Study on using fatty acid data in the botanical chemotaxonomy for Vietnamese seaweed species

Vietnam's sea is assessed to be very diverse and rich in seaweed species. It is about 1000 different species of seaweed, of which more than 800 species have been identified, classify into genus, families, classes, phylums, and continuously updated from the 1950s to the present. Previous studies by Vietnamese and international scientists have shown that lipids from seaweed contain many valuable active ingredients such as acids C20: 4n-6 (AA), C20:5n-3 (EPA), C22:6n-3 (DHA), prostaglandin E2… In this study, fatty acids were converted to methyl esters and identified by gas chromatography using flame ionization detector (GC-FID) with column Cap Mao Equity 5 (Merck, L×ID 30m×0.25 mm, df 0.25 µm). From the total lipid of 50 of Vietnamese seaweed, we have identified 30 fatty acids, in which, C16:0, C18:1n-9, C20:4n-6 (AA) fatty acids have the high content, and C20:5n-3 (EPA), C22:6n-3 (DHA), C22:5n-3 (DPA) fatty acids have the high bioactivities. By the method of PCA main component analysis, from the dataset of fatty acids, we have identified 8 main fatty acids with high correlation and used to represent the distribution of seaweed species on the two-way plane. Three phylums were classified by different fatty acid groups with the high reliability. In the detail, the distribution of the phylum Phaeophyta depends on the content of 3 fatty acids including C16:1n-7, C18:1n-9 và C20:4n-6, the phylum Rhodophyta depends on C15:0, C16:0, C18:0 fatty acids, and the phylum Chlorophyta depends on C18:1n-7, C18:3n-6 fatty acids. This method can may help provides more chemical data in the taxonomy of Vietnamese seaweed species.

Fatty Acid Data and Crop Surveys Indicate Sources of Red Sunflower Seed Weevil (Coleoptera: Curculionidae), Populations and Suggest Strategies for Management

The red sunflower seed weevil, Smicronyx fulvus L., is a univoltine seed-feeding pest of cultivated sunflower, Helianthus annuus L. Artificial infestations of S. fulvus onto sunflowers with traditional (<25% oleic acid), mid-oleic (55–75%), or high oleic (>80%) fatty acid profiles were used to test if fatty acids could be used as natural markers to estimate the proportion of weevils developing on oilseed sunflowers rather than wild Helianthus spp. and confection (non-oil) types. Oleic acid (%) in S. fulvus confirmed the fatty acid compositions of mature larvae and weevil adults reflected their diets, making primary (oleic or linoleic) fatty acids feasible as natural markers for this crop-insect combination. Oleic acid in wild S. fulvus populations in North Dakota suggests at least 84 and 90% of adults originated from mid-oleic or high oleic sunflower hybrids in 2017 and 2018, respectively. Surveys in 2017 (n = 156 fields) and 2019 (n = 120 fields) extended information provided by S. fulvus fatty acid data; no significant spatial patterns of S. fulvus damage were detected in samples, damage to oilseed sunflowers was greater than confection (non-oil) types, and the majority of damage occurred in ≈10% of surveyed fields. Combined, data suggest a few unmanaged or mismanaged oilseed sunflower fields are responsible for producing most S. fulvus in an area. Improved management seems possible with a combination of grower education and expanded use of non-insecticidal tactics, including cultural practices and S. fulvus-resistant hybrids.

Evaluating the performance of the Bayesian mixing tool MixSIAR with fatty acid data for quantitative estimation of diet

We test the performance of the Bayesian mixing model, MixSIAR, to quantitatively predict diets of consumers based on their fatty acids (FAs). The known diets of six species, undergoing controlled-feeding experiments, were compared with dietary predictions modelled from their FAs. Test subjects included fish, birds and mammals, and represent consumers with disparate FA compositions. We show that MixSIAR with FA data accurately identifies a consumer’s diet, the contribution of major prey items, when they change their diet (diet switching) and can detect an absent prey. Results were impacted if the consumer had a low-fat diet due to physiological constraints. Incorporating prior information on the potential prey species into the model improves model performance. Dietary predictions were reasonable even when using trophic modification values (calibration coefficients, CCs) derived from different prey. Models performed well when using CCs derived from consumers fed a varied diet or when using CC values averaged across diets. We demonstrate that MixSIAR with FAs is a powerful approach to correctly estimate diet, in particular if used to complement other methods.

Human Milk Fatty Acid Composition of Allergic and Non-Allergic Mothers: The Ulm SPATZ Health Study

The aim of this study was to determine the differences in human milk fatty acid composition in relation to maternal allergy within a large birth cohort study using statistical methods accounting for the correlations that exist in compositional data. We observed marginal differences in human milk fatty acid composition of allergic and non-allergic mothers. However, our results do not support the hypothesis that human milk fatty acid composition is influenced by allergy or that it differs between mothers with or without allergy. Observed differences in our results between transformed and untransformed fatty acid data call for re-evaluation of previous, as well as future, studies using statistical methods appropriate for compositionality of fatty acid data.

Fatty Acids-Based Quality Index to Differentiate Worldwide Commercial Pistachio Cultivars

The fatty acid profiles of five main commercial pistachio cultivars, including Ahmad-Aghaei, Akbari, Chrok, Kalle-Ghouchi, and Ohadi, were determined by gas chromatography: palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3), arachidic (C20:0), and gondoic (C20:1) acid. Based on the oleic to linoleic acid (O/L) ratio, a quality index was determined for these five cultivars: Ohadi (2.40) < Ahmad-Aghaei (2.60) < Kale-Ghouchi (2.94) < Chrok (3.05) < Akbari (3.66). Principal component analysis (PCA) of the fatty acid data yielded three significant PCs, which together account for 80.0% of the total variance in the dataset. A linear discriminant analysis (LDA) model that was evaluated with cross-validation correctly classified almost all of the samples: the average percent accuracy for the prediction set was 98.0%. The high predictive power for the prediction set shows the ability to indicate the cultivar of an unknown sample based on its fatty acid chromatographic fingerprint.

Fatty Acids-Based Quality Index to Differentiate Worldwide Commercial Pistachio Cultivars

The fatty-acid profiles of five main commercial pistachio cultivars, including Ahmad-Aghaei, Akbari, Chrok, Kalle-Ghouchi and Ohadi, were determined by gas chromatography: palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3) arachidic (C20:0) and gondoic (C20:1) acid. Based on the oleic to linoleic acid (O/L) ratio, a quality index was determined for these five cultivars: Ohadi (2.40) &lt; Ahmad-Aghaei (2.60) &lt; Kale-Ghouchi (2.94) &lt; Chrok (3.05) &lt; Akbari (3.66). Principal component analysis (PCA) of the fatty-acid data yielded three significant PCs, which together account for 80.0% of the total variance in the data set. A linear discriminant analysis (LDA) model evaluated with cross validation correctly classified almost all samples: the average percent accuracy for the prediction set was 98.0%. The high predictive power for the prediction set shows the ability to indicate the cultivar of an unknown sample based on its fatty-acid chromatographic fingerprint.

Distinguishing Coconut Oil from Coconut Paring Oil using Principle Component Analysis of Fatty Acid Data

A study was carried out to distinguish coconut oil from coconut pairing oil by the application of principal component analysis (PCA) to fatty acid compositional and iodine value data. Five samples of ordinary coconut oil extracted from five different batches of copra and five samples of coconut pairing oil obtained from five batches of dried coconut pairings were employed. Fatty acid composition and iodine values of oil samples were determined individually and the data were analyzed statistically. PCA analysis showed that lauric and oleic acid contents and iodine value data are the most influencing parameters to discriminate coconut oil from coconut pairing oil. Hence, the application of PCA to fatty acid compositional and iodine value data was successful in distinguishing coconut oil from coconut pairing oil.