Bini Ramachandran Bini Ramachandran is a postdoctoral research associate at the food allergen research and resource program (FARRP), University of Nebraska-Lincoln. Here, she developed targeted mass spectrometry methods for detection and quantification of milk proteins in foods which can trigger an allergic response in susceptible individuals. We used a high-resolution PRM approach for targeted detection of representative milk allergen peptides and employed a matrix-independent external calibration approach for quantification of the detected targets. Skyline is used as a tool from the initial stages of method development from target selection to target detection, calibration, and target quantification. Bini's association with mass spectrometry and proteomics started with her doctoral research where she studied early host responses to infection employing plasma proteome profiling of a mice model. She was fascinated by the technology behind mass spectrometry and its applications, especially in the field of proteomics. In her career thus far she has had opportunities to employ mass spectrometry and proteomics to address challenges in food allergy, cell biology, infectious disease biology, cancer biology, and bacterial strain improvement. She gained experience in adapting the mass spectrometry-based proteomics strategies in different fields of research provided an easy platform to develop a consensus quantitative method applicable to different types of test samples.

Matrix-independent Calibration: A Consensus Strategy to Quantify an Analyte from Different Types of Matrices

What if you have to develop a method to quantify an analyte from multiple different matrices? These matrices are entirely different in the composition, complexity, and physicochemical properties. Some of these matrices are rich in protein, some are rich in fat, some are rich in carbohydrates, some are extremely acidic in nature, while some others are basic in nature. As the matrices can have a significant impact on the response of the analyte, the analytical community always recommends preparing the calibration curve in a blank or homologous matrix. But what do you do if the number of matrices you must test is innumerous?? Welcome to the world of targeted quantitative detection of allergens in foods. In the US, regulatory bodies mandate declaration of the presence of 8 allergens (milk, egg, fish, crustacean shellfish, peanut, tree nut, wheat, and soy) in foods, and most of these allergens can be present as an ingredient in many foods.In this study, we are demonstrating a matrix-independent strategy for targeted detection of milk allergens from multiple food matrices. An inert carrier protein was incorporated as background matrix for both the calibrants and test samples from the sample preparation stages. Quantitative accuracy and precision in milk allergen detection using matrix-independent strategy was evaluated in test matrices and compared with conventional matrix-matched strategies. The novel method appeared to be sensitive, accurate, and precise in detection of milk allergens in five different test matrices evaluated, containing known amounts allergen. The method has shown robustness and high precision in quantifying milk allergen from baked cookie and dough samples containing as low as 1 ppm nonfat dry milk, from chocolates containing as low as 1 ppm casein, and from beverages containing as low as 0.5 ppm total milk protein. This matrix-independent strategy could improve the adoption and applicability of targeted mass spectrometry approaches for samples which are otherwise challenging for immunochemistry approaches in routine testing labs.