Search Results (153)

The modern food industry faces increasing pressure to reduce environmental impacts, while at the same time preserving product safety, quality, nutritional value, and industrial relevance. This review synthesizes three related pillars of sustainable food processing: green extraction technologies, natural fermentation, and analytical quality validation. Green extraction methods can reduce dependence on conventional organic solvents, shorten processing time, and support the extraction of bioactive compounds from plant materials and by-products of the food industry. Natural fermentation is a low-impact biotechnological approach to improve sensory quality, shelf life, nutritional value, and valorization of low-cost raw materials or residues. However, sustainability cannot be judged only through lower consumption of resources or general “green” claims. It also requires analytical confirmation of the content of bioactive compounds, oxidative stability, contaminants, authenticity, traceability, standardization, and product safety. In response to reviewers’ recommendations, the review includes a transparent literature selection protocol, a clearer distinction of challenges, research gaps, and future perspectives, as well as additional quantitative comparative tables covering extraction technologies, fermentation applications, and analytical methods. The review shows that the future of sustainable food processing depends on integrating extraction, fermentation, by-product valorization, foodomics approaches, life cycle thinking, real-time monitoring, and industrial-scale validation within the circular economy. Full article

Background/Objectives: Course-based Undergraduate Research Experiences (CUREs) represent a form of student crowdsourcing in which individuals perform authentic discovery-based research in a class setting with interest to outside stakeholders. Here, the renowned Tiny Earth (TE) CURE is being utilized to teach microbiology and perform natural product discovery research by students in the course. Methods: In our TE CURE, students collect soil samples from their hometown and characterize bacteria that can inhibit plant and animal pathogens. This unique growing collection of isolates from across Ohio has provided opportunities to facilitate drug discovery and establish biotechnology collaborations. Results: In this study, we describe two outcomes using our environmental strain collection that initiated biotechnology collaborations and identified bacterial candidates for drug discovery. Results from one project led to a partnership with an aquaculture company. A novel biosynthetic gene cluster involved in antagonistic activity was identified, whose product inhibits Aeromonas pathogens, which cause disease in freshwater fish. The other project involves a collaboration with a global commercial cleaning and equipment company to identify lipase activity among Bacillus strains for its potential use in bioremediation. Conclusions: The unique strain collection generated by students in the CURE led to collaboration with biotechnology companies, which contributed to natural product discovery of an antimicrobial product and active enzymatic activity, all of which benefit education and scientific discovery. Full article

The geographical origin of aniseed (Pimpinella anisum L.) represents a key quality determinant, as it directly influences the chemical composition and commercial value of its essential oil. Agronomic traits of aniseed (plant height, umbel diameter, number of umbels per plant), productivity-related traits (number of seeds, thousand-seed weight, yield per plant, plant biomass, harvest index, yield per hectare, essential oil content and yield), and physiological traits (germination energy and total germination) exhibit variations depending on geographical origin. The study proposes an integrated framework for accurate classification by combining agronomic, productivity, and physiological data with GC-MS profiles and advanced machine learning (ML) techniques. A total of 144 samples were analyzed, based on a factorial design including three locations, six fertilizer treatments, two years, and four replications. trans-Anethole was the dominant compound in all samples (89.508–101.441%). Several classification models, including artificial neural networks, random forests, MARSplines, boosted trees, interactive trees, naïve Bayes, and support vector machines, were evaluated to discriminate samples by geographical origin using agro-meteorological and GC-MS data. The results indicate that AI and ML approaches effectively captured complex non-linear relationships. Overall, the multi-model framework highlights the strong potential of machine learning for agro-food authentication, supporting improved traceability, site-specific decision-making, and quality control. Full article

Crete, a major Mediterranean biodiversity hotspot, hosts many local endemic, threatened and/or protected plant taxa (species and subspecies). Besides their ecological and conservation significance, these unique phytogenetic resources hold significant economic potential for sustainable utilization. Since DNA barcoding is critical for conservation, taxonomy, and plant-derived product authentication, we studied 15 local Cretan endemic taxa using three chloroplast DNA (cpDNA) regions (rbcL, trnL, trnH-psbA). A comparative analysis against GenBank (NCBI) records revealed significant new data: (i) the first genetic information for five taxa (Centaurea redempta subsp. redempta, Galium fruticosum, Micromeria hispida, Salix kaptarae, Teucrium cuneifolium); (ii) new marker-specific sequences for seven taxa (Helichrysum heldreichii, Scutellaria hirta, Sesleria doerfleri, Staehelina petiolata, Teucrium alpestre, Campanula pelviformis, Phlomis lanata); and (iii) novel genotypes of already represented markers for three species (Phlomis lanata, Scutellaria sieberi, Staehelina petiolata). Phylogenetic analyses were performed for all three molecular markers across selected members of Scutellaria section Scutellaria, Teucrium section Polium, and Campanula section Quinqueloculares. The overall results indicated that, amongst the studied species, the trnH-psbA marker is more suitable for species-level identification, whereas the rbcL and trnL markers were more helpful to genus-level identification within Lamiaceae and Campanulaceae. These results enrich the DNA barcoding reference library and form a concrete contribution towards the protection, conservation and traceability of Crete’s unique botanical heritage. Full article

The demands of competitiveness in global markets require the integration of Industry 4.0 (I4.0) digital technologies for any manufacturing company, regardless of size. Industrial operations require complete supply chain visibility to ensure data protection and authenticity throughout the process. This document presents a distributed architecture based on RAMI 4.0, designed for product traceability in industrial environments. It integrates automation tools, IIoT communication, cloud storage, artificial intelligence, and secure data transmission using encrypted communication protocols. The system consists of a hybrid architecture; only the first, lower-level layer corresponds to a simulated manufacturing plant with deterministic and stochastic dynamics within the production line. In the second part, the middle and upper layers are implemented, where plant data is transmitted to a cloud instance, stored in a PostgreSQL database, and subsequently analyzed using automated scripts. Reporting capabilities are incorporated with ChatGPT-3.5 Turbo, and visualization is provided through Odoo. Experimental tests demonstrated an average end-to-end communication latency of less than 200 ms, a packet loss rate of 2.67%, and 100% reliability in verifying requested reports when using the cognitive computing service. Furthermore, the results of the systematic vulnerability identification process for the architecture show a significant reduction in overall risk for most assets, with a predominant shift from high or moderate to low or moderate. The proposed architecture is validated in a simulated industrial environment under controlled conditions, demonstrating its viability as a prototype rather than as a fully implemented industrial solution. Full article

Angelica sinensis (Oliv.) Diels (AS) is a medicinal and food plant that has long faced a persistent challenge: its quality and price are often influenced by environmental conditions and geographical origins. To achieve substantial profits, items that are not produced in primary regions, along with counterfeit products, are frequently misbranded as originating from main production areas; this leads to fraud regarding geographic origin and product tampering. Rapid, effective and feasible methods for distinguishing the geographic origin of AS are important for ensuring consumer safety and protecting their interests. This study establishes the authenticity and geographical origins of AS. Meanwhile, diverse machine learning strategies are used to identify the optimal combination by incorporating spectral pre-processing techniques, feature wavenumber selection methods and classification algorithms. The findings reveal that the backpropagation neural network (BPNN), convolutional neural network (CNN) and radial basis function neural network (RBF) excel in determining the authenticity of AS. To distinguish among different growing environments of AS, three models obtained 98.94% classification accuracy on the test set: (1) multiplicative scatter correction (MSC) pre-processing with an RBF classifier, (2) standard normalised variate (SNV) pre-processing with an RBF classifier and (3) Savitzky–Golay (SG) smoothing pre-processing, competitive adaptive reweighted sampling (CARS) for selecting features and a BPNN for classification. This study validates the feasibility of ensemble learning combined with MIR for discriminating AS from authenticity and different geographical sources. Full article

Background: Piper is one of the largest genera in the family Piperaceae, with approximately 2100 species. Most Piper species are used as spices or as medicinal plants. Piper methysticum G. Forst., popularly known as kava-kava (or kava), is widely used to treat anxiety disorders. Due to similar morphological features, P. auritum Kunth (known as “false kava”) is sometimes mistakenly or intentionally used as an alternative botanical source for “kava” extracts. The false kava extracts do not contain active kavalactones but contain safrole, which is hepatotoxic. It is important to verify the component botanical materials in order to evaluate the quality and safety attributes of a potential botanical drug. Some studies have evaluated genetic variation in Piper sp. using the chloroplast regions matK, rbcL, rpoC1 and trnH-psbA and the nuclear ITS2 markers. However, none has focused on the identification of P. methysticum using DNA barcodes. In the present investigation, the ITS2 DNA barcode region from the nuclear genome was tested to confirm the identification and authentication of kava-kava samples. Methods: Seven P. methysticum samples were collected from three different geographic lo-cations and two P. auritum samples were collected and the ITS2 region from the nuclear genome, was amplified, sequenced and aligned to determine their genetic distances. Results: The ITS2 locus showed high amplification and sequence output with a discriminating barcode gap. A distance-based phylogenetic tree and BLAST confirmation (using blastn) revealed the ITS2 locus as a diagnostic DNA barcode for the accurate identification of kava-kava species. Discussion: In conclusion, the ITS2 region proves to be an effective and reliable DNA barcode for distinguishing P. methysticum from closely related species such as P. auritum. Its application can significantly improve the safety, quality, and traceability of kava-containing products, addressing a critical need in the standardization of botanical drugs. Full article

Background: Radish seeds are our model on glucosinolates (GSLs), which is a class of secondary metabolites in medicinal plants of the Brassicaceae family. Multilayer perceptron (MLP) network is highly effective in the study of complex plants. This study came up with a smart plan through the Python language. Methods: First, we used the MLP network to pick out GSL precursor ions, running them through a deep learning filter. Next, we set up an automated screening system and looked at how standard chemicals break down. To speed things up, we created a scoring system that flagged promising compounds. After that, we built a tracer molecular network, basically connecting compounds according to how the plant makes them, which helped us label everything accurately. Finally, we brought in a math-based tool that pieces together different chemical parts to predict new GSL structures. Results: With this workflow, we annotated 195 glucosinolate-related compounds in radish seeds. That includes 86 regular GSLs, 34 malonyl products, 40 sinapoyl compounds, and 35 diglycosides. Among them, eight compounds were confirmed by comparison with authentic standards (retention time and MS/MS data), whereas the remaining compounds were tentatively annotated based on accurate mass measurements, diagnostic fragment ions, Tracer Molecular Nnetworking, and literature/database matching. In addition, 36 compounds were considered putatively novel derivatives pending further structural confirmation. Conclusions: This new approach reduces the time spent on determining chemicals in complicated samples. This can be done with other vegetables and medicinal herbs by researchers. It assists us in knowing the chemistry of plants in a deeper manner. Full article

The growing global population and increasing pressure on conventional food systems have intensified the search for sustainable and nutrient-rich protein sources. Blue foods derived from marine and freshwater organisms offer significant nutritional advantages and lower environmental footprints compared with many terrestrial animal proteins. However, challenges related to resource sustainability, processing, preservation, and product traceability limit their full potential. This review provides a broad overview of emerging technologies shaping the future of blue food systems, covering innovative production strategies, advanced processing techniques, and omics-based analytical approaches. Key developments in cellular aquaculture and cellular mariculture are discussed as promising alternatives to traditional fisheries and aquaculture, enabling the production of blue food through controlled cell cultivation. Additionally, alternative protein platforms including plant-based, fermentation-derived, and cultivated blue food analogues are assessed for their potential to enhance sustainability and diversify aquatic protein sources. Advanced structuring technologies such as extrusion, electrospinning, wet spinning, and 3D printing are highlighted for their roles in developing blue food analogues with improved texture and sensory attributes. Furthermore, non-thermal preservation techniques, including cold plasma (CP), high-pressure processing (HPP), pulsed electric fields (PEFs), and ultraviolet-based treatments, are reviewed for their effectiveness in improving microbial safety and extending shelf life while maintaining nutritional quality. The integration of omics technologies (proteomics, metabolomics, and lipidomics) provides deeper molecular insights into product quality, authenticity, and traceability within blue food supply chains. Collectively, these interdisciplinary advancements demonstrate strong potential to transform blue food production into a more resilient, sustainable, and technology-driven sector. Future progress will depend on overcoming challenges related to scalability, regulatory frameworks, and consumer acceptance to enable the successful commercialization of next-generation blue food products. Full article

The increasing global demand for plant-based meat alternatives, driven by concerns for environmental sustainability, animal welfare, and health, has led to a growing need for reliable food authentication methods. Animal-based and plant-based meat products are visually similar, which poses a challenge for consumers to distinguish them. We developed an electronic nose (e-nose) system with an array of MQ gas sensors (MQ-2, MQ-3, MQ-7, MQ-135, MQ-136, MQ-138), an Arduino MEGA microcontroller, and an LCD for displaying results. A fuzzy logic algorithm was implemented to process sensor data and enable decision-making through membership functions and IF-THEN rule evaluation to classify meat products as either animal meat or plant-based meat. The system performance was validated with 20 independent test samples. Determination accuracy for both categories, as well as the overall accuracy, was assessed using a confusion matrix. The findings demonstrate that the e-nose system can reliably distinguish between animal-based and plant-based meat products. Full article

European pork production pursues traceability and authenticity to ensure animal welfare, food safety, and support products with geographical indications. This study reports a European survey integrating stable isotope ratios (δ¹³C, δ¹⁵N, δ³⁴S, δ¹⁸O, δ²H) and multi-element profiling using IRMS and ICP-MS, on 612 samples collected across Denmark, Poland, Italy, and Spain, with diverse production systems, breeds, feeding, and slaughter ages. Geographical and climatic gradients influenced δ²H and δ¹⁸O, which ranged from −111‰ to −89‰ in samples from Denmark and Spain and from 13.3‰ to 16.0‰ in samples from Italy and Spain, respectively. In selected farms, δ¹³C ranged from −22.7‰ to −17.0‰ depending on diet composition based on C₃ and C₄ plants. The wide variability in pig management practices suggested that δ¹⁵N (2.50 ÷ 4.96‰) increased with slaughter age and was positively correlated with Fe (3.38 ÷ 8.39 mg/kg) and Zn (9.39 ÷ 23.6 mg/kg). Most mineral components were mainly driven by feed formulation and supplementation. Principal component analysis (PCA) showed that samples were grouped based on their origin and husbandry system, confirming the key role of isotopic and elemental markers for the development of a database supporting the pork supply chains across Europe. Full article

The textile sector provides essential goods, yet it remains environmentally and socially intensive, driven by high water use, pesticide dependent monocropping, chemical pollution during processing, and growing waste streams. This review examines credible pathways to sustainability by integrating emerging plant-based fibres from hemp, abaca, stinging nettle, and pineapple leaf fibre. These underutilised crops combine favourable agronomic profiles with competitive mechanical performance and are gaining momentum as the demand for demonstrably sustainable textiles increases. However, conventional fibre identification methods, including microscopy and spectroscopy, often lose reliability after wet processing and in blended fabrics, creating opportunities for mislabelling, greenwashing, and weak certification. We synthesise how advanced molecular approaches, including DNA fingerprinting, species-specific assays, and metagenomic tools, can support the authentication of fibre identity and provenance and enable linkage to Digital Product Passports. We also critically assess environmental Life Cycle Assessment (LCA) and social assessment frameworks, including S-LCA and SO-LCA, as complementary methodologies to quantify climate burden, water use, labour conditions, and supply chain risks. We argue that aligning fibre innovation with molecular traceability and harmonised life cycle evidence is essential to replace generic sustainability claims with verifiable metrics, strengthen policy and certification, and accelerate transparent, circular, and socially responsible textile value chains. Key research priorities include validated marker panels and reference libraries for non-cotton fibres, expanded region-specific LCA inventories and end-of-life scenarios, scalable fibre-to-fibre recycling routes, and practical operationalisation of SO-LCA across diverse enterprises. Full article

Background: “Tejocote, manzanita, tejocotera”, and Mexican hawthorn are the popular common and commercial names of Crataegus mexicana Moc. & Sessé ex DC. This medicinal and edible plant species is widely used for weight loss and for treatment of cardiovascular, inflammatory, neurological, and respiratory infections. Several commercial products are marketed as “Raíz de Tejocote” for weight loss; however, these are frequently adulterated with other plants, other Crataegus species, or other parts of genuine C. mexicana. In this sense, this work aims to provide the anatomical features of the leaf and stem, and especially to authenticate the root of C. mexicana. Methods: The study utilized light microscopy, fluorescence microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy to identify the key characteristics that differentiate the market sample sold under the name Raíz de Tejocote. Results: Anatomical features revealed that the sample sold as Raiz de Tejocote is not a root but a stem. The absence of key diagnostic features such as cork, cortex, cambial layers, and sclereids in the cortex, and the presence of pith, uniseriate rays, radial vessel patterns, and clustered pits, strongly suggests that the market sample is adulterated, most likely derived from a stem of a Crataegus species, but not the C. mexicana. Conclusions: The anatomical comparison indicates that the market sample does not match the root or stem characters of C. mexicana. This comparative anatomical profiling can serve as a reliable authentication parameter, especially if the sample is taken for quality check as a whole, cut and sifted, or coarse powder form, based on the wood characteristics, xylem vessel and fiber characteristics provided. Full article

Domestic demand for honey in Kenya consistently exceeds national production, resulting in periodic reliance on imports. Kenyan honey is typically branded and marketed according to its geographical origin, whereas information regarding botanical origin is rarely communicated. This study was undertaken in two phases: a systematic review of the literature on honey varieties in Kenya—with an emphasis on legislation, production, and processing—and an online survey assessing front-of-pack (FoP) labeling descriptions. Legislatively, Kenyan honey varieties are categorized based on (i) the bee species producing the honey (honeybee or stingless bee), (ii) the intended use (direct human consumption or industrial application), and (iii) the presence of added flavoring agents. The results from the FoP labeling survey indicated that all domestic honey samples (n = 24) failed to comply with labeling requirements, instead emphasizing descriptors such as “natural” and “pure.” Only 40% of imported honey brands (n = 10) declared the botanical origin and processing method. Mellisopalynological studies showed that honey produced in the Acacia woodlands of Baringo, West Pokot, and Kitui can legitimately be marketed as Acacia honey. In contrast, honey from the Eastern Mau forest can be characterized as monofloral Eucalyptus, Croton, Albizia, or Cordia spp. honeys, with numerous bifloral and multifloral combinations. Sisal and mangrove honeys were also identifiable in landscapes dominated by these plant species. The lack of legislative classification for Kenyan monofloral honeys appears to contribute to widespread non-compliance in industry labeling practices. Although Kenyan honey remains competitive, inadequate product differentiation and weak labeling hinder access to niche domestic and international markets. To strengthen competitiveness, Kenyan honey legislation should incorporate provisions for characterizing monofloral honey types, processing standards, and mellisopalynological authentication. Such measures will enhance producer awareness, promote adoption of good processing practices, strengthen compliance with trade regulations, and support the development of a robust national honey value chain. Full article

Honey adulteration has long been a nuisance in local and international trade. Sugar syrup addition and false labeling of botanical origin have created a challenge in identifying fraudulent honey supplies and products. Stable carbon isotope ratio analysis (SCIRA) has been widely employed in honey authentication. While it is effective in identifying the addition of C4 plant-derived sugars, it does not provide information related to honey’s botanical source. This research investigated the combination of SCIRA and melissopalynology to provide a more robust assessment of honey integrity and showed that PCA analysis of δ¹³C together with sugar profiles could further improve the decision involving addition of sugar syrups. A total of 34 beekeeper honey samples were analyzed from 7 provinces of Thailand with a focus on longan honey. Twenty-four samples passed the δ¹³C criteria, exhibiting δ¹³C of bulk honey ranging from −28.53 ± 0.19‰ to −22.89 ± 0.08‰ and δ¹³C of extracted protein ranging from −29.30 ± 0.07‰ to −22.76 ± 0.03‰. Pollen profiling further eliminated honey of questionable and multifloral origins, yielding only eight samples that passed both criteria of being monofloral and not being adulterated with C4-derived sugars. These included six samples of longan honey and two honey samples of other botanical origins, yielding an overall passing rate of 23.5%. Our study showed that by combining SCIRA and melissopalynology, a robust determination of honey integrity could be achieved. Full article