发酵处理对天然桑叶中降糖活性成分的影响

doi:10.16178/j.issn.0528-9017.20231233

摘要/Abstract

摘要：

选取新鲜的桑叶嫩芽作为研究对象,借鉴传统的红茶发酵工艺,以期通过发酵处理来提高天然桑叶中各降糖活性成分的含量。采用不同降糖活性成分的含量作为综合评价指标,通过单因素试验和响应面试验对影响发酵效果的不同加工条件进行优化,确定最佳的发酵工艺参数。结果表明,在发酵温度40 ℃、发酵时间8.70 h和发酵湿度85%的条件下,与天然桑叶相比,发酵后桑叶中总生物碱、总黄酮和总多糖的含量分别增加了27.92%、28.71%和20.10%。综上所述,发酵处理有助于提升天然桑叶中各有效降糖活性成分的含量,可为促进天然桑叶的高效利用以及提高其附加值提供参考。

关键词: 天然桑叶, 发酵条件, 响应面法, 生物碱, 黄酮, 多糖

Abstract:

Fresh mulberry leaf sprouts were selected as the research object, and the traditional black tea fermentation process was used to improve the contents of hypoglycemic active ingredients in natural mulberry leaves through fermentation. The contents of different hypoglycemic active ingredients was used as a comprehensive evaluation index, and the different processing conditions affecting the fermentation effect were optimized through single factor test and response surface test, and the optimal fermentation process parameters were determined. The results showed that the contents of total alkaloids, total flavonoids and total polysaccharides in mulberry leaves increased by 27.92%, 28.71% and 20.10%, respectively, compared with natural mulberry leaves under the conditions of fermentation temperature of 40 ℃, fermentation time of 8.70 h and fermentation humidity of 85%. In conclusion, fermentation treatment can help to increase the content of effective hypoglycemic active ingredients in natural mulberry leaves, which can provide a reference for promoting the efficient utilization of natural mulberry leaves and increasing their added value.

Key words: natural mulberry leaves, fermentation conditions, response surface methodology, alkaloids, flavonoids, polysaccharides

中图分类号:

TS205.5

郭洁丽, 高鑫, 杨立梅, 林正山, 潘革波. 发酵处理对天然桑叶中降糖活性成分的影响[J]. 浙江农业科学, 2025, 66(1): 189-196.

GUO Jieli, GAO Xin, YANG Limei, LIN Zhengshan, PAN Gebo. Effect of fermentation on hypoglycemic active ingredients in natural mulberry leaves[J]. Journal of Zhejiang Agricultural Sciences, 2025, 66(1): 189-196.

图/表 7

表1 试验因素水平设计

Table 1 Design and coding of experimental factor

水平	因素
水平	X₁/℃	X₂/h	X₃/%
-1	35	8	80
0	40	9	85
1	45	10	90

图1 发酵温度对桑叶中降糖活性成分含量的影响

Fig.1 Influence of fermentation temperature on the contents of hypoglycemic ingredients in mulberry leaves

图2 发酵时间对桑叶中降糖活性成分含量的影响

Fig.2 Influence of fermentation time on the contents of hypoglycemic ingredients in mulberry leaves

图3 发酵湿度对桑叶中降糖活性成分含量的影响

Fig.3 Influence of fermentation humidity on the contents of hypoglycemic ingredients in mulberry leaves

表2 响应面参数优化结果

Table 2 Response surface parameter optimization results

序号	因素			响应值
序号	X₁/ ℃	X₂/ h	X₃/ %	Y₁/ (mg·g^-1)	Y₂/ (mg·g^-1)	Y₃/ (mg·g^-1)
1	0	-1	1	2.36±0.05	18.37±0.03	24.11±0.16
2	-1	0	-1	2.23±0.03	17.94±0.06	24.82±0.17
3	0	0	0	2.50±0.02	19.13±0.05	27.18±0.14
4	0	0	0	2.51±0.04	19.25±0.04	26.92±0.15
5	0	1	-1	2.34±0.06	18.32±0.07	22.13±0.12
6	-1	0	1	2.20±0.03	16.32±0.06	23.84±0.16
7	1	0	1	2.24±0.02	15.41±0.03	23.46±0.15
8	1	0	-1	2.28±0.01	16.11±0.05	23.74±0.14
9	0	1	1	2.26±0.04	17.19±0.06	23.37±0.18
10	0	-1	-1	2.39±0.05	18.68±0.04	25.76±0.15
11	1	1	0	2.21±0.03	15.07±0.07	23.13±0.13
12	0	0	0	2.51±0.06	19.23±0.08	27.00±0.18
13	-1	1	0	2.16±0.02	17.43±0.05	23.35±0.16
14	0	0	0	2.51±0.01	19.33±0.06	26.99±0.15
15	-1	-1	0	2.26±0.03	17.48±0.04	25.52±0.17
16	0	0	0	2.50±0.02	19.20±0.07	26.90±0.18
17	1	-1	0	2.31±0.05	16.85±0.08	24.37±0.15

表3 回归模型的方差分析

Table 3 Analysis of variance in regression models

方差来源	Y₁/(mg·g^-1)			Y₂/(mg·g^-1)			Y₃/(mg·g^-1)
方差来源	F值	P值	显著性	F值	P值	显著性	F值	P值	显著性
模型	348.20	<0.000 1	^**	129.14	<0.000 1	^**	133.29	<0.000 1	^**
X₁	56.01	0.000 1	^**	153.89	<0.000 1	^**	27.12	0.001 2	^**
X₂	183.18	<0.000 1	^**	54.85	0.000 1	^**	206.48	<0.000 1	^**
X₃	47.97	0.000 2	^**	69.79	<0.000 1	^**	9.65	0.017 2	^*
X₁X₂	3.20×10^-3	0.956 7		28.67	0.001 1	^**	5.93	0.045 1	^*
X₁X₃	7.92×10^-2	0.786 5		7.06	0.032 6	^*	3.32	0.111 1
X₂X₃	10.30	0.014 9	^*	6.25	0.041 0	^*	57.25	0.000 1	^**
$X 1 2$	1 838.13	<0.000 1	^**	698.98	<0.000 1	^**	222.75	<0.000 1	^**
$X 2 2$	372.81	<0.000 1	^**	25.71	0.001 4	^**	264.08	<0.000 1	^**
$X 3 2$	381.79	<0.000 1	^**	68.30	<0.000 1	^**	309.86	<0.000 1	^**
失拟项	2.19	0.222 3	不显著	4.02	0.106 2	不显著	5.36	0.069 3	不显著
R²	0.997 8			0.994 0			0.994 2

表3 回归模型的方差分析

Table 3 Analysis of variance in regression models

方差来源	Y₁/(mg·g^-1)			Y₂/(mg·g^-1)			Y₃/(mg·g^-1)
方差来源	F值	P值	显著性	F值	P值	显著性	F值	P值	显著性
模型	348.20	<0.000 1	^**	129.14	<0.000 1	^**	133.29	<0.000 1	^**
X₁	56.01	0.000 1	^**	153.89	<0.000 1	^**	27.12	0.001 2	^**
X₂	183.18	<0.000 1	^**	54.85	0.000 1	^**	206.48	<0.000 1	^**
X₃	47.97	0.000 2	^**	69.79	<0.000 1	^**	9.65	0.017 2	^*
X₁X₂	3.20×10^-3	0.956 7		28.67	0.001 1	^**	5.93	0.045 1	^*
X₁X₃	7.92×10^-2	0.786 5		7.06	0.032 6	^*	3.32	0.111 1
X₂X₃	10.30	0.014 9	^*	6.25	0.041 0	^*	57.25	0.000 1	^**
$X 1 2$	1 838.13	<0.000 1	^**	698.98	<0.000 1	^**	222.75	<0.000 1	^**
$X 2 2$	372.81	<0.000 1	^**	25.71	0.001 4	^**	264.08	<0.000 1	^**
$X 3 2$	381.79	<0.000 1	^**	68.30	<0.000 1	^**	309.86	<0.000 1	^**
失拟项	2.19	0.222 3	不显著	4.02	0.106 2	不显著	5.36	0.069 3	不显著
R²	0.997 8			0.994 0			0.994 2

图4 各因素间的交互作用

Fig.4 Interaction effects among the various factors

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