CN113609775A - 固态复合调味料鲜美度生津感感官评价分值的定量预报方法 - Google Patents
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Abstract
本发明提出一种固态复合调味料鲜美度生津感感官评价分值的定量预报方法,以多种固态复合调味料中7种成分IMP、GMP、Na+、Asp、Glu、Gly和Ala的含量作为自变量;人工对每种固态复合调味料进行鲜美度生津感打分,以及采用公式对7种自变量的含量进行综合处理;对综合处理后的数据以及生津感进行定量建模,生成固态复合调味料鲜美度生津感感官评价的定量预报模型;将待测固态复合调味料中7种自变量含量综合处理后,代入所述定量预报模型,得到待测固态复合调味料的鲜美度生津感感官评价分值。本发明的方法相比人工测评,预报出来的结果更客观、准确以及便捷。
Description
技术领域
本发明涉及调味料感官评价技术领域,尤其涉及一种固态复合调味料鲜美度生津感感官评价分值的定量预报方法。
背景技术
固态复合调味料由两种以上的调味品复合而成,复合调味料的主要功能在于调味,赋予食品、菜肴特殊风味。近年来,中国调味品行业有了较大发展,人们对于调味品的要求也从单一需求逐渐转向多元化,固态复合调味料产品在市场上越来越受欢迎。但对于固态复合调味料的感官评价目前主要是通过品尝师品尝这些调味料,然后根据自己的感受、以往积累的经验给出感官评价的分数。这样给出的感官评价分数往往带有强烈的个人主观性,不同的品尝师品尝同样的固态复合调味料,给出的感官评价分数往往存在差异,有些还有着比较大的差异。如对于某种固态复合调味料的鲜美度生津感,有的品尝师给出的是1.5分,有的品尝师给出了3.0分。原因是多方面的,可能是不同品尝师的经验积累的时间不一样,有的品尝师有十多年的经验积累,有的品尝师只有三四年时间的积累。也可能是与品尝师的年龄、喜好、身体、精神状态有关,有些品尝师可能就是对生津感敏感,有些品尝师品尝的时候身体可能出现问题,其精神、味觉感觉都出现迟钝等现象。这些原因都导致对固态复合调味料的感官评价出现差异。但是由于复合调味料在人们日常生活中的应用越来越广泛,人们对于美味的要求也越来越高,对其感官评价的准确性要求也越来越高,如何找到更加客观的感官评价打分方法,逐步代替人工主观打分方法,成为本领域技术人员的当务之急。
发明内容
本发明的目的在于提出一种对复合调味料鲜美度生津感给出客观、准确分数的定量预报方法。
为达到上述目的,本发明提出一种固态复合调味料鲜美度生津感感官评价分值的定量预报方法,以多种固态复合调味料中7种成分IMP、GMP、Na+、Asp、Glu、Gly和Ala的含量作为自变量;人工对每种固态复合调味料进行生津感打分,以及采用公式对7种自变量的含量进行综合处理;对综合处理后的数据以及鲜美度生津感进行定量建模,生成固态复合调味料鲜美度生津感感官评价的定量预报模型;
将待测固态复合调味料中7种自变量含量综合处理后,代入所述定量预报模型,得到待测固态复合调味料的鲜美度生津感感官评价分值。
进一步的,所述综合处理包括利用如下转换方程对参数含量进行处理:
X1=+0.260[IMP]+4.955E-2[GMP]+1.971E-2[Na]+0.271[Asp]-2.521E-3[Glu]+0.453[Gly]+1.169[Ala]-0.515;
X2=+0.795[IMP]+0.340[GMP]+0.133[Na]-0.617[Asp]-4.217E-3[Glu]-0.765[Gly]+0.806[Ala]-1.488;
X3=+2.354[IMP]+1.660[GMP]-0.103[Na]-0.340[Asp]+2.758E-3[Glu]-0.663[Gly]+0.686[Ala]-0.862;
X4=-0.257[IMP]-1.850[GMP]-0.179[Na]-3.978E-2[Asp]+3.134E-3[Glu]-1.035[Gly]+0.665[Ala]+3.013;
X5=+2.457[IMP]-2.734[GMP]+0.130[Na]+0.169[Asp]+2.082E-3[Glu]-0.106[Gly]+6.545E-2[Ala]-3.024;
X6=+5.119[IMP]-5.251[GMP]-6.594E-2[Na]-5.659E-2[Asp]-9.137E-4[Glu]+0.132[Gly]-0.122[Ala]+1.209;
X7=+0.917[IMP]+0.463[GMP]-2.781E-2[Na]+1.008[Asp]-3.006E-3[Glu]-0.719[Gly]-0.216[Ala]+0.754;
式中,X1、X2、X3、X4、X5、X6和X7分别代表每种固态复合调味料综合处理后的7个建模数据。
进一步的,建模前,所述固态复合调味料的数量大于等于70个,品尝师对每一种固态复合调味料均进行品尝以及鲜美度生津感打分。
进一步的,利用支持向量机回归算法对每种的固态复合调味料中,7种自变量综合处理后的数据以及鲜美度生津感进行定量建模。
进一步的,在所述支持向量机回归算法中,为了保证建模的准确性,选取径向基核函数,惩罚因子选取为13,不敏感函数选取为0.06。
与现有技术相比,本发明的优势之处在于:
1.本发明可以实现固态复合调味料鲜美度生津感感官评价分值的预报,为企业监控产品质量提供数据支撑。
2.在对新的固态复合调味料鲜美度生津感感官评价分值预报过程中,不再需要品尝师的参与,减少了品尝师的主观因素,预报出来的结果更客观、准确。
3.相比于人工评分,本发明整个评分过程减少了样品的浪费,提高企业经济效益。
4.通过本发明建模测出来的鲜美度生津感数值与实际值的结果几乎完全相同,准确度更高。
附图说明
图1为固态复合调味料生津感感官评价分值的支持向量机回归模型建模结果图。
图2为固态复合调味料生津感感官评价分值的支持向量机回归模型留一法交叉验证结果图。
具体实施方式
为使本发明的目的、技术方案和优点更加清楚,下面将对本发明的技术方案作进一步地说明。
本发明提出一种固态复合调味料鲜美度生津感感官评价分值的定量预报方法,具体方法如下:
步骤1:从市场中收集70种固态复合调味料,在实验室检测这70种固态复合调味料中的IMP、GMP、Na+、Asp、Glu、Gly和Ala含量,作为自变量。品尝师再对这70种固态复合调味料的鲜美度生津感进行人工打分,这样构建了7*70的数据矩阵。这个数据矩阵中,鲜美度生津感分值为因变量,7种成分含量的值为自变量。这样得到了基础数据。部分数据如表1所示:
表1.成分含量和鲜美度生津感示例数据
步骤2:对基础数据中的自变量进行综合处理,综合处理后的新变量包含所有的原始自变量。转换方程为:
X1=+0.260[IMP]+4.955E-2[GMP]+1.971E-2[Na]+0.271[Asp]-2.521E-3[Glu]+0.453[Gly]+1.169[Ala]-0.515
X2=+0.795[IMP]+0.340[GMP]+0.133[Na]-0.617[Asp]-4.217E-3[Glu]-0.765[Gly]+0.806[Ala]-1.488
X3=+2.354[IMP]+1.660[GMP]-0.103[Na]-0.340[Asp]+2.758E-3[Glu]-0.663[Gly]+0.686[Ala]-0.862
X4=-0.257[IMP]-1.850[GMP]-0.179[Na]-3.978E-2[Asp]+3.134E-3[Glu]-1.035[Gly]+0.665[Ala]+3.013
X5=+2.457[IMP]-2.734[GMP]+0.130[Na]+0.169[Asp]+2.082E-3[Glu]-0.106[Gly]+6.545E-2[Ala]-3.024
X6=+5.119[IMP]-5.251[GMP]-6.594E-2[Na]-5.659E-2[Asp]-9.137E-4[Glu]+0.132[Gly]-0.122[Ala]+1.209
X7=+0.917[IMP]+0.463[GMP]-2.781E-2[Na]+1.008[Asp]-3.006E-3[Glu]-0.719[Gly]-0.216[Ala]+0.754
综合处理后的示例数据如表2所示。
表2.综合处理后示例数据
步骤3:利用支持向量机回归对综合处理后的数据进行定量建模,得到固态复合调味料鲜美度生津感感官评价分值的定量预报模型。
步骤4:新收集4种新固态复合调味料,在实验室检测其IMP、GMP、Na+、Asp、Glu、Gly和Ala含量,但品尝师不再进行人工品尝给出生津感分值,这样形成了独立测试集。收集新的示例数据如表3所示:
表3.独立测试集的新数据
步骤5:将收集到的新数据代入转换方程,得到综合映射处理后数据,转换后数据如表4所示。
表4:新数据综合处理后部分示例
X<sub>1</sub> | X<sub>2</sub> | X<sub>3</sub> | X<sub>4</sub> | X<sub>5</sub> | X<sub>6</sub> | X<sub>7</sub> |
1.0837 | 1.0532 | -1.2147 | 0.6144 | -0.2118 | 0.2467 | 0.1258 |
-0.4864 | 0.7246 | -0.5372 | -0.7026 | 0.5274 | -0.7870 | -0.3617 |
-0.7389 | -0.2215 | 3.6973 | -0.0676 | -0.1357 | 0.0560 | 0.2312 |
1.9510 | -0.9925 | -0.6365 | -0.1138 | 0.5756 | -0.4588 | 1.4037 |
步骤6:然后将综合处理后的数据直接代入固态复合调味料鲜美度生津感感官评价分值的定量预报模型,预报得到独立测试集的固态复合调味料生津感感官评价分值。
独立测试集的固态复合调味料鲜美度生津感感官评价分值的支持向量机回归模型独立测试结果,其结果如表5所示。
表5预测结果
样本序号 | 鲜美度生津感分值 |
1 | 3.0373 |
2 | 2.8018 |
3 | 2.4994 |
4 | 2.7460 |
对于70个数据,固态复合调味料鲜美度生津感感官评价分值的支持向量机回归模型建模结果如图1所示。
由图1可以看出:生津感实际值与计算值的相关系数为0.91,数值很高,模型的准确率比较高,建立的模型可信度高。
对于70个数据,固态复合调味料鲜美度生津感感官评价分值的支持向量机回归模型留一法交叉验证结果如图2所示。
由图2可以看出:模型的预报值与实测值的相关系数为0.80,说明建立的支持向量机回归模型内部交叉验证结果的准确率比较高,进一步说明建立的模型比较可信。
上述仅为本发明的优选实施例而已,并不对本发明起到任何限制作用。任何所属技术领域的技术人员,在不脱离本发明的技术方案的范围内,对本发明揭露的技术方案和技术内容做任何形式的等同替换或修改等变动,均属未脱离本发明的技术方案的内容,仍属于本发明的保护范围之内。
Claims (5)
1.固态复合调味料鲜美度生津感感官评价分值的定量预报方法,其特征在于,以多种固态复合调味料中7种成分IMP、GMP、Na+、Asp、Glu、Gly和Ala的含量作为自变量;人工对每种固态复合调味料进行鲜美度生津感打分,以及采用公式对7种自变量的含量进行综合处理;对综合处理后的数据以及生津感进行定量建模,生成固态复合调味料鲜美度生津感感官评价的定量预报模型;
将待测固态复合调味料中7种自变量含量综合处理后,代入所述定量预报模型,得到待测固态复合调味料的鲜美度生津感感官评价分值。
2.根据权利要求1所述的固态复合调味料鲜美度生津感感官评价分值的定量预报方法,其特征在于,所述综合处理包括利用如下转换方程对参数含量进行处理:
X1=+0.260[IMP]+4.955E-2[GMP]+1.971E-2[Na]+0.271[Asp]-2.521E-3[Glu]+0.453[Gly]+1.169[Ala]-0.515;
X2=+0.795[IMP]+0.340[GMP]+0.133[Na]-0.617[Asp]-4.217E-3[Glu]-0.765[Gly]+0.806[Ala]-1.488;
X3=+2.354[IMP]+1.660[GMP]-0.103[Na]-0.340[Asp]+2.758E-3[Glu]-0.663[Gly]+0.686[Ala]-0.862;
X4=-0.257[IMP]-1.850[GMP]-0.179[Na]-3.978E-2[Asp]+3.134E-3[Glu]-1.035[Gly]+0.665[Ala]+3.013;
X5=+2.457[IMP]-2.734[GMP]+0.130[Na]+0.169[Asp]+2.082E-3[Glu]-0.106[Gly]+6.545E-2[Ala]-3.024;
X6=+5.119[IMP]-5.251[GMP]-6.594E-2[Na]-5.659E-2[Asp]-9.137E-4[Glu]+0.132[Gly]-0.122[Ala]+1.209;
X7=+0.917[IMP]+0.463[GMP]-2.781E-2[Na]+1.008[Asp]-3.006E-3[Glu]-0.719[Gly]-0.216[Ala]+0.754;
式中,X1、X2、X3、X4、X5、X6和X7分别代表每种固态复合调味料综合处理后的7个建模数据。
3.根据权利要求1所述的固态复合调味料鲜美度生津感感官评价分值的定量预报方法,其特征在于,建模前,所述固态复合调味料的数量大于等于70个,品尝师对每一种固态复合调味料均进行品尝以及鲜美度生津感打分。
4.根据权利要求1所述的固态复合调味料鲜美度生津感感官评价分值的定量预报方法,其特征在于,利用支持向量机回归算法对每种的固态复合调味料中,7种自变量综合处理后的数据以及鲜美度生津感进行定量建模。
5.根据权利要求4所述的固态复合调味料鲜美度生津感感官评价分值的定量预报方法,其特征在于,在所述支持向量机回归算法中,选取径向基核函数,惩罚因子选取为13,不敏感函数选取为0.06。
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