Application of LC‐HRMS identified marker peptides in an LC‐MS/MS method for detection and quantification of heat‐resistant proteolytic activity in raw milk

Verhegghe, Marijke; Block, Jan De; Heyndrickx, Marc; Coillie, Els Van; Poucke, Christof Van; Duquenne, Barbara

doi:10.1111/1471-0307.12754

Cited by 2 publications

(1 citation statement)

References 20 publications

(44 reference statements)

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“…Furthermore, efforts have also been made to investigate the proteome during storage to identify peptide markers of different foods, e.g., milk products and oysters. These identified peptide markers are possibly able to reflect the shelf-life [109][110][111].…”

Section: Prediction Of Shelf-lifementioning

confidence: 99%

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Class

Kuhnen

Rohn

et al. 2021

Foods

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Deep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review’s focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.

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Section: Prediction Of Shelf-lifementioning

confidence: 99%

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Class

Kuhnen

Rohn

et al. 2021

Foods

View full text Add to dashboard Cite

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Biochemical, microbiological, and structural evaluations to early detect age gelation of milk caused by proteolytic activity of Pseudomonas fluorescens

D’Incecco

Rosi

Fortina

et al. 2022

Eur Food Res Technol

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Heat–stable peptidase AprX, released by Pseudomonas species in raw milk during cold storage, can cause gelation of UHT milk since it is able to split caseinomacropeptides (CMPtot) from κ-casein, so inducing aggregation of casein micelles. Identifying raw milk susceptibility to gelation would allow UHT milk manufacturers to select appropriate processing conditions or give the milk a different destination. Two approaches, i.e., detection of free CMPtot and evidence of casein aggregates, were evaluated as possible indicators for early detecting milk destabilization. With this aim, microfiltered milk was inoculated with a P. fluorescence strain and incubated at either 4 or 25 °C. The presence of CMPtot was detected using capillary electrophoresis after 96 and 24 h at the two temperatures, respectively, when milk also became heat unstable and small flocks of protein appeared. Confocal laser scanning microscopy evidenced initial aggregates of casein micelles after 48 and 24 h at 4 and 25 °C, respectively. Keeping the milk at 25 °C/24 h could be a useful condition to accelerate milk destabilization. Despite the similar timing of instability detection, presence of CMPtot was the only trait specific for AprX activity.

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Application of LC‐HRMS identified marker peptides in an LC‐MS/MS method for detection and quantification of heat‐resistant proteolytic activity in raw milk

Cited by 2 publications

References 20 publications

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Biochemical, microbiological, and structural evaluations to early detect age gelation of milk caused by proteolytic activity of Pseudomonas fluorescens

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