陈 磊, 王金勇, 李学伟. 仪器测定的猪肉质构性状与感官性状的回归分析[J]. 农业工程学报, 2010, 26(6): 357-362.
    引用本文: 陈 磊, 王金勇, 李学伟. 仪器测定的猪肉质构性状与感官性状的回归分析[J]. 农业工程学报, 2010, 26(6): 357-362.
    Regression analysis of instrumental texture characteristics and sensory characteristics of pork[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(6): 357-362.
    Citation: Regression analysis of instrumental texture characteristics and sensory characteristics of pork[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(6): 357-362.

    仪器测定的猪肉质构性状与感官性状的回归分析

    Regression analysis of instrumental texture characteristics and sensory characteristics of pork

    • 摘要: 为了分析猪肉感官性状与仪器质构性状间的相关性,并利用仪器质构测定指标建立感官性状的预测模型,采集18头6月龄杜洛克×(长白×大约克)杂交商品猪的背最长肌样品,通过24、72、144 h 3种不同时间嫩化处理得到54份质构性状表现有差异的样品。利用warner-bratzler 剪切力测定、质构剖面分析(TPA)和感官评定,分析了各样品的质构性状,并分析了仪器测定与感官测定数据间的线性关系。大部分仪器测定指标均与感官评定值表现出显著或极显著(P<0.05或P<0.01)的相关关系。进一步采用主成分-逐步回归法,以仪器测定指标为自变量,感官评定数据为依变量进行回归分析,分别得到具有统计意义的感官硬度、感官弹性和多汁性的预测方程,其中对感官硬度的预测效果较好,方程决定系数(r2)达到0.61;而对感官弹性和多汁性的预测效果较差,方程决定系数(r2)为0.40和0.42。由此明确了猪肉感官性状与仪器质构性状间广泛的相关关系,并采用客观、全面的仪器测定手段和合理的回归方法的优化将肉类感官性状模型的预测效果提高到一个新的水平。

       

      Abstract: In order to analyze the correlation between sensory attributes and instrumental texture characteristics of pork, and validate model for predict sensory attributes, two instrumental methods for assessing texture characteristics of pork, warner–bratzler (W-B) and texture profile analysis (TPA), were conducted on 54 samples of m. longissimus dorsi muscle from 18 DLY commercial pigs, aging at 24, 72 and 144 hours post-mortem. A trained panelists sensory analysis was also performed on the similar 54 samples. The results showed that, most of the instrumental measures were significantly or marked significantly correlated (P<0.05 or P<0.01) with sensory texture characteristics. Principal component-stepwise regression analysis were used to generate prediction equations with the data of TPA and warner-bratzler measures as independent variables respectively and the data of sensory analysis as dependent variable. The prediction equations of sensory hardness, springiness and juiciness were of significance in statistics and the determination coefficients were 61.46%, 39.77% and 42.34%, respectively. Therefore, the widespread correlation between sensory attributes and instrumental texture characteristics of pork are comfirmed, and the prediction efficiency of meat sensory attributes prediction model is brought to a new level by more proper testing and statistical methods.

       

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