不同摊放环境下茶鲜叶失水的变化规律

doi:10.11924/j.issn.1000-6850.casb2022-0146

中国农学通报 ›› 2023, Vol. 39 ›› Issue (4): 160-164.doi: 10.11924/j.issn.1000-6850.casb2022-0146

• 食品·营养·检测·安全 • 上一篇

不同摊放环境下茶鲜叶失水的变化规律

廖珺¹(), 方洪生², 苏有健¹, 王烨军¹, 张永利¹, 孙宇龙¹, 方雅各¹

¹安徽省农业科学院茶叶研究所，安徽黄山 245000
²黄山市洪通农业科技有限公司，安徽黄山 245000

收稿日期:2022-03-04 修回日期:2022-09-09 出版日期:2023-02-05 发布日期:2023-01-31
作者简介:
廖珺，女，1990年出生，安徽黄山人，助理研究员，硕士，研究方向为茶叶精深加工。通信地址：245000 安徽省黄山市屯溪区鬲山大道28号安徽省农科院茶叶研究所，Tel：0559-2591977，E-mail：632693239@qq.com。
基金资助:
黄山市科技计划项目“黄山毛峰滋味品质提升关键加工技术研究与应用”(2020KN-10); 安徽省现代农业产业技术体系建设专项资金资助(AHCYJSTX-11)

Change Law of Water Loss in Fresh Tea Leaves Under Different Spreading Environments

LIAO Jun¹(), FANG Hongsheng², SU Youjian¹, WANG Yejun¹, ZHANG Yongli¹, SUN Yulong¹, FANG Yage¹

¹Tea Research Institute, Anhui Academy of Agricultural Sciences, Huangshan, Anhui 245000
²Huangshan Hongtong Agricultural Technology Co., Ltd., Huangshan, Anhui 245000

Received:2022-03-04 Revised:2022-09-09 Online:2023-02-05 Published:2023-01-31

摘要/Abstract

摘要：

明确不同环境条件下茶鲜叶摊放过程失水动态变化规律，构建鲜叶水分含量变化的预测模型，从而有效预判摊放程度。本研究通过设置不同的温湿度，考察在一定的摊放时间内茶鲜叶水分的变化情况，利用响应面分析软件建立预测模型。结果表明，在不同环境下茶鲜叶水分含量随摊放时间的延长均呈现先快速下降后缓慢减少的趋势，且低温高湿环境能显著延缓鲜叶的失水速率。通过响应面分析得到鲜叶水分含量和温湿度、时间之间关系的预测模型(R²=0.9977)，其具备较高的显著性和拟合度，且预测效果良好。构建的模型能较好地预测不同温湿度摊放环境下茶鲜叶水分的变化情况，对摊放进程的调控具有实际应用价值。

关键词: 茶鲜叶, 摊放温度, 摊放湿度, 水分含量, 变化规律, 响应面预测模型

Abstract:

To clarify the dynamic changes of water loss during the spreading process of fresh tea leaves under different environmental conditions, a prediction model for the changes of the moisture content of fresh tea leaves was constructed to predict the degree of spreading. In this experiment, different temperature and humidity were set to investigate the change of moisture content of fresh tea leaves during a certain spreading time, and the prediction model was established by response surface analysis software. The results showed that the moisture content of fresh tea leaves decreased quickly and then slowly with the extension of spreading time in different environments, and the low temperature and high humidity environment could significantly delay the water loss rate of fresh leaves. The prediction model (R²=0.9977) of the relationship between fresh tea leaf moisture content and temperature, humidity and time was obtained by response surface analysis, which had high significance and fitting degree, and good prediction effect. Therefore, the model constructed in this experiment could be used to forecast the moisture changes of fresh tea leaves under different temperature and humidity conditions and have practical application value for the regulation of the spreading process.

Key words: fresh tea leaves, spreading temperature, spreading humidity, moisture content, change law, response surface prediction model

廖珺, 方洪生, 苏有健, 王烨军, 张永利, 孙宇龙, 方雅各. 不同摊放环境下茶鲜叶失水的变化规律[J]. 中国农学通报, 2023, 39(4): 160-164.

LIAO Jun, FANG Hongsheng, SU Youjian, WANG Yejun, ZHANG Yongli, SUN Yulong, FANG Yage. Change Law of Water Loss in Fresh Tea Leaves Under Different Spreading Environments[J]. Chinese Agricultural Science Bulletin, 2023, 39(4): 160-164.

图/表 5

参考文献 26

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时间	T₁	T₂	T₃	T₄	T₅	T₆
0 h	77.91±0.20aA	77.91±0.20aA	77.91±0.20aA	77.91±0.20aA	77.91±0.20aA	77.91±0.20aA
2 h	75.25±0.38bC	76.00±0.33bA	74.43±0.23bD	75.42±0.23bB	73.02±0.29bF	74.04±0.25bE
4 h	73.21±0.27cC	74.28±0.17cA	72.26±0.34cD	73.53±0.31cB	70.01±0.18cF	71.45±0.31cE
6 h	71.67±0.19dC	73.14±0.28dA	70.69±0.21dD	72.29±0.35dB	68.22±0.25dF	69.87±0.39dE
8 h	70.71±0.22eC	72.42±0.24eA	69.72±0.18eD	71.33±0.22eB	67.04±0.32eF	68.83±0.27eE
10 h	70.12±0.26fC	71.91±0.21fA	69.11±0.14fD	70.75±0.41fB	66.22±0.29fF	68.02±0.26fE

时间	T₁	T₂	T₃	T₄	T₅	T₆
0 h	77.91±0.20aA	77.91±0.20aA	77.91±0.20aA	77.91±0.20aA	77.91±0.20aA	77.91±0.20aA
2 h	75.25±0.38bC	76.00±0.33bA	74.43±0.23bD	75.42±0.23bB	73.02±0.29bF	74.04±0.25bE
4 h	73.21±0.27cC	74.28±0.17cA	72.26±0.34cD	73.53±0.31cB	70.01±0.18cF	71.45±0.31cE
6 h	71.67±0.19dC	73.14±0.28dA	70.69±0.21dD	72.29±0.35dB	68.22±0.25dF	69.87±0.39dE
8 h	70.71±0.22eC	72.42±0.24eA	69.72±0.18eD	71.33±0.22eB	67.04±0.32eF	68.83±0.27eE
10 h	70.12±0.26fC	71.91±0.21fA	69.11±0.14fD	70.75±0.41fB	66.22±0.29fF	68.02±0.26fE

方差来源	平方和	自由度	均方	F值	P值	显著性
模型	293.53	9	32.61	1085.56	<0.0001	^**
A	39.69	1	39.69	1320.97	<0.0001	^**
B	11.89	1	11.89	395.77	<0.0001	^**
C	86.80	1	86.80	2888.95	<0.0001	^**
AB	0.041	1	0.041	1.38	0.2524
AC	1.78	1	1.78	59.13	<0.0001	^**
BC	0.66	1	0.66	21.81	0.0001	^**
A²	3.10	1	3.10	103.06	<0.0001	^**
B²	1.13	1	1.13	37.71	<0.0001	^**
C²	5.78	1	5.78	192.31	<0.0001	^**
残差	0.69	23	0.030
失拟项	0.61	21	0.029	0.73	0.7253
纯误差	0.080	2	0.040
总和	294.22	32

方差来源	平方和	自由度	均方	F值	P值	显著性
模型	293.53	9	32.61	1085.56	<0.0001	^**
A	39.69	1	39.69	1320.97	<0.0001	^**
B	11.89	1	11.89	395.77	<0.0001	^**
C	86.80	1	86.80	2888.95	<0.0001	^**
AB	0.041	1	0.041	1.38	0.2524
AC	1.78	1	1.78	59.13	<0.0001	^**
BC	0.66	1	0.66	21.81	0.0001	^**
A²	3.10	1	3.10	103.06	<0.0001	^**
B²	1.13	1	1.13	37.71	<0.0001	^**
C²	5.78	1	5.78	192.31	<0.0001	^**
残差	0.69	23	0.030
失拟项	0.61	21	0.029	0.73	0.7253
纯误差	0.080	2	0.040
总和	294.22	32

不同摊放环境下茶鲜叶失水的变化规律

Change Law of Water Loss in Fresh Tea Leaves Under Different Spreading Environments

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