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Rapid identification of dark spots and damage areas in apples and oranges by hyperspectral imaging technology
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Yi-Xuan SUN1, Mei SUN1, *, Yi YANG1, Yu HUANG2
Journal of Food Safety & Quality | 2026, 17(7) : 243 - 250
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Journal of Food Safety & Quality | 2026, 17(7): 243-250
• Special Topic: Rapid Detection Technology for Food Safety •
Rapid identification of dark spots and damage areas in apples and oranges by hyperspectral imaging technology
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Yi-Xuan SUN1, Mei SUN1, *, Yi YANG1, Yu HUANG2
Affiliations
  • 1.School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
  • 2.Wuxi Spectrum Vision Technology Co., Ltd., Wuxi 214142, China
doi: 10.19812/j.cnki.jfsq11-5956/ts.20251120003
Outline
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Objective

To quickly and accurately identify the black spot areas and damage areas of 2 types of fruits, apples and oranges.

Methods

Hyperspectral imaging technology was employed for the rapid detection of black spots and damage areas on these 2 types of fruits: By analyzing the spectra of apples and oranges in the range of 550–780 nm. The minimum noise fraction rotation (MNF) was applied to analyze the grayscale images of the first 7 eigenvalues for both fruits, which reduced computational demands for data processing to facilitate differentiation.The normalized difference spectral index (NDSI) method was utilized to construct novel two-band spectral indices, thereby making better use of the information contained within the wavelengths.

Results

Using the MNF Rotation method, it was found that the grayscale image of the fourth eigenvalue could effectively identify the dark spots and damage areas of the 2 types of fruits. In the NDSI method, two wavelength combinations, NDSI (723.6, 673.6) and NDSI (706, 590), were constructed. Then, through grayscale density slicing and arithmetic operations using the two bands, rapid identification of fruit damage and spots was achieved.

Conclusion

Both methods can effectively identify the damaged and black spot areas of fruits. However, the MNF Rotation method is relatively complex with a slow computing speed, making it unsuitable for industrial applications. In contrast, the NDSI algorithm features high computational efficiency and simple implementation, which renders it more applicable for the rapid online identification in fruit quality inspection.

hyperspectral imaging technology  /  dark spot area  /  damage area  /  normalized difference spectral index  /  minimum noise fraction rotation
孙怡萱, 孙梅, 杨一, 黄宇. 高光谱成像技术快速识别苹果和橙子的黑斑及损伤区域. 食品安全质量检测学报, 2026 , 17 (7) : 243 -250 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20251120003
Yi-Xuan SUN, Mei SUN, Yi YANG, Yu HUANG. Rapid identification of dark spots and damage areas in apples and oranges by hyperspectral imaging technology[J]. Journal of Food Safety & Quality, 2026 , 17 (7) : 243 -250 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20251120003
Year 2026 volume 17 Issue 7
11
2
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Article Info
doi: 10.19812/j.cnki.jfsq11-5956/ts.20251120003
  • Receive Date:2025-11-20
  • Online Date:2026-11-05
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  • Received:2025-11-20
Funding
Affiliations
    1.School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    2.Wuxi Spectrum Vision Technology Co., Ltd., Wuxi 214142, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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