董庆利,金曼,胡孟晗,刘宝林,林玉海.基于生物散斑技术的两部位牛肉质构特性预测模型改进[J].农业机械学报,2016,47(4):209-215.
Dong Qingli,Jin Man,Hu Menghan,Liu Baolin,Lin Yuhai.Improvement of Modeling Texture Characteristics of Different Parts of Beef Based on Biospeckle Technique[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(4):209-215.
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基于生物散斑技术的两部位牛肉质构特性预测模型改进   [下载全文]
Improvement of Modeling Texture Characteristics of Different Parts of Beef Based on Biospeckle Technique   [Download Pdf][in English]
投稿时间:2015-10-14  
DOI:10.6041/j.issn.1000-1298.2016.04.028
中文关键词:  牛肉  生物散斑  质构特性  模型改进
基金项目:“十二五”国家科技支撑计划项目(2015BAK36B04)、国家自然科学基金面上项目(31271896)、上海市科委长三角科技联合攻关领域项目(15395810900)和上海市研究生创新基金项目(JWCXSL1401)
作者单位
董庆利 上海理工大学 
金曼 上海理工大学 
胡孟晗 上海理工大学 
刘宝林 上海理工大学 
林玉海 荷美尔中国研发创新中心 
中文摘要:以时间序列散斑图的惯性力矩表征图像的散斑活性,采用感官评定法、质构剖面分析法(TPA)和Warner—Bratzler(W—B)剪切力法分析牛里脊肉的质构特性,研究了不同测定方法的相关性,并建立了散斑活性对里脊肉质构特性的预测模型;同时,针对里脊肉和腱子肉2种部位牛肉间质构特性差异较大,不能用同一模型进行预测的问题,应用斜率/截距法(S/B)和Kennard—Stone(K—S)样本添加法对模型进行改进,选择一种较准确易行的方法,使模型在2部位间得到快速的传递。结果表明,感官评定和TPA测得的硬度和咀嚼性间具有较高的正相关性,相关系数分别达到0.98和0.90,且W—B剪切力法与TPA的硬度决定系数也达到了0.95,证明了3种测定方法的可靠性。通过散斑活性值对质构特性进行预测时,硬度、咀嚼性及W—B剪切力的预测决定系数分别达到了0.83、0.77和0.69。分别用2种方法对模型进行改进,可知采用S/B法时,改进后的里脊肉模型对腱子肉的预测均方根误差RMSE为26.65,准确因子Af和偏差因子Bf分别为1.15和1.08。而采用K—S样本添加法,加入代表性样本数为12时,模型对腱子肉的预测达到较理想水平,RMSE为13.21,Af和Bf分别为1.07和1.02。K—S样本添加法能够在预测过程中更好地降低部位间差异,提高模型对腱子肉的预测精度,且改进效果优于S/B法。
Dong Qingli  Jin Man  Hu Menghan  Liu Baolin  Lin Yuhai
University of Shanghai for Science and Technology,University of Shanghai for Science and Technology,University of Shanghai for Science and Technology,University of Shanghai for Science and Technology and Hormel China Idea and Innovation Center
Key Words:beef  biospeckle  texture characteristics  model improvement
Abstract:Biospeckle is one of the low-cost, portable and online screening tools for optical non-destructive testing technologies, and it shows potential for application to agricultural products quality prediction. Sensory evaluation, texture profile analysis (TPA) and Warner—Bratzler (W—B) shear force were applied to analyze the texture characteristics of beef tenderloin, the correlation between different measuring methods was investigated, and the prediction model of biospeckle for texture characteristics was established. Since the significant difference between tenderloin and shin, it seems not possible to predict their texture characteristics with a same model. Two methods, including slope/bias (S/B) correction method and Kennard—Stone (K—S) typical samples adding method were used to improve the tenderloin prediction model. Compared with the effect of two modified methods, the more accurate and convenient method was chosen to make the model transfer to shin fast. The results showed that the hardness and chewiness of sensory evaluation and TPA had high positive correlation, the determination coefficient (R2) reached 0.98 and 0.90, respectively, and R2 between W—B shear force and hardness of TPA reached 0.95, which proved the reliability of the three texture characteristics measurement methods. The values of R2 for predicting the texture characteristics of hardness, chewiness and W—B shear force with biospeckle activity were 0.83, 0.77 and 0.69, respectively. The results of improvement for the loin model were as follows: as improved with S/B correction method, the root mean square error (RMSE) was 26.65, bias factor (Bf) and accuracy factor (Af) were 1.08 and 1.15, respectively. While the effect of modified with K—S adding method of typical samples was better than that of S/B correction method, and when the adding number of samples was 12, the RMSE was 13.21, Bf and Af values were 1.07 and 1.02, respectively. In conclusion, K—S typical samples adding method could reduce the differences between the different parts, improve the goodness-of-fit of predictive shin model, and produce better effect than S/B correction method.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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