Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra
Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra
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DOI：
10.1016/j.foodchem.2022.134321
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发表时间：
2022-09-30
期刊：
影响因子：
8.8
通讯作者：
Jeffery, David W.
中科院分区：
文献类型：
--
作者：
Armstrong, Claire E. J.;Gilmore, Adam M.;Jeffery, David W.
利用吸收透射和荧光激发发射矩阵（a - teem）光谱技术，对赤霞珠葡萄在两个生长季节、四种葡萄栽培处理下的成熟度指标进行了快速预测。利用融合光谱数据建立机器学习模型，在传统分析方法的基础上预测3-异丁基-2-甲氧基吡嗪（IBMP）、pH、总单宁（Tannin）、总可溶性固形物（TSS）、苹果酸和酒石酸。极端梯度增强（XGB）回归对外部验证（Test）模型的IBMP、苹果酸、pH和TSS的R-2值为0.92-0.96，偏最小二乘回归对TSS的预测更优（R-2 = 0.97）。两种方法预测酒石酸和单宁的R-2值均为0.64-0.81。葡萄成熟度的分类，由红色、IBMP、苹果酸和TSS的分位数范围定义，使用XGB判别分析进行了研究，为测试模型提供了平均78%的正确分类样本。
Absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy was investigated as a rapid method for predicting maturity indices using Cabernet Sauvignon grapes produced under four viticulture treatments during two growing seasons. Machine learning models were developed with fused spectral data to predict 3-isobutyl-2-methoxypyrazine (IBMP), pH, total tannins (Tannin), total soluble solids (TSS), and malic and tartaric acids based on the results from traditional analysis methods. Extreme gradient boosting (XGB) regression yielded R-2 values of 0.92-0.96 for IBMP, malic acid, pH, and TSS for externally validated (Test) models, with partial least squares regression being superior for TSS prediction (R-2 = 0.97). R-2 values of 0.64-0.81 were achieved with either approach for tartaric acid and Tannin predictions. Classification of grape maturity, defined by quantile ranges for red colour, IBMP, malic acid, and TSS, was investigated using XGB discriminant analysis, providing an average of 78 % correctly classified samples for the Test model.