Prediction of Center Temperature of Beijing Roast Duck Based on Quality Index
LIU YanXia1,2, WANG ZhenYu1, ZHENG XiaoChun1, ZHU YaoDi2, CHEN Li1, ZHANG DeQuan,11 Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Beijing 100193 2 College of Food science and Technology, Henan Agricultural University/Henan Key Laboratory of Meat Processing and Quality Safety Control, Zhengzhou 450002
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 2C 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. R2C and R2CV of 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 R2V 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. Keywords:Beijing roast duck;center temperature;quality;prediction model
PDF (499KB)元数据多维度评价相关文章导出EndNote|Ris|Bibtex收藏本文 本文引用格式 柳艳霞, 王振宇, 郑晓春, 朱瑶迪, 陈丽, 张德权. 基于品质指标预测北京烤鸭的中心温度[J]. 中国农业科学, 2020, 53(8): 1655-1663 doi:10.3864/j.issn.0578-1752.2020.08.014 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 Acricultura Sinica, 2020, 53(8): 1655-1663 doi:10.3864/j.issn.0578-1752.2020.08.014
0 引言
【研究意义】北京烤鸭历史悠久,被誉为“天下美味”而驰名中外[1,2,3]。目前餐饮店加工的北京烤鸭95%以上采用明火挂炉烤制,烤制过程火候控制和产品熟度(degree of doneness)判定皆依赖于操作师傅的经验,缺乏自动控温设备和熟度量化标准,从而经常造成产品品质不均、质量不稳等问题,难以实现标准化生产。熟度是指肉类的烹调成熟度,直接影响肉品的适口性和消费者的接受程度[4,5]。烤制终点的中心温度对肉制品的嫩度、色泽和风味等均产生较大的影响[6,7,8],是标准化生产的关键控制点,是判断其熟度的核心参数。肉类烹调程度受烹调时间和温度的控制,“适度”为主要原则,测温法被确定为肉类烹调程度的最常用方法[4]。实时掌握烤制过程中产品的中心温度,可以较好地判定产品的熟度。【前人研究进展】肉品中心温度的监测常使用专用温度计。肉用温度计有两种类型—直接型和间接型[4]。目前,在烤制环境温度较低及没有明火存在的情况下,使用温度记录仪连接探针可以方便快捷地反映中心温度变化情况[6,9],例如微波烤制牛肉[10]、荣昌烤制猪肉[8]等,从而控制其嫩度、风味等品质。国内外****开展了中心温度与肉品品质相关的研究工作[6-7,9-14]。李宏燕等[11]利用红外测温仪测定羊肉的中心温度。潘腾等[9]采用热电偶联合多路巡检控制仪监测烤制羊排中心温度。【本研究切入点】北京烤鸭挂炉烤制过程中采用明火加热,其中心温度的监测对于温度记录仪的设备要求苛刻,温度探针的寿命非常短,监测成本大幅增加,亟需开发新的中心温度监测技术。【拟解决的关键问题】本研究利用品质指标构建中心温度的预测模型,实现传统挂炉烤鸭的中心温度实时预测,为北京烤鸭熟度的判定提供一种新的途径。
1.3.1 样品制备 在北京东直门东兴楼饭庄由烤鸭师按照传统技法[15]对鸭坯进行处理,所用挂炉烤鸭炉内空气温度维持在203—254℃,挂鸭炉中部区域温度为224—235℃。试验设置0、10、20、30、40、50和60 min 7个时间点,每个时间点各取出15只鸭坯并分离出鸭胸肉,12只用于模型构建,3只用于模型验证,立即测定或-80℃冻存后测定。
采用MATLAB软件的多元线性回归(multiple linear regression,MLR)和偏最小二乘回归(partial least square method,PLSR)[18]两种线性建模方法及支持向量回归(support vector regression,SVR)和人工神经网络(artificial neural network,ANN)[19]2种非线性建模方法,基于北京烤鸭胸肉的色泽、肌红蛋白、水分含量、脂肪含量、蛋白二级结构等构建北京烤鸭中心温度预测模型,通过校正集、留一法交叉验证以及验证集(外部)的决定系数和均方根误差评价北京烤鸭中心温度预测模型的预测效果、稳定性和适用性。
本研究利用pls_toolbox工具包(来源于Eigenvector Research Incorporated)进行算法优化和建模分析。采用支持向量机模型建立烤鸭中心温度预测模型时,选择核函数时,对比线性核函数和径向核函数后发现径向核函数的预测效果较好,基于径向核函数建立支持向量机模型。选择最佳惩罚因子时,设置从10-3—100范围优化,优化的惩罚因子为31.26。使用ANN算法时,首先将数据进行归一化处理,然后利用工具包的默认设置用于超函数的优化,构建ANN模型。
公式中X1代表L*,X2代表a*,X3代表b*,X4代表脱氧肌红蛋白含量,X5代表氧合肌红蛋白含量,X6代表高铁肌红蛋白含量,X7代表水分含量,X8代表脂肪含量,X9代表α-螺旋含量,X10代表β-折叠含量,X11代表β-转角含量,X12代表无规则卷曲含量 The X1-12 in the formula represent L*, a*, b*, content of deoxymyoglobin, content of oxymyoglobin, content of metmyoglobin, moisture content, fat content, content of α-helix, content of β-sheet, content of β-turn, and content of random coil, respectively
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