基于优化植被指数的多生育期茶叶游离氨基酸含量估算

不同季节的茶叶外形和品质均具有较大差异，针对运用植被指数检测不同季节茶鲜叶游离氨基酸含量存在难度，选取了10个茶树品种3个季节（春茶、夏茶和秋茶）茶鲜叶中游离氨基酸含量数据和高光谱数据进行分析。首先，对原始光谱数据进行5种光谱变换：倒数T_1/R、对数T_1gR、一阶微分T_R’、倒数的一阶微分T_（1/R）’和对数的一阶微分T_（1gR）’,并进一步利用不同光谱变换优化了经典植被指数，最终比较了经典植被指数和优化植被指数对不同季节茶鲜叶游离氨基酸模型的影响。结果表明：茶鲜叶建模集和验证集游离氨基酸含量的变化趋势从大到小均为春茶游离氨基酸含量、秋茶游离氨基酸含量、夏茶游离氨基酸含量；光谱变换优化后的植被指数与茶鲜叶游离氨基酸含量的相关性均高于经典植被指数与茶鲜叶游离氨基酸含量相关性，相关系数绝对值范围为0.10～0.30;基于T_lgR-VI构建的多元线性回归（MLR）模型在不同季节的建模集和验证集中均得到了较好的精度，且适用于多生育时期茶鲜叶氨基酸含量的估测。基于T...

Estimation of Free Amino Acid Content in Fresh Tea Leaves at Multiple Growth Periods Based on Optimized Vegetation Index

The appearance and quality of tea in seasons of spring,summer and autumn are quite different.Using vegetation index to monitor the free amino acid content of fresh tea leaves in different seasons is facing great challenges.The spectral transformation played an important role in highlighting the characteristic spectrum and eliminating the influence of background and noise.Whether the optimization of vegetation index(VI)by transformation was beneficial to the free amino acid content of tea leaves at multiple growth stages was concerned.The free amino acid content data and hyperspectral data of ten tea varieties(summer tea,autumn tea and spring tea)were analyzed in three consecutive seasons.Firstly,the original spectral data was transformed by spectral transformations(reciprocal(T_1/R),logarithm(T_1gR),first-order differential T_R′,first-order differential T_(1/R′) of reciprocal and first-order differential T_(1gR′) of the logarithm).The correlation between the vegetation index of different transformation spectra and the combination of spectral transformation and the amino acid of fresh tea leaves in different seasons was further analyzed.Finally,the effects of different spectral transformations on the free amino acid model of fresh tea leaves in different seasons were compared.The results showed that the changing trend of free amino acid content in modeling set and validation set of fresh tea leaves was spring tea free amino acid content(modeling mean:4.03%,validation mean:3.98%),autumn tea free amino acid content(modeling mean:3.72%,validation mean:3.56%)and summer tea free amino acid content(modeling mean:2.91%,validation mean:2.93%).Except for T_lgRTCARI,the correlation between other vegetation indices optimized by spectral transformation and free amino acids in fresh tea leaves was higher than that between classical vegetation indices and free amino acids in fresh tea leaves,with the absolute correlation coefficients of 0.10~0.30.The accuracy of the MLR model based on T_lgRVI was obtained in the calibration sets and verification sets of different seasons,and it was suitable for the estimation of the amino acid content of tea fresh leaves during multiple growth periods.The multiple linear regression(MLR)model based on T_lgRVI had high accuracy,with determination coefficient(R²)of 0.38 and root mean squared error(RMSE)of 0.72%for calibration sets and R² of 0.38 and RMSE of 0.84%,respectively.The overall results indicated that spectral pretreatment had a positive effect on the monitoring of free amino acids in different growth seasons,which provided a technical reference for the estimation of tea quality.