Journal of Food Science最新文献

{"title":"Peer Review—Can AI Help?","authors":"Richard Hartel","doi":"10.1111/1750-3841.70537","DOIUrl":"10.1111/1750-3841.70537","url":null,"abstract":"<p>One of my biggest concerns related to journal editing is the inconsistency in peer review. The fate of a manuscript often seems to depend on who is assigned as AE and who is called on to provide peer review comments. Let me qualify that. A really top-end paper will get accepted no matter what, and a really poor submission will be rejected accordingly. It's the manuscripts that fall in between where there is variability.</p><p>In my experience, each editor has a slightly different bar by which to judge a manuscript. First, the scientific editors evaluate each manuscript as assigned. They look for a variety of parameters to judge: new and novel work, robust experimental design with appropriate replications and statistical assessment, clear and concise presentation and discussion, appropriate abstract and conclusions, low similarity index, among others.</p><p>Each editor has their own bar regarding grounds for rejection, some much lower than others. This is evident in the rejection rates of individual editors. On average, SEs reject about half of the manuscripts that come to them, the other half being forwarded to AEs to process. But the variation among SEs runs from a low of 3.6% rejected immediately to as high as 84%. That is, one SE essentially sends everything to an AE, while another sends less than 2 out of 10 forward. Although some of this variation may be related to the topic area of interest for each SE, the variation among editors is still high.</p><p>Of those manuscripts sent to an AE, on average, about 50% more are rejected, but again, each AE has their own bar. A couple of AEs reject only about 15% of their manuscripts, while a couple of others reject over 80%. One AE rejects virtually everything sent to them.</p><p>Then we need to factor in the quality of the peer reviewers who evaluate a manuscript. Again, in my experience, this is widely variable; some reviewers provide 2–3 pages of insightful commentary, while others barely provide a sentence with little to no justification of their decision. We strive to get three good critical reviews for each manuscript, but often we don't reach that goal. AEs get to rate each review, so we know who does a consistently good job and who not to trust as much.</p><p>It's these numbers that raise concerns about the equity of our peer review system.</p><p>Some have suggested that we use AI to help make the peer review process easier and perhaps more consistent. But there are some huge roadblocks to this practice, and we still do not allow its use in peer review. The main issue is that it is not appropriate to feed a manuscript into a program like ChatGPT, since that breaks the confidentiality of the peer review process itself. And still, can you really trust everything that ChatGPT says?</p><p>Some LLMs do not require material to be added to the database, like Copilot. Still, we do not allow Copilot's use to conduct the review. While AI can provide interesting insights, depending on the questions as","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ift.onlinelibrary.wiley.com/doi/epdf/10.1111/1750-3841.70537","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"A Comparative Analysis on the Classification of Pineapple Varieties Using Thermal Imaging Coupled With Transfer Learning","authors":"Norhashila Hashim,&nbsp;Maimunah Mohd Ali","doi":"10.1111/1750-3841.70530","DOIUrl":"10.1111/1750-3841.70530","url":null,"abstract":"<div>\u0000 \u0000 <p>Advanced intelligent systems are becoming a significant trend, especially in the classification of tropical fruits due to their unique flavor and taste. As one of the most popular tropical fruits worldwide, pineapple (<i>Ananas comosus</i>) has a great chemical composition and is high in nutritional value. A non-destructive method for the determination of pineapple varieties was developed, which utilized thermal imaging and deep learning techniques. This study presents a comparative analysis of three deep learning models, including ResNet, VGG16, and InceptionV3, for the rapid classification of pineapple varieties using thermal imaging and transfer learning. The dataset comprises 3240 thermal images from three different pineapple varieties, including Moris, Josapine, and N36, under controlled temperature conditions (5°C, 10°C, and 25°C), resulting in a total of three classification classes. All convolutional neural network (CNN) architectures were fine-tuned, and data augmentation techniques were applied to improve model generalization. The efficiency of hyperparameters was evaluated to improve the model accuracy, whereas the data augmentation was carried out to avoid model overfitting. The highest classification accuracy of 99 % was achieved via InceptionV3. The precision, recall, and F1-score demonstrate promising results with the values higher than 0.85 for all pineapple varieties. This approach demonstrated that transfer learning with CNNs is significantly promising as a feature extraction method for the determination of physicochemical properties in pineapple fruit. An ablation study confirmed the added benefit of using both data augmentation and transfer learning. While model architecture innovation was not the primary goal, this work contributes by benchmarking established CNN models for agricultural thermal imaging applications.</p>\u0000 </div>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Coffee Roast Level Classification Using Machine Learning and Deep Learning Models","authors":"René Ernesto García Rivas,&nbsp;Pedro Luiz Lima Bertarini,&nbsp;Henrique Fernandes","doi":"10.1111/1750-3841.70532","DOIUrl":"10.1111/1750-3841.70532","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>The coffee roasting process is a critical factor in determining the final quality of the beverage, influencing its flavour, aroma, and acidity. Traditionally, roast-level classification has relied on manual inspection, which is time-consuming, subjective, and prone to inconsistencies. However, advancements in machine learning (ML) and computer vision, particularly convolutional neural networks (CNNs), have shown great promise in automating and improving the accuracy of this process. This study evaluates multiple ML models for coffee roast level classification, including a CNN with Xception as a feature extractor, alongside AdaBoost, random forest (RF), and support vector machine (SVM). The models were trained and tested on a public dataset of 1,600 high-quality images, balanced across four roast levels: green, light, medium, and dark, to ensure robust performance. Experimental results demonstrate that all models achieved 100 % accuracy and F-1 scores, confirming their effectiveness in accurately distinguishing roast levels. Furthermore, the proposed approach was compared with previous studies, showing strong performance in roast classification. Image augmentation techniques were applied to improve generalizability in real-world applications. This research presents a reliable, scalable, and fully automated solution for roast-level classification, significantly contributing to quality control in the coffee industry.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Practical Applications</h3>\u0000 \u0000 <p>This research offers a reliable and automated way to classify coffee bean roast levels using image analysis and ML. It can help coffee producers and roasters improve quality control by providing faster, more consistent, and objective assessments of roast levels, ultimately ensuring a better product for consumers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ift.onlinelibrary.wiley.com/doi/epdf/10.1111/1750-3841.70532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Effects of α-Lipoic Acid, vitamin E, and Selenium Combination on Inflammation-Independent ROS-Induced Damage in Kidney Tissue of Diabetic Mice","authors":"Ayse Karatug Kacar,&nbsp;Onur Ertik,&nbsp;Nilay Dinckurt,&nbsp;Unal Arabaci,&nbsp;Pinar Obakan Yerlikaya,&nbsp;Refiye Yanardag,&nbsp;Sehnaz Bolkent","doi":"10.1111/1750-3841.70544","DOIUrl":"10.1111/1750-3841.70544","url":null,"abstract":"<div>\u0000 \u0000 <p>Diabetes is a metabolic and chronic disease affecting different tissues' metabolism. Genetic factors, lifestyles, and dietary habits can cause it. In diabetes, oxidative stress can occur in metabolic disorders, negatively affecting it. The antioxidants are essential in reducing or completely stopping the harmful effects of these adverse effects on the tissues. In the present study, we aimed to determine the combined effects of lipoic acid, vitamin E, and selenium in the kidneys of diabetic mice. For this experiment, the Balb/c mice were used and divided into five groups: citrate buffer, the solvents of the antioxidants, combined the antioxidants (α-lipoic acid, vitamin E, and selenium), streptozotocin, combined with the antioxidants and streptozotocin (A+D). At the end of 30 days of this process, the mice were sacrificed by cervical dislocation. Kidney tissues were taken for morphological, Western blotting, and biochemical analyses. The tissue was used for staining with Masson's trichrome and periodic acid–Schiff (PAS) of renal tissue sections taken for histological analysis; Western blotting such as the level of IL-10, IL-1β, TGF-β, p38, cCas3, NRF2; biochemical parameters such as the level of GSH, LPO, SOD, CAT, GR, TAS, TOS, ROS, OSI, PON, CA, LDH, AR, ADA, arginase, OH-proline, and AOPP. The histological findings showed mild damage to the kidney tissue of diabetic mice. Western blot results showed that the damage was independent of inflammation. Biochemical results revealed that administering combined antioxidants to diabetic mice protects the kidney tissue.</p>\u0000 </div>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Analysis on the Classification of Pineapple Varieties Using Thermal Imaging Coupled With Transfer Learning","authors":"Norhashila Hashim,&nbsp;Maimunah Mohd Ali","doi":"10.1111/1750-3841.70530","DOIUrl":"10.1111/1750-3841.70530","url":null,"abstract":"<div>\u0000 \u0000 <p>Advanced intelligent systems are becoming a significant trend, especially in the classification of tropical fruits due to their unique flavor and taste. As one of the most popular tropical fruits worldwide, pineapple (<i>Ananas comosus</i>) has a great chemical composition and is high in nutritional value. A non-destructive method for the determination of pineapple varieties was developed, which utilized thermal imaging and deep learning techniques. This study presents a comparative analysis of three deep learning models, including ResNet, VGG16, and InceptionV3, for the rapid classification of pineapple varieties using thermal imaging and transfer learning. The dataset comprises 3240 thermal images from three different pineapple varieties, including Moris, Josapine, and N36, under controlled temperature conditions (5°C, 10°C, and 25°C), resulting in a total of three classification classes. All convolutional neural network (CNN) architectures were fine-tuned, and data augmentation techniques were applied to improve model generalization. The efficiency of hyperparameters was evaluated to improve the model accuracy, whereas the data augmentation was carried out to avoid model overfitting. The highest classification accuracy of 99 % was achieved via InceptionV3. The precision, recall, and F1-score demonstrate promising results with the values higher than 0.85 for all pineapple varieties. This approach demonstrated that transfer learning with CNNs is significantly promising as a feature extraction method for the determination of physicochemical properties in pineapple fruit. An ablation study confirmed the added benefit of using both data augmentation and transfer learning. While model architecture innovation was not the primary goal, this work contributes by benchmarking established CNN models for agricultural thermal imaging applications.</p>\u0000 </div>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Deep Learning in Primary Agricultural Products Freshness Assessment: A Systematic Review","authors":"Yifan Kang,&nbsp;Yijie Li,&nbsp;Hanyu Wang,&nbsp;JiangLi Guo,&nbsp;Ziqi Huang,&nbsp;Juan Du","doi":"10.1111/1750-3841.70535","DOIUrl":"10.1111/1750-3841.70535","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> ABSTRACT</h3>\u0000 \u0000 <p>Primary agricultural products are closely related to our daily lives, as they serve not only as raw materials for food processing but also as products directly purchased by consumers. These products face the issue of freshness decline and spoilage during both production and consumption. Freshness degradation induces sensory deterioration and nutritional loss and promotes harmful substance accumulation, causing gastrointestinal issues or even endangering life. Currently, the freshness of primary agricultural products evaluation methods primarily includes sensory evaluation, spectroscopy, and colorimetric analysis. However, these techniques generally suffer from strong subjectivity, high requirements for specialized skills, and lengthy detection times. As a significant branch of machine learning, deep learning is based on neural networks and employs multi-layer architectures to process and learn from large-scale, complex data. It is capable of automatically extracting features from data and, by learning these features, can recognize and predict complex patterns. The application of deep learning for primary agricultural product freshness assessment not only enables automation and intelligent analysis of the evaluation process but also allows trained models to rapidly and accurately assess primary agricultural product freshness, thereby reducing the influence of manual intervention and subjective judgment. This paper reviews the integration of machine vision (based on physicochemical properties and smart visual labels), spectroscopy (hyperspectral imaging, near infrared spectrum, fluorescence spectra, and Raman spectrum), and electronic noses with deep learning for freshness of primary agricultural products evaluation, and highlights the current limitations of these technologies along with future development directions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ift.onlinelibrary.wiley.com/doi/epdf/10.1111/1750-3841.70535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"The Use of Deep Learning in Primary Agricultural Products Freshness Assessment: A Systematic Review","authors":"Yifan Kang,&nbsp;Yijie Li,&nbsp;Hanyu Wang,&nbsp;JiangLi Guo,&nbsp;Ziqi Huang,&nbsp;Juan Du","doi":"10.1111/1750-3841.70535","DOIUrl":"https://doi.org/10.1111/1750-3841.70535","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> ABSTRACT</h3>\u0000 \u0000 <p>Primary agricultural products are closely related to our daily lives, as they serve not only as raw materials for food processing but also as products directly purchased by consumers. These products face the issue of freshness decline and spoilage during both production and consumption. Freshness degradation induces sensory deterioration and nutritional loss and promotes harmful substance accumulation, causing gastrointestinal issues or even endangering life. Currently, the freshness of primary agricultural products evaluation methods primarily includes sensory evaluation, spectroscopy, and colorimetric analysis. However, these techniques generally suffer from strong subjectivity, high requirements for specialized skills, and lengthy detection times. As a significant branch of machine learning, deep learning is based on neural networks and employs multi-layer architectures to process and learn from large-scale, complex data. It is capable of automatically extracting features from data and, by learning these features, can recognize and predict complex patterns. The application of deep learning for primary agricultural product freshness assessment not only enables automation and intelligent analysis of the evaluation process but also allows trained models to rapidly and accurately assess primary agricultural product freshness, thereby reducing the influence of manual intervention and subjective judgment. This paper reviews the integration of machine vision (based on physicochemical properties and smart visual labels), spectroscopy (hyperspectral imaging, near infrared spectrum, fluorescence spectra, and Raman spectrum), and electronic noses with deep learning for freshness of primary agricultural products evaluation, and highlights the current limitations of these technologies along with future development directions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ift.onlinelibrary.wiley.com/doi/epdf/10.1111/1750-3841.70535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Specific Flavor Substances of Liqueur Koji Fermented Foxtail Millet Beverage: As Indicators of Flavor Characteristics and Antioxidant Capacity","authors":"Junli Liu,&nbsp;Rui Zhang,&nbsp;Zhimin Chen,&nbsp;Wei Zhao,&nbsp;Aixia Zhang,&nbsp;Jingke Liu","doi":"10.1111/1750-3841.70496","DOIUrl":"10.1111/1750-3841.70496","url":null,"abstract":"<div>\u0000 \u0000 <p>Liqueur koji-fermented foxtail millet beverages offer distinctive flavors and health benefits, but the interrelationships among flavor compounds, sensory properties, and antioxidant activity remain unelucidated. This study systematically mapped dynamic changes across a standardized 72 h fermentation using chromatographic, electronic sensory approaches, and antioxidant assays. Key results revealed glucose, lactic acid, and succinic acid as primary taste-active indicators through HPLC. Early-stage fermentation (0-48 h) accumulated sweet amino acids, while extended fermentation (60-72 h) significantly elevated bitter amino acids, enhancing flavor complexity. GC-MS coupled with OPLS-DA identified 14 signature aroma markers (rOAV&gt;1 and VIP&gt;1), including isoamyl acetate, decanal, and phenylacetaldehyde, predominantly accumulating in later stages to impart floral, fruity, and fatty notes. Radical scavenging assays demonstrated progressively enhanced antioxidant activity, with correlation analyses confirming dual functionality of taste compounds. Specifically, lactic acid, succinic acid, and glucose strongly correlated with sourness, richness, and sweetness, while phenolic compounds, organic acids (lactic, succinic, and malic acids), trehalose, and amino acids (e.g., Cys, Phe, and Val) served as indicators of antioxidant capacity. These bioactive markers provide practical metrics for flavor and bioactivity assessment, advancing quality optimization in functional millet beverages.</p>\u0000 </div>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolite and Sensory Profiling of Microwave-Assisted and Ultrasonic-Assisted Alkalized Cocoa Powders","authors":"Mary Faith Adan,&nbsp;Dimas Rahadian Aji Muhammad,&nbsp;Danar Praseptiangga,&nbsp;Eiichiro Fukusaki,&nbsp;Sastia Prama Putri","doi":"10.1111/1750-3841.70508","DOIUrl":"10.1111/1750-3841.70508","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Microwave (MA)- and ultrasonic-assisted (UA) alkalization were applied to cocoa powders for 5, 10, and 15 min to investigate their effects on metabolite composition, sensory profile, and consumer liking. Widely targeted gas chromatography–mass spectrometry (GC-MS) analysis showed alkalization altered metabolite profiles: Natural cocoa powders contained more amino acids, sugars, and flavonoids, while alkalized samples had elevated organic acids. Compared to conventionally alkalized (CA) samples, only UA15 showed a significant increase in 3,4-dihydroxybenzoic acid. UA15 also exhibited the greatest reduction in reducing sugars and hydrophobic amino acids, specific precursors for cocoa aroma via the Maillard reaction. Headspace solid-phase microextraction GC-MS revealed that alkalized samples contained more Maillard-reaction-derived aroma-active compounds, including aldehydes, esters, ketones, pyrazines, and furan, with UA15 showing the highest levels, suggesting enhanced Maillard activity. MA10 and UA5 had elevated n-hexadecane and 2,3-pentanedione, suggesting enhanced release and fragmentation–recombination of sugar-derived intermediates. Rate-All-That-Apply results showed alkalized samples were associated with sweeter and more intense cocoa flavor. Hedonic test results indicated consumers preferred alkalized samples over natural cocoa powder, which was perceived as overly acidic and burnt. However, differences in sensory attributes and overall liking among the alkalized samples were not significant and showed high variability, suggesting that consumer panelists cannot detect subtle sensory differences between CA, MA, and UA samples. Future studies should focus on determining the appropriate MA and UA treatment conditions to produce cocoa powders that better meet consumer preferences. This is the first report on MA and UA alkalization effects on cocoa powder quality and offers valuable insights for product development using hybrid alkalization processes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Practical Applications</h3>\u0000 \u0000 <p>The changes in the sensory and chemical profiles of cocoa powders upon microwave-assisted and ultrasonic-assisted alkalization were revealed. This information on the effect of the hybrid alkalization process on food quality and consumer preference of the cocoa powders is of value for consideration in cocoa powder processing and development.</p>\u0000 </section>\u0000 </div>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge as a Key Factor in Consumption and Its Importance for Agrifood Product Communication","authors":"Elisa Garrido-Castro,&nbsp;Sergio Valdelomar-Muñoz,&nbsp;Francisco J. Torres-Ruiz,&nbsp;Eva M. Murgado-Armenteros","doi":"10.1111/1750-3841.70528","DOIUrl":"10.1111/1750-3841.70528","url":null,"abstract":"<p>Consumer knowledge is a variable that has been widely studied in the literature due to its strong influence on behavior, which could be crucial in some specific contexts where product consumption largely depends on what the consumer specifically knows about the product in question. Communication campaigns designed to convey clear and useful information for the purchase decision are the key to increasing knowledge and, consequently, consumption. Using data collected from a total of 3200 olive oils consumers (Spain, Germany, the United Kingdom, and Denmark), this work studies the level of consumer objective knowledge of olive oils, an essential component of the Mediterranean diet and offers several recommendations for informing communication strategy in this sector to improve knowledge and drive up the consumption of high-quality oil.</p><p>The results reveal significant differences and, in general, a great lack of knowledge and confusion around olive oils in the analyzed countries, particularly in nonproducing countries.</p>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ift.onlinelibrary.wiley.com/doi/epdf/10.1111/1750-3841.70528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}