Nebraska U FARRP - Soy Quantification Method- Matrix Extension

Quantification of Soy-Derived Ingredients in Model Bread and Frankfurter Matrices with an Optimized LC-MS/MS External Standard Calibration Workflow
Data License: CC BY 4.0 | ProteomeXchange: PXD029137 | doi: https://doi.org/10.6069/rzyh-9n48
  • Organism: Glycine max
  • Instrument: Q Exactive Plus
  • SpikeIn: Yes
  • Keywords: Food Allergen, Soybean Allergy, Parallel Reaction Monitoring, Incurred Food Matrices, Mass Spectrometry
  • Lab head: Melanie Downs Submitter: Melanie Downs
Abstract
The detection and quantification of soy protein is important for food allergen management and identifying the presence of undeclared soy proteins. Heat processing and matrix interactions can affect the accuracy of allergen detection methods. The sensitivity of ELISA methods can be compromised if protein epitopes are modified during processing. Therefore, an MS-based method was evaluated for the recovery of total soy protein in incurred matrices. MS-based quantification of total soy protein was assessed using a combination of external and internal standards. The reproducibility of the standard curves was investigated by comparing within-day and among among-day variation. Incurred samples were prepared using bread and frankfurters as model food matrices. Several soy-derived ingredients were used to prepare the matrices with varying levels of soy protein (1, 10, 50, or 100 ppm total soy protein). A pooled standard curve was used to estimate the total soy protein concentration of the incurred food matrices and the percent total protein recovery. The variation of replicate standard curves between days and among all days was not significant. The differences in slopes obtained from replicate standards run on different days were minimal. The most influential factor on the quantitative protein recovery in incurred samples was the effect of the physical matrix structure on protein extraction. The lowest percent protein recoveries, less than 50%, were calculated for uncooked matrices. The cooked matrices had percent recoveries between 50-150% for all total soy protein levels. Other factors, such as type of ingredient, were determined to be not as impactful on recovery. The MS method described in this study was able to provide sensitive detection and accurate quantification of total soy protein from various soy-derived ingredients present in processed food matrices.
Experiment Description
Model food matrices incurred with different soy-derived ingredients (as described below) were analyzed with a targeted, parallel reaction monitoring (PRM) method for the detection and quantification of soy protein in foods. Quantification was conducted utilizing an external calibration curve of nonroasted soy flour (NRSF) with internal stable heavy isotope-labeled peptide standards, in order to obtain final quantification results of mg total soy protein per kg food product (i.e., ppm total soy protein). All test samples were also spiked with heavy peptides prior to analysis for interpolation of L:H peak area ratios from the calibration curve. Calibration curve repeatability was evaluated with analysis of three independent calibration curves conducted on each of three days. Quantitative recovery from model foods was evaluated with analysis of raw and cooked matrices containing known amounts of soy-derived ingredients.
Sample Description
Soy-derived ingredients were incurred into two model food matrices (bread and frankfurters) at varying concentrations of total soy protein. The incurred matrices were subsequently thermally processed. - Bread Matrix Soy Ingredients: nonroasted soy flour (NRSF), soy protein concentrate (SPC), and toasted soy flour (TSF); each incurred at concentrations of 0, 1, 10, and 100 ppm total soy protein; unbaked dough, inner crumb, and crust portions were analyzed - Frankfurter Matrix Soy Ingredients: nonroasted soy flour (NRSF), soy protein concentrate (SPC), soy protein isolate (SPI), and textured vegetable protein (TVP); incurred at concentrations of 0, 10, or 50 ppm total soy protein
Created on 10/14/21, 12:10 PM
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AllMatrices_QuantPep_2021-10-08_11-35-52.sky.zip2021-10-14 12:09:593510301785
Calibration Curves_FinalQuantPep_2021-10-08_11-39-17.sky.zip2021-10-14 12:09:59336181443