{"title":"Unraveling material characteristics of different pork belly cuts from raw to dish: a comparative study of texture and flavor.","authors":"Mi Tang, Yong Yu, Weijie Hu, Hankun Zhu, Liang Ma, Kangting Sun, Xin Feng, Jinyong Wang, Xiaoqian Wu, Yuhao Zhang","doi":"10.1038/s41538-026-00967-5","DOIUrl":"10.1038/s41538-026-00967-5","url":null,"abstract":"<p><p>Pork belly is widely favored for its alternating fat-lean layers, yet the raw material characteristics of different cuts-upper (UPB), middle (MPB), lower (LPB), and second-knife meat (SKM)-remain unclear. This study selected twice-cooked pork belly (TPB) as the model dish to systematically compare the texture, collagen, microstructure, and flavor of four cuts from raw to cooked stages. Texture analysis showed that the skin layer was mainly related to collagen, while the fat layer was influenced not only by collagen content and composition but also by the extracellular matrix network, as revealed by Masson staining. Lean meat texture was primarily determined by its proportion, resulting in 57.55 ~ 124.45% higher chewiness in SKM. Flavor and precursor analysis revealed that UPB was characterized by higher proline and C20:4n6 and was associated with higher relative abundance of sweet/citrus aldehydes. MPB generated fatty aroma linked to C18:1n9c. LPB delivered complex meaty/roasted flavors from C18:1n9c, C18:2n6c, and Phe. SKM exhibited a flavor profile dominated by 2,3-butanedione due to its high branched-chain amino acid content. Furthermore, biplot analysis elucidated the relationships between raw material characteristics and final quality attributes, providing a theoretical basis for selecting the pork belly cuts in industrial production.</p>","PeriodicalId":19367,"journal":{"name":"NPJ Science of Food","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148339364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chia-Chen Wu, Ying-Ju Chen, Jung-Min Chang, Tzu-Cheng Chang, Tse-Yen Liu
{"title":"Chloroplast genome comparison and chemical profiling support InDel based identification across cinnamon plants.","authors":"Chia-Chen Wu, Ying-Ju Chen, Jung-Min Chang, Tzu-Cheng Chang, Tse-Yen Liu","doi":"10.1038/s41538-026-00941-1","DOIUrl":"https://doi.org/10.1038/s41538-026-00941-1","url":null,"abstract":"<p><p>Cinnamomum osmophloeum, an endemic edible species in Taiwan, is frequently misidentified as the invasive C. burmannii in commercial products. This study developed a DNA-based identification system using chloroplast insertion/deletion (indel) markers, supported by chemical profiling. The comparative analysis of chloroplast genomes between C. osmophloeum and other cinnamon species, yielding 15 indel loci, of which 14 were polymorphic and 6 revealed intraspecific variation. Eight validated indel markers were further used to discriminate cinnamon species. Volatile profiles analyzed by SPME-GC/MS showed that cinnamaldehyde and linalool dominated in C. osmophloeum, whereas borneol and eucalyptol characterized C. burmannii. Application to seven commercial products revealed that five products matched C. osmophloeum genotypes, consistent with labeling. These results demonstrate that chloroplast indel markers provide a robust and rapid tool for cinnamon authentication, while chemical profiling serves as a complementary approach for species discrimination.</p>","PeriodicalId":19367,"journal":{"name":"NPJ Science of Food","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148339352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Generative artificial intelligence creates delicious, sustainable, and nutritious burgers.","authors":"Vahidullah Tac, Christopher D Gardner, Ellen Kuhl","doi":"10.1038/s41538-026-00953-x","DOIUrl":"10.1038/s41538-026-00953-x","url":null,"abstract":"<p><p>Food choices shape both human and planetary health; yet, designing foods that are delicious, nutritious, and sustainable remains challenging. Here we show that generative artificial intelligence can learn the structure of the human palate directly from large-scale, human-generated recipe data to create novel foods within a structured design space. Using burgers as a model system, the generative AI rediscovers the classic Big Mac without explicit supervision and generates novel burgers optimized for deliciousness, sustainability, or nutrition. Compared to the Big Mac, its delicious burgers score the same or better in overall liking, flavor, and texture in a blinded sensory evaluation conducted in a restaurant setting with 101 participants; its mushroom burger achieves an environmental impact score more than an order of magnitude lower; and its bean burger attains nearly twice the nutritional score. Together, these results establish generative AI as a quantitative framework for learning human taste and navigating complex trade-offs in principled food design.</p>","PeriodicalId":19367,"journal":{"name":"NPJ Science of Food","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13309617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148339306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amirhosein Ghozatfar, Tina Sardashti, Deniz Berfin Karakoc
{"title":"Underlying export characteristics and projected country positions in the agri-food trade: A global system analysis.","authors":"Amirhosein Ghozatfar, Tina Sardashti, Deniz Berfin Karakoc","doi":"10.1038/s41538-026-00944-y","DOIUrl":"10.1038/s41538-026-00944-y","url":null,"abstract":"<p><p>The global agriculture and food trade plays a critical role in ensuring food security and economic stability. A country's agri-food export share is contingent upon various social, economic, and physical factors, and it defines its position within the global trade system. In this study, we propose a machine learning framework to systematically identify the most informative characteristics associated with export shares of countries and explore their projected positions within the agri-food trade. Our study focuses on 25 countries with varying export shares spanning from 1988 to 2022. The results show that the performance of logistics systems, population, reliability of government, and value of agricultural production have the greatest impact on shaping the export share of a country. Further, by 2035, the United States, the Netherlands, Brazil, Germany, China, and Australia are projected to be the leading exporters of global agriculture and food trade, following current trends closely. Our findings can offer insights to policymakers and researchers, enabling more strategic decision-making.</p>","PeriodicalId":19367,"journal":{"name":"NPJ Science of Food","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148339341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexandra Steuer, Francesco Ferri, Laura Eckrich, Julia Heidenkampf, Verena Karolin Mittermeier-Kleßinger, Silvia Schaefer, Maik Behrens, Noelia Ferruz, Corinna Dawid, Antonella Di Pizio
{"title":"De novo design and experimental characterization of bitter peptides.","authors":"Alexandra Steuer, Francesco Ferri, Laura Eckrich, Julia Heidenkampf, Verena Karolin Mittermeier-Kleßinger, Silvia Schaefer, Maik Behrens, Noelia Ferruz, Corinna Dawid, Antonella Di Pizio","doi":"10.1038/s41538-026-00942-0","DOIUrl":"10.1038/s41538-026-00942-0","url":null,"abstract":"<p><p>Bitter taste is a critical quality determinant in food systems, particularly those using sustainable protein hydrolysates, where the unpredictable formation of bitter peptides severely limits consumer acceptance. Achieving predictive control over flavor chemistry requires deciphering the complex sequence-activity relationship. To address this, we integrated the generative capacity of a protein language model with BitterPep-GCN, a Graph Convolutional Network (GCN) capable of robust in silico bitter/non-bitter classification, to target the de novo design of functional bitter and non-bitter sequences. We achieved this by generating two strategic peptide libraries: a targeted tripeptide library derived from known bitter and non-bitter peptide sequences, and a set of de novo designed sequences. For the de novo designed peptides, we fine-tuned the conditional language model ZymCTRL on our curated dataset of sensory-validated bitter peptides (BPS-1000). Both libraries were subjected to classification and rigorous filtering using BitterPep-GCN to select high-confidence candidates for validation. The selected peptides were purchased and rigorously assessed for high purity. Sensory tests were conducted by an expert human panel to determine intrinsic taste quality and taste recognition thresholds. The results validated the high predictive fidelity of our pipeline: out of the 31 tested peptides, 25 were correctly classified, including 15 confirmed bitter and 10 confirmed non-bitter sequences. This study successfully demonstrates the application of machine learning frameworks in the design of bioactive peptides. It provides a set of novel taste-active peptides that can be used to accelerate the rational mitigation of off-tastes in next-generation food products.</p>","PeriodicalId":19367,"journal":{"name":"NPJ Science of Food","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13303823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148331699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jie Liu, Wenshan Shi, Shuhui Wan, Da Shi, Jiahao Song, Le Hong, Feifei Wang, Yani Xiong, Wendi Shi, Qing Liu, Zhiying Huo, Jinqiang Li, Pengfei Chen, Yongfang Zhang, Jiamin Xu, Weihong Chen, Bin Wang
{"title":"Urinary caffeine and caffeine metabolites, biological aging, and cardiovascular-kidney-metabolic syndrome: deciphering association, mediation, and mechanism via epidemiology and bioinformatics.","authors":"Jie Liu, Wenshan Shi, Shuhui Wan, Da Shi, Jiahao Song, Le Hong, Feifei Wang, Yani Xiong, Wendi Shi, Qing Liu, Zhiying Huo, Jinqiang Li, Pengfei Chen, Yongfang Zhang, Jiamin Xu, Weihong Chen, Bin Wang","doi":"10.1038/s41538-026-00955-9","DOIUrl":"10.1038/s41538-026-00955-9","url":null,"abstract":"<p><p>Associations of caffeine (the most routinely consumed bioactive compound worldwide) and caffeine metabolites (CAF/CAFMs) with cardiovascular-kidney-metabolic (CKM) syndrome (a newly-introduced high-burden clinical condition) and role of biological aging and underlying mechanisms are uncharted territories warranting pressing decryption. Among 3020 adults, 15 urinary CAF/CAFMs were determined to investigate their single-analyte (by weighted logistic regression) and mixture (by Bayesian kernel machine regression [BKMR] and weighted quantile sum [WQS]) relationships between advanced CKM syndrome. Role of emerging biological aging metric-Gompertz-Law-Based Biological Age Difference (GOLD BioAgeDiff)-was explored by mediation analysis. Underlying mechanisms were explored by network pharmacology. Specific CAF/CAFMs (1-methylxanthine, theophylline, paraxanthine, and theobromine) were negatively linked to advanced CKM syndrome (P and false discovery rate < 0.05). WQS and BKMR uncovered negative CAF/CAFMs mixture-advanced CKM syndrome association (odds ratio = 0.782; 95% confidential interval: 0.670, 0.912). Exploratorily, GOLD BioAgeDiff mediated 13.85-20.06% of the CAF/CAFMs-advanced CKM syndrome associations. Metabolic, mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase-protein kinase B (PI3K-Akt), and apoptosis signaling pathways were exploratorily enriched in the CAF/CAFMs-CKM syndrome relationships. Overall, several CAF/CAFMs and, especially, their mixture showed protective associations with CKM syndrome, which was exploratorily mediated by delayed biological aging, while metabolic, MAPK, PI3K-Akt, and apoptosis signaling pathways might be involved in the underlying mechanisms.</p>","PeriodicalId":19367,"journal":{"name":"NPJ Science of Food","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13658184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148318424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"DNA methylation, transcriptomic, and metabolomic analyses provide new mechanistic insights into cold shock-induced chilling tolerance in cucumber.","authors":"Yiting Ren, Jianlou Mu, Yunxiang Wang, Chunmei Bai, Lili Ma, Jiejie Tao, Xinyi Feng, Tianyu Li, Jinhua Zuo, Xinhuan Yan, Yanyan Zheng","doi":"10.1038/s41538-026-00926-0","DOIUrl":"10.1038/s41538-026-00926-0","url":null,"abstract":"<p><p>Cucumber is cold-sensitive; improper storage induces chilling injury. To investigate how 3 h 0 °C cold shock (CS) alleviates chilling injury in 'Baijinyu' cucumbers at 4 °C, we performed integrated DNA methylation, transcriptomic, and metabolomic analyses. CS alleviated chilling injury, delayed quality decline (soluble solids, firmness, color), and altered accumulation of lipids, keto acids/esters, amino acids, and organic acids. CS increased methylation of peroxidase, caffeic acid 3‑O‑methyltransferase, and cinnamyl‑alcohol dehydrogenase, upregulating peroxidase but downregulating the latter two, accompanied by accumulation of (E)-ferulic acid, L‑tyrosine, and trans‑5‑O‑(p‑coumaroyl)shikimate. CS induced hypomethylation of CG‑type distal intergenic region of NCED and CHH‑type promoter region of abscisic acid receptor PYL, leading to downregulation of NCED and upregulation of PYL, thereby suppressing ABA biosynthesis and modulating ABA signaling. Moreover, CS maintained membrane lipid stability. Overall, CS enhances chilling tolerance by DNA methylation-mediated regulation of transcriptional and metabolic networks, supporting its potential as a green postharvest treatment.</p>","PeriodicalId":19367,"journal":{"name":"NPJ Science of Food","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13655157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148318419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Linda Kalunga, Katherine Koebel, Carrie Alexander, Martin Wiedmann, Aaron Smith, Aaron Adalja, Renata Ivanek
{"title":"Perceptions of voluntary horizontal confidential food safety data sharing: an exploratory interview study with food industry leadership.","authors":"Linda Kalunga, Katherine Koebel, Carrie Alexander, Martin Wiedmann, Aaron Smith, Aaron Adalja, Renata Ivanek","doi":"10.1038/s41538-026-00939-9","DOIUrl":"10.1038/s41538-026-00939-9","url":null,"abstract":"<p><p>Voluntary horizontal sharing of confidential food safety data among companies for joint analysis can improve food safety, efficiency, and decision-making, especially for rare events. Despite its potential benefits, horizontal data sharing in the food industry has lagged, with limited research exploring the reasons for hesitation. To address this gap, we conducted in-depth semi-structured interviews with 27 food industry leaders. Four themes emerged: (1) benefits of data sharing, (2) technical barriers, (3) trust as a determinant of data sharing decisions, and (4) data governance as a solution. Across these themes, we found that companies face trade-offs when deciding to share food safety data, weighing risks against benefits. Data sharing decisions were strongly influenced by trust in stakeholders (e.g., industry peers, regulatory bodies, customers) and in data protection measures. Data governance emerged as a solution to concerns stemming from trust, such as loss of control once data is shared. Taken together, the findings reveal underlying tensions between individual firm incentives and collective benefits, including uneven cost-benefit distributions, opportunism concerns, and participation cost asymmetries. These findings offer timely insights to guide data sharing initiatives and prioritize areas for future research.</p>","PeriodicalId":19367,"journal":{"name":"NPJ Science of Food","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148308556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}