基于智能仿生仪的发酵小米椒感官品质评价模型
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摘要
为利用智能感官分析技术评价小米椒发酵过程中感官品质的变化,以恒温条件发酵的小米椒为试验对象,利用电子鼻、电子舌等智能感官仪器结合感官评价,采用相关性分析、主成分分析、逐步回归分析及拟合检验等数学方法,建立发酵小米椒感官品质评价模型。结果表明:简化后的感官指标为滋味、香味及色泽,权重分别是38.96%,35.06%,25.97%,拟合检验R2=0.998,表明模型可代替原始的人工感官评价法。以智能感官指标的PCA为自变量,感官评价模型结果为因变量,进行因子分析建立主成分回归方程:Y=67.672+8.296×X_1PC1-1.571×X_2PC1-3.431×X_3PC1+0.288×X_4PC1,方差扩大因子检验表明该模型无明显多重共线性(VIF<10)。新的品质评价模型能较好地拟合小米椒发酵过程中感官品质的变化,弥补了人工感官重现性及稳定性差的缺陷。
In order to study the quality change of fermented millet pepper,the sensory evaluation model of fermented millet pepper was established using the intelligent sensory instruments(electronic nose and electronic tongue)and sensory evaluation,combinating with the method of correlation analysis, principal component analysis,stepwise regression analysis and fitting test.The results showed that the weight of the simplified sensory indicators were 38.96%,35.06% and 25.97%,respectively,and the fitting test R2=0.998 indicated that the model can replace the original evaluation method.Taking the PCA of the intelligent sensory index as the independent variable and the sensory evaluation model as the dependent variable,the principal component regression equation was established by factor analysis:Y(sensory score)=67.672+8.296 × color PC1-1.571 × texture PC1-3.431× PC1 electronic nose+0.288×PC1 electronic tongue,and the variance inflation factor test showed that the model had no obvious multicollinearity(VIF<10).The new quality evaluation model can fit the change of sensory quality during fermentation process of millet pepper,and offset the defect of the reproducibility and stability of the artificial sensory.
In order to study the quality change of fermented millet pepper,the sensory evaluation model of fermented millet pepper was established using the intelligent sensory instruments(electronic nose and electronic tongue)and sensory evaluation,combinating with the method of correlation analysis, principal component analysis,stepwise regression analysis and fitting test.The results showed that the weight of the simplified sensory indicators were 38.96%,35.06% and 25.97%,respectively,and the fitting test R2=0.998 indicated that the model can replace the original evaluation method.Taking the PCA of the intelligent sensory index as the independent variable and the sensory evaluation model as the dependent variable,the principal component regression equation was established by factor analysis:Y(sensory score)=67.672+8.296 × color PC1-1.571 × texture PC1-3.431× PC1 electronic nose+0.288×PC1 electronic tongue,and the variance inflation factor test showed that the model had no obvious multicollinearity(VIF<10).The new quality evaluation model can fit the change of sensory quality during fermentation process of millet pepper,and offset the defect of the reproducibility and stability of the artificial sensory.
引文
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[22]钱丽丽,李平惠,杨义杰,等.不同产地芸豆中矿物元素的因子分析与聚类分析[J].食品科学,2015,36(14):102-106.
[2]杨峰,巫朝华,范大明,等.四川泡椒对鲢鱼鱼糜凝胶风味特性的影响[J].食品科学,2017,38(16):152-157.
[3]赵雪梅,廖诚成,丁捷,等.挤压型速冻青稞鱼面品质评价模型的建立[J].食品科技,2018,43(2):189-195.
[4]易宇文,范文教,彭毅秦,等.鱼香调味汁人工感官评价与电子舌感官分析相关性研究[J].食品科技,2017,42(11):290-294.
[5]易宇文,刘阳,彭毅秦,等.东坡肘子风味电子鼻分析与感官评价相关性探究[J].食品与发酵工业,2018,44(1):238-244.
[6]许文方,张海东,李贵荣,等.仿生传感智能感官检测技术及其在云南特色农产品品质检测中的应用[J].安徽农业科学,2013,41(10):4 592-4 595.
[7]谢安国,王金水,渠琛玲,等.电子鼻在食品风味分析中的应用研究进展[J].农产品加工:学刊,2011(1):71-73,87.
[8]易宇文,范文教,乔明锋,等.7种鱼香肉丝调料智能嗅觉识别[J].食品与机械,2017,33(1):27-31.
[9]林科.电子舌研究进展及其在食品检测中的应用研究[J].安徽农业科学,2008,36(15):6 602-6 604.
[10]王素霞.花椒酰胺电子舌响应规律及其应用研究[D].成都:西南交通大学,2014:24-31.
[11]国艳梅,杜永臣,王孝宣,等.利用色差仪估测番茄果实番茄红素含量的研究[J].中国蔬菜,2008,1(11):10-14.
[12]马庆华,王贵禧,梁丽松,等.冬枣的穿刺质地及其影响因素[J].林业科学研究,2011,24(5):596-601.
[13]王莉,张丽,郭兆斌,等.泡椒牦牛黄喉感官评价描述词的建立[J].食品科学,2015,36(21):74-78.
[14]李敬,崔繁荣,叶治兵,等.泡椒牛皮感官评价指标体系的构建[J].食品工业科技,2016,37(13):206-209,214.
[15]马雄威.线性回归方程中多重共线性诊断方法及其实证分析[J].华中农业大学学报:社会科学版,2008(2):78-81,85.
[16]金彪.一元线性回归分析在处理实验数据中的应用[J].物理教师,2009,30(6):24,26.
[17]杨炜,蔺艳君,刘丽娅,等.小麦馒头品质评价方法优化[J].食品科学技术学报,2016,34(2):31-38.
[18]刘雷,曹柳,杜玖珍,等.基于主成分及聚类分析的川产半夏产量与品质的综合评价[J].中草药,2016,47(14):2 519-2 525.
[19]刘景,王荫榆,郭本恒,等.发酵乳储存期内风味模型的建立与检验[J].食品科学,2011,32(12):296-300.
[20]张景阳,潘光友.多元线性回归与BP神经网络预测模型对比与运用研究[J].昆明理工大学学报,2013,38(6):61-67.
[21]吴玉红,田霄鸿,南雄雄,等.基于因子和聚类分析的保护性耕作土壤质量评价研究[J].中国生态农业学报,2010,18(20):223-228.
[22]钱丽丽,李平惠,杨义杰,等.不同产地芸豆中矿物元素的因子分析与聚类分析[J].食品科学,2015,36(14):102-106.