Targeted mass spectrometry quantification of total soy protein residues from commercially processed ingredients for food allergen management

Development of a Mass Spectrometry-Based Method for Quantification of Total Cashew Protein in Roasting Oil

BACKGROUND

Food allergen cross-contact during food preparation and production is one of the causes of unintentional allergen presence in packaged foods. However, little is known about allergen cross-contact in shared frying or roasting oil, which prevents the establishment of effective allergen controls and may put allergic individuals at risk. To better understand the quantity of allergen transferred to frying oil and subsequent products, an analytical method is needed for quantifying protein in oil that has been exposed to frying/roasting conditions.

OBJECTIVE

The goal of this study was to develop a parallel reaction monitoring LC-MS/MS method to quantify the amount of cashew protein in shared roasting oil.

METHODS

The sample preparation method was evaluated to improve protein extractability and peptide performance. Four quantitative peptides representing cashew 2S and 11S proteins were selected as targets based on their sensitivity, heat stability, and specificity. A calibration strategy was developed to quantify the amount of total cashew protein in oil. Method performance was evaluated using a heated cashew-in-oil model system.

RESULTS

The method showed high recovery in oil samples spiked with 100 or 10 parts per million (ppm) total cashew protein heated at 138 or 166°C for 2-30 min. Samples (100 ppm total cashew protein) heated for 30 min had more than 90% recovery when treated at 138°C and more than 50% when heated at 166°C.

CONCLUSION

The method is fit-for-purpose for the analysis of cashew allergen cross-contact in oil.

HIGHLIGHTS

A novel MS-based method was developed that can accurately quantify the amount of cashew protein present in heated oil.

An integrated calibration strategy for the development and validation of an LC-MS/MS method for accurate quantification of egg allergens (Gal d 1-6) in foods

Accurate determination of egg allergens in food is vital for allergen management and labeling. However, quantifying egg allergens with mass spectrometry poses challenges and lacks validation methods. Here, we developed and validated an LC-MS/MS method for quantifying egg allergens (Gal d 1-6) in foods. Sample extraction, enzymatic digestion, purification, proteins/peptides selection, and calibration curves were optimized. VMVLC[+57]NR (Gal d 1) and GTDVQAWIR (Gal d 5) exhibited outstanding sensitivity and stability, serving as quantitation markers for egg white and yolk. Using a matrix-matched calibration curve with allergen ingredients as calibrants and labeled peptides as standards, we achieved highly accurate quantitation. Validation involved spiking egg protein into egg-free foods, showing excellent sensitivity (LOQ: 1-5 mg/kg), accuracy (62.4 %-88.5 %), and reproducibility (intra-/inter-day precision: 3.5 %-14.2 %/8.2 %-14.6 %). Additionally, we successfully applied this method to commercial food analysis. These findings demonstrate optimal allergen selection, peptides, and calibration strategy are crucial parameters for food allergen quantification via MS-based methods.

Advanced Proteomic and Bioinformatic Tools for Predictive Analysis of Allergens in Novel Foods

In recent years, novel food is becoming an emerging trend increasingly more demanding in developed countries. Food proteins from vegetables (pulses, legumes, cereals), fungi, bacteria and insects are being researched to introduce them in meat alternatives, beverages, baked products and others. One of the most complex challenges for introducing novel foods on the market is to ensure food safety. New alimentary scenarios drive the detection of novel allergens that need to be identified and quantified with the aim of appropriate labelling. Allergenic reactions are mostly caused by proteins of great abundance in foods, most frequently of small molecular mass, glycosylated, water-soluble and with high stability to proteolysis. The most relevant plant and animal food allergens, such as lipid transfer proteins, profilins, seed storage proteins, lactoglobulins, caseins, tropomyosins and parvalbumins from fruits, vegetables, nuts, milk, eggs, shellfish and fish, have been investigated. New methods for massive screening in search of potential allergens must be developed, particularly concerning protein databases and other online tools. Moreover, several bioinformatic tools based on sequence alignment, motif identification or 3-D structure predictions should be implemented as well. Finally, targeted proteomics will become a powerful technology for the quantification of these hazardous proteins. The ultimate objective is to build an effective and resilient surveillance network with this cutting-edge technology.

Quantification of Soy-Derived Ingredients in Model Bread and Frankfurter Matrices with an Optimized Liquid Chromatography-Tandem Mass Spectrometry External Standard Calibration Workflow

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 enzyme-linked immunosorbent assay methods can be compromised if protein epitopes are modified during processing. Therefore, a mass spectrometry (MS)-based method was evaluated for the recovery of total soy protein in incurred matrices. MS-based quantification of total soy protein was assessed by using a combination of external and internal standards. The reproducibility of the standard curves was investigated by comparing within-day and 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 of 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 percentage recoveries between 50 and 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.

HIGHLIGHTS