Quantification of phospholipids in infant formula and growing up milk by high-performance liquid chromatography with evaporative light scattering detector.

Phospholipids (PLs) are well known for their excellent emulsifier properties and more recently for their biological functions, such as cell signing, brain development, immune function, heart health, and cancer prevention, besides their physiological role in membrane composition. In dairy products, PLs represent 0.2-1% of milk fat. The milk PLs comprise phosphatidylcholine (PC), phosphatidylethanolamine (PE), and sphingomyelin (SPH) as the major compounds; phosphatidylinositol and phosphatidylserine are minor PLs. A new generation of dairy products claiming PL family content, such as SPH, is being produced; therefore, a validated method for quantifying PL families in dairy products is needed. In this study, an HPLC-evaporative light scattering detector method to quantify the most abundant milk PL families, i.e., PC, PE, and SPH, in infant formula and growing up milk was developed and validated.

Improvement of AOAC Official Method 984.27 for the determination of nine nutritional elements in food products by Inductively coupled plasma-atomic emission spectroscopy after microwave digestion: single-laboratory validation and ring trial.

A single-laboratory validation (SLV) and a ring trial (RT) were undertaken to determine nine nutritional elements in food products by inductively coupled plasma-atomic emission spectroscopy in order to improve and update AOAC Official Method 984.27. The improvements involved optimized microwave digestion, selected analytical lines, internal standardization, and ion buffering. Simultaneous determination of nine elements (calcium, copper, iron, potassium, magnesium, manganese, sodium, phosphorus, and zinc) was made in food products. Sample digestion was performed through wet digestion of food samples by microwave technology with either closed or open vessel systems. Validation was performed to characterize the method for selectivity, sensitivity, linearity, accuracy, precision, recovery, ruggedness, and uncertainty. The robustness and efficiency of this method was proved through a successful internal RT using experienced food industry laboratories. Performance characteristics are reported for 13 certified and in-house reference materials, populating the AOAC triangle food sectors, which fulfilled AOAC criteria and recommendations for accuracy (trueness, recovery, and z-scores) and precision (repeatability and reproducibility RSD and HorRat values) regarding SLV and RT. This multielemental method is cost-efficient, time-saving, accurate, and fit-for-purpose according to ISO 17025 Norm and AOAC acceptability criteria, and is proposed as an improved version of AOAC Official Method 984.27 for fortified food products, including infant formula.

Model-based high-throughput design of ion exchange protein chromatography.

This work describes the development of a model-based high-throughput design (MHD) tool for the operating space determination of a chromatographic cation-exchange protein purification process. Based on a previously developed thermodynamic mechanistic model, the MHD tool generates a large amount of system knowledge and thereby permits minimizing the required experimental workload. In particular, each new experiment is designed to generate information needed to help refine and improve the model. Unnecessary experiments that do not increase system knowledge are avoided. Instead of aspiring to a perfectly parameterized model, the goal of this design tool is to use early model parameter estimates to find interesting experimental spaces, and to refine the model parameter estimates with each new experiment until a satisfactory set of process parameters is found. The MHD tool is split into four sections: (1) prediction, high throughput experimentation using experiments in (2) diluted conditions and (3) robotic automated liquid handling workstations (robotic workstation), and (4) operating space determination and validation. (1) Protein and resin information, in conjunction with the thermodynamic model, is used to predict protein resin capacity. (2) The predicted model parameters are refined based on gradient experiments in diluted conditions. (3) Experiments on the robotic workstation are used to further refine the model parameters. (4) The refined model is used to determine operating parameter space that allows for satisfactory purification of the protein of interest on the HPLC scale. Each section of the MHD tool is used to define the adequate experimental procedures for the next section, thus avoiding any unnecessary experimental work. We used the MHD tool to design a polishing step for two proteins, a monoclonal antibody and a fusion protein, on two chromatographic resins, in order to demonstrate it has the ability to strongly accelerate the early phases of process development.