Armstrong Claire E J, Niimi Jun, Boss Paul K, Pagay Vinay, Jeffery David W
Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia.
School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia.
Foods. 2023 Feb 9;12(4):757. doi: 10.3390/foods12040757.
Generations of sensors have been developed for predicting food sensory profiles to circumvent the use of a human sensory panel, but a technology that can rapidly predict a suite of sensory attributes from one spectral measurement remains unavailable. Using spectra from grape extracts, this novel study aimed to address this challenge by exploring the use of a machine learning algorithm, extreme gradient boosting (XGBoost), to predict twenty-two wine sensory attribute scores from five sensory stimuli: aroma, colour, taste, flavour, and mouthfeel. Two datasets were obtained from absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy with different fusion methods: variable-level data fusion of absorbance and fluorescence spectral fingerprints, and feature-level data fusion of A-TEEM and CIELAB datasets. The results for externally validated models showed slightly better performance using only A-TEEM data, predicting five out of twenty-two wine sensory attributes with R values above 0.7 and fifteen with R values above 0.5. Considering the complex biotransformation involved in processing grapes to wine, the ability to predict sensory properties based on underlying chemical composition in this way suggests that the approach could be more broadly applicable to the agri-food sector and other transformed foodstuffs to predict a product's sensory characteristics from raw material spectral attributes.
为了避免使用人类感官评定小组，人们已经开发了几代用于预测食品感官特征的传感器，但仍未出现一种能够通过一次光谱测量快速预测一系列感官属性的技术。本项新研究利用葡萄提取物的光谱，旨在通过探索使用一种机器学习算法——极端梯度提升（XGBoost），从香气、颜色、味道、风味和口感这五种感官刺激来预测22种葡萄酒感官属性得分，以应对这一挑战。通过不同的融合方法，从吸光度-透射和荧光激发-发射矩阵（A-TEEM）光谱中获得了两个数据集：吸光度和荧光光谱指纹的可变级数据融合，以及A-TEEM和CIELAB数据集的特征级数据融合。外部验证模型的结果表明，仅使用A-TEEM数据时性能略好，在22种葡萄酒感官属性中预测出5种R值高于0.7，15种R值高于0.5。考虑到葡萄加工成葡萄酒过程中涉及的复杂生物转化，以这种方式基于潜在化学成分预测感官特性的能力表明，该方法可能更广泛地适用于农业食品部门和其他加工食品，以从原材料光谱属性预测产品的感官特征。
