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      An overview of food lipids toward food lipidomics

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          Abstract

          Increasing evidence regarding lipids’ beneficial effects on human health has changed the common perception of consumers and dietary officials about the role(s) of food lipids in a healthy diet. However, lipids are a wide group of molecules with specific nutritional and bioactive properties. To understand their true nutritional and functional value, robust methods are needed for accurate identification and quantification. Specific analytical strategies are crucial to target specific classes, especially the ones present in trace amounts. Finding a unique and comprehensive methodology to cover the full lipidome of each foodstuff is still a challenge. This review presents an overview of the lipids nutritionally relevant in foods and new trends in food lipid analysis for each type/class of lipids. Food lipid classes are described following the LipidMaps classification, fatty acids, endocannabinoids, waxes, C 8 compounds, glycerophospholipids, glycerolipids (i.e., glycolipids, betaine lipids, and triglycerides), sphingolipids, sterols, sercosterols (vitamin D), isoprenoids (i.e., carotenoids and retinoids (vitamin A)), quinones (i.e., coenzyme Q, vitamin K, and vitamin E), terpenes, oxidized lipids, and oxylipin are highlighted. The uniqueness of each food group: oil‐, protein‐, and starch‐rich, as well as marine foods, fruits, and vegetables (water‐rich) regarding its lipid composition, is included. The effect of cooking, food processing, and storage, in addition to the importance of lipidomics in food quality and authenticity, are also discussed. A critical review of challenges and future trends of the analytical approaches and computational methods in global food lipidomics as the basis to increase consumer awareness of the significant role of lipids in food quality and food security worldwide is presented.

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          Adjusting batch effects in microarray expression data using empirical Bayes methods.

          Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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            Vitamin D Deficiency

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              Intestinal Short Chain Fatty Acids and their Link with Diet and Human Health

              The colon is inhabited by a dense population of microorganisms, the so-called “gut microbiota,” able to ferment carbohydrates and proteins that escape absorption in the small intestine during digestion. This microbiota produces a wide range of metabolites, including short chain fatty acids (SCFA). These compounds are absorbed in the large bowel and are defined as 1-6 carbon volatile fatty acids which can present straight or branched-chain conformation. Their production is influenced by the pattern of food intake and diet-mediated changes in the gut microbiota. SCFA have distinct physiological effects: they contribute to shaping the gut environment, influence the physiology of the colon, they can be used as energy sources by host cells and the intestinal microbiota and they also participate in different host-signaling mechanisms. We summarize the current knowledge about the production of SCFA, including bacterial cross-feedings interactions, and the biological properties of these metabolites with impact on the human health.
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                Author and article information

                Contributors
                Journal
                Comprehensive Reviews in Food Science and Food Safety
                Comp Rev Food Sci Food Safe
                Wiley
                1541-4337
                1541-4337
                November 2023
                August 24 2023
                November 2023
                : 22
                : 6
                : 4302-4354
                Affiliations
                [1 ] Department of Food Science, Gilat Research Center, Agricultural Research Organization Volcani Institute M.P. Negev Israel
                [2 ] Department of Chemistry and Pharmacy Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany
                [3 ] Applied Analytical Chemistry University of Duisburg‐Essen Essen Germany
                [4 ] Department of Postharvest Science Agricultural Research Organization, Volcani Center Rishon LeZion Israel
                [5 ] LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences Technical University of Munich (TUM) Freising Germany
                [6 ] Lipid Metabolism: Analysis and Integration; Center of Membrane Biochemistry and Lipid Research; Faculty of Medicine Carl Gustav Carus Technical University Dresden Dresden Germany
                [7 ] Mass Spectrometry Centre, LAQV‐REQUIMTE, Department of Chemistry Santiago University Campus, University of Aveiro Aveiro Portugal
                [8 ] Centre for Environmental and Marine Studies, CESAM, Department of Chemistry, Santiago University Campus University of Aveiro Aveiro Portugal
                Article
                10.1111/1541-4337.13225
                a57d73b0-fdfc-4fc7-b584-86e0b43fc86e
                © 2023

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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