基于品质指标预测北京烤鸭的中心温度

doi:10.3864/j.issn.0578-1752.2020.08.014

Abstract

Abstract:

【Objective】 The aim of this study was to solve the problem in detecting center temperature of Beijing roast duck during traditional Gua-lu roasting accurately and timely. 【Method】 The prediction models of center temperature were established by using multiple linear regression, partial least-squares regression, support vector regression and artificial neural network according to the quality indicators. 【Result】 The results showed that the models were effective to identify center temperature of Beijing roast duck by L*, a*, b*, deoxymyoglobin, oxymyoglobin, metmyoglobin, moisture and fat content, as well as protein secondary structure of duck breast. The R ²_C of multiple linear regression and partial least-squares regression were 0.9543 and 0.9384, and SEC of 5.8205℃ and 6.7634℃, respectively. The prediction effect of multiple linear regression was better than partial least-squares regression, while the prediction model of support vector regression was superior to artificial neural network. R ²_C and R ²_CVof support vector regression were 0.9837 and 0.9496, SEC and SECV were 3.5215℃ and 6.1236℃, respectively, so the support vector regression was the best prediction model of center temperature. The R ²_V of the verified models of support vector regression was 0.9748, and the SEV was 5.5204℃. The model obtained by support vector regression together with the modeling results could accurately predict the center temperature of Beijing roast duck. 【Conclusion】 The color, myoglobin, water content, fat content and protein secondary structure of the breast of Beijing roast duck could effectively identify the central temperature. The SVR model was the most accurate prediction model for the center temperature.

Key words: Beijing roast duck, center temperature, quality, prediction model

LIU YanXia,WANG ZhenYu,ZHENG XiaoChun,ZHU YaoDi,CHEN Li,ZHANG DeQuan. Prediction of Center Temperature of Beijing Roast Duck Based on Quality Index[J].Scientia Agricultura Sinica, 2020, 53(8): 1655-1663.

Figures/Tables 7

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References 36

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建模方法 Modeling methods	校正集决定系数R²_C R²_C for Calibration set	校正集均方根误差 SEC for Calibration set	交叉验证决定系数R²_CV R²_CV for Cross-validation set	交叉验证均方根误差 SECV for Cross-validation set	模型表征 Model characterization
MLR	0.9543	5.8205	0.9376	6.8249	Y=0.5138X₁-1.2166X₂+1.8129X₃+14.4454X₄+ 14.6893X₅+13.6934X₆-1.7653X₇+0.2512X₈- 13.2067X₉-12.7812X₁₀-12.6912X₁₁-12.6671X₁₂
PLSR	0.9384	6.7634	0.9221	7.6114	Y=0.5831X₁-0.5723X₂+0.9120X₃+1.7496X₄+ 1.0477X₅+0.4625X₆-1.7756X₇-0.0101X₈+ 0.2155X₉+0.4015X₁₀+1.2262X₁₁+0.8696X₁₂
SVR	0.9837	3.5215	0.9496	6.1236
ANN	0.9712	4.6260	0.9055	8.5726

Prediction of Center Temperature of Beijing Roast Duck Based on Quality Index

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