偏最小二乘回归
吸光度
苹果酸
化学
成熟度(心理)
酒石酸
线性判别分析
数学
色谱法
人工智能
食品科学
统计
计算机科学
心理学
发展心理学
柠檬酸
作者
Claire E.J. Armstrong,Adam M. Gilmore,Paul K. Boss,Vinay Pagay,David W. Jeffery
出处
期刊:Food Chemistry
[Elsevier]
日期:2022-09-01
卷期号:403: 134321-134321
被引量:2
标识
DOI:10.1016/j.foodchem.2022.134321
摘要
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 R2 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 (R2 = 0.97). R2 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.
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