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Effects of Thermal Processing Methods on Tuna Quality and Establishment of a Moisture Prediction Model
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Zehui ZHANG1, Qiaoyu LIU1, *, Sili XIAO1, Jiasheng ZHANG1, Shaotai XIE2, *, Zhongcheng XIE2, Haihui HUANG2, Weidong BAI1
Science and Technology of Food Industry | 2026, 47(15) : 56 - 66
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Science and Technology of Food Industry | 2026, 47(15): 56-66
• Papers Invited by Guest Editors •
Effects of Thermal Processing Methods on Tuna Quality and Establishment of a Moisture Prediction Model
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Zehui ZHANG1, Qiaoyu LIU1, *, Sili XIAO1, Jiasheng ZHANG1, Shaotai XIE2, *, Zhongcheng XIE2, Haihui HUANG2, Weidong BAI1
Affiliations
  • 1College of Light Industry and Food Sciences, Guangdong Provincial Key Laboratory of Lingnan Specialty Food Science and Technology, Key Laboratory of Green Processing and Intelligent Manufacturing of Lingnan Specialty Food, Ministry of Agriculture, Academy of Contemporary Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
  • 2Guangdong Yingjinqian Haibao Food Co., Ltd., Maoming 525400, China
Published: 2026-08-01 doi: 10.13386/j.issn1002-0306.2025090324
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Tuna samples were processed using steaming, roasting, air frying, and low-temperature boiling methods. Changes in sensory attributes, color, texture, moisture content, pH, crude protein, and crude fat were systematically analyzed. Three prediction models—Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Back-Propagation Neural Network (BPNN)—were developed based on moisture content as the characteristic parameter, combined with spectral preprocessing techniques, to establish a non-destructive detection method using hyperspectral imaging technology. The results demonstrated that moist-heat treatments (low-temperature boiling and steaming) resulted in lower hardness, effectively preserved moisture, and yielded a soft texture. In contrast, dry-heat treatments (roasting and air frying) promoted the Maillard reaction and moisture evaporation, imparting a crisp texture and rich aroma. As the thermal processing intensity increased, the moisture content decreased significantly (P<0.05), while the relative contents of protein and fat increased due to the concentration effect. Among the three models developed, the BPNN model exhibited superior prediction accuracy and stability compared to the PLSR and SVR models, achieving optimal performance (R2=0.9239, RMSEP=2.0284). The BPNN model enables accurate and rapid non-destructive detection of moisture content in tuna, providing a highly reliable spectral analysis solution for quality monitoring of thermally processed tuna products.

hyperspectral  /  tuna  /  hot working  /  physical and chemical properties  /  moisture prediction
张泽辉, 刘巧瑜, 肖斯立, 张佳生, 谢绍泰, 谢忠诚, 黄海辉, 白卫东. 不同热加工方式对金枪鱼品质的影响及水分预测模型建立. 食品工业科技, 2026 , 47 (15) : 56 -66 . DOI: 10.13386/j.issn1002-0306.2025090324
Zehui ZHANG, Qiaoyu LIU, Sili XIAO, Jiasheng ZHANG, Shaotai XIE, Zhongcheng XIE, Haihui HUANG, Weidong BAI. Effects of Thermal Processing Methods on Tuna Quality and Establishment of a Moisture Prediction Model[J]. Science and Technology of Food Industry, 2026 , 47 (15) : 56 -66 . DOI: 10.13386/j.issn1002-0306.2025090324
Year 2026 volume 47 Issue 15
120
48
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doi: 10.13386/j.issn1002-0306.2025090324
  • Receive Date:2025-09-29
  • Online Date:2026-10-24
  • Published:2026-08-01
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  • Received:2025-09-29
Affiliations
    1College of Light Industry and Food Sciences, Guangdong Provincial Key Laboratory of Lingnan Specialty Food Science and Technology, Key Laboratory of Green Processing and Intelligent Manufacturing of Lingnan Specialty Food, Ministry of Agriculture, Academy of Contemporary Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    2Guangdong Yingjinqian Haibao Food Co., Ltd., Maoming 525400, 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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