Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611
Performing Department
Food Science and Human Nutrition
Non Technical Summary
The Food & Health Survey of the International Food Information Council (IFIC) has revealed the importance of food selection criteria for the American consumers in 2018. Ninety percent of consumers believed that flavor is the most important factor impacting their purchases. Flavor is defined as the combination of aroma and taste sensations. Aroma is the perception of selected volatile compounds or odorants, by the olfactory system, whereas taste is perceived by taste receptors in the oral cavity. Taste compounds are normally non-volatile, water-soluble and are perceived as sweet, sour, salty, bitter or umami. When a food is consumed, aroma and taste are perceived simultaneously and not as discrete events. In order to keep pace with the market and consumer expectation, flavor chemists have been continuously working on a means to have a better understanding of the mechanisms involved in flavor generation, degradation and perception via pre- and post- harvest interventions. However, there are still so many questions waiting to be answered. For example, can we have some new agricultural commodities with better flavor profile for food production? How does food processing or storage affect flavor quality? Would any specific flavor compounds relate to consumer preference?Traditional flavor research usually only focuses on the identification of the single compound, and lack the capability of studying a whole flavor profile and relationships within and across stimuli (taste and aroma). In addition, flavor is a complex phenomenon that can be influenced by intrinsic and extrinsic factors such as food shape, physiological and emotional factors. Instead of focusing only on identifying compounds, an alternative approach will be developed in this proposal and is referred as "flavoromics". Flavoromics was first referred to as "flavor metabolomics" which borrows the idea of metabolomics to study flavor. Then the term and concept was changed to flavoromics, but the techniques are still adapted from metabolomics such as untargeted/targeted analysis, chemometrics, ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS), and time of flight (TOF), for example. Therefore, Flavoromics is an analytical methodology focused on studying flavor compounds (aroma and taste) in agricultural commodities and food products and aimed at linking chemical composition with sensory quality using chemometrics.A major challenge in meeting consumer expectations through new variety development (pre-harvest) is to identify specific genes controlling the traits relating to consumer preferences for flavor. To bridge these two isolated concepts, a flavoromics platform will be developed. This platform will be used to identify and quantify some specific flavor compounds, which are key factors in determining consumer preference. This platform will focus on setting up a correlation between chemical compounds and personal preferences, which will also help quantify consumer insight and perspective. Once compounds relating to consumer preferences are clearly identified and validated, the genes regulating biosynthesis of these compounds can be identified and selection tools can be developed to more effectively manipulate consumer-relevant traits by breeders in future projects.The appealing flavor compounds in a food product have multiple origins and fates. Some are endogenous to the food commodity and may be retained in the finished product. Others significantly increase due to food processing such as fermentation, roasting and aging, and are also retained in finished product.Besides the development of a new food commodity, modification of food processing and adding flavor modulators are alternative ways to manipulate flavor during post-harvest. This will be achieved using flavoromics.Therefore, the overall goal of this Hatch project is to set up a flavoromics platform to provide fundamental information on flavor improvements through pre- and post-harvest processes; We will better understand consumer preferences to develop new cultivars with desirable flavor attributes and develop post-harvest treatments for flavor modification to maximize product quality and increase consumer acceptance.
Animal Health Component
40%
Research Effort Categories
Basic
60%
Applied
40%
Developmental
(N/A)
Goals / Objectives
The overall goal of this Hatch project is to set up a flavoromics platform to provide fundamental information on flavor improvements through pre- and post-harvest processes; We will better understand consumer preferences to develop new cultivars with desirable flavor attributes and develop post-harvest treatments for flavor modification to maximize product quality and increase consumer acceptance.Objective 1:Identify fruit cultivars (citrus, mango pomegranate etc.) with appealing flavor traits and consumer preferenceObjective 2: Investigate the correlations between analytical data and sensory evaluation and identify key drivers for the citrus breeding program
Project Methods
Flavoromics (flavor-targeted metabolomics):The Wang lab has extensive experience in the flavor chemistry/flavoromics approaches proposed hereandhas created GC/MS/MS and LC-MS/MS libraries for primary and secondary citrus metabolites, most of which are known to function as odor or taste stimuli and thus contribute to flavor perception. Recently, a comprehensive two-dimensional gas chromatography−mass spectrometry method has been used in combination with stable isotope dilution assays to quantify the entire set of aroma compounds previously established by GC−olfactometry (coined "flavoromics") of raw and roasted hazelnuts (Corylus avellana L. 'Tonda Gentile'). To date, no study aimed at the entire "flavoromics" of fruit products has been conducted.OdorAnalysis:For compound identification, mass spectra will be acquired using aClarus 680 gas chromatograph (PerkinElmer) equipped with a Clarus SQ 8T mass spectrometry and a SNFR olfactory port. A TR-FFAP column (30 m × 0.25 mm, 0.25 µm film thickness) will be used for separation. Oven temperature will be programmed based on compound characteristics. Mass spectra in the electron impact mode (MS-EI) will be applied at 70 eV ionization energy. The MS will be set to scan from m/z 50 to 300. ASwafer™S2 mode will be used to split the sample into the MS and the SNFR olfactory port (240°C).Identification of aroma compounds will be achievedby using retention indices on the FFAP capillary column, mass spectra in EI modes, odor quality perceived at the SNFR port, standards and the NIST library and online database (Flavornet, The Pherobase, PubChem and LRI& Odor Database).Tastant Analysis:(1)Sugars will be derivatized to their corresponding trimethylsilyl derivatives by addition of 80mL methyl-n-(trimethylsiyl)trifluoroacetamide. Derivatized sugars will be identified using an Agilent 7890 gas chromatograph coupled with a mass spectrometer with electron impact mode. The separationis carried out using an Rxi-5 MS capillary column (30 m´0.25 mm; 0.25mm film thickness).(2)Organic and amino acids will be analyzed with aThermo Ultimate 3000 HPLC equipped with a Thermo Quantiva triple quadrupole electrospray ionization tandem mass spectrometer. Chromatographic separations for organic acids and amino acids will be performed using a Gemini C18 column (3 µm, 3 x 150 mm) and a TSKgel Amide-80 column (3 µm, 2 x 150 mm), respectively. Selected reaction monitoring (SRM) will be used for quantification.(3)Flavonoids will be analyzed with the same LC/MS/MS system as above. Chromatographic separations will be performed using a Gemini C18 column (3 µm, 3 x 150 mm).Metabolomic analyses (untargeted):Metabolomics is the technology designed to provide an untargeted comprehensive qualitative and quantitative overview of the pool of small molecules present in a sample. Metabolic profiling is useful in the identification and quantification of metabolites or components differentially present in a sample set. This traditional approach will be employed to examine flavor precursor formation. Samples will proceed through a multi-step process including analysis using three platforms: LC-MS/MS(+ESI), LC-MS/MS(-ESI) and GC-MS. Detected biochemicals will be compared to a reference standard in a database, using retention index and mass spectrum. The result is a broad range of biochemicals identified including amino acids, carbohydrates, lipids, nucleic acids and cofactors. Statistical analyses will be used to determine which biochemicals show significant changes, and these are then organized by biochemical pathways to provide a comprehensive view of metabolic outcomes; heat maps allow a quick identification of biochemicals that increase or decrease relative to the control. This pathway-centric statistical analysis of affected biochemicals allows for the rapid identification of areas for further investigation.Descriptive Sensory tests.For the descriptive sensory tests, panelists (20-50) will be presented with the treated and untreated samples. The panelists will be asked to taste the samples and describe the perceived sensory attributes of the samples. These sensory attributes will then be used in further descriptive analysis, specifically rating the intensity of the attributes on a standard 9-point scale (1 = none, 9 = strong). Results will show the sensory attributes that are found to be differentiated the different treatments. Methodology for descriptive tests will be taken from the ASTM manual for descriptive analysis for sensory evaluation.Consumer Acceptability.For consumer acceptability tests, panelists (20-50) will be presented with fruit samples. The panelists will be asked to taste the products and rate their acceptability of the samples on a standard 9-point scale (1 = dislike extremely, 9 = like extremely). Results will be analyzed to see if there is a difference in acceptability between the treated and untreated samples. Methodology for consumer preference is described.Statistical Analyses: Both parametric (Student's t-test, ANOVA) and nonparametric (Wilcoxon test, Kruskal-Wallis test) tests will be used to calculate cultivars means comparisons using SPSS and R software. P-value thresholds will be determined by calculating the value controlling family-wise error rate or false discovery rate. Consumer acceptability will be regressed onto flavor/metabolite measurements using a PLS regression model.