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

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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Publication Details

Journal
Journal of the American Oil Chemists Society
Published
2024-09-02
DOI
https://doi.org/10.1002/aocs.12897
Citations
1
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.18

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article

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

Xuebing Xu, Kevin Lim, Sumin Ma, Yanlan Bi et al.
1 citations
Journal of the American Oil Chemists Society
Advanced Chemical Sensor Technologies
0.18
article

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

Xuebing Xu, Kevin Lim, Sumin Ma, Yanlan Bi, Hong Zhang, Haiming Shi, Caixia Ding, Haoyu Xu
article en
1 citations

Abstract

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.

Journal of the American Oil Chemists SocietyVol. 102(2)
Henan University of Technology (CN), Wilmar International (Singapore) (SG)
Henan University of Technology, Henan University
Zero hunger
Openalex Percentile: Top 53%
Advanced Chemical Sensor Technologies
0.18
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