{"title":"Physicochemical, mineral, and metabolic variation across coconut varieties and coastal landscape types","authors":"Lina Cardona, Heidy Gallego, Félix Alberto Guzmán, Anggy Camacho, Fabrice Vaillant","doi":"10.1007/s00217-026-05321-3","DOIUrl":null,"url":null,"abstract":"<div><p>Coconut (<i>Cocos nucifera</i> L.) supports coastal value chains, yet compositional variation across varieties and production landscapes remains insufficiently characterized. This study evaluated physical, physicochemical, mineral, and metabolic variation across two coastal landscape types (alluvial and fluviomarine plains) and three coconut varieties (Manila Red, Hybrid, and Alto Pacífico) using a 2 × 3 factorial sampling design (<i>n</i> = 36 biological replicates; each replicate comprising six fruits from six different palms). Coconut water was analyzed for pH, titratable acidity, soluble solids, and mineral composition; kernel physical characteristics, moisture, and total oil content (dry basis) were determined; and coconut milk was characterized physicochemically and profiled by untargeted UPLC–ESI–QTOF–MS. Kernel circumference differed among varieties (<i>p</i> = 1.21 × 10<sup>−6</sup>), and the kernel-to-water ratio differed between landscapes (<i>p</i> = 0.0401), whereas no significant landscape, variety, or interaction effects were detected for kernel moisture, total oil content, or the evaluated coconut-water and coconut-milk physicochemical characteristics. Mineral composition showed analyte-specific variation: Ca was associated with variety (<i>p</i> = 1.04 × 10<sup>−4</sup>), whereas Mg and K exhibited significant variety × landscape interactions (<i>p</i> = 4.88 × 10<sup>−7</sup> and <i>p</i> = 0.00214, respectively). Untargeted metabolomics retained 709 features after QC filtering; PCA showed no evident clustering by landscape or variety, and no valid PLS-DA model was obtained. Overall, variation was trait-specific, with limited detectable effects for most physicochemical characteristics and more pronounced variation in coconut-water mineral composition.</p></div>","PeriodicalId":549,"journal":{"name":"European Food Research and Technology","volume":"252 11","pages":""},"PeriodicalIF":4.2000,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00217-026-05321-3.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Food Research and Technology","FirstCategoryId":"97","ListUrlMain":"https://link.springer.com/article/10.1007/s00217-026-05321-3","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Coconut (Cocos nucifera L.) supports coastal value chains, yet compositional variation across varieties and production landscapes remains insufficiently characterized. This study evaluated physical, physicochemical, mineral, and metabolic variation across two coastal landscape types (alluvial and fluviomarine plains) and three coconut varieties (Manila Red, Hybrid, and Alto Pacífico) using a 2 × 3 factorial sampling design (n = 36 biological replicates; each replicate comprising six fruits from six different palms). Coconut water was analyzed for pH, titratable acidity, soluble solids, and mineral composition; kernel physical characteristics, moisture, and total oil content (dry basis) were determined; and coconut milk was characterized physicochemically and profiled by untargeted UPLC–ESI–QTOF–MS. Kernel circumference differed among varieties (p = 1.21 × 10−6), and the kernel-to-water ratio differed between landscapes (p = 0.0401), whereas no significant landscape, variety, or interaction effects were detected for kernel moisture, total oil content, or the evaluated coconut-water and coconut-milk physicochemical characteristics. Mineral composition showed analyte-specific variation: Ca was associated with variety (p = 1.04 × 10−4), whereas Mg and K exhibited significant variety × landscape interactions (p = 4.88 × 10−7 and p = 0.00214, respectively). Untargeted metabolomics retained 709 features after QC filtering; PCA showed no evident clustering by landscape or variety, and no valid PLS-DA model was obtained. Overall, variation was trait-specific, with limited detectable effects for most physicochemical characteristics and more pronounced variation in coconut-water mineral composition.
期刊介绍:
The journal European Food Research and Technology publishes state-of-the-art research papers and review articles on fundamental and applied food research. The journal''s mission is the fast publication of high quality papers on front-line research, newest techniques and on developing trends in the following sections:
-chemistry and biochemistry-
technology and molecular biotechnology-
nutritional chemistry and toxicology-
analytical and sensory methodologies-
food physics.
Out of the scope of the journal are:
- contributions which are not of international interest or do not have a substantial impact on food sciences,
- submissions which comprise merely data collections, based on the use of routine analytical or bacteriological methods,
- contributions reporting biological or functional effects without profound chemical and/or physical structure characterization of the compound(s) under research.