Application of Image Computing in Non-Destructive Detection of Chinese Cuisine Article Swipe
Xiaowei Huang
,
Zexiang Li
,
Zhihua Li
,
Jiyong Shi
,
Ning Zhang
,
Qin Zhou
,
Liuzi Du
,
Tingting Shen
,
Roujia Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/foods14142488
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/foods14142488
Food quality and safety are paramount in preserving the culinary authenticity and cultural integrity of Chinese cuisine, characterized by intricate ingredient combinations, diverse cooking techniques (e.g., stir-frying, steaming, and braising), and region-specific flavor profiles. Traditional non-destructive detection methods often struggle with the unique challenges posed by Chinese dishes, including complex textural variations in staple foods (e.g., noodles, dumplings), layered seasoning compositions (e.g., soy sauce, Sichuan peppercorns), and oil-rich cooking media. This study pioneers a hyperspectral imaging framework enhanced with domain-specific deep learning algorithms (spatial–spectral convolutional networks with attention mechanisms) to address these challenges. Our approach effectively deciphers the subtle spectral fingerprints of Chinese-specific ingredients (e.g., fermented black beans, lotus root) and quantifies critical quality indicators, achieving an average classification accuracy of 97.8% across 15 major Chinese dish categories. Specifically, the model demonstrates high precision in quantifying chili oil content in Mapo Tofu with a Mean Absolute Error (MAE) of 0.43% w/w and assessing freshness gradients in Cantonese dim sum (Shrimp Har Gow) with a classification accuracy of 95.2% for three distinct freshness levels. This approach leverages the detailed spectral information provided by hyperspectral imaging to automate the classification and detection of Chinese dishes, significantly improving both the accuracy of image-based food classification by >15 percentage points compared to traditional RGB methods and enhancing food quality safety assessment.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/foods14142488
- https://www.mdpi.com/2304-8158/14/14/2488/pdf?version=1752671430
- OA Status
- gold
- References
- 162
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412480812
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412480812Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/foods14142488Digital Object Identifier
- Title
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Application of Image Computing in Non-Destructive Detection of Chinese CuisineWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-16Full publication date if available
- Authors
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Xiaowei Huang, Zexiang Li, Zhihua Li, Jiyong Shi, Ning Zhang, Qin Zhou, Liuzi Du, Tingting Shen, Roujia ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/foods14142488Publisher landing page
- PDF URL
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https://www.mdpi.com/2304-8158/14/14/2488/pdf?version=1752671430Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2304-8158/14/14/2488/pdf?version=1752671430Direct OA link when available
- Concepts
-
Hyperspectral imaging, RGB color model, Artificial intelligence, Flavor, Steaming, Seasoning, Computer science, Food science, Mathematics, Pattern recognition (psychology), Raw material, Chemistry, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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162Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.region-specific | 31 |
| abstract_inverted_index.Chinese-specific | 102 |
| abstract_inverted_index.(spatial–spectral | 83 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.33173192 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |