A novel strategy for discriminating marine oils by using the positional distribution (sn-1, sn-2, sn-3) of omega-3 polyunsaturated fatty acids in triacylglycerols

Detection and Quantification of Adulteration in Krill Oil with Raman and Infrared Spectroscopic Methods

Raman and infrared spectroscopy, used as individual and low-level fused datasets, were evaluated to identify and quantify the presence of adulterants (palm oil, PO; ω-3 concentrates in ethyl ester, O3C and fish oil, FO) in krill oil. These datasets were qualitatively analysed with principal component analysis (PCA) and classified as adulterated or unadulterated using support vector machines (SVM). Using partial least squares regression (PLSR), it was possible to identify and quantify the adulterant present in the KO mixture. Raman spectroscopy performed better (r² = 0.98; RMSEP = 2.3%) than IR spectroscopy (r² = 0.91; RMSEP = 4.2%) for quantification of O3C in KO. A data fusion approach further improved the analysis with model performance for quantification of PO (r² = 0.98; RMSEP = 2.7%) and FO (r² = 0.76; RMSEP = 9.1%). This study demonstrates the potential use of Raman and IR spectroscopy to quantify adulterants present in KO.

Authentication of fish oil (omega-3) supplements using class-oriented chemometrics and comprehensive two-dimensional gas chromatography coupled to mass spectrometry

Food supplement authentication is an important concern worldwide due to the ascending consumption related to health benefits and its lack of effective regulation in underdeveloped countries, making it a target of fraudulent activities. In this context, this study evaluated fish oil supplements by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) to obtain fingerprints, which were used to build predictive models for automated authentication of the most popular products sold in Brazil. The authentication process relied on a one-class classifier model using data-driven soft independent modeling of class analogy (DD-SIMCA). The output of the model was a binary classifier: certified IFOS fish oils and non-certified ones - regardless of the source of adulteration. The compositional analysis showed a significant variation in the samples, which validated the need for reliable statistical models. The DD-SIMCA algorithm is still incipient in GC×GC studies, but it proved to be an excellent tool for authenticity purposes, achieving a chemometric model with a sensitivity of 100%, specificity of 98.6%, and accuracy of 99.0% for fish oil authentication. Finally, orthogonalized partial least square discriminant analysis (OPLS-DA) was used to identify the features that distinguished the groups, which ascertained the results of the DD-SIMCA model that IFOS-certified oils are positively correlated to omega-3 fatty acids, including eicosapentaenoic acid (EPA, C20:5 n-3) and docosahexaenoic acid (DHA, C22:6 n-3).

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.

Omega-3 mechanism of action in inflammation and endoplasmic reticulum stress in mononuclear cells from overweight non-alcoholic fatty liver disease participants: study protocol for the "Brazilian Omega Study" (BROS)-a randomized controlled trial

Abstract

The low-grade inflammation is pivotal in obesity and its comorbidities; however, the inflammatory proteins are out of target for traditional drug therapy. Omega-3 (ω3) fatty acids can modulate the downstream signaling of Toll-like receptor (TLR) and tumor necrosis factor-α receptor (TNFα) through GPR120, a G-protein-coupled receptor, a mechanism not yet elucidated in humans. This work aims to investigate if the ω3 supplementation, at a feasible level below the previously recommended level in the literature, is enough to disrupt the inflammation and endoplasmic reticulum stress (ER-stress), and also if in acute treatment (3 h) ω3 can activate the GPR120 in peripheral blood mononuclear cells (PBMC) and leukocytes from overweight non-alcoholic fatty liver disease (NAFLD) participants. The R270H variant of the Ffar4 (GPR120 gene) will also be explored about molecular responses and blood lipid profiles. A triple-blind, prospective clinical trial will be conducted in overweight men and women, aged 19–75 years, randomized into placebo or supplemented (2.2 g of ω3 [EPA+DHA]) groups for 28 days. For sample calculation, it was considered the variation of TNFα protein and a 40% dropout rate, obtaining 22 individuals in each group. Volunteers will be recruited among patients with NAFLD diagnosis. Anthropometric parameters, food intake, physical activity, total serum lipids, complete fatty acid blood profile, and glycemia will be evaluated pre- and post-supplementation. In the PBMC and neutrophils, the protein content and gene expression of markers related to inflammation (TNFα, MCP1, IL1β, IL6, IL10, JNK, and TAK1), ER-stress (ATF1, ATF6, IRE1, XBP1, CHOP, eIF2α, eIF4, HSP), and ω3 pathway (GPR120, β-arrestin2, Tab1/2, and TAK1) will be evaluated using Western blot and RT-qPCR. Participants will be genotyped for the R270H (rs116454156) variant using the TaqMan assay. It is hypothesized that attenuation of inflammation and ER-stress signaling pathways in overweight and NAFLD participants will be achieved through ω3 supplementation through binding to the GPR120 receptor.

Trial registration

ClinicalTrials.gov #RBR-7x8tbx. Registered on May 10, 2018, with the Brazilian Registry of Clinical Trials.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05702-x.

Rapid and direct determination of fatty acids and glycerides profiles in Schisandra chinensis oil by using UPLC-Q/TOF-MSE

Fatty acids and glycerides are globally accepted quality and nutrition indicators of oils. Schisandra chinensis (S. chinensis) is a good functional oil source, with an oil content of 10-50% (dry weight). In this study, the UPLC-Q/TOF-MS^E technique was developed to profile FFA and glycerides in the S. chinensis oils directly. The results showed that all of the 36 FFA calibration equations of the mixture standard had good linear relationships (R² > 0.99). The limit of detection for the tested compounds ranged from 0.0001 to 0.0200 μg/mL, while the limit of quantification ranged from 0.0005 to 0.1300 μg/mL. In total, seventeen FFAs, six diglycerides and 20 triglycerides were identified. Linoleic, oleic, stearic and palmitic acids were the most abundant FFAs in the S. chinensis oils. It was also found that S. chinensis oil is rich in the L-L, L-L-L, O-L-L and O-L-O glycerides. These results will be helpful for the use of this technique in physicochemical evaluation and for further application development.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.