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Rapid detection of dynamic changes in acid content during the fermentation process of sour meat based on hyperspectral imaging technology
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Shi-Jia CAO1, Dong LIANG1, 2, Yao-Di ZHU1, 2, Li-Jun ZHAO1, Miao-Yun LI1, *, Ling-Xia SUN1, Gai-Ming ZHAO1, Yan-Xia LIU1
Journal of Food Safety & Quality | 2025, 16(2) : 187 - 195
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Journal of Food Safety & Quality | 2025, 16(2): 187-195
Special Topic: Application of Modern Analysis Instrument in Food Detection
Rapid detection of dynamic changes in acid content during the fermentation process of sour meat based on hyperspectral imaging technology
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Shi-Jia CAO1, Dong LIANG1, 2, Yao-Di ZHU1, 2, Li-Jun ZHAO1, Miao-Yun LI1, *, Ling-Xia SUN1, Gai-Ming ZHAO1, Yan-Xia LIU1
Affiliations
  • 1. College of Food Science and Technology, Henan Agricltural University, Zhengzhou 450000, China
  • 2. Henan Jiuyuquan Food Co., Ltd., Xinxiang 453000, China
Published: 2025-01-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20241017008
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Objective To achieve rapid and non-destructive detection of lactic acid and total acidity during the fermentation process of sour meat. Methods Utilizing hyperspectral imaging technology, spectral reflectance images in the range of 408 to 1049 nm were collected to obtain spectral information of sour meat at different fermentation stages. After extracting the reflectance spectra from the regions of interest in the images, the kennard-stone algorithm (KS) was employed to divide the data into training and testing sets. The raw data underwent preprocessing through standard normal variate transformation (SNV) and multivariate scatter correction (MSC), followed by model establishment using partial least squares regression (PLSR). Feature wavelengths were extracted using the successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE). Models were developed based on PLSR and compared with full-wavelength prediction models. Results The optimal prediction model for lactic acid in sour meat was SNV-CARS-PLSR, with a coefficient of determination (R2) of 0.9113 and a root mean square error of cross-validation (RMSECV) of 0.7236 for the training set, while the testing set yielded an R2 of 0.9104 and RMSECV of 0.7342. The MSC-CARS-PLSR model for total acidity demonstrated the best predictive performance, with training set R2 and RMSECV values of 0.9307 and 0.6782, respectively, and prediction set R2 and RMSECV values of 0.8682 and 0.6907. Conclusion The models constructed using hyperspectral imaging technology have the potential to enable rapid and non-destructive detection of lactic acid and total acidity in sour meat, indicating significant application value.

hyperspectral imaging technology  /  sour meat  /  lactic acid  /  partial least squares regression
Shi-Jia CAO, Dong LIANG, Yao-Di ZHU, Li-Jun ZHAO, Miao-Yun LI, Ling-Xia SUN, Gai-Ming ZHAO, Yan-Xia LIU. Rapid detection of dynamic changes in acid content during the fermentation process of sour meat based on hyperspectral imaging technology[J]. Journal of Food Safety & Quality, 2025 , 16 (2) : 187 -195 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241017008
Year 2025 volume 16 Issue 2
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Article Info
doi: 10.19812/j.cnki.jfsq11-5956/ts.20241017008
  • Receive Date:2024-10-17
  • Online Date:2025-07-21
  • Published:2025-01-25
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  • Received:2024-10-17
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    1. College of Food Science and Technology, Henan Agricltural University, Zhengzhou 450000, China
    2. Henan Jiuyuquan Food Co., Ltd., Xinxiang 453000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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