Cis-Vaccenic Acid as a Compositional Marker for Detecting High-Oleic Seed Oil Adulteration in Avocado Oil

IF 2.4 4区 农林科学 Q3 CHEMISTRY, APPLIED
Journal of the American Oil Chemists Society Pub Date : 2026-10-07 Epub Date: 2026-06-29 DOI:10.1002/aocs.70129
Donpon Wannasin, Selina C. Wang
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引用次数: 0

Abstract

The rapid expansion of the global avocado oil market has intensified concerns about economic adulteration with lower-cost, high-oleic seed oils that resemble the fatty acid specifications of avocado oil. Cis-vaccenic acid C18:1 (n−7) occurs at higher proportions in avocado oil than in common seed oil adulterants, making it a promising complementary marker for avocado oil authenticity. However, natural compositional variability must be incorporated into threshold development to ensure reliable analytical classification of authentic oils. This study evaluates the analytical performance of cis-vaccenic acid as a compositional marker for detecting high-oleic seed oil adulteration in avocado oil using 298 authentic avocado oils representing 24 cultivars and 13 producing regions. Linear mixed-effects modeling demonstrated that geographic origin accounted for 73.7% of total variability in cis-vaccenic acid content, exceeding the contribution of cultivar (18.4%). A one-sided tolerance interval (α = 0.05, p = 0.99) established a statistically supported lower bound of 3.2%, encompassing 99.7% of authentic oils. Controlled blending experiments with common seed oil adulterants demonstrated linear decreases in cis-vaccenic acid with increasing adulteration (R 2 ≥ 0.99). Receiver operating characteristic analysis confirmed strong discriminatory capacity (AUC = 0.89) and identified a balanced operational threshold of 4.6%. These findings support the use of cis-vaccenic acid as a complementary screening marker within the Codex framework for improving detection of high-oleic seed oil adulteration in avocado oil.

顺式异丙酸作为检测鳄梨油中高油籽油掺假的成分标记
全球鳄梨油市场的迅速扩张加剧了人们对低成本、高油酸籽油的经济掺假的担忧,这些油与鳄梨油的脂肪酸规格相似。顺式异丙酸C18:1 (n−7)在牛油果油中的含量高于普通种子油掺假物,使其成为牛油果油真伪的有前途的补充标记。然而,自然成分的可变性必须纳入阈值开发,以确保可靠的正宗油的分析分类。本研究利用来自13个产区、24个品种的298种正宗鳄梨油,对顺式异丙酸作为成分标记检测鳄梨油中高油籽油掺假的分析性能进行了评价。线性混合效应模型表明,地理来源对顺式异丙酸含量的总变异贡献率为73.7%,超过品种贡献率(18.4%)。单侧容忍区间(α = 0.05, p = 0.99)建立了统计支持的下限3.2%,包括99.7%的正宗油。与普通种子油掺假的对照混合实验表明,顺式异丙酸随掺假量的增加呈线性下降(r2≥0.99)。受试者操作特征分析证实了较强的区分能力(AUC = 0.89),并确定了4.6%的平衡操作阈值。这些发现支持在食品法典框架内使用顺式异痘酸作为补充筛选标记,以改进对鳄梨油中高油酸籽油掺假的检测。
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来源期刊
CiteScore
4.10
自引率
5.00%
发文量
95
审稿时长
2.4 months
期刊介绍: The Journal of the American Oil Chemists’ Society (JAOCS) is an international peer-reviewed journal that publishes significant original scientific research and technological advances on fats, oils, oilseed proteins, and related materials through original research articles, invited reviews, short communications, and letters to the editor. We seek to publish reports that will significantly advance scientific understanding through hypothesis driven research, innovations, and important new information pertaining to analysis, properties, processing, products, and applications of these food and industrial resources. Breakthroughs in food science and technology, biotechnology (including genomics, biomechanisms, biocatalysis and bioprocessing), and industrial products and applications are particularly appropriate. JAOCS also considers reports on the lipid composition of new, unique, and traditional sources of lipids that definitively address a research hypothesis and advances scientific understanding. However, the genus and species of the source must be verified by appropriate means of classification. In addition, the GPS location of the harvested materials and seed or vegetative samples should be deposited in an accredited germplasm repository. Compositional data suitable for Original Research Articles must embody replicated estimate of tissue constituents, such as oil, protein, carbohydrate, fatty acid, phospholipid, tocopherol, sterol, and carotenoid compositions. Other components unique to the specific plant or animal source may be reported. Furthermore, lipid composition papers should incorporate elements of year­to­year, environmental, and/ or cultivar variations through use of appropriate statistical analyses.
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