerstone of the global food supply, require quality inspection that is crucial for ensuring food safety and improving production efficiency. Traditional laboratory methods, while accurate, are time-consuming and costly, making them unsuitable for online, real-time monitoring. Near-infrared (NIR) spectroscopy, with its advantages of rapid, non-destructive, and multi-component simultaneous detection, has been widely applied in grain quality inspection. However, the high dimensionality, complexity of NIR spectral data, and variations among different instruments, environments, and samples pose challenges to modeling methods and model transfer. This review summarizes the application of NIR spectroscopy in online grain quality inspection, systematically outlining the development from traditional linear modeling (e.g., partial least squares regression), nonlinear modeling (e.g., support vector machines, artificial neural networks) to deep learning methods (e.g., convolutional neural networks). It focuses on the strategies, challenges, and latest advances of model transfer techniques in addressing issues such as instrument differences, environmental changes, and sample diversity, including calibration transfer with and without standards. Furthermore, this review summarizes the challenges, experiences, and future research directions in practical industrial applications, aiming to provide references for the widespread application of NIR technology in the grain industry. |
| format | Article |
| id | doaj-art-0a6782e9cb5349fca4585a711f1809dc |
| institution | Kabale University |
| issn | 1007-7561 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Academy of National Food and Strategic Reserves Administration |
| record_format | Article |
| series | Liang you shipin ke-ji |
| spelling | doaj-art-0a6782e9cb5349fca4585a711f1809dc2025-08-20T03:46:17ZengAcademy of National Food and Strategic Reserves AdministrationLiang you shipin ke-ji1007-75612025-05-01333748410.16210/J.CNKI.1007-7561.2025.03.006Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer StrategiesCUI Chen-hao0FAN Chen1Bühler Group, Innovation Center, Wuxi, Jiangsu 214142, ChinaState Key Laboratory for Manufacturing Systems Engineering, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, ChinaGrains, as the cornerstone of the global food supply, require quality inspection that is crucial for ensuring food safety and improving production efficiency. Traditional laboratory methods, while accurate, are time-consuming and costly, making them unsuitable for online, real-time monitoring. Near-infrared (NIR) spectroscopy, with its advantages of rapid, non-destructive, and multi-component simultaneous detection, has been widely applied in grain quality inspection. However, the high dimensionality, complexity of NIR spectral data, and variations among different instruments, environments, and samples pose challenges to modeling methods and model transfer. This review summarizes the application of NIR spectroscopy in online grain quality inspection, systematically outlining the development from traditional linear modeling (e.g., partial least squares regression), nonlinear modeling (e.g., support vector machines, artificial neural networks) to deep learning methods (e.g., convolutional neural networks). It focuses on the strategies, challenges, and latest advances of model transfer techniques in addressing issues such as instrument differences, environmental changes, and sample diversity, including calibration transfer with and without standards. Furthermore, this review summarizes the challenges, experiences, and future research directions in practical industrial applications, aiming to provide references for the widespread application of NIR technology in the grain industry.near-infrared spectroscopygrain qualityonline detectionchemometricscalibration transferdeep learning |
| spellingShingle | CUI Chen-hao FAN Chen Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies Liang you shipin ke-ji near-infrared spectroscopy grain quality online detection chemometrics calibration transfer deep learning |
| title | Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies |
| title_full | Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies |
| title_fullStr | Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies |
| title_full_unstemmed | Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies |
| title_short | Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies |
| title_sort | advances in online grain quality assessment near infrared spectroscopic modeling and transfer strategies |
| topic | near-infrared spectroscopy grain quality online detection chemometrics calibration transfer deep learning |
| work_keys_str_mv | AT cuichenhao advancesinonlinegrainqualityassessmentnearinfraredspectroscopicmodelingandtransferstrategies AT fanchen advancesinonlinegrainqualityassessmentnearinfraredspectroscopicmodelingandtransferstrategies |