Application of Untargeted LC/MS Techniques (Flavoromics) To Identify Changes Related to Freshness of Food

Abstract

Freshness in food is a highly desirable attribute that has proven difficult to chemically characterize using traditional targeted methods. This work focuses on applying untargeted chemometric techniques to investigate differences in the chemical composition of orange extracts as they age as a strategy to identify compounds that contribute to the "fresh" flavor character. Ethanol extracts of oranges products were aged and sampled every 48 hours. RP-UPLC-MS (ESI-NEG) was used for data collection and two modeling techniques including the projection to latent variables (PLS) and Random Forest analysis were utilized for data analysis. Random forest and PLS provide different modeling criteria and identified common as well as unique features in the data set. Future work will focus on the compound identification and further sensory characterization of the selected markers. In summary, a method was developed to chemically profile the changes in a food product during aging to provide a unique basis to investigate changes in flavor profiles, identifying chemical attributes that may relate to freshness perception in food.

Characterization of the Key Aroma Compounds in Three Truffle Varieties from China by Flavoromics Approach

“…Flavoromics was applied to find markers of cooked and fermented flavor in strawberry juices submitted to different treatments (heat, storage, and freeze-drying) [18]. Researchers also developed flavoromics to chemically profile the changes in a food product during aging to provide a unique basis to investigate changes in flavor profiles, identifying chemical attributes that may relate to freshness perception in food [19].…”

Identification and Validation of Sensory-Active Compounds from Data-Driven Research: A Flavoromics Approach

“…Oranges were extracted as previously described. , In brief, oranges were thinly sliced (<5 mm thick) and extracted with 200 proof ethanol for 24 h, limiting headspace, and stored under dark ambient conditions. The resulting extract was aged for 0, 48, 96, and 144 h.…”

“…Identified features were then fractionated from the sample with the highest prevalence using a Shimadzu VP series binary HPLC system (Columbia, MD, USA) coupled to a Waters Quattro Micro (Milford, MA, USA) triple-quadrupole mass spectrometer using electrospray ionization (ESI) with the flow split to a Waters fraction collector (WFC III). Twenty compounds, previously identified as significant features to model aging of orange extracts, were isolated using the nominal mass from the compounds listed in Table . , Acquisition was done in single ion monitoring (SIM) mode under negative ionization mode using the following source conditions; desolvation temperature, 350 °C; source temperature, 110 °C; capillary voltage, 2 kV; cone voltage, 35 V. First-dimension separation was conducted on a Waters Xbridge Prep Shield RP18 column (10 × 250 mm, 5 μm particle size) held at 40 °C. Chromatographic separation was performed using a solvent gradient starting at 95% solvent A (water acidified with 0.1% formic acid) and 5% solvent B (methanol acidified with 0.1% formic acid) and held for 3 min; solvent B was ramped to 30% at 10 min and to 100% at 38 min, at which it was held until 41 min.…”

“…After isolation, methanol was removed from fractions using a rotary evaporator and further lyophilized twice to ensure removal of residual organic solvent. To verify the correct feature was isolated, each isolate was re-injected onto a Waters Acquity I-Class ultraperformance liquid chromatograph (UPLC) coupled to a Waters Xevo G2 time of flight mass spectrometer (TOF) system under identical LC-MS conditions as previously utilized for feature identification to confirm retention time and MS fragmentation of the targeted compounds . Feature purity ranged from 20 to 40% on the basis of LC-MS-TOF total ion chromatogram (TIC) peak area analysis.…”

“…In a previous phase of the current study, two orange extracts and one hybrid orange citrus fruit ethanol extract aged over 6 days were chemically profiled by LC-MS, and high-quality MVA models were developed to predict sample age, which also corresponded to changes in flavor profiles observed by a trained sensory panel. , The experimental design emphasized common chemistry changes in diverse biological systems to find commonalities, which has shown success in identifying predictors of phenotypes in complex biological systems before . To these ends modeling was directed to understand aging across the citrus platform and mined for features with a statistical relationship to aging.…”