Assessment of lemon juice adulteration by targeted screening using LC-UV-MS and untargeted screening using UHPLC-QTOF/MS with machine learning

Changes of low-abundance peptides in frozen orange juice before and after frozen storage and pasteurization processing, and their contribution to ACE inhibition activity

The direct-to-business product is the core sector for the sustainable production of all kinds of direct-to-consumer fruit and vegetable products. Additionally, it's also an effective way to reduce fruit and postharvest loss. However, unlike other agri-foods, few studies focus on the quality of frozen fruit juice. Fruit juice freezing is a vital practice to ensure sustainable supply chain in food and beverage industry. Our research centered on a six-year monitoring analysis of unpasteurized and pasteurized frozen orange juice. We examined the metabolite and peptide profiles using metabolomics and peptidomics approaches, as well as assessing angiotensin-converting enzyme (ACE) inhibition activity through a rapid ACE assay. Based on metabolomics and peptidomics analysis of orange juice, low-abundance peptides in orange juice have demonstrated a heightened sensitivity to frozen storage and pasteurization processing, outperforming overall metabolites. Frozen storage and pasteurization processing affect the level of predicted bioactive peptides as well as ACE inhibition activity in orange juice, and the peptide-rich fraction contributes to the ACE inhibition activity of orange juice. We synthesized 14 peptides and measured their ACE inhibition activity. Five strong ACE inhibitory peptides with IC50 values ranging from 0.31 to 1.93 mg/mL were identified, and their concentrations were found to decrease after frozen storage and pasteurization. As far as we know, this study is the first to focus on low-abundance peptides in industrial samples of orange juice, providing data on their alterations during a six-year monitoring analysis of frozen storage and before and after pasteurization.

Lemon Juice-Infused PVA Nanofibers for the Development of Sustainable Antioxidant and Antibacterial Electrospun Hydrogel Biomaterials

Cross-linking bonds adjacent polymer chains into a three-dimensional network. Cross-linked poly(vinyl alcohol) (PVA) turns into a hydrogel, insoluble structure exhibiting outstanding sorption properties. As an electrospinnable polymer, PVA enables the creation of nanofibrous hydrogels resembling biological tissues, thus ideal for nature-inspired platforms. PVA properties are easily adjustable through additives and an appropriate cross-linking method. Drawing inspiration from environmentally safe approaches, this work developed a new "green" method of low-temperature PVA cross-linking. Nanofibers were electrospun from a precursor solution of PVA dissolved in fresh lemon juice, stabilized by heating at 60 °C for 7 days, and thoroughly characterized. The obtained nanoplatform demonstrated long-term stability and enhanced mechanical properties. Its biocompatibility was confirmed, and its antibacterial and health-promoting effects were attributed to lemon juice-rich in vitamin C, a potent antioxidant with anti-inflammatory properties. The developed system has future potential for use in the biomedical engineering field as a dressing accelerating wound healing.

Exploration of Freshness Identification Method for Refrigerated Vegetables Based on Metabolomics

Background: The rapid development of refrigerated transportation technology for fresh vegetables has extended their shelf life. Some vegetables may appear undamaged on the surface, but their freshness may have decreased, often resulting in the phenomenon of passing off inferior vegetables as good. It is very important to establish a detection method for identifying and assessing the freshness of vegetables. Methods: Therefore, based on metabolomics methods, this study innovatively employed UHPLC-Q-Exactive Orbitrap MS and GC-MS techniques to investigate the metabolites in the refrigerated storage of four vegetables, namely chard (Beta vulgaris var. cicla L), lettuce (Lactuca sativa var. ramose Hort.), crown daisy (Glebionis coronaria (L.) Cass. ex Spach), and tomato (Solanum lycopersicum L.), exploring key biomarkers for assessing their freshness. UPLC-TQ MS was used for the quantitative analysis of key metabolites. Results: The results showed that arginine biosynthesis and the metabolism of alanine, aspartate, and glutamate are key pathways in vegetable metabolism. Four key metabolites were selected from chard, five from lettuce, three from crown daisy, and five from tomato. Conclusions: Comparing the content of substances such as alanine and arginine can help infer the freshness and nutritional value of the vegetables, providing important references for detecting spoilage, determining storage time, and improving transportation conditions. This research holds significant relevance for the vegetable transportation industry.

Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

In silico guided pre-treatment of sugarcane juice with natural inhibitors of polyphenol oxidase (PPO) is a new and effective strategy for development of spray-dried formulation

Sugarcane juice is a popular beverage and is also processed to produce sugar. The polyphenol oxidase (PPO) in sugarcane juice causes enzymatic browning and makes the process of sugar production complex and cumbersome. Storage of sugarcane juice is also hampered by the high sugar content and rapid microbial fermentation. The present research assessed the potential of lemon juice (LJ) and ginger extract (GE) as natural inhibitors of PPO. Enzyme kinetics and the mechanism of inhibition of LJ and GE were studied. Primary investigation was carried out using molecular docking approach to assess the inhibitory potential of LJ and GE and to determine the nature of interaction between the enzyme and inhibitors. Extracts were used as inhibitors and studies revealed that both reduced the PPO activity. Subsequently, pure bioactive inhibitors such as ascorbic acid, citric acid, and 6-shogaol present in these natural extracts were used to study the mode of inhibition of PPO. Citric acid decreased PPO activity by lowering pH, while ascorbic acid was found to be a competitive inhibitor of PPO with a Ki of 75.69 µM. The proportion of LJ and GE required in sugarcane juice was optimized on the basis of browning index and sensory acceptance. Further, the sugarcane cane juice after inhibition of PPO under optimized conditions was spray dried and evaluated for reconstitution properties. The product formulated in the present study is a new and effective approach to address quality-compromising issues associated with long-term storage of cane juice.

Insights into metabolic characteristics and biological activity changes in Zangju (Citrus reticulata cv. Manau Gan) peel at different maturity stages through UPLC-MS/MS-based metabolomics

In this study, comprehensive and systematic nontargeted metabolomics analysis was performed with the metabolites of Zangju peel (Citrus reticulata cv. Manau Gan, CRZP, which has been cultivated for over 400 years in Derong County, China.) at four different mature stages. A total of 1878 metabolites were identified, among which flavonoids were the most abundant (62.04 %), and identified 62 key differential metabolites significantly affected by maturity. Based on biological activity measurements, CRZP showed better antioxidant activity, lipase inhibition ability, inhibition of adipogenic differentiation in 3TT-L1 cells and promotion of lipid metabolism, with the biological activity of CRZP at different maturity stages being associated with key differential metabolite. Thus, CRZP is natural antioxidants and possess anti-obesity potential, and industrial production needs to consider the Maturity stage of its collection.

Comparative antioxidant activity and untargeted metabolomic analyses of cherry extracts of two Chinese cherry species based on UPLC-QTOF/MS and machine learning algorithms

P. pseudocerasus and P. tomentosa are the two native Chinese cherry species of high economic and ornamental worths. Little is known about the metabolic information of P. pseudocerasus and P. tomentosa. Effective means are lacking for distinguishing these two similar species. In this study, the differences in total phenolic content (TPC), total flavonoid content (TFC), and in vitro antioxidant activities in 21 batches of two species of cherries were compared. A comparative UPLC-QTOF/MS-based metabolomics coupled with three machine learning algorithms was established for differentiating the cherry species. The results demonstrated that P. tomentosa had higher TPC and TFC with average content differences of 12.07 times and 39.30 times, respectively, and depicted better antioxidant activity. Total of 104 differential compounds were identified by UPLC-QTOF/MS metabolomics. The major differential compounds were flavonoids, organooxygen compounds, and cinnamic acids and derivatives. Correlation analysis revealed differences in flavonoids content such as procyanidin B1 or isomer and (Epi)catechin. They could be responsible for differences in antioxidant activities between the two species. Among three machine learning algorithms, the prediction accuracy of support vector machine (SVM) was 85.7%, and those of random forest (RF) and back propagation neural network (BPNN) were 100%. BPNN exhibited better classification performance and higher prediction rate for all testing set samples than those of RF. The study herein found that P. tomentosa had higher nutritional value and biological functions, and thus considered for usage in health products. Machine models based on untargeted metabolomics can be effective tools for distinguishing these two species.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

Quantitative metabolomic analysis reveals the fractionation of active compounds during lemon fruit juicing

In this study, metabolomic analysis was employed to investigate the separation (fractionation) of active compounds into lemon juice (LJ) and lemon pomace (LP) during lemon juicing. A total of 968 metabolites were identified, and 438 differentially abundant metabolites (DAMs) were screened out between LJ and LP, suggesting significant metabolite fractionation during juicing. The "flavonoids", "phenolic acids", and "saccharides and alcohols" were mainly retained in the LP, while the fractionation of major "organic acids" was differentiated. Seven of the 12 potential bitter metabolites were more abundant in the LP and two were more abundant in the LJ, suggesting that LP would be more bitter. L-Ascorbic acid, thiamine, and acitretin were significantly lost during juicing, while riboflavin was newly dissolved during juicing. The antioxidant capacity of LP was significantly higher than that of LJ, which was closely related to the higher abundance of phenolic acid metabolites in LP. These findings suggtested that promoting the release of flavonoids and phenolic acids from LP is a potential strategy to improve the quality of LJ. Results also provides important information and reference for developing high-value products by using LP.

Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices

Fruit juices are one of the most widely consumed beverages worldwide, and their production is subject to strict regulations. Therefore, this study presents a methodology based on the use of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) in combination with machine-learning algorithms for the characterization juices of different raw material (orange, pineapple, or apple and grape). For this purpose, the ion mobility sum spectrum (IMSS) was used. First, an optimization of the most important conditions in generating the HS was carried out using a Box-Behnken design coupled with a response surface methodology. The following factors were studied: temperature, time, and sample volume. The optimum values were 46.3 °C, 5 min, and 750 µL, respectively. Once the conditions were optimized, 76 samples of the different types of juices were analyzed and the IMSS was combined with different machine-learning algorithms for its characterization. The exploratory analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed a clear tendency to group the samples according to the type of fruit juice and, to a lesser extent, the commercial brand. The combination of IMSS with supervised classification techniques reported an excellent result with 100% accuracy on the test set for support vector machines (SVM) and random forest (RF) models regarding the specific fruit used. Nevertheless, all the models have proven to be an effective alternative for characterizing and classifying the different types of juices.

Integration of Untargeted Metabolomics and Object-Oriented Data-Processing Protocols to Characterize Acerola Powder Composition as Functional Food Ingredient

Acerola powder has been experiencing a surge in demand as a functional food ingredient, particularly due to its usage in vitamin C supplements. However, limited research has been conducted on its other bioactive compounds. In this study, we employed metabolomics and object-oriented data-processing protocols to comprehensively characterize acerola powder. To ensure maximum coverage of metabolomics, we selected a 50% methanol aqueous solution as the extraction solvent and utilized the HSS T3 column for chromatography analysis. Through this approach, we successfully identified a total of 175 compounds in acerola powder, encompassing amino acids and peptides, polyphenols, organic acids, and various other compounds. Additionally, we measured the total phenolic content (TPC) and assessed the antioxidant activity of acerola powder. Furthermore, we analyzed the differential composition of acerola fruit and juice powder, identifying polyphenols and lipids as primary markers in fruit powder, while peptides emerged as key markers in juice powder. Notably, two specific peptides, Thr-Trp and Val-Tyr, were identified as antioxidant peptides. Overall, our study provides novel composition data for acerola powder, shedding light on its potential as a functional food ingredient. These findings contribute to the development and utilization of acerola powder in the formulation of functional food products.

Pseudotargeted metabolomics-based random forest model for tracking plant species from herbal products

BACKGROUND

The "one-to-multiple" phenomenon is prevalent in medicinal herbs. Accurate species identification is critical to ensure the safety and efficacy of herbal products but is extremely challenging due to their complex matrices and diverse compositions.

PURPOSE

This study aimed to identify the determinable chemicalome of herbs and develop a reasonable strategy to track their relevant species from herbal products.

METHODS

Take Astragali Radix-the typical "one to multiple" herb, as a case. An in-house database-driven identification of the potentially bioactive chemicalome (saponins and flavonoids) in AR was performed. Furthermore, a pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data. Then based on the data matrix, the random forest algorithm was trained to predict Astragali Radix species from commercial products.

RESULTS

The pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data (including 56 saponins and 49 flavonoids) from 26 batches of AR. Then the random forest algorithm was well-trained by importing the valid data matrix and showed high performance in predicting Astragalus species from ten commercial products.

CONCLUSION

This strategy could learn species-special combination features for accurate herbal species tracing and could be expected to promote the traceability of herbal materials in herbal products, contributing to manufacturing standardization.

Characterization and quantification of ginsenosides from the root of Panax quinquefolius L. by integrating untargeted metabolites and targeted analysis using UPLC-Triple TOF-MS coupled with UFLC-ESI-MS/MS

The root of Panax quinquefolius L. (RPQ) is considered as an important functional food and rich in bioactive components, ginsenosides. To comprehensively characterize ginsenosides and evaluate the quality of RPQ from different sources, UPLC-Triple TOF-MS coupled with UFLC-ESI-MS/MS was applied to untargeted metabolites and targeted analysis for the first time. In untargeted metabolites analysis, a total of 225 ginsenosides were identified from RPQ using UPLC-Triple TOF-MS combined with SWATH data-independent strategy. Furthermore, the contents of 39 targeted ginsenoside markers in 14 RPQ samples were analyzed by a rapid and sensitive UFLC-ESI-MS/MS method. In addition, the results of chemometric analysis showed the quality of American RPQ was distinguished from that of Chinese RPQ according to the amount of targeted ginsenosides. This newly developed approach provides a powerful tool for enriching the diversity of saponins database and assessing the quality of RPQ, which can be further extended to other ginseng products and functional foods.