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. 2024 Nov 4;13(21):3524.
doi: 10.3390/foods13213524.

Diversity Analysis and Comprehensive Evaluation of 101 Soybean (Glycine max L.) Germplasms Based on Sprout Quality Characteristics

Xiaoyan Zhang  1 , Gufeng Wu  1   2 , Yuhe Wu  3 , Ning Tang  3 , Lu Huang  1 , Dongqing Dai  1 , Xingxing Yuan  1 , Chenchen Xue  1 , Xin Chen  1   2
Affiliations

Diversity Analysis and Comprehensive Evaluation of 101 Soybean (Glycine max L.) Germplasms Based on Sprout Quality Characteristics

Xiaoyan Zhang et al. Foods. .

Abstract

Soybean sprouts are a common culinary vegetable due to their high nutrition and tasty flavors. To select soybean varieties with excellent sprout quality, 101 soybean materials were collected from different regions of China, and eight sprout quality parameters were determined for overall evaluation. The results showed that eight sprout quality parameters achieved varying degrees of difference and correlation. Based on the principal component analysis (PCA), three principal components were extracted, with a cumulative contribution rate of 78.314%. Further, the comprehensive evaluation value (D) of soybean sprout quality was calculated by membership function analysis based on PCA, and the quality of soybean sprouts was ranked accordingly. Subsequently, a regression equation for the prediction of soybean sprout quality was established using a stepwise regression analysis, and the model showed a good prediction performance (correlation coefficient of prediction > 0.8; residual predictive deviation > 2.0). On these grounds, it was proposed that the quality of soybean sprouts could be comprehensively predicted by four parameters: hypocotyl length, edible rate, 100-seed weight, and total isoflavone content and saponin content. In conclusion, this study provides excellent varieties for soybean sprout production and new variety breeding, and it provides an important reference for the prediction of soybean sprout quality.

Keywords: comprehensive evaluation; parameter screening; principal component analysis (PCA); quality characteristics; soybean sprouts.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The heat map of the quality characteristics of 101 soybean sprouts.
Figure 2
Figure 2
Correlation analysis of quality characteristics of soybean sprouts. The size of the circle represents the value of the Pearson coefficient, and red indicates a positive correlation, green indicates a negative correlation. * means the significant is at the level of p < 0.05; ** means the significant is at the level of p < 0.01; *** means the significant is at the level of p < 0.001.
Figure 3
Figure 3
The PCA plot of the quality characteristics of 101 soybean sprouts.
Figure 4
Figure 4
The scatter plots of the calibration and prediction D value of quality characteristics of soybean sprouts. R(c) and R(p) represent the correlation coefficient of the calibration and prediction set, respectively. RMSE(c) and RMSE(p) represent the correlation coefficient of the calibration and prediction set, respectively.
See this image and copyright information in PMC

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