Real-time authentication of minced shrimp by rapid evaporative ionization mass spectrometry

Effect of κ-carrageenan on the quality of crayfish surimi gels

The demand for crayfish surimi products has grown recently due to its high protein content. This study examined the effects of varying κ-carrageenan (CAR) and crayfish surimi (CSM) concentrations on the gelling properties of CAR-CSM composite gel and its intrinsic formation process. Our findings demonstrated that with the increasing concentration of carrageenan, the quality of CAR-CSM exhibited rising trend followed by subsequently fall. Based on the textural qualities, the highest quality CAR-CSM was achieved at 0.3% carrageenan addition. With the exception of chewiness, and the cooking loss of the gel system was 1.62%, whiteness was 82.35%, and the percentage of β-sheets increased to 57.18%. Further increase in CAR (0.4-0.5%) addition resulted in internal build-up of LCAR-CSM, conversion of intermolecular forces into disulfide bonds and gel breakage. This study exudes timely recommendations for extending the CAR application for the continuous development of crayfish surimi and its derivatives and its overall economic worth.

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms

Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect. With the organic phase ratio from low to high, most compounds changed by 1.0 log units in log IE. The addition of formic acid enhanced the signal but also promoted the matrix effect, which often occurred in compounds with strong ionization capacity. It was speculated that the matrix effect was mainly in the form of competitive charge and charged droplet' gasification sites during MS detection. Subsequently, we present a log IE prediction method built using the COSMO-RS software and the artificial neural network (ANN) algorithm to address this difficulty and overcome the shortcomings of previous models, which always ignore the matrix effect. This model was developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). Furthermore, we validated this approach by predicting the log IE of 70 compounds, including those not involved in the log IE model development. The results presented demonstrate that the method we put forward has an excellent prediction accuracy for log IE (R2pred = 0.880), which means that it has the potential to predict the log IE of new compounds without authentic standards.

Development of sequential online extraction electrospray ionization mass spectrometry for accurate authentication of highly-similar Atractylodis Macrocephalae

The accurate and rapid authentication techniques and strategies for highly-similar foods are still lacking. Herein, a novel sequential online extraction electrospray ionization mass spectrometry (S-oEESI-MS) was developed to achieve spatio-temporally resolved ionization and comprehensive characterization of complex foods with multi-components (high, medium, and low polarity substances). Meanwhile, a characteristic marker screening method and an integrated research strategy based on MS fingerprinting, characteristic marker and chemometrics modeling were established, which are especially suitable for the accurate and rapid authentication of highly-similar foods that are difficult to be authenticated by traditional techniques (e.g., LC-MS). Thirty-two batches of highly-similar Atractylodis macrocephalae rhizome from four different origins were used as model samples. As a result, S-oEESI-MS enabled a more comprehensive MS characterization of substance profiles in complex plant samples in 1.0 min. Further, 22 characteristic markers of Atractylodis macrocephalae were ingeniously screened out and combined with multivariate statistical analysis model, the accurate authentication of highly-similar Atractylodis macrocephalae was realized. This study presents a comprehensive strategy for accurate authentication and origin analysis of highly-similar foods, which has potentially significant applications for ensuring food quality and safety.

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.

Applications of ambient ionization mass spectrometry in 2022: An annual review

The development of ambient ionization mass spectrometry (AIMS) has transformed analytical science, providing the means of performing rapid analysis of samples in their native state, both in and out of the laboratory. The capacity to eliminate sample preparation and pre-MS separation techniques, leading to true real-time analysis, has led to AIMS naturally gaining a broad interest across the scientific community. Since the introduction of the first AIMS techniques in the mid-2000s, the field has exploded with dozens of novel ion sources, an array of intriguing applications, and an evident growing interest across diverse areas of study. As the field continues to surge forward each year, ambient ionization techniques are increasingly becoming commonplace in laboratories around the world. This annual review provides an overview of AIMS techniques and applications throughout 2022, with a specific focus on some of the major fields of research, including forensic science, disease diagnostics, pharmaceuticals and food sciences. New techniques and methods are introduced, demonstrating the unwavering drive of the analytical community to further advance this exciting field and push the boundaries of what analytical chemistry can achieve.

Nutritional lipidomics for the characterization of lipids in food

Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.

Study on the physicochemical indexes, nutritional quality, and flavor compounds of Trichiurus lepturus from three representative origins for geographical traceability

Trichiurus lepturus (hairtail) is an important economic component of China's marine fishing industry. However, due to the difficulty in identifying the appearance of hairtail from different geographical distributions, hairtails with geographical indication trademarks were imitated by general varieties. In this study, the texture characteristics, color, basic nutrients, amino acids, mineral, fatty acids, and volatile flavor substances were used as indicators for multivariate statistical analysis to determine whether three origins of hairtails from the habitats of Zhoushan (East China Sea, T.Z), Hainan (South China Sea, T.N), and Qingdao (Yellow Sea, T.Q) in the market could be distinguished. The findings revealed that there were significant differences in amino acids composition, mineral composition, fatty acid composition in lipids, and volatile flavor substances among the hairtails of three origins (P < 0.05), but no differences in color, texture, protein content. T.Z had moisture, crude fat, essential amino acids (EAA), flavor amino acids (FAA), unsaturated fatty acids (UFA), and docosahexaenoic acids and dicosapentaenoic acids (ΣEPA + DHA) contents of 74.33, 5.4%, 58.25 mg⋅g-1, 46.20 mg⋅g-1, 66.84 and 19.38%, respectively, and the contents of volatile alcohols, aldehydes and ketones were 7.44, 5.30, and 5.38%, respectively. T.N contains moisture, crude fat, EAA, FAA, UFA and ΣEPA + DHA as 77.69, 2.38%, 64.76 mg⋅g-1, 52.44 mg⋅g-1, 65.52 and 29.45%, respectively, and the contents of volatile alcohols, aldehydes and ketones as 3.21, 8.92, and 10.98%, respectively. T.Q had the contents of moisture, crude fat, EAA, FAA, UFA, and ΣEPA + DHA 79.69, 1.43%, 60.9 mg⋅g-1, and 49.42 mg⋅g-1, respectively. The contents of unsaturated fatty acid and ΣEPA + DHA were 63.75 and 26.12%, respectively, while the volatile alcohols, aldehydes, and ketones were 5.14, 5.99, and 7.85%, respectively. Partial least squares-discriminant analysis (PLS-DA) multivariate statistical analysis showed that volatile flavor compounds could be used as the most ideal indicators for tracing the source of hairtail. In conclusion, the findings of this study can distinguish the three hairtail origins using some basic indicators, providing ideas for hairtail geographical identification.