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

Integrating transfer learning and spectroscopy for enhanced pork spoilage assessment using correlation analysis

Accurate Total Viable Count (TVC) detection is vital for food quality monitoring. In this study, we investigated the feasibility of using visible near-infrared (VNIR) spectroscopy (400-1000 nm) combined with transfer learning (TL) to track the chemical spoilage of pork. The base models developed using the full band for pork TVC, total volatile basic nitrogen, pH, and color showed predictability; the correlation coefficient of prediction set (RP) for all models ranged from 0.821 to 0.916; and the root mean square error of prediction set (RMSEP) of the TVC model was 0.617 (lg CFU/g). A correlation analysis of the different indexes of pork was carried out to optimize the TVC calibration model. Different TL methods for TVC optimization were designed. The results showed that multiple correlation chain stacking-partial least squares performed best with RP, RMSEP, and the relative percent deviation of 0.947, 0.425 lg CFU/g, and 2.355, respectively, the RMSEP of TVC was reduced by 31.12 % as compared to the base model. This study demonstrated the possibility of combining the VNIR spectroscopy system with TL to monitor the degree of meat's chemical spoilage.

Comparative transcriptomics and metabolomics provide insight into degeneration-related physiological mechanisms of Morchella importuna after long-term preservation

Ascomycetes fungi are often prone to degeneration. Agricultural production of the prized ascomycete mushroom Morchella importuna (black morel) typically suffers from reduced yield and malformed ascocarps owing to culture degeneration. This study compared M. importuna cultures subjected to five different long-term preservation treatments, using transcriptomics and metabolomics. Avoiding repeated subculturing in combination with nutrient-limited conditions was found to be the most beneficial method for maintaining the fruiting capability of morels. The expression of the gene sets involved in cysteine and methionine metabolism and nucleocytoplasmic transport was upregulated under nutrient-limited and nutrient-rich conditions, respectively. This increased expression was accompanied by differential accumulation of metabolites involved in nucleobase metabolism. Repeated subculturing triggered dissimilar changes in the functional modules under nutrient-rich and nutrient-limited conditions. A diverse set of cellular biochemical processes related to carbon metabolism were altered by repeated subculturing under nutrient-rich conditions, whereas glycerophospholipid and purine metabolism were key functions affected by repeated subculturing under nutrient-limited conditions. Altogether, metabolic alterations related to sulfur-containing amino-acid biosynthesis, DNA repair, and cellular structural maintenance contributed to improved preservation outcomes in terms of morel fruiting capability. Our findings contribute to a more detailed understanding of the molecular mechanisms related to subculturing and fruiting of ascomycete macrofungi after long-term preservation.

Serum metabolic profiles in quails: Effects of age, breed, and sex

Over the past decade, metabolomic research in livestock and poultry has gained considerable momentum; however, quail metabolomics still lags behind that of livestock species such as chickens, pigs, and cattle. Quails are important models due to their low-cost protein sources-both eggs and meat-and practical benefits such as minimal space requirements, high egg production, disease resistance, and rapid reproduction. Therefore, it is necessary to systematically understand the effects of various factors on quail metabolism to provide a theoretical basis for accurate feeding and breeding practices. In this study, liquid chromatography with tandem mass spectroscopy (LC-MS/MS)-based metabolomics was used to examine the effects of age, breed, and sex on the serum metabolic profile of quails. A total of 550 metabolites were identified. Relative to breed and sex, we found that age played a crucial role in influencing quail serum metabolites. At 20 days of age (D20), quails had high levels of serum thymidine and alpha-D-glucose, while at 70 days of age (D70), the lipids, including 3-isothujone, 15-deoxy-d-12,14-PGJ2, and 2-aminobut-2-enoate dominated the serum. Additionally, xanthine, hypoxanthine, diaminopimelic acid, and 2-deoxy-scyllo-inosose appeared to be specific metabolites of Japanese quail (JAPQ). Serum levels of N-acetylglutamic acid, hydroxypyruvic acid, carnosine, alloepipregnanolone, lumichrome, 6-hydroxynicotinate, and myristic acid were higher in D70 Hengyan white feather quails (HYWQ) than those in D70 JAPQ. Notably, this study also identified 2-hydroxy-2-ethylsuccinic acid and riboflavin as potential specific metabolites in female quails. Furthermore, integration analysis showed that amino acid biosynthesis and metabolism, as well as ABC transporters, were the key pathways distinguishing D20 from D70. Purine metabolism, pyrimidine metabolism, ABC transporters, and TCA cycle were the key pathways distinguishing HYWQ from JAPQ. Differences in energy metabolism and amino acid biosynthesis and metabolism were observed between males and females. These findings enhance our understanding of the dynamic changes in quail serum metabolites influenced by various factors and address the knowledge gap regarding serum metabolic changes at different stages in quails.

Zein/chitosan Janus film incorporated with tannic acid and cinnamon essential oil co-loaded Pickering emulsion for sustained controlled release and pork preservation

The development of active packaging offers a promising approach to reducing food waste. However, challenges remain, particularly in achieving efficient release dynamics of active compounds and balancing the barrier properties. Herein, a Janus structure zein/chitosan film is custom designed by layer-by-layer casting method to achieve sustainable and unidirectional release performance of antimicrobial agent, which comprises an inner loading layer of tannic acid (TA) and cinnamon essential oil (CEO) co-loaded Pickering emulsion incorporated with chitosan and an outer barrier layer of zein. The good interfacial compatibility between the entities of Pickering emulsion/chitosan loading layer and zein barrier layer had be confirmed via physicochemical structure characterization. The lower swelling rate of Pickering emulsion/chitosan film (47.61 %-51.71 %) indicated the sustained and stable release rate of substances from the inner loading layer, while the zein barrier layer restricted the diffusion of active molecules due to the high swelling rate (162.52 %). In addition, the films showed excellent antimicrobial activity (>99 % against key foodborne pathogens) and radical scavenging activity (2.5-fold enhancement). Moreover, the film loading layer showed predominantly controlled by a quasi-Fickian diffusion, and prolonged the shelf life of pork by 6 days under the unidirectional sustained release. Our work presents a promising fabrication strategy of antimicrobial packaging film with sustainable release performance for food preservation.

Exploring the freshness biomarker and volatiles formation in stored pork by means of lipidomics and volatilomics

Raw pork is prone to oxidation and rancidity as it contains a high level of unsaturated lipid molecules. Reliable biomarkers to benchmark pork freshness and their formation have not been systematically investigated. The results indicated that the peroxide values, TVB-N and rancid volatiles dramatically increased in pork during the storage period (4 °C, 0-9 d). Concentrations of most volatile compounds with carbonyl groups were increased markedly in pork during storing, including hexanal, acetic acid, and hexadecanoic acid methyl ester. Lipidomics, volatilomics and chemometrics methods were used to discriminate the freshness of pork, among which acetic acid and PC O-20:3 emerged as the most reliable freshness biomarkers. Phospholipids and neutral lipids, including phosphatidylcholines (PC), triglycerides (TG), and phosphatidylethanolamine (PE), played a crucial role in the formation of rancid volatiles and the decreased freshness. This work will provide technical supports for the efficient storage and preservation of raw meat.

Non-Targeted Metabolomics Analysis of Small Molecular Metabolites in Refrigerated Goose Breast Meat

Poultry represents a rich source of multiple nutrients. Refrigeration is commonly employed for poultry preservation, although extended storage duration can adversely affect the meat quality. Current research on this topic has focused on the analysis of biochemical indices in chilled goose meat, with limited information on changes in metabolites that influence the quality of the meat during storage. This study used non-targeted metabolomics and the random forest algorithm to investigate metabolite changes in goose meat over an extended storage period. The results showed a significant change in the composition of the meat as the duration of storage increased, with the identification of 121 distinct metabolites. Further analysis identified 18 metabolites that could be used as indicators of the degradation of carbohydrates, amino acids, nucleotides, and lipids. These metabolites could be used as markers to monitor the deterioration process. These intermediate metabolites tended to be transformed into lower-level products involving pyruvate, acetyl coenzyme A, and fumaric acid, used in the tricarboxylic acid cycle, performing substance transformation. This comprehensive analysis of metabolites provides a valuable reference for monitoring the freshness of goose meat, potentially improving the safety of domestic poultry products.

Unraveling the spoilage characteristics of refrigerated pork using high-throughput sequencing coupled with UHPLC-MS/MS-based non-targeted metabolomics

The spoilage of refrigerated pork involves nutrient depletion and the production of spoilage metabolites by spoilage bacteria, yet the microbe-metabolite interactions during this process remain unclear. This study employed 16S rRNA high-throughput sequencing and non-targeted metabolomics based on ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) to reveal the core microbiota and metabolite profiles of pork during refrigeration. A total of 45 potential biomarkers were screened through random forest model analysis. Metabolic pathway analysis indicated that eleven pathways, including biogenic amine metabolism, pentose metabolism, purine metabolism, pyrimidine metabolism, phospholipid metabolism, and fatty acid degradation, were potential mechanisms of pork spoilage. Correlation analysis revealed nine metabolites-histamine, tyramine, tryptamine, D-gluconic acid, UDP-d-glucose, xanthine, glutamine, phosphatidylcholine, and hexadecanoic acid-as spoilage biomarkers, with Pseudomonas, Serratia, and Photobacterium playing significant roles. This study provides new insights into the changes in microbial and metabolic characteristics during the spoilage of refrigerated pork.

Characterization and exploration of dynamic variation of volatile compounds in vine tea during processing by GC-IMS and HS-SPME/GC-MS combined with machine learning algorithm

It is imperative to unravel the dynamic variation of volatile components of vine tea during processing to provide guidance for tea quality evaluation. In this study, the dynamic changes of volatile compounds of vine tea during processing were characterized by GC-IMS and HS-SPME/GC-MS. As a result, 103 volatile compounds were characterized by the two technologies with three overlapped ones. The random forest approach was employed to develop the models and explore key volatile compounds. 23 key compounds were explored, among which 13 were derived from GC-IMS and ten were from HS-SPME/GC-MS. Moreover, the area under the receiver operating characteristics curve with 100 cross validations by the pair-wised models were all 1 for the established models. Furthermore, the primary aroma formation mechanism for the key volatile compounds were mainly involved in fatty acid and amino acid metabolism. Besides, this study provides a theoretical support for directed processing and quality control of vine tea.

A review of taste-active compounds in meat: Identification, influencing factors, and taste transduction mechanism

Poultry and livestock meat are important parts of the human diet. As living standards have improved, food taste has become a major influence on consumer quality assessment and meat purchasing choices. There is increasing research interest in meat taste and meat taste-active compounds, which include free amino acids, flavor nucleotides, taste-active peptides, organic acids, soluble sugars, and inorganic ions. Taste component research is also an important part of sensory science. A deeper understanding of the meat taste perception mechanism and interactions among different taste compounds will promote the development of meat science and sensory evaluation. This article reviews the main taste compounds in meat, factors influencing their concentrations, and the identification and measurement of taste-active compounds, as well as summarizing the mechanisms of taste sensing and perception. Finally, the future of scientific taste component evaluation is discussed. This review provides a theoretical basis for research on meat taste and an important reference for the development of the meat industry.

Metabolomics integrated with mass spectrometry imaging reveals novel action of tetramethylpyrazine in migraine

Migraine as a common neurological disorder still lacks effective therapies. Tetramethylpyrazine (TMP) is the main bioactive component from Ligusticum chuanxiong hort., a traditional edible-medicinal herb. This study aimed to investigate the action of TMP on migraine by metabolomics with mass spectrometry imaging (MSI) analysis and molecular exploring, including random forest model analysis, KEGG enrichment analysis and metabolite-metabolite interaction network analysis. The results indicated that 26 key representative metabolic biomarkers were identified, especially γ-glu-cys, which were highly related to glutathione (GSH) metabolism. MSI found the abundance of eleven endogenous metabolites were modulated by TMP, particularly glucose, the most important energy metabolism molecule, and GSH were increased that maintains intracellular redox balance, which was consistent with activation of Nrf2 signals by TMP. These findings provide insights into the effectiveness of metabolomics integrated with MSI in explaining the metabolic mechanisms of TMP, and afford valuable information for healthy development of TMP in migraine.

A Comparative Metabolomics Study of the Potential Marker Compounds in Feces from Different Hybrid Offspring of Huainan Pigs

As a notable native Chinese genetic population, the Huainan pig has an exceptional meat quality but a low percentage of lean meat and subpar genetic performance. To better exploit the superior genetic traits of the Huainan pig and address knowledge gaps regarding the optimization of its hybrid offspring, this study used Huainan pigs as the maternal line and bred them with Yorkshire, Landrace, and Berkshire sires. This approach produced three hybrid combinations: Yorkshire × Huainan (YH), Landrace × Huainan (LH), and Berkshire × Huainan (BH). The body size, fat ratio, and average backfat thickness of these hybrid progeny were evaluated under the same feeding management and nutritional circumstances. The results revealed that the average backfat thickness of YH was significantly lower than that of LH and BH. In order to better understand the causes of these variations, fecal samples were taken from three pigs in each group for metabolomic analysis. A total of 2291 metabolites were identified, including benzene derivatives (16.6%), amino acids and their metabolites (14.5%), and organic acids (13.4%), with pyruvaldehyde and norethindrone acetate elevated in YH compared to LH and BH. In addition, the three hybrid pig groups commonly exhibited differences in the "glycerophospholipid metabolism" pathway. This variation may also contribute to differences in their fat ratio and backfat thickness. Our findings provide a novel perspective on the role of hybrid vigor in advancing the genetic population of Huainan pigs, while also revealing the unique metabolic characteristics of the YH with regard to fat deposition. This study is expected to enhance the conservation and effective utilization of genetic resources within the Huainan pig population.

Lipidomics combined with random forest machine learning algorithms to reveal freshness markers for duck eggs during storage in different rearing systems

The differences in lipids in duck eggs between the 2 rearing systems during storage have not been fully studied. Herein, we propose untargeted lipidomics combined with a random forest (RF) algorithm to identify potential marker lipids based on ultra-performance liquid chromatography‒mass spectrometry (UPLPC-MS/MS). A total of 106 and 16 differential lipids (DL) were screened in egg yolk and white, respectively. In yolk, metabolic pathway analysis of DLs revealed that glycerophospholipid metabolism and sphingolipid metabolism were the key metabolic pathways in the traditional free-range system (TFS) during storage, glycosylphosphatidylinositol-anchored biosynthesis and glyceride metabolism were the key pathways in the floor-rearing system (FRS). In egg white, the key pathway in both systems is the biosynthesis of unsaturated fatty acids. Combined with RF algorithm, 12 marker lipids were screened during storage. Therefore, this study elucidates the changes in lipids in duck eggs during storage in 2 rearing systems and provides new ideas for screening marker lipids during storage. This approach is highly important for evaluating the quality of egg and egg products and provides guidance for duck egg production.

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays

Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring.

A comparative metabolomics analysis of phytochemcials and antioxidant activity between broccoli floret and by-products (leaves and stalks)

Leaves and stalks, which account for about 45% and 25% of broccoli biomass, respectively, are usually discarded during broccoli production, leading to the waste of green resources. In this study, the phytochemical composition and antioxidant capacity of broccoli florets and their by-products (leaves and stalks) were comprehensively analyzed. The metabolomics identified several unique metabolites (e.g., scopoletin, Harpagoside, and sinalbin) in the leaves and stalks compared to florets. Notably, the leaves were found to be a rich source of flavonoids and coumarins, with superior antioxidant capacity. The random forest model and correlation analysis indicated that flavonoids, coumarin, and indole compounds were the important factors contributing to the antioxidant activity. Moreover, the stalks contained higher levels of carbohydrates and exhibited better antioxidant enzyme activity. Together, these results provided valuable data to support the comprehensive utilization of broccoli waste, the development of new products, and the expansion of the broccoli industry chain.

Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma

Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.

On-site quantitation of xanthine in fish and serum using a smartphone-based spectrophotometer integrated with a dual-readout nanosensing assay

Rapid and quantitative biochemical analysis at points-of-need is imperative for food safety inspection. This work reports on: 1) a stand-alone smartphone-based "two-in-one" spectrophotometer (the SAFS) installed with a self-developed application (the SAFS-App) which can precisely collect both absorption spectra and fluorescence spectra in a reproducible manner within 5 s; and 2) a straightforward protocol for xanthine detection using fluorescent carbon nanodots and silver nanoparticles. The assay performed with the SAFS demonstrates high specificity towards xanthine, and a linear range of 1-60 μM with LODs of 0.38 and 0.58 μM for colorimetric and fluorometric readouts, respectively. The reliability and robustness of the SAFS are validated by on-site quantitation of xanthine in fish and serum samples, with comparable accuracy to HPLC method. More importantly, the SAFS presents itself as an appealing device which is accessible to everyone through the Internet of Things and can be tailored for diverse point-of-care testing applications.