School of Agriculture and Wine Sciences, Charles Sturt University, Wagga Wagga, New South Wales 2678, Australia.
Anal Chim Acta. 2012 Jun 30;732:16-25. doi: 10.1016/j.aca.2011.10.055. Epub 2011 Nov 6.
Predictions of grapevine yield and the management of sugar accumulation and secondary metabolite production during berry ripening may be improved by monitoring nitrogen and starch reserves in the perennial parts of the vine. The standard method for determining nitrogen concentration in plant tissue is by combustion analysis, while enzymatic hydrolysis followed by glucose quantification is commonly used for starch. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FT-IR) combined with chemometric modelling offers a rapid means for the determination of a range of analytes in powdered or ground samples. ATR-FT-IR offers significant advantages over combustion or enzymatic analysis of samples due to the simplicity of instrument operation, reproducibility and speed of data collection. In the present investigation, 1880 root and wood samples were collected from Shiraz, Semillon and Riesling vineyards in Australia and Germany. Nitrogen and starch concentrations were determined using standard analytical methods, and ATR-FT-IR spectra collected for each sample using a Bruker Alpha instrument. Samples were randomly assigned to either calibration or test data sets representing two thirds and one third of the samples respectively. Signal preprocessing included extended multiplicative scatter correction for water and carbon dioxide vapour, standard normal variate scaling with second derivative and variable selection prior to regression. Excellent predictive models for percent dry weight (DW) of nitrogen (range: 0.10-2.65% DW, median: 0.45% DW) and starch (range: 0.25-42.82% DW, median: 7.77% DW) using partial least squares (PLS) or support vector machine (SVM) analysis for linear and nonlinear regression respectively, were constructed and cross validated with low root mean square errors of prediction (RMSEP). Calibrations employing SVM-regression provided the optimum predictive models for nitrogen (R(2)=0.98 and RMSEP=0.07% DW) compared to PLS regression (R(2)=0.97 and RMSEP=0.08% DW). The best predictive models for starch was obtained using PLS regression (R(2)=0.95 and RSMEP=1.43% DW) compared to SVR (R(2)=0.95; RMSEP=1.56% DW). The RMSEP for both nitrogen and starch is below the reported seasonal flux for these analytes in Vitis vinifera. Nitrogen and starch concentrations in grapevine tissues can thus be accurately determined using ATR-FT-IR, providing a rapid method for monitoring vine reserve status under commercial grape production.
通过监测葡萄藤多年生部分的氮和淀粉储备，可以提高对葡萄产量和浆果成熟过程中糖积累和次生代谢产物生产的预测。植物组织中氮浓度的标准测定方法是燃烧分析，而酶水解后葡萄糖定量通常用于淀粉。衰减全反射傅里叶变换红外光谱（ATR-FT-IR）结合化学计量建模为粉末或研磨样品中一系列分析物的快速测定提供了一种手段。与样品的燃烧或酶分析相比，ATR-FT-IR 具有仪器操作简单、重现性好和数据采集速度快等显著优势。在本研究中，从澳大利亚和德国的设拉子、赛美蓉和雷司令葡萄园采集了 1880 个根和木材样本。使用标准分析方法测定氮和淀粉浓度，并使用 Bruker Alpha 仪器为每个样本采集 ATR-FT-IR 光谱。样品随机分配到校准或测试数据集，分别代表三分之二和三分之一的样品。信号预处理包括水和二氧化碳蒸气的扩展多重散射校正、二阶导数的标准正态变量标准化和回归前的变量选择。使用偏最小二乘（PLS）或支持向量机（SVM）分析分别对线性和非线性回归进行分析，建立了氮（范围：0.10-2.65% DW，中位数：0.45% DW）和淀粉（范围：0.25-42.82% DW，中位数：7.77% DW）的干重（DW）百分比的优秀预测模型，交叉验证的预测均方根误差（RMSEP）较低。与 PLS 回归（R(2)=0.97，RMSEP=0.08% DW）相比，SVM 回归（R(2)=0.98，RMSEP=0.07% DW）为氮提供了最佳的预测模型。与 SVR（R(2)=0.95；RMSEP=1.56% DW）相比，PLS 回归（R(2)=0.95，RSMEP=1.43% DW）为淀粉获得了最佳预测模型。氮和淀粉的 RMSEP 均低于 Vitis vinifera 中这些分析物的报告季节性通量。因此，ATR-FT-IR 可用于准确测定葡萄藤组织中的氮和淀粉浓度，为商业葡萄生产下监测葡萄藤储备状况提供了一种快速方法。
