A thorough evaluation of flavor characteristics among various retail peanut oil products using multivariate statistical methods

Sumin Ma, Kevin Lim, Caixia Ding, Haoyu Xu, Haiming Shi, Yanlan Bi, Hong Zhang, Xuebing Xu
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Abstract

Flavor is a crucial characteristic of peanut oils, exerting a significant impact on their quality and acceptability. However, given the uncertainty in the correlations between flavor attributes and volatile compounds, assessing the flavor quality of commercial peanut oil retail products (PORPs) based on updated analysis techniques remains challenging. In this study, 21 commercial samples were collected for investigating this correlation. From the sensory evaluations, the 16 fragrant PORPs show differences esp. for the roasted (4.25–10), the sweet (0–4.25), and the steamed (0–4.25) attributes while the roasted attribute is dominant for all the products. For volatile profile analysis with updated analytical technology, the fragrant PORPs also show significant difference in terms of volatile profiles and intensities. The total intensity can differ from 16.37 to 66.59 mg/kg. Multivariate statistical methods (MSMs) revealed the significant correlations between volatile compounds and specific sensory attributes. Notably, 1‐hexanol and phenylethyl alcohol demonstrate a strong correlation with the raw peanut flavor attribute. Benzeneacetaldehyde (BA) exhibited a positive correlation with sweet flavor. dl‐Pantolactone has a positive effect on roasted flavor. Hexanal and 3‐octen‐2‐one are highly related to off‐flavor notes. In addition, based on machine learning (ML), three groups of volatile compounds were found to be highly correlated with specific flavor attributes. Notably, a cluster comprising seven compounds, namely 2‐ethenyl‐6‐methylpyrazine, 2‐methyl‐6‐[(E)‐1‐propenyl]pyrazine, benzaldehyde, BA, nonanal, dl‐pantolactone, and 2‐acetopyridine exhibiting a positive correlation with roasted, steamed, and sweet flavor attributes. Furthermore, recombination experiments were conducted to verify the synergistic effect of these seven compounds on flavor performance and agreements were reached.
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使用多元统计方法全面评估各种零售花生油产品的风味特征
风味是花生油的一个重要特征,对其质量和可接受性有重大影响。然而,由于风味属性与挥发性化合物之间的相关性存在不确定性,因此根据最新的分析技术来评估商业花生油零售产品(PORPs)的风味质量仍然具有挑战性。本研究收集了 21 个商业样品来研究这种相关性。从感官评价来看,16 种芳香型花生油零售产品在烘烤(4.25-10)、甜味(0-4.25)和蒸煮(0-4.25)属性方面存在差异,而烘烤属性在所有产品中都占主导地位。使用最新分析技术进行挥发性特征分析时,芳香型 PORP 在挥发性特征和强度方面也显示出显著差异。总强度从 16.37 毫克/千克到 66.59 毫克/千克不等。多元统计方法(MSMs)揭示了挥发性化合物与特定感官属性之间的显著相关性。值得注意的是,1-己醇和苯乙醇与生花生的风味属性有很强的相关性。苯乙醛(BA)与甜味呈正相关。己醛和 3-辛烯-2-酮与异味高度相关。此外,基于机器学习(ML),还发现三组挥发性化合物与特定风味属性高度相关。值得注意的是,由七种化合物组成的群组,即 2-乙烯基-6-甲基吡嗪、2-甲基-6-[(E)-1-丙烯基]吡嗪、苯甲醛、BA、壬醛、dl-泛内酯和 2-乙酰基吡啶与烘烤、蒸煮和甜味属性呈现正相关。此外,还进行了重组实验,以验证这七种化合物对风味表现的协同效应,并达成了一致。
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