花生烘烤食用品质评价及指标筛选

doi:10.3864/j.issn.0578-1752.2022.04.002

Abstract

Abstract:

【Objective】The objective of this study was to explore the evaluation methods of edible quality of roasted peanuts, screen identification indexes, establish prediction model, and provide basis for peanut edible quality breeding. 【Method】The kernels of 51 peanut varieties (lines) with different quality types were used as experimental materials. A total of 27 edible quality indexes related to tastes, appearances, textures and nutrition of roasted kernels were measured. Correlation analysis and principal component analysis were used to comprehensively evaluate the edible quality of roasted peanut kernels. Cluster analysis was used to classify edible quality of 51 peanut varieties (lines). Regression analysis were used to establish predictive model and index screening. 【Result】27 indexes had different ranges of variation in 51 peanut varieties (lines), with coefficients of variation ranging from 5.86% to 39.65%. There were significant or extremely significant related indexes for each individual index. The correlation coefficients of 175 groups reached significant level, and the 140 groups reached extremely significant level. The 27 individual indexes were converted into 5 independent comprehensive indexes through principal component analysis, and their contribution rates respectively were 35.70%, 20.63%, 10.07%, 8.19% and 6.38%, representing the information of 80.97% of all data. The comprehensive evaluation analysis showed that the average F value of the roasted edible quality of 51 peanut varieties (lines) was 0.76. Xuhuatian 29 had the highest F value (F=1.51) and the best roasting edible quality. Xuhua 15 had the lowest F value (F=0.03) and the worst roasting edible quality. The correlation analysis showed that 21 indexes were significantly correlated with F value. Cluster analysis was performed on the comprehensive value F of peanut roasting edible quality, and 51 varieties (lines) were divided into 3 categories. The first category was of good edible quality, including 4 varieties of Xuhuatian 29, Jihuatian 1, Linhua 16 and Xuhuatian 30. The second category was of general edible quality and contained 33 varieties (lines). The third category was of poor edible quality and contained 14 varieties (lines). Using stepwise regression analysis method, the prediction model of roasting edible quality was established as: Y=0.979+ 0.021X₇+0.081X₂₁+0.009X₂₀-0.034X₁₉-0.074X₂₇ (R²=0.953). Then, 5 identification indexes were screened, which were the hundred kernel weight, sucrose content, protein content, fat content and behenic acid content. The analysis of characteristics showed that the four varieties with good roasting edible quality had medium to high hundred kernel weight, high sucrose content, medium protein content, low fat content, and low to medium behenic acid content. According to the prediction model, this category varieties still need to be improved to increase protein content and reduce behenic acid content. 【Conclusion】Hundred kernel weight, sucrose content, protein content, fat content and behenic acid content could be used to identify the edible quality of roasted peanuts. It is determined that high-quality roasted peanut varieties should have the characteristics of medium to large kernels, high oleic acid content, high sucrose content, high protein content, low fat content and low behenic acid content.

Key words: peanut, roast, edible quality, comprehensive evaluation, index screening

BIAN NengFei, SUN DongLei, GONG JiaLi, WANG Xing, XING XingHua, JIN XiaHong, WANG XiaoJun. Evaluation of Edible Quality of Roasted Peanuts and Indexes Screening[J].Scientia Agricultura Sinica, 2022, 55(4): 641-652.

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Figures/Tables 8

Table 1

Table 2

Correlative coefficients of 27 indexes"

指标 Index	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂	X₁₃	X₁₄	X₁₅	X₁₆	X₁₇	X₁₈	X₁₉	X₂₀	X₂₁	X₂₂	X₂₃	X₂₄	X₂₅	X₂₆
X₂	-0.14
X₃	0.62**	-0.45**
X₄	0.59**	-0.26	0.66**
X₅	-0.33*	0.17	-0.46**	-0.51**
X₆	-0.23	-0.07	-0.31*	-0.47**	0.67**
X₇	0.32*	0.03	0.15	0.09	-0.03	0.24
X₈	0.27	0.03	0.16	0.07	-0.01	0.22	0.96**
X₉	0.28*	-0.04	0.21	0.07	-0.04	0.19	0.90**	0.98**
X₁₀	0.25	-0.08	0.22	0.06	-0.03	0.18	0.80**	0.91**	0.98**
X₁₁	0.22	0.15	0.01	0.06	0.01	0.19	0.85**	0.76**	0.61**	0.44**
X₁₂	0.15	-0.19	0.27	0.05	-0.07	0.05	0.28*	0.46**	0.62**	0.77**	-0.22
X₁₃	0.34*	-0.14	0.43**	0.37**	-0.31*	-0.17	0.25	0.12	0.06	-0.03	0.36**	-0.25
X₁₄	0.41**	-0.42**	0.60**	0.38**	-0.20	0.06	0.59**	0.53**	0.55**	0.53**	0.33*	0.37**	0.55**
X₁₅	0.40**	-0.55**	0.65**	0.38**	-0.17	0.10	0.51**	0.48**	0.52**	0.52**	0.23	0.42**	0.43**	0.95**
X₁₆	0.30*	-0.10	0.35*	0.22	-0.03	-0.06	0.08	0.00	-0.04	-0.09	0.18	-0.24	0.56**	0.36**	0.31*
X₁₇	0.42**	-0.55**	0.70**	0.35*	-0.28*	0.01	0.44**	0.45**	0.52**	0.54**	0.17	0.46**	0.37**	0.82**	0.85**	0.40**
X₁₈	0.35*	-0.66**	0.72**	0.34*	-0.30*	-0.01	0.34*	0.36**	0.44**	0.47**	0.07	0.47**	0.31*	0.79**	0.85**	0.24	0.93**
X₁₉	-0.46**	0.63**	-0.73**	-0.43**	0.35*	0.08	-0.01	0.03	-0.03	-0.06	0.13	-0.18	-0.41**	-.64**	-0.73**	-0.25	-0.710**	-0.75**
X₂₀	0.06	-0.14	-0.10	0.02	-0.09	0.11	0.20	0.18	0.19	0.18	0.16	0.10	0.15	0.25	0.20	-0.09	0.14	0.16	-0.26
X₂₁	0.50**	-0.53**	0.87**	0.54**	-0.37**	-0.18	0.02	0.05	0.13	0.19	-0.18	0.36**	0.35*	0.59**	0.68**	0.25	0.68**	0.73**	-0.81**	-0.11
X₂₂	-0.41**	0.39**	-0.32*	-0.34*	0.50**	0.21	0.07	0.13	0.07	0.05	0.18	-0.11	0.02	-0.18	-0.23	0.09	-0.26	-0.35*	0.49**	-0.26	-0.34*
X₂₃	0.42**	-0.41**	0.35*	0.37**	-0.49**	-0.21	-0.07	-0.11	-0.05	-0.01	-0.20	0.17	-0.01	0.20	0.26	-0.09	0.29*	0.38**	-0.50**	0.30*	0.37**	-0.99**
X₂₄	0.37**	-0.30*	0.24	0.30*	-0.46**	-0.20	-0.07	-0.15	-0.10	-0.08	-0.17	0.07	-0.06	0.12	0.16	-0.12	0.15	0.24	-0.39**	0.19	0.25	-0.97**	0.95**
X₂₅	0.51**	-0.36**	0.59**	0.36**	-0.45**	-0.13	0.18	0.11	0.12	0.10	0.11	0.06	0.29*	0.47**	0.49**	0.05	0.52**	0.55**	-0.75**	0.27	0.59**	-0.60**	0.57**	0.52**
X₂₆	0.51**	-0.32*	0.53**	0.41**	-0.45**	-0.13	0.29*	0.27	0.29*	0.29*	0.18	0.21	0.24	0.46**	0.46**	-0.03	0.52**	0.53**	-0.63**	0.46**	0.51**	-0.55**	0.57**	0.44**	0.91**
X₂₇	-0.17	0.044	-0.06	-0.13	0.06	-0.15	-0.77**	-0.83**	-0.82**	-0.79**	-0.60**	-0.44**	-0.16	-0.38**	-0.32*	0.00	-0.32*	-0.25	-0.14	-0.23	-0.02	-0.10	0.07	0.12	0.07	-0.18

Table 2

Table 3

Table 4

Table 5

Fig. 1

Fig. 2

Table 6

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指标 Index	极小值 Min	极大值 Max	均值 Mean	标准差 SD	变异系数 CV (%)
X₁	3.00	4.22	3.60	0.26	7.34
X₂	2.67	3.89	3.44	0.27	7.71
X₃	1.78	4.44	2.44	0.68	27.78
X₄	2.00	3.89	2.93	0.40	13.80
X₅	1.11	3.00	1.79	0.43	24.32
X₆	1.00	3.22	1.69	0.43	25.27
X₇	30.86	80.73	58.61	11.03	18.82
X₈	69.97	145.83	109.89	17.45	15.88
X₉	33.97	50.13	42.50	3.99	9.39
X₁₀	12.08	19.27	15.91	1.82	11.46
X₁₁	7.12	10.04	8.89	0.67	7.52
X₁₂	1.49	2.24	1.81	0.20	10.94
X₁₃	31.35	69.50	48.91	8.05	16.46
X₁₄	105.64	258.63	158.94	30.77	19.36
X₁₅	7.66	39.44	17.09	6.78	39.65
X₁₆	17.38	38.50	23.29	4.28	18.37
X₁₇	88.53	201.62	128.13	26.69	20.83
X₁₈	7.41	29.04	14.35	4.82	33.59
X₁₉	42.02	56.50	52.15	3.05	5.86
X₂₀	18.05	27.04	22.50	1.99	8.84
X₂₁	1.88	7.41	3.56	1.23	34.64
X₂₂	28.07	80.83	42.97	15.31	35.63
X₂₃	5.13	52.76	37.24	12.97	34.84
X₂₄	5.16	13.38	10.82	1.96	18.15
X₂₅	1.89	4.73	3.06	0.72	23.53
X₂₆	1.12	1.94	1.52	0.22	14.22
X₂₇	0.89	3.96	2.08	0.71	33.97

类别 Cluster	X₇		X₂₁		X₂₀		X₁₉		X₂₇
类别 Cluster	均值 Mean	范围 Range	均值 Mean	范围 Range	均值 Mean	范围 Range	均值 Mean	范围 Range	均值 Mean	范围 Range
第一类First cluster	64.21	55.44-74.56	6.86	6.24-7.41	22.19	20.84-23.38	44.84	42.02-46.62	2.13	1.78-2.77
第二类Second cluster	63.90	52.92-80.73	3.32	1.88-5.45	22.95	19.56-27.04	52.69	47.49-56.5	1.73	0.89-2.53
第三类Third cluster	44.54	30.86-55.02	3.20	2.25-3.93	21.53	18.05-24.7	52.95	49.96-54.61	2.90	1.59-3.96

Evaluation of Edible Quality of Roasted Peanuts and Indexes Screening

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主成分 Principle factor	指标 Index	主成分1 Principle factor 1	主成分2 Principle factor 2	主成分3 Principle factor 3	主成分4 Principle factor 4	主成分5 Principle factor 5
特征向量Eigenvector	X₁	0.21	-0.05	0.02	0.24	-0.11
	X₂	-0.17	0.12	-0.04	0.29	-0.14
	X₃	0.25	-0.11	0.20	-0.01	-0.20
	X₄	0.18	-0.12	0.10	0.24	-0.26
	X₅	-0.15	0.16	0.08	-0.29	0.28
	X₆	-0.04	0.18	-0.03	-0.29	0.45
	X₇	0.17	0.32	-0.07	0.18	0.08
	X₈	0.16	0.35	-0.10	0.09	-0.04
	X₉	0.18	0.33	-0.14	0.00	-0.11
	X₁₀	0.18	0.31	-0.16	-0.10	-0.18
	X₁₁	0.08	0.28	0.03	0.38	0.22
	X₁₂	0.14	0.13	-0.20	-0.36	-0.34
	X₁₃	0.14	-0.01	0.35	0.29	0.17
	X₁₄	0.27	0.09	0.15	-0.06	0.11
	X₁₅	0.28	0.06	0.14	-0.16	0.10
	X₁₆	0.08	-0.01	0.42	0.14	0.11
	X₁₇	0.28	0.04	0.13	-0.16	0.01
	X₁₈	0.28	-0.01	0.08	-0.23	0.02
	X₁₉	-0.25	0.18	-0.11	0.14	-0.16
	X₂₀	0.09	0.02	-0.22	0.07	0.40
	X₂₁	0.24	-0.14	0.17	-0.17	-0.17
	X₂₂	-0.16	0.25	0.33	-0.06	-0.09
	X₂₃	0.17	-0.24	-0.33	0.04	0.06
	X₂₄	0.13	-0.24	-0.35	0.09	0.07
	X₂₅	0.23	-0.15	-0.08	0.09	0.19
	X₂₆	0.24	-0.08	-0.17	0.12	0.14
	X₂₇	-0.13	-0.31	0.12	-0.11	0.10
特征值Eigenvalue		9.64	5.57	2.72	2.212	1.72
贡献率Contribution rate		35.70	20.63	10.07	8.19	6.38
累计贡献率Cumulative contribution rate		35.70	56.32	66.40	74.59	80.97

编号 No.	综合值F Comprehensive value (F)	编号 No.	综合值F Comprehensive value (F)	编号 No.	综合值F Comprehensive value (F)
1	0.72	18	1.17	35	0.80
2	0.86	19	0.83	36	1.06
3	1.08	20	0.46	37	0.56
4	0.89	21	0.61	38	0.48
5	0.89	22	0.79	39	0.34
6	1.07	23	0.92	40	1.18
7	0.90	24	1.09	41	0.89
8	1.11	25	0.37	42	1.49
9	0.67	26	0.53	43	0.84
10	0.70	27	0.73	44	0.16
11	0.63	28	0.86	45	0.35
12	0.44	29	0.66	46	0.10
13	1.11	30	0.36	47	0.27
14	1.01	31	0.66	48	0.20
15	0.03	32	1.37	49	1.04
16	0.81	33	0.68	50	1.51
17	0.79	34	0.69	51	1.25

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