Metabonomic Profiling of Chicken Eggs during Storage Using High-Performance Liquid Chromatography–Quadrupole Time-of-Flight Mass Spectrometry

Discrimination of overheated pasteurized milk using mass spectrometry-based proteomics

Milk is one of the most widely consumed foods globally. To protect consumer interests, it is essential to establish an analytical method to detect the degree of heating in milk. A novel approach using nano liquid chromatography-orbitrap fusion mass spectrometer was developed for screening and identifing thermally sensitive peptides markers in the milk heating process (below 100 °C). This method integrates untargeted proteomics and chemometric tools to analyze protein quantitation data from differently heat-treated milk. Thirteen potential markers were screened out and identified, and further confirmed using by standard substances. Then, the accurate concentrations of 13 potential markers determined by isotope-dilution ultra-performance liquid chromatography-tandem triple quadrupole mass spectrometry were further mining the highly specific and thermally sensitive peptides markers. And Four peptides-INLFDTPLETQYVR, FELLGCELNGCTEPLGLK, QFQFIQVAGR, and GEADALNLDGGYIYTAGK-were selected as marker peptides to differentiate normal pasteurized milk from overheated pasteurized milk. The concentrations of INLFDTPLETQYVR ranges from 150 ± 11 µg/L to 350 ± 23 µg/L, while the concentrations of FELLGCELNGCTEPLGLK ranges from 40 ± 5 µg/L to 92 ± 3 µg/L, can distinguish normal pasteurized milk from overheated pasteurized milk. QFQFIQVAGR indicates overheated pasteurized milk at 230 ± 21 µg/L, and GEADALNLDGGYIYTAGK signifies 750 ± 43 µg/L. This study provides new insights for distinguishing overheated pasteurized milk.

Identification of metabolite biomarkers in Salmonella enteritidis-contaminated chickens using UHPLC-QTRAP-MS-based targeted metabolomics

This study aimed to characterize the metabolic profile of Salmonella enteritidis (S. enteritidis) in chicken matrix and to identify metabolic biomarkers of S. enteritidis in chicken. The UHPLC-QTRAP-MS high-throughput targeted metabolomics approach was employed to analyze the metabolic profiles of contaminated and control group chickens. A total of 348 metabolites were quantified, and the application of deep learning least absolute shrinkage and selection operator (LASSO) modelling analysis obtained eight potential metabolite biomarkers for S. enteritidis. Metabolic abundance change analysis revealed significantly enriched abundances of anthranilic acid, l-pyroglutamic acid, 5-hydroxylysine, n,n-dimethylarginine, 4-hydroxybenzoic acid, and menatetrenone in contaminated chicken samples. The receiver operating characteristic (ROC) curve analysis demonstrated the strong ability of these six metabolites as biomarkers to distinguish S. enteritidis contaminated and fresh chicken samples. The findings presented in this study offer a theoretical foundation for developing an innovative approach to identify and detect foodborne contamination caused by S. enteritidis.

Health outcomes of 100% orange juice and orange flavored beverage: A comparative analysis of gut microbiota and metabolomics in rats

A high intake of sugar-sweetened fruity beverage (FB) is associated with a higher risk of metabolic syndromes, but the health outcome of 100% fruit juice (FJ) intake remains unclear. We aim to reveal health outcomes of diet intervention (FJ or FB) with system profiling via interaction of gut microbiota and metabolomics in a rat (Rattus norvegicus) model. Firstly, the glucose, sucrose, fructose, and bioactive metabolites of FJ and FB were analyzed, and FJ possessed higher sucrose and flavonoids, while FB showed higher glucose and fructose. Secondly, C0 was set as the control group on Day 0, and a 4-week diet invention was performed to control, FJ-intake, and FB-intake groups with normal saline, FJ, and FB, respectively. The results showed that FJ improved alpha diversity and decreased the Firmicutes/Bacteroidota ratio (F/B ratio) of gut microbiota and prevented insulin resistance. However, FB possessed unchanged microbial diversity and enhanced F/B ratio, causing insulin resistance with renal triglyceride accumulation. In summary, FJ, although naturally containing similar amounts of total free sugars as FB, could be a healthier drink choice.

Comparison of freshly squeezed, Non-thermally and thermally processed orange juice based on traditional quality characters, untargeted metabolomics, and volatile overview

The NOVA food classification system, divides foods into four categories, namely unprocessed and minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods. With the recently increasing pursuit of healthy diets, special attention to minimally processed foods has become crucial. According to NOVA, freshly squeezed, high pressure processing (HPP) and pasteurized orange juice are minimally processed foods. In this study, the differences in the quality and composition of these minimally processed juice are explored, as it was found that their traditional quality characteristics were too weak to illustrate their difference. However, based on untargeted metabolomics, two differential compounds were identified between freshly squeezed and HPP orange juice, in addition to 15 differential compounds between freshly squeezed and pasteurized orange juice. Moreover, all the pasteurized orange juice in this study was deemed to be out of the acceptance area of freshly squeezed and HPP orange juice in a data-driven soft independent modeling of class analogy (dd-SIMCA) model based on volatile overview. The results of this study provide data for clarifying the compositional differences between minimally processed juice for their further subclassification.

An untargeted metabolomics approach to identify markers to distinguish duck eggs that come from different poultry breeding systems by ultra high performance liquid chromatography-high resolution mass spectrometry

Untargeted metabolomics approach based on ultra high performance liquid chromatography coupled with high resolution mass spectrometry (UHPLC-HRMS) was used to investigate the differences in cage duck eggs and sea duck eggs that from different poultry breeding system, which could help to combat fraud within the egg industry. High dimensions and complex data collected by UHPLC-HRMS were analyzed by multivariate statistical analysis. Identification model of sea duck eggs based on was established. After matching with the chemical databases, four potential markers were putatively matched. Further analysis showed that three of them were confirmed by reference standards. All these three markers (n-behenoyl-d-erythro-sphingosine, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine and n-nervonoyl-d-erythro-sphingosine) have higher content in sea duck eggs. The quantitative analysis showed that the content difference of three markers in farm samples were in highly consistent with the concentration changes measured in experimental samples, which indicate that these three markers are reliable.

Discovery of Spoilage Markers for Chicken Eggs Using Liquid Chromatography-High Resolution Mass Spectrometry-Based Untargeted and Targeted Foodomics

The current approaches remain insufficient for measuring chicken egg spoilage or present analytical limitations. This study aimed to complement the existing analyses and identify novel markers using liquid chromatography-high resolution mass spectrometry-based foodomics strategies. In the discovery set, comparative untargeted metabolomics was utilized to identify marker candidates in microbially inoculated chicken eggs. Markers were annotated by spectral matching with authentic standards, experimental libraries, or in silico fragmentation. In the validation set, targeted metabolomics was employed to verify the markers in stored chicken eggs from five farms. Statistical differences at a p-value < 0.001 revealed increases in lactic and 3-hydroxybutyric acids and decreases in phosphocholine, LPE(O-18:1), LPC(16:0), and LPC(18:0) in stored eggs. Receiver operating characteristic curve analysis of the six combined markers yielded an AUC of 0.956 and a sensitivity and specificity of ∼90%. Four phospholipids were highlighted as a novel class of spoilage markers. Our findings may contribute to further industrial implementation, benefiting the quality assurance and food safety of poultry egg production.

An integrated method for monitoring thermal processing temperature of pork based on Q-Exactive mass spectrometry and chemometrics

Meat heating endpoint temperature (EPT) is an important indicator to ensure the safety of cooked meat. Accurately determining the EPT of cooked meat and ready-to-eat meat products is an important strategy to ensure food safety. In this study, a comprehensive metabolic method based on UPLC-Q Exactive and chemometrics was developed to study the metabolites differences among pork roasted at different temperatures in order to select markers indicating EPT and discover new toxic heat-induced compounds. A two-step extraction method was applied to avoid the loss of metabolite information caused by sample preparation. Using chemometrics, the five compounds of creatine, creatinine, 2-amino-1-methyl-6-phenylimidazo (4,5-b) pyridine (PhIP), 2-methyl-6-amino-5-hydroxymethylpyrimidine (TMP) and compound with the m/z of 114.04316 were selected as markers, and four of them were further confirmed by chemical standards. It is worth noting that TMP was discovered in roasted pork for the first time. In addition, targeting studies aimed at quantifying the selected markers were conducted at different thermal processing temperatures. From the quantification results, it can be concluded that the heat temperature not exceed 180 °C is recommended to reduce the content of toxic compounds. This study has proved that the integration of UPLC-Q Exactive and chemometrics could provide an efficient method for the study of markers related to thermal process and new toxic heat-induced compounds.

UHPLC-MS/MS-Based Nontargeted Metabolomics Analysis Reveals Biomarkers Related to the Freshness of Chilled Chicken

To identify metabolic biomarkers related to the freshness of chilled chicken, ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) was used to obtain profiles of the metabolites present in chilled chicken stored for different lengths of time. Random forest regression analysis and stepwise multiple linear regression were used to identify key metabolic biomarkers related to the freshness of chilled chicken. A total of 265 differential metabolites were identified during storage of chilled chicken. Of these various metabolites, 37 were selected as potential biomarkers by random forest regression analysis. Receiver operating characteristic (ROC) curve analysis indicated that the biomarkers identified using random forest regression analysis showed a strong correlation with the freshness of chilled chicken. Subsequently, stepwise multiple linear regression analysis based on the biomarkers identified by using random forest regression analysis identified indole-3-carboxaldehyde, uridine monophosphate, s-phenylmercapturic acid, gluconic acid, tyramine, and Serylphenylalanine as key metabolic biomarkers. In conclusion, our study characterized the metabolic profiles of chilled chicken stored for different lengths of time and identified six key metabolic biomarkers related to the freshness of chilled chicken. These findings can contribute to a better understanding of the changes in the metabolic profiles of chilled chicken during storage and provide a basis for the further development of novel detection methods for the freshness of chilled chicken.

Nitrate exposure induces intestinal microbiota dysbiosis and metabolism disorder in Bufo gargarizans tadpoles

Excess nitrate has been reported to be associated with many adverse effects in humans and experimental animals. However, there is a paucity of information of the effects of nitrate on intestinal microbial community. In this study, the effects of nitrate on development, intestinal microbial community, and metabolites of Bufo gargarizans tadpoles were investigated. B. gargarizans were exposed to control, 5, 20 and 100 mg/L nitrate-nitrogen (NO₃-N) from eggs to Gosner stage 38. Our data showed that the body size of tadpoles significantly decreased in the 20 and 100 mg/L NO₃-N treatment group when compared to control tadpoles. Exposure to 20 and 100 mg/L NO₃-N also caused indistinct cell boundaries and nuclear pyknosis of mucosal epithelial cells in intestine of tadpoles. In addition, exposure to NO₃-N significantly altered the intestinal microbiota diversity and structure. The facultative anaerobic Proteobacteria occupy the niche of the obligately anaerobic Bacteroidetes and Fusobacteria under the pressure of NO₃-N exposure. According to the results of functional prediction, NO₃-N exposure affected the fatty acid metabolism pathway and amino acid metabolism pathway. The whole-body fatty acid components were found to be changed after exposure to 100 mg/L NO₃-N. Therefore, we concluded that exposure to 20 and 100 mg/L NO₃-N could induce deficient nutrient absorption in intestine, resulting in malnutrition of B. gargarizans tadpoles. High levels of NO₃-N could also change the intestinal microbial communities, causing dysregulation of fatty acid metabolism and amino acid metabolism in B. gargarizans tadpoles.

Use of information dependent acquisition mass spectra and sequential window acquisition of all theoretical fragment-ion mass spectra for fruit juices metabolomics and authentication

INTRODUCTION

LC-MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry.

OBJECTIVES

To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study.

METHODS

IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets.

RESULTS

IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%.

CONCLUSION

SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.

Discrimination of Milk from Different Animal Species by a Foodomics Approach Based on High-Resolution Mass Spectrometry

An untargeted foodomics strategy based on ultra-high-performance liquid chromatography coupled with quadrupole orbitrap and chemometrics was used to observe subtle differences in the molecule profiles of raw milk from different animal species (cow milk, goat milk, and water buffalo milk), which could prevent the fraud activities in the dairy industry. In data-dependent acquisition (DIA), spectra for all precursor ions facilitated the comprehensive identification of unknown compounds in untargeted foodomics. Chemometrics techniques were used to analyze large amounts of complex data to observe the separation of different sample groups and find the potential markers of sample groups. Finally, five markers were putatively identified by the potential marker identification workflow. The quantification results showed that β-carotene was found only in cow milk; ergocalciferol was found only in water buffalo milk; and the contents of nonanoic acid, decanoic acid, and octanoic acid were higher in goat milk than those in cow milk and water buffalo milk. The quantification of β-carotene enabled the detection of cow milk with a sensitivity threshold of 5% (w/w). This work provided an efficient approach for the discrimination of cow milk, goat milk, and water buffalo milk. Compared with proteomics and genomics, the simpler analytical procedures, lower costs, and higher speed of this work make it of great benefit for routine operations.

Classification of Hen Eggs by HPLC-UV Fingerprinting and Chemometric Methods

Hen eggs are classified into four groups according to their production method: Organic, free-range, barn, or caged. It is known that a fraudulent practice is the misrepresentation of a high-quality egg with a lower one. In this work, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were proposed as a source of potential chemical descriptors to achieve the classification of hen eggs according to their labelled type. A reversed-phase separation was optimized to obtain discriminant enough chromatographic fingerprints, which were subsequently processed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Particular trends were observed for organic and caged hen eggs by PCA and, as expected, these groupings were improved by PLS-DA. The applicability of the method to distinguish egg manufacturer and size was also studied by PLS-DA, observing variations in the HPLC-UV fingerprints in both cases. Moreover, the classification of higher class eggs, in front of any other with one lower, and hence cheaper, was studied by building paired PLS-DA models, reaching a classification rate of at least 82.6% (100% for organic vs. non-organic hen eggs) and demonstrating the suitability of the proposed method.