基于灰色关联模型的黄桃采摘贮藏分析
The Analysis of Peach Harvest and Storage Based on Grey Correlation Model

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 < 11.44%，色差x₆ < 6.4747。构建灰色关联分析模型，先将人工评分y作为参考序列，人工评分(x₂、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 < 11.44%, and color difference x₆ < 6.4747 in mechanical indexes. The grey correlation analysis model is constructed. Firstly, the artificial score y is taken as the reference sequence, and the artificial score (x₂、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