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首页> 外文期刊>Journal of near infrared spectroscopy >Near and mid-infrared spectroscopy for the quantification of botrytis bunch rot in white wine grapes
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Near and mid-infrared spectroscopy for the quantification of botrytis bunch rot in white wine grapes

机译:近红外和中红外光谱法定量白葡萄酒葡萄中的葡萄孢菌

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Botrytis bunch rot (BBR), one of the most important diseases of wine grapes, is usually quantified in the vineyard by visual estimation of percentage disease severity on individual grape bunches. This method is prone to assessor error and there is a need for a more objective quantification method that is cost-effective and practical. Near infrared (NIR; 800-2690nm) and mid-infrared (mid-IR; 2510-25,770nm) spectroscopy were investigated as alternatives to visual estimation. Partial least squares (PLS) analysis of the NIR and mid-IR spectra from near-ripe grape bunches from Tasmanian vineyards was used to generate prediction models from both raw data and data pre-processed using the Savitzky-Golay derivative. The entire spectral range for each spectral region was analysed first, after which specific spectral ranges were analysed based on their influence on the initial PLS analysis. The spectral range of 1260-1370nm with Savitzky-Golay smoothing and first derivative pre-processing produced the PLS model with the highest predictive ability in the NIR spectral region, with a ratio of standard error of prediction to standard deviation (RPD) of 2.2. The spectral range of 8760-9520 nm with Savitzky-Golay smoothing and first derivative pre-processing produced the PLS model with the highest predictive ability in the mid-IR spectral region, with a RPD of 1.7. Both methods demonstrated the potential for spectroscopic quantification of BBR. However, further calibration is required to increase the accuracy of these models, particularly at low BBR severities, if they are to be considered suitable for use in the vineyard.
机译:葡萄孢最重要的疾病之一葡萄孢串腐病(BBR)通常在葡萄园中通过肉眼估计单个葡萄串的疾病严重程度百分比来量化。该方法容易出现评估者错误,因此需要一种更具成本效益和实用性的更客观的量化方法。研究了近红外(NIR; 800-2690nm)和中红外(mid-IR; 2510-25,770nm)光谱作为视觉估计的替代方法。塔斯马尼亚葡萄园近熟葡萄串的近红外和中红外光谱的偏最小二乘(PLS)分析用于从原始数据和使用Savitzky-Golay衍生物预处理的数据生成预测模型。首先分析每个光谱区域的整个光谱范围,然后根据它们对初始PLS分析的影响来分析特定的光谱范围。使用Savitzky-Golay平滑和一阶导数预处理的1260-1370nm光谱范围产生的PLS模型在NIR光谱区域具有最高的预测能力,预测的标准误与标准差(RPD)的比率为2.2。使用Savitzky-Golay平滑和一阶导数预处理的8760-9520 nm光谱范围产生的PLS模型在中红外光谱区域具有最高的预测能力,RPD为1.7。两种方法都证明了BBR光谱定量的潜力。但是,如果认为它们适合在葡萄园中使用,则需要进一步校准以提高这些模型的准确性,尤其是在BBR强度较低的情况下。

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