背景:橄榄油的感官特征是人类感官面板获得的基本质量参数。在这项工作中,使用便携式电子鼻预测了来自西班牙不同地区和栽培品种(Picual, Arbequina和Cornicabra)的199种橄榄油样品的水果香气强度,并特别强调了预测与栽培品种变异性的稳健性。电子鼻给出的原始数据用于获得两个不同的特征向量,用于将脊和拉索回归模型拟合到两个数据集:一个由所有样本组成,另一个仅由cv组成。Picual样本。结果:所得结果显示,所有情况下的平均误差(Mean Average Error, MAE)均小于0.88,其中典型模型的MAE为0.67。这些MAE值和模型参数对不同数据折叠拟合的相似性与前人的研究结果一致。结论:分析的大量样本和得到的结果显示了该方法的稳健性和适用性。结果还表明,对特定品种进行特定模型拟合可以获得更好的生产性能。总的来说,所提出的方法能够为快速筛选橄榄油的水果香气强度提供有用的信息。这篇文章受版权保护。版权所有。
Assessment of fruity aroma intensity in olive oils from different Spanish regions using a portable electronic nose.
Background: The organoleptic profile of an olive oil is a fundamental quality parameter obtained by human sensory panels. In this work, a portable electronic nose was employed to predict the fruity aroma intensity of 199 olive oil samples from different Spanish regions and cultivar varieties ('Picual', 'Arbequina', and 'Cornicabra'), with special emphasis in testing the robustness of the predictions versus cultivar variety variability. The primary data given by the electronic nose were used to obtain two different feature vectors that were employed to fit ridge and lasso regressions models to two datasets: one consisting of all the samples and another just the cv. Picual samples.
Results: The results obtained showed mean average error (MAE) values below 0.88 in all cases, with an MAE of 0.67 for the 'Picual' model. These MAE values and the similarities in the model parameters fitted for the different data folds are in agreement with the results obtained in previous studies.
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The Journal of the Science of Food and Agriculture publishes peer-reviewed original research, reviews, mini-reviews, perspectives and spotlights in these areas, with particular emphasis on interdisciplinary studies at the agriculture/ food interface.
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