摘要
为更好地筛选优质黄桃,本文通过构建灰色关联分析模型来研究黄桃品质评判指标间的关系。本文将人工评分的三个指标(色泽x2、质地x3、味道x4)取均值作为人工评分y,通过绘制散点图将y和x1、x5、x6可视化,通过拟合得出机械指标和人工评分的关系表达式,以此来研究黄桃的机械指标(果实硬度x1、TSS下降含量x5和色差x6)与人工评分之间的关系。结果表明:随果实硬度增加、TSS含量下降率增加以及色差值的增大,对应的人工评分有下降趋势。进而得出黄桃品质为优质的限制条件为机械指标中硬度x1 > 1.66 kg/cm2,TSS含量下降率x5 6 2、x3、x4)和机械指标(x1、x5和x6)作为比较数列,计算模型比较数列和参考数列的灰色关联系数得,x1 = 0.9012,x2 = 0.9886,x3 = 0.9818,x4 = 0.9753,x5 = 0.5720,x6 = 0.8793,因此总体上评判机械指标和人工评分两类方法具有较高的一致性。
In order to better screen high-quality yellow peach, this paper studies the relationship between the quality evaluation indexes of yellow peach by constructing the grey correlation analysis model. In this paper, the mean values of the three artificial scoring indexes (color x2, texture x3, taste x4) were taken as the artificial scoring y, and y and x1、x5、x6 were visualized by plotting to scatter plot. The relationship expression between mechanical indexes and artificial scoring was obtained by fitting, so as to study the relationship between mechanical indexes (fruit firmness x1, TSS decline content x5, and color difference x6) of yellow peach and artificial scoring. The results showed that with the increase of fruit hardness, the decrease rate of TSS content and the increase of color difference, the corresponding artificial score showed a downward trend. It was further concluded that the limiting conditions for yellow peach quality to be high quality were as follows: hardness x1 > 1.66 kg/cm2, TSS content decline rate x5 6 2、x3、x4) and mechanical index (x1、x5和x6) are taken as the comparison sequence. The grey correlation coefficients of the comparison sequence and the reference sequence of the model are calculated, and x1 = 0.9012, x2 = 0.9886, x3 = 0.9818, x4 = 0.9753, x5 = 0.5720, x6 = 0.8793. Therefore, in general, the two methods of evaluating mechanical index and artificial score have high consistency.
出处
《建模与仿真》
2022年第4期1203-1210,共8页
Modeling and Simulation
