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Optimization research on non-destructive detection model of Vaccinium spp. sugar content based on hyperspectral imaging technology
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Xin-Yue GUO1, Guo-Liang CHEN2, Liang-Kuan ZHU1, *, Da-Yang LIU2, Xiao-Xiong SUN1
Journal of Food Safety & Quality | 2025, 16(11) : 207 - 214
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Journal of Food Safety & Quality | 2025, 16(11): 207-214
• Food Analysis and Detection •
Optimization research on non-destructive detection model of Vaccinium spp. sugar content based on hyperspectral imaging technology
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Xin-Yue GUO1, Guo-Liang CHEN2, Liang-Kuan ZHU1, *, Da-Yang LIU2, Xiao-Xiong SUN1
Affiliations
  • 1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
  • 2. College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
Published: 2025-06-15 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250205002
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Objective To optimize a non-destructive detection model for predicting Vaccinium spp. sugar content using hyperspectral imaging technology. Methods The L25 variety of blueberries from Dandong was selected as the subject, and hyperspectral imaging technology was acquired in the wavelength range of 900-1700 nm. The average spectrum of the region of interest was calculated as the raw data. The 3 kinds of preprocessing methods, including multiple scatter correction (MSC), standard normal variate (SNV) and Savitzky-Golay (SG), were applied to improve the spectral data quality. Non-destructive sugar content prediction models were established using partial least squares regression (PLSR), back propagation neural network (BPNN), and support vector regression (SVR) based on the full-wavelength data after preprocessing. Results The experimental results demonstrated that the PLSR model, with MSC and SNV preprocessing, exhibited the best performance, achieving root mean square error of prediction (RMSEP) values of 0.3586 and 0.3599, respectively. Conclusion This study provides an optimized non-destructive detection model for Vaccinium spp. sugar content, offering effective technical support for rapid and accurate sugar content prediction with significant practical potential.

Vaccinium spp. sugar content  /  non-destructive detection  /  hyperspectral imaging technology  /  machine learning
Xin-Yue GUO, Guo-Liang CHEN, Liang-Kuan ZHU, Da-Yang LIU, Xiao-Xiong SUN. Optimization research on non-destructive detection model of Vaccinium spp. sugar content based on hyperspectral imaging technology[J]. Journal of Food Safety & Quality, 2025 , 16 (11) : 207 -214 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250205002
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Year 2025 volume 16 Issue 11
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Article Info
doi: 10.19812/j.cnki.jfsq11-5956/ts.20250205002
  • Receive Date：2025-02-05
  • Online Date：2025-07-14
  • Published：2025-06-15
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  • Received：2025-02-05
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Affiliations
    1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
    2. College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, 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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