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Quantitative Prediction of White Striping Defects in Chicken Breast Fillets Using Hyperspectral Imaging and Generative Adversarial Network
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Cong ZHANG, Feng TAN, Dachen WANG, Hongping ZHOU, Hongzhe JIANG*
Science and Technology of Food Industry | 2026, 47(14) : 289 - 299
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Science and Technology of Food Industry | 2026, 47(14): 289-299
• Analysis and Determination •
Quantitative Prediction of White Striping Defects in Chicken Breast Fillets Using Hyperspectral Imaging and Generative Adversarial Network
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Cong ZHANG, Feng TAN, Dachen WANG, Hongping ZHOU, Hongzhe JIANG*
Affiliations
  • College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Published: 2026-07-15 doi: 10.13386/j.issn1002-0306.2025060131
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Chicken meat was widely favored by consumers for its excellent texture and reasonable price. However, the frequent occurrence of white striping (WS) myopathy significantly impacted its quality and marketability. In this study, the comprehensive myopathy index (CMI) was developed as a quantitative predictor of WS myopathy using multiple physicochemical parameters, including pH, shear force, cooking loss, drip loss, and color. In combination with hyperspectral imaging (HSI), a CMI prediction model was developed to achieve rapid and non-destructive identification of WS myopathy. To address the limitation of insufficient samples, a regression generative adversarial network (RGAN) was proposed. Spectral data and the corresponding CMI values were generated simultaneously. And the generated data were highly consistent with the real data in spectral features, t-distributed stochastic neighbor embedding (t-SNE), and CMI values. The comparison of models showed that the convolutional neural network regression (CNNR) model achieved the best performance (\begin{document}$R_{\mathrm{p}}^2 $\end{document} of 0.835, RMSEP of 0.057) when 300 generated samples were added. This result confirmed the effectiveness of RGAN in improving modeling accuracy and generalization. In conclusion, the proposed CMI prediction method integrating HSI with RGAN enabled accurate quantification of WS myopathy in chicken breast. It would also provide an effective solution for intelligent detection under limited sample conditions.

white striping  /  hyperspectral imaging  /  comprehensive myopathy index  /  generative adversarial network  /  convolutional neural network regression
张聪, 谭烽, 王大臣, 周宏平, 姜洪喆. 基于高光谱成像与生成对抗网络的鸡胸肉白纹缺陷量化预测研究. 食品工业科技, 2026 , 47 (14) : 289 -299 . DOI: 10.13386/j.issn1002-0306.2025060131
Cong ZHANG, Feng TAN, Dachen WANG, Hongping ZHOU, Hongzhe JIANG. Quantitative Prediction of White Striping Defects in Chicken Breast Fillets Using Hyperspectral Imaging and Generative Adversarial Network[J]. Science and Technology of Food Industry, 2026 , 47 (14) : 289 -299 . DOI: 10.13386/j.issn1002-0306.2025060131
Appendix
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Year 2026 volume 47 Issue 14
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doi: 10.13386/j.issn1002-0306.2025060131
  • Receive Date：2025-06-12
  • Online Date：2026-10-22
  • Published：2026-07-15
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  • Received：2025-06-12
Affiliations
    College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, 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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