Discover food最新文献

{"title":"Toward accurate prediction of apple firmness and brix across countries, seasons and cultivars with hyperspectral imaging.","authors":"Haidee Tang, Yuan Liu, Issam Boukhennoufa, Wangyang Yu, Xiangming Xu, Xiaojun Zhai","doi":"10.1007/s44187-026-01072-y","DOIUrl":"https://doi.org/10.1007/s44187-026-01072-y","url":null,"abstract":"<p><p>Traditional apple maturity assessment methods are destructive and time- and labour-intensive, yielding only population-level approximations. Hyperspectral imaging provides a non-destructive alternative to assess individual fruit, but progress has been constrained by the lack of large, diverse datasets that support robust model generalisation. This study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness. Using this dataset, we adopt an iterative modelling framework to evaluate deep learning architectures, image resolutions, cultivar encoding, seasonal effects, and feature-specific models. Wavelength and spatial region importance were also analysed. The best predictive performance was achieved using Vision Transformer (ViT) models trained on edge-cropped 40 <i>×</i> 40 pixel images with explicit cultivar encoding, with Brix and firmness modelled independently. Although seasonal specificity was observed, models trained across all three seasons achieved the strongest overall performance. A 50% reduction in spectral wavebands did not compromise prediction accuracy. Key wavelength ranges contributing to Brix and firmness prediction were identified across the visible-near-infrared spectrum. Spatial regions were unimportant for Brix prediction but showed relevance for firmness. The optimised ViT model achieved firmness prediction performance comparable to previous studies (RMSE = 0.76 kgf, R[Formula: see text] = 0.63), while Brix prediction accuracy was lower (RMSE = 0.91 [Formula: see text]Brix, R[Formula: see text] = 0.75), likely reflecting increased biological and environmental variability captured in the dataset. Overall, this work demonstrates that hyperspectral imaging combined with deep learning and large, diverse datasets enables robust, non-destructive prediction of apple quality attributes across production conditions.</p>","PeriodicalId":520360,"journal":{"name":"Discover food","volume":"6 1","pages":"335"},"PeriodicalIF":3.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13473259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whole maize flour could enhance food and nutrition security in Malawi.","authors":"Theresa Nakoma Ngoma, Victor Taleon, Brighton M Mvumi, Aggrey P Gama, Natalia Palacios-Rojas, Limbikani Matumba","doi":"10.1007/s44187-025-00311-y","DOIUrl":"10.1007/s44187-025-00311-y","url":null,"abstract":"<p><p>Maize is the staple cereal in Malawi, with a daily per capita consumption of 383 g (dry matter basis), primarily consumed in the form of <i>nsima</i>, a thick porridge. We combined a milling experiment with focus group discussions (FGDs) to provide insights into mass and nutrient losses during maize grain dehulling and maize flour consumption patterns in rural Malawi. Milling batches (30 kg) of four maize grain varieties were dehulled at three abrasive disk dehullers under controlled conditions. The impact of maize variety and dehuller design on mass and nutrient losses during dehulling was statistically significant (p < 0.05), with a mean mass loss of 28.1 ± 5.7%, and nutrient losses of 9.8 ± 1.9% for protein, 61.7 ± 2.0% for zinc, and 47.7 ± 3.6% for iron. Six FGDs conducted in rural areas of Lilongwe District revealed a preference for refined flour due to convenience and cultural norms, despite the nutritional benefits of whole grain flour, which was recognized for its ability to provide satiety, particularly during periods of maize scarcity. Participants also highlighted switching between flour types based on seasonal maize availability, social stigma associated with whole grain flour, and awareness of nutrient losses during dehulling. Given Malawi's precarious food insecurity situation, transitioning from dehulled maize flour <i>nsima</i> to whole maize flour or less refined <i>nsima</i>, is imperative. Our study findings can have food and nutritional savings for other southern Africa countries where the dehulling is a common practice.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s44187-025-00311-y.</p>","PeriodicalId":520360,"journal":{"name":"Discover food","volume":"5 1","pages":"40"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defining food well-being from the perspective of young Canadian consumers: an exploratory study.","authors":"Tian Zeng","doi":"10.1007/s44187-024-00257-7","DOIUrl":"https://doi.org/10.1007/s44187-024-00257-7","url":null,"abstract":"<p><p>Food well-being (FWB) is fundamental for consumers' overall well-being. Previous studies have explored FWB through five domains (food socialization, literacy, marketing, availability, and policy) or food-related concepts (e.g., food pleasure, healthiness, and satisfaction). However, the understanding of FWB is limited due to two main issues: (1) the absence of a clear definition and reliable measurement tools for this multifaceted and multidisciplinary phenomenon, and (2) the tendency of studies to focus on individual dimensions rather than examining it as an integrated whole, making it difficult to understand how these dimensions are interconnected. To address these gaps, a qualitative exploratory study was conducted with young Canadian consumers using a focus group and 15 individual interviews. A directed content analysis revealed a disconnect between consumer perceptions and the existing definition of FWB. This study makes three key contributions to understanding FWB: first, it provides a deeper insight into FWB conceptualization by identifying new themes within its domains. These include: (1) food socialization, shaped by family, peers, and meal traditions; (2) food literacy, encompassing both conceptual and procedural knowledge, as well as individual goals; and (3) food marketing, shaped by the impact of marketing activities (the 4Ps) on food experiences. Second, this study presents a comprehensive framework that illustrates the interconnections among FWB domains. It highlights, for example, how food socialization influences food literacy, the interactions between food literacy and food marketing, and the role of policy in shaping FWB. Third, it offers practical implications for enhancing FWB in contemporary consumer society.</p>","PeriodicalId":520360,"journal":{"name":"Discover food","volume":"4 1","pages":"185"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}