Real-time detection of authenticity and adulteration of krill phospholipids with soybean phospholipids using rapid evaporative ionization mass spectrometry: Application on commercial samples

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.

Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification

The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.

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.

Real-time traceability of sorghum origin by soldering iron-based rapid evaporative ionization mass spectrometry and chemometrics

Sorghum is an important grain with a high economic value for liquor production. Tracing the geographical origin of sorghum is vital to guarantee the liquor flavor. Soldering iron-based rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics was developed for the real-time discrimination of the sorghum's geographical origin. The working conditions of soldering iron-based ionization were optimized, and then the obtained MS profiling data were processed using chemometrics analysis methods, including principal component analysis-linear discriminant analysis and orthogonal projection to latent structures discriminant analysis (OPLS-DA). A recognition model was established, and discriminations of sorghum samples from 10 provinces in China were achieved with a correct rate higher than 90%. On the basis of OPLS-DA, the specific ions of m/z 279.2327, 281.2479, and 283.2639 had relatively strong discrimination power for the geographical origins of sorghum. The developed method was successfully applied in the discrimination of sorghum origins. The results indicated that the soldering iron-based REIMS technique combined with chemometrics is a useful tool for direct, fast, and real-time ionization of poor conductivity samples and acquisition of metabolic profiling data.

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

Minced shrimp is popular seafood due to its delicious flavor and nutritional value. However, the biological species of raw material of minced shrimp are not distinguished by naked eyes after processing. Thus, an in situ and real-time minced shrimp authentication method was established using iKnife rapid evaporative ionization mass spectrometry (REIMS) based lipidomics. The samples were analyzed under ambient ionization without any tedious preparation step. Seven economic shrimp samples were tested, whose phenotypes were used to develop a real-time recognition model. A total of 19 fatty acids and 45 phospholipid molecular species were efficiently identified and statistically analyzed by multivariate statistical analysis. The results showed that the seven shrimp species were well distinguished, and the most contributing ions at m/z 255.2, 279.2, 301.2, 327.2, 699.5, 742.5, etc., were revealed by variable importance in projection. The proposed iKnife REIMS showed excellent performance in minced shrimp authentication.

Real-Time Profiling and Distinction of Lipids from Different Mammalian Milks Using Rapid Evaporative Ionization Mass Spectrometry Combined with Chemometric Analysis

The price of mammalian milk from different animal species varies greatly due to differences in their yield and nutritional value. Therefore, the authenticity of dairy products has become a hotspot issue in the market due to the replacement or partial admixture of high-cost milk with its low-cost analog. Herein, four common commercial varieties of milk, including goat milk, buffalo milk, Holstein cow milk, and Jersey cow milk, were successfully profiled and differentiated from each other by rapid evaporative ionization mass spectrometry (REIMS) combined with chemometric analysis. This method was developed as a real-time lipid fingerprinting technique. Moreover, the established chemometric algorithms based on multivariate statistical methods mainly involved principal component analysis, orthogonal partial least squares-discriminant analysis, and linear discriminant analysis as the screening and verifying tools to provide insights into the distinctive molecules constituting the four varieties of milk. The ions with m/z 229.1800, 243.1976, 257.2112, 285.2443, 299.2596, 313.2746, 341.3057, 355.2863, 383.3174, 411.3488, 439.3822, 551.5051, 577.5200, 628.5547, 656.5884, 661.5455, 682.6015, and 684.6146 were selected as potential classified markers. The results of the present work suggest that the proposed method could serve as a reference for recognizing dairy fraudulence related to animal species and expand the application field of REIMS technology.

Rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics for real-time beer analysis

The beer industry plays an important role in the economy since this is the third most consumed beverage worldwide. Efficient analytical methods must be developed to ensure the quality of the product. Rapid evaporative ionization mass spectrometry (REIMS) can provide molecular-level information, while enabling fast analysis. REIMS requires minimal sample preparation and it is ideal for the analysis of homogeneous liquid samples, such as beers, within only five seconds. In this article, 32 different beers were analyzed by REIMS in positive and negative ionization modes using a hybrid quadrupole time-of-flight mass spectrometer. The positive and negative MS spectrum blocks were augmented for data fusion. A predictive model by partial least squares discriminant analysis (PLS-DA) was used to discriminate the samples (i) by their brands and (ii) by the beer type (Premium and Standard American lagers). The results showed that REIMS provided a rich fingerprint of beers, which was successfully used to discriminate the brands and types with 96.9% and 97.9% accuracy, respectively. We believe that this proof-of-concept has great potential to be applied on a larger scale for industrial purposes due to its high-throughput.

In Situ Quality Assessment of Dried Sea Cucumber (Stichopus japonicus) Oxidation Characteristics during Storage by iKnife Rapid Evaporative Ionization Mass Spectrometry

Sea cucumber (Stichopus japonicus) is one of the most luxurious and nutritious seafoods in Asia. It is always processed into dried products to prevent autolysis, but its quality is easily destructed during storage. Herein, an extremely simplified workflow was established for real-time and in situ quality assessment of dried sea cucumbers (DSCs) during storage based on the lipid oxidation characteristics using an intelligent surgical knife (iKnife) coupled with rapid evaporative ionization mass spectrometry (REIMS). The lipidomic phenotypes of DSCs at different storage times were acquired successfully, which were then processed by multivariate statistical analysis. The results showed that the discrepancy in the characteristic ions in different DSCs was significant (p < 0.05) with high R²(Y) and Q² values (0.975 and 0.986, respectively). The receiver operating characteristic curve revealed that the ions of m/z 739.5, m/z 831.5, m/z 847.6, and m/z 859.6 were the most specific and characteristic candidate biomarkers for quality assessment of DSCs during accelerated storage. Finally, this method was validated to be qualified in precision (RSD_intraday ≤ 9.65% and RSD_interday ≤ 9.36%). In conclusion, the results showed that the well-established iKnife-REIMS method was high-throughput, rapid, and reliable in the real-time quality assessment of DSCs.

Lipid profile migration during the tilapia muscle steaming process revealed by a transactional analysis between MS data and lipidomics data

In this work, lipid profile migration from muscle to juice during the tilapia muscle steaming process was revealed by a transactional analysis of data from ultra-high-performance liquid chromatography coupled with Q Exactive (UHPLC-QE) Orbitrap mass spectrometry (MS) and lipidomics. Firstly, the lipids in tilapia muscles and juices at different steaming time points were extracted and examined by UHPLC-QE Orbitrap mass spectrometry. Secondly, a transactional analysis procedure was developed to analyze the data from UHPLC-QE Orbitrap MS and lipidomics. Finally, the corrected lipidomics data and the normalized MS data were used for lipid migration analysis. The results suggested that the transactional analysis procedure was efficient to significantly decrease UHPLC-QE Orbitrap MS workloads and delete the false-positive data (22.4-36.7%) in lipidomics data, which compensated the disadvantages of the current lipidomics method. The lipid changes could be disappearance, full migration into juice, appearance in juice, appearance in muscle, appearance in both muscle and juice, and retention in the muscle. Moreover, the results showed 9 (compared with 52), 5 (compared with 116), and 10 (compared with 178) of lipid class (compared with individual lipid) variables showed significant differences among the different steaming times (0, 10, 30, and 60 min) in all the muscles, juices, and muscle-juice systems, respectively. These results showed significant lipid profile migration from muscle to juice during the tilapia steaming process.

Fast and Specific Screening of EPA/DHA-Enriched Phospholipids in Fish Oil Extracted from Different Species by HILIC-MS

Eicosapentaenoic acid- and docosahexaenoic acid-enriched phospholipids (PL_EPA/DHA) have versatile health-beneficial functions and can be well absorbed in the intestine. Herein, a precursor ion scan-driven hydrophilic interaction chromatography mass spectrometry (PreIS-HILIC-MS) method with the fatty acyl moieties of m/z 301.6 and 327.6 locked was established to specifically and selectively screen PL_EPA/DHA in different fish oil samples, including saury, grass carp, hairtail, and yellow croaker. Taking saury oil as an example, a total of 24 PL_EPA/DHA were successfully identified and quantified, including 20 PC_EPA/DHA and 4 PE_EPA/DHA. Finally, this method was validated in terms of sensitivity (limit of detection ≤ 4.15 μg·mL^-1), linearity (≥0.9979), precision (RSD_intraday ≤ 4.65%), and recovery (≥78.6%). The performance of the PreIS-HILIC-MS method was also compared with that of the traditional full-scan mode, and the former demonstrated its unique superiority in targeted screening of PL_EPA/DHA in fish oils.

Differentiation between Fresh and Frozen-Thawed Meat using Rapid Evaporative Ionization Mass Spectrometry: The Case of Beef Muscle

An intelligent surgical knife (iKnife) coupled with rapid evaporative ionization mass spectrometry (REIMS) was employed for the lipidomic profiling of fresh and frozen-thawed beef muscle. The data were obtained by REIMS and then processed using multivariate statistical analysis methods including principal component analysis-linear discriminant analysis (PCA-LDA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The discrimination of fresh and frozen-thawed meat has been achieved, and the real-time identification accuracy was 92-100%. Changes in the composition and content of fatty acids and phospholipids were statistically analyzed by OPLS-DA, and the ions of m/z 279.2317, m/z 681.4830, and m/z 697.4882 were selected as differential compounds/metabolites. The developed method was also successfully applied in the discrimination of fresh and frozen-thawed meat samples. These results showed that REIMS as a high-throughput, rapid, and real-time mass spectrometry detection technology can be used for the identification of fresh and frozen-thawed meat samples.

Untargeted Screening of EPA/DHA Structured Phospholipids in Krill Oil by Chain-Lock-Driven Hydrophilic Interaction Liquid Chromatography Tandem Mass Spectrometry

Eicosapentaenoic and docosahexaenoic acids structured phospholipids (PL_EPA/DHA) have multiple biochemical and pharmacological effects on human health. In this study, EPA and DHA chains were locked under precursor ion scan (PreIS) mode for untargeted screening PL_EPA/DHA in krill oil using hydrophilic interaction liquid chromatography tandem mass spectrometry (HILIC-MS/MS). The effect of collision energy and declustering potential on the fragmentation of EPA (m/z 301.2) and DHA (m/z 327.2) chains was studied. A total of 33 PL_EPA/DHA were characterized (sn-1/sn-2) and quantified using regression models, including 16 PC_EPA/DHA, 11 PE_EPA/DHA, and 6 PI_EPA/DHA. Afterward, this method was validated in terms of linearity (≥0.9978), sensitivity (LOD ≤ 4.02 μg·L^-1), precision (RSD_intraday ≤ 4.71%), and recovery (≥78.9%). Finally, the performance of HILIC-PreIS-MS/MS was compared with those of conventional methods, and the results indicated its superiority in selective screening PL_EPA/DHA in krill oil.