Detection of Quality Deterioration of Packaged Raw Beef Based on Hyperspectral Technology
-д хадгалсан:
| -д хэвлэсэн: | Food Science & Nutrition vol. 13, no. 3 (Mar 1, 2025) |
|---|---|
| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , , , , |
| Хэвлэсэн: |
John Wiley & Sons, Inc.
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3180020204 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2048-7177 | ||
| 024 | 7 | |a 10.1002/fsn3.70022 |2 doi | |
| 035 | |a 3180020204 | ||
| 045 | 0 | |b d20250301 | |
| 084 | |a 233132 |2 nlm | ||
| 100 | 1 | |a Wu, Cheng |u School of Food Science and Engineering, Ningxia University, Yinchuan, China | |
| 245 | 1 | |a Detection of Quality Deterioration of Packaged Raw Beef Based on Hyperspectral Technology | |
| 260 | |b John Wiley & Sons, Inc. |c Mar 1, 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a ABSTRACT It is an important measure to ensure food quality and safety that real‐time monitoring of the key quality indicators of fresh meat after packaging in the process of storage and transportation. The feasibility of combining hyperspectral imaging (HSI) technology with chemometrics and deep learning to detect the quality deterioration of polyethylene (PE)‐packaged raw beef, especially for a key lipid oxidation indicator of malondialdehyde (MDA) content, was explored in this study. The feasibility of filtering to overcome the interference of packaging film on the spectral data was further investigated. Near‐infrared HSI (400–1000 nm) was used to collect spectral and spatial data of beef samples during short‐term storage. A least squares regression (PLSR) and echo‐neural network optimized by vulture optimization algorithms (BES‐ESN) models were developed by multivariate data processing methods. The results showed that the performance of models established by PE‐packed beef samples was usually inferior to that established by unpacked beef samples. The changes of MDA content in beef were visualized according to the optimal model. In addition, Gaussian filtering was applied to reduce the interference effect caused by the packaging film. The results have demonstrated that HSI combined with Gaussian filter preprocessing and multivariate data processing provided an efficient and reliable tool for detecting the freshness of beef in PE packaging. The best model had a coefficient of determination (R2P) of 0.8309 and a root mean squared error of prediction (RMSEP) of 0.2180, demonstrating the potential of hyperspectral techniques for real‐time monitoring of packaged raw meat quality. The findings can provide some references for the meat industry to develop advanced non‐invasive quality assurance systems in the meat industry. | |
| 653 | |a Beef | ||
| 653 | |a Software | ||
| 653 | |a Food safety | ||
| 653 | |a Acids | ||
| 653 | |a Data processing | ||
| 653 | |a Quality assurance | ||
| 653 | |a Meat processing industry | ||
| 653 | |a Packaging | ||
| 653 | |a Least squares method | ||
| 653 | |a Food quality | ||
| 653 | |a Oxidation | ||
| 653 | |a Chromatography | ||
| 653 | |a Lipid peroxidation | ||
| 653 | |a Feasibility studies | ||
| 653 | |a Freshness | ||
| 653 | |a Monitoring | ||
| 653 | |a Machine learning | ||
| 653 | |a Deep learning | ||
| 653 | |a Food packaging | ||
| 653 | |a Cameras | ||
| 653 | |a Spatial data | ||
| 653 | |a Neural networks | ||
| 653 | |a Meat industry | ||
| 653 | |a Experiments | ||
| 653 | |a Optimization | ||
| 653 | |a Multivariate analysis | ||
| 653 | |a Meat | ||
| 653 | |a Algorithms | ||
| 653 | |a Lipids | ||
| 653 | |a Meat quality | ||
| 653 | |a Filtration | ||
| 653 | |a Hyperspectral imaging | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Feng, Yingjie |u School of Food Science and Engineering, Ningxia University, Yinchuan, China | |
| 700 | 1 | |a Cui, Jiarui |u School of Food Science and Engineering, Ningxia University, Yinchuan, China | |
| 700 | 1 | |a Yao, Zhang |u School of Food Science and Engineering, Ningxia University, Yinchuan, China | |
| 700 | 1 | |a Xu, Hailong |u School of Food Science and Engineering, Ningxia University, Yinchuan, China | |
| 700 | 1 | |a Wang, Songlei |u School of Food Science and Engineering, Ningxia University, Yinchuan, China | |
| 773 | 0 | |t Food Science & Nutrition |g vol. 13, no. 3 (Mar 1, 2025) | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3180020204/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3180020204/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3180020204/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |