Proteomics and Metabolomics Profiling of Pork Exudate Reveals Meat Spoilage during Storage

A non-destructive predictive model for estimating the freshness/spoilage of packaged chicken meat using changes in drip metabolites

This study investigates the possibility of utilizing drip as a non-destructive method for assessing the freshness and spoilage of chicken meat. The quality parameters [pH, volatile base nitrogen (VBN), and total aerobic bacterial counts (TAB)] of chicken meat were evaluated over a 13-day storage period in vacuum packaging at 4 °C. Simultaneously, the metabolites in the chicken meat and its drip were measured by nuclear magnetic resonance. Correlation (Pearson's and Spearman's rank) and pathway analyses were conducted to select the metabolites for model training. Binary logistic regression (model 1 and model 2) and multiple linear regression models (model 3-1 and model 3-2) were trained using selected metabolites, and their performance was evaluated using receiver operating characteristic (ROC) curves. As a result, the chicken meat was spoiled after 7 days of storage, exceeding 20 mg/100 g VBN and 5.7 log CFU/g TAB. The correlation analysis identified one organic acid, eight free amino acids, and five nucleic acids as highly correlated with chicken meat and its drip during storage. Pathway analysis revealed tyrosine and purine metabolism as metabolic pathways highly correlated with spoilage. Based on these findings, specific metabolites were selected for model training: ATP, glutamine, hypoxanthine, IMP, tyrosine, and tyramine. To predict the freshness and spoilage of chicken meat, model 1, trained using tyramine, ATP, tyrosine, and IMP from chicken meat, achieved a 99.9 % accuracy and had an ROC value of 0.884 when validated using drip metabolites. This model 1 was improved by training with tyramine and IMP from both chicken meat and its drip (model 2), which increased the ROC value for drip metabolites from 0.884 to 0.997. Finally, selected two metabolites (tyramine and IMP) can predict TAB and VBN quantitatively through models 3-1 and 3-2, respectively. Therefore, the model developed using metabolic changes in drip demonstrated the capability to non-destructively predict the freshness and spoilage of chicken meat at 4 °C. To make generic predictions, it is necessary to expand the model's applicability to various conditions, such as different temperatures, and validate its performance across multiple chicken batches.

MS based foodomics: An edge tool integrated metabolomics and proteomics for food science

Foodomics has become a popular methodology in food science studies. Mass spectrometry (MS) based metabolomics and proteomics analysis played indispensable roles in foodomics research. So far, several methodologies have been developed to detect the metabolites and proteins in diets and consumers, including sample preparation, MS data acquisition, annotation and interpretation. Moreover, multiomics analysis integrated metabolomics and proteomics have received considerable attentions in the field of food safety and nutrition, because of more comprehensive and deeply. In this context, we intended to review the emerging strategies and their applications in MS-based foodomics, as well as future challenges and trends. The principle and application of multiomics were also discussed, such as the optimization of data acquisition, development of analysis algorithm and exploration of systems biology.

Selection of texture-associated biomarkers in chilled and iced grass carp (Ctenopharyngodon idella) fillets via DIA-based proteomics

Consumers care about the texture of fresh fish flesh, but a rapid quantitative analytical method for this has not been properly established. In this study, texture-associated biomarkers were selected by DIA-based proteomics for possible future application. Results indicated a significant decline in texture and moisture characteristics with extended storage under chilled and iced conditions, and flesh quality was categorized into three intervals. A total of 8 texture-associated biomarkers were identified in the chilled storage group, and 3 distinct ones in the iced storage group. Biomarkers were further refined based on their expression levels. Isobutyryl-CoA dehydrogenase, mitochondrial and [Phosphatase 2A protein]-leucine-carboxy methyltransferase were identified as effective texture-associated biomarkers for chilled fish, and Staphylococcal nuclease domain-containing protein 1 for iced fish. This study provided suitable proteins as indicators of fresh fish flesh texture, which could help establish a rapid and convenient texture testing method in future studies.

Integrated proteomics and metabolomics analysis revealed the mechanisms underlying the effect of irradiation on the fat quality of Chinese bacon

Irradiation increases the security and storage period of preserved Chinese bacon; nevertheless, the biological mechanisms underlying the changes in fat quality caused by irradiation are unknown. We investigated the influence of irradiation on Chinese bacon by proteomic and metabolomic. We identified 24 proteins that participated in metabolism and 40 common differential metabolites enriched in 16 signalling pathways. Correlation analysis revealed that irradiation altered 11 pathways shared between the proteome and metabolome, including two lipid metabolism pathways. Acetyl-CoA carboxylase, ACSL, octanoic acid, decanoic acid, palmitic acid, and oleic acid participated in fatty acid biosynthesis. Acyl-CoA thioesterase 1/2/4, enoyl-CoA reductase, acetyl-CoA acyltransferase 1, enoyl-CoA hydratase 2, palmitic acid, and oleic acid participated in unsaturated fatty acid biosynthesis. These findings lay the groundwork for multi-omics research on the effects of irradiation on Chinese bacon quality, assisting in assessing irradiated Chinese bacon quality, and developing effective strategies to standardise quality parameters.

Insight from untargeted metabolomics: Revealing the potential marker compounds changes in refrigerated pork based on random forests machine learning algorithm

Data on changes in non-volatile components and metabolic pathways during pork storage were inadequately investigated. Herein, an untargeted metabolomics coupled with random forests machine learning algorithm was proposed to identify the potential marker compounds and their effects on non-volatile production during pork storage by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS). A total of 873 differential metabolites were identified based on analysis of variance (ANOVA). Bioinformatics analysis shows that the key metabolic pathways for protein degradation and amino acid transport are amino acid metabolism and nucleotide metabolism. Finally, 40 potential marker compounds were screened using the random forest regression model, innovatively proposing the key role of pentose-related metabolism in pork spoilage. Multiple linear regression analysis revealed that d-xylose, xanthine, and pyruvaldehyde could be key marker compounds related to the freshness of refrigerated pork. Therefore, this study could provide new ideas for the identification of marker compounds in refrigerated pork.

The potential of proteomics in the study of processed meat products

Proteomics is a field that has grown rapidly since its emergence in the mid-1990s, reaching many disciplines such as food technology. The application of proteomic techniques in the study of complex biological samples such as foods, specifically meat products, allows scientists to decipher the underlying cellular mechanisms behind different quality traits. Lately, much emphasis has been placed on the discovery of biomarkers that facilitate the prediction of biochemical transformations of the product and provide key information on parameters associated with traceability and food safety. This review study focuses on the contribution of proteomics in the improvement of processed meat products. Different techniques and strategies have recently been successfully carried out in the study of the proteome of these products that can help the development of foods with a higher sensory quality, while ensuring consumer safety through early detection of microbiological contamination and fraud. SIGNIFICANCE: The food industry and the academic world work together with the aim of responding to market demands, always seeking excellence. In particular, the meat industry has to face a series of challenges such as, achieving sensory attributes in accordance with the standards required by the consumer and maintaining a high level of safety and transparency, avoiding deliver adulterated and/or contaminated products. This review work exposes how the aforementioned challenges are attempted to be solved through proteomic technology, discussing the latest and most outstanding research in this regard, which undoubtedly contribute to improving the quality, in all the extension of the word, of meat products, providing relevant knowledge in the field of proteomic research.