Search Results (1,986)

The quality evaluation of processed potatoes is vital in the food industry. In this study, the effect of three different drying methods on the post-processing quality of potatoes utilizing 4, 8, 12, and 16 h of freeze drying (FD), infrared drying (ID), and oven drying (OD) was investigated. The impact of the drying methods on the potato’s microstructure was analyzed and quantified using 3D X-ray micro-computed tomography images. A new Hybrid Quality Score Evaluator (HQSE) was introduced and used to assess the Quality Index (QI) and Specific Energy Consumption Index (SECI) across various drying methods and durations. Mathematical models were developed to predict the optimal drying method. FD showed significantly higher (p < 0.05) colour retention, rehydration ratio, and total porosity, with minimal shrinkage, although it had higher energy consumption. ID had the shortest drying time, followed by OD and FD. The optimization showed that for FD, the optimal time of 5.78 h increased QI by 9.7% and SECI by 30.6%. The mathematical models could accurately predict the QI and SECI under different drying methods, balancing quality preservation with energy efficiency. The findings suggest that a hybrid drying system could optimize potato quality and energy consumption. Full article

The advent of self-driving technology marks a significant milestone in the evolution of modern transportation and logistics services. More importantly, self-driving food delivery services are expected to play a significant role in environmental protection by operating on batteries instead of the traditional gasoline. The current study examines the relationship between perceived attributes, image, normative factors, and behavioral intentions in the context of eco-friendly self-driving food delivery services. The study deepens the framework by identifying the moderating role of country development status. The study gathered samples from 313 panels in South Korea, a developed country, and 315 respondents in Mongolia, a developing country. The results of the South Korean dataset showed that two types of perceived attributes, perceived innovativeness and perceived risk significantly affect image, which in turn leads to the formation of behavioral intentions. Normative factors, such as subjective norms and personal norms, also positively affect behavioral intentions, and subjective norms increase personal norms. The results of the Mongolian dataset indicated that all paths are statistically supported. Lastly, the moderating role of the country development status was found in the relationship between (1) perceived innovativeness and perceived risk, (2) subjective norms and personal norms, and (3) subjective norms and behavioral intentions. Full article

This study presents the development and characterization of medium-chain fatty acid (MCFA)-loaded bigels, using coconut oil as the MCFA source. The bigels exhibited high oil binding capacity, ranging from 87% to 98%, effectively retaining MCFAs within the matrix, with lauric acid (C12) being the main component detected within the bigels at 178.32 ± 0.10 mg/g. Physicochemical analysis, including FTIR and scanning electron microscopy, confirmed stable fatty acid incorporation and a cohesive, smooth structure. The FTIR spectra displayed O-H and C=O stretching vibrations, indicating hydrogen bonding within the matrix, while the SEM images showed uniform lipid droplet distribution with stable phase separation. Thermal stability tests showed that the bigels were stable for 5 days at 50 °C, with oil retention and structural integrity unchanged. Rheological testing indicated a solid-like behavior, with a high elastic modulus (G′) that consistently exceeded the viscous modulus (G″), which is indicative of a strong internal structure. In simulated gastrointestinal digestion, the bigels achieved significantly higher MCFA retention than the pure oil, particularly in the gastric phase, with recovery percentages of 38.1% for the bigels and 1.7% for the oil (p < 0.05), suggesting enhanced bioavailability. Cell-based cytotoxicity assays showed low cytotoxicity, and permeability testing in a co-culture Caco-2/HT29-MTX model revealed a controlled, gradual MCFA release, with approximately 10% reaching the basolateral side over 6 h. These findings highlight MCFA-loaded bigels as a promising platform for nutraceutical applications; they provided stability, safety, and controlled MCFA release, with significant potential for functional foods aimed at enhancing fatty acid bioavailability. Full article

Although most American eat at a fast-food restaurant 1 to 3 times a week, they would realize that fast food consumption is highly associated with chronic diseases and generates negative impacts on their health. As fast-food consumers become more health-conscious, fast-food brands strive to build a more health-oriented image on their fast-food products. Thus, this study proposes a conceptual model that aims to examine direct and indirect impacts of consumers’ BMI, self-efficacy, perceived brand trust, and brand commitment on their fast-food consumption. An online survey using Amazon Mechanical Turk is conducted with a total of 484 female and 380 male participants included in the final analysis. Results show that the mediating effects of self-efficacy, brand trust in, and brand commitment with the fast-food product with nutrition facts are significantly generated on consumers’ fast-food consumption. Moreover, indirect effects are found on consumers’ fast-food consumption via the nexus of their self-efficacy, brand trust, and brand commitment. The study also offers practical insights into the impact of health-conscious consumers’ brand perceptions on their fast-food consumption. Full article

Pest infestations significantly impact rice production and threaten food security. Remote sensing offers a vital tool for the non-destructive, rapid detection of rice pests. Existing studies often focus on laboratory conditions at the leaf level, limiting their applicability for precise pesticide application. Therefore, this study aimed to develop a method for detecting rice pests (rice leaf folders) in paddy fields based on unmanned aerial vehicle (UAV) hyperspectral data. Firstly, a UAV imaging system collected hyperspectral images of rice plants in both the jointing and heading stages. A total of 222 field plots for investigating rice leaf folders was established during these two periods. Secondly, 23 vegetation indices were calculated as candidates for identifying rice pests. Then, hyperspectral data and field investigation data from the jointing stage were used to construct a machine learning (extreme gradient boosting, XGBoost) algorithm for detecting rice pests. The results showed that the XGBoost model exhibited the best performance when eight vegetation indices were considered as the selected input features for model construction: the Red-edge Normalized Difference Vegetation Index (red-edge NDVI), Structure Insensitive Pigment Index (SIPI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Soil-Adjusted Vegetation Index (SAVI), Red-edge Chlorophyll Index (CIred-edge), Pigment-Specific Simple Ratio₆₈₀ (PSSR₆₈₀), and Carotenoid Reflectance Index₇₀₀ (CPI₇₀₀). The training and testing accuracies reached 87.46% and 86%, respectively. Furthermore, the heading stage application confirmed the model’s feasibility. Thus, the XGBoost model with input features of eight vegetation indices provides an effective and reliable method for detecting rice leaf folders, supporting real-time, precise pesticide use in rice cultivation. Full article

Conventional food fraud detection using hyperspectral imaging (HSI) relies on the discriminative power of machine learning. However, these approaches often assume a balanced class distribution in an ideal laboratory environment, which is impractical in real-world scenarios with diverse label distributions. This results in suboptimal performance when less frequent classes are overshadowed by the majority class during training. Thus, the critical research challenge emerges of how to develop an effective classifier on a small-scale imbalanced dataset without significant bias from the dominant class. In this paper, we propose a novel nondestructive detection approach, which we call the Dice Loss Improved Self-Supervised Learning-Based Prototypical Network (Proto-DS), designed to address this imbalanced learning challenge. The proposed amalgamation mitigates the label bias on the most frequent class, further improving robustness. We validate our proposed method on three collected hyperspectral food image datasets with varying degrees of data imbalance: Citri Reticulatae Pericarpium (Chenpi), Chinese herbs, and coffee beans. Comparisons with state-of-the-art imbalanced learning techniques, including the Synthetic Minority Oversampling Technique (SMOTE) and class-importance reweighting, reveal our method’s superiority. Notably, our experiments demonstrate that Proto-DS consistently outperforms conventional approaches, achieving the best average balanced accuracy of 88.18% across various training sample sizes, whereas the Logistic Model Tree (LMT), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) approaches attain only 59.42%, 60.38%, and 66.34%, respectively. Overall, self-supervised learning is key to improving imbalanced learning performance and outperforms related approaches, while both prototypical networks and the Dice loss can further enhance classification performance. Intriguingly, self-supervised learning can provide complementary information to existing imbalanced learning approaches. Combining these approaches may serve as a potential solution for building effective models with limited training data. Full article

This study evaluated the differences in oral processing and texture perception of breads with varying compositions. The research investigated the dynamic changes in moisture content (MC), reducing sugars (RSs), and chewiness of the bolus formed from white bread (B0) and 50% whole-wheat bread (B50) during oral processing. Hyperspectral imaging (HSI) combined with chemometric methods was used to establish quantitative prediction models for MC, RSs, and chewiness, and to create visual distribution maps of these parameters. The results showed that B0 had a higher moisture content and a faster hydration rate than B50 during the initial stages of oral processing, indicating greater hydrophilicity and ease of saliva wetting. Additionally, the uniformity of moisture distribution in the bolus of B0 was higher than that of B50. B50 exhibited significantly lower RSs content and poorer distribution uniformity compared to B0. The primary differences in chewiness between the two types of bread were observed during the early stages of oral processing, with B50 requiring more chewing effort initially. This study demonstrated that HSI technology can effectively monitor and elucidate the compositional changes in food particles during oral processing, providing new insights into bread texture perception and offering a scientific basis for improving bread processing and texture. Full article

Background: Front-of-pack-labels (FOPLs) on packaged foods provide essential information to help consumers make informed dietary choices. However, evidence on their effectiveness, particularly in low- and middle-income countries like Kenya, is limited. Objective: This study assessed the effectiveness of three FOPLs in helping consumers identify nutrients of concern in packaged food products and influencing their purchase intention in Kenya. Methods: A total of 2198 shoppers from supermarkets in Nairobi, Mombasa, Kisumu, and Garissa were randomized into three groups: Red and Green Octagon label (RG), Red and Green Octagon with icons (RGI), and Black Octagon Warning label (WL). In the control phase, participants were shown unlabeled images of packaged foods, followed by questions. In the experimental phase, the same images were presented with one assigned FOPL, and participants responded again to the same set of questions. Differences in correct identification of nutrients of concern and changes in purchase intention were analyzed using frequency tables and Chi-Square tests, while modified Poisson regression assessed FOPL effectiveness. Results: FOPLs significantly improved correct nutrient identification and reduced the intention to purchase unhealthy foods, with the WL proving most effective. Conclusions: These findings highlight the potential of FOPLs, particularly the WL, as an effective regulatory tool for promoting healthier food choices in Kenya. Full article

Paddy fields are essential for food security and sustaining global dietary needs, yet urban expansion often encroaches on agricultural lands. Analyzing paddy fields and land use/land cover changes over time using satellite images provides critical insights for sustainable food production and balanced urban growth. However, mapping the paddy fields in tropical monsoon areas presents challenges due to persistent weather interference, monsoon-submerged fields, and a lack of training data. To address these challenges, this study proposed a flooding-assisted maximum likelihood classification (F-MLC) method. This approach utilizes accurate training datasets from intersecting flooded paddy field maps from the rainy and dry seasons, combined with the Automated Water Extraction Index (AWEI) to distinguish natural water bodies. The F-MLC method offers a robust solution for accurately mapping paddy fields and land use changes in challenging tropical monsoon climates. The classified images for 1989, 2000, 2013, and 2021 were produced and categorized into the following five major classes: urban areas, vegetation, paddy fields, water bodies, and other lands. The paddy field class derived for each year was validated using samples from various sources, contributing to the overall accuracies ranging from 83.6% to 90.4%, with a Kappa coefficient of between 0.80 and 0.88. The study highlights a significant decrease in paddy fields, while urban areas rapidly increased, replacing 23% of paddy fields between 1989 and 2021 in the watershed. This study demonstrates the potential of the F-MLC method for analyzing paddy fields and other land use changes over time in the tropical watershed. These findings underscore the urgent need for robust policy measures to protect paddy fields by clearly defining urban expansion boundaries, prioritizing paddy field preservation, and integrating these green spaces into urban development plans. Such measures are vital for ensuring a sustainable local food supply, promoting balanced urban growth, and maintaining ecological balance within the watershed. Full article

Modern bioeconomy and sustainability demands lead food technology in the development of novel biobased edible food preservatives. Herein, the development and characterization of novel polysaccharide (xanthan gum and kappa-carrageenan)-based nanoemulgels (NGs) enhanced with essential oil derivatives; pure citral (CT); pure carvacrol (CV); and various CT:CV ratios (25:75, 50:50, and 75:25) are presented. The obtained NGs are applied as active edible coatings for extending the shelf life of Protected Designation of Origin (PDO) green table olives of Chalkidiki. The zeta potential demonstrated the high stability of the treatments, while light scattering measurement and scanning electron microscopy images confirmed the <100 nm droplet size. EC₅₀ indicated high antioxidant activity for all the tested samples. The fractional inhibitory concentration (FIC) confirmed the synergistic effect of NG with a CT:CV ratio at 50:50 against Staphylococcus aureus and at CT:CV ratios 25:75 and 75:25 against E. coli O157:H7. NG coatings with CT:CV ratios at 50:50 and at 25:75 effectively controlled the weight loss at 0.5%, maintained stable pH levels, and preserved the visual quality of green olives on day 21. The synergistic effect between CT and CV was confirmed as they reduced the spoilage microorganisms of yeasts and molds by 2-log [CFU/g] compared to the control and almost 1 log [CFU/g] difference from pure CT and CV-based NGs without affecting the growth of beneficial lactic acid bacteria crucial for fermentation. NGs with CT:CV ratios at 50:50 and at 25:75 demonstrated superior effectiveness in preventing discoloration and maintaining the main sensory attributes. Overall, shelf life extension was achieved in 21 compared to only 7 of the uncoated ones. Finally, this study demonstrates the potential of polysaccharide-based NGs in mixtures of CT and CV for the shelf life extension of fermented food products. Full article

By-products generated in the winemaking industry contain compounds with health-promoting properties, which can be reintroduced into the food production chain. This study evaluated the use of a by-product from the industrial processing of grapes as an ingredient in the manufacture of Petit Suisse cheese, made with A2A2 milk and the addition of the probiotic Bifidobacterium animalis subsp. lactis HN019. Two Petit Suisse formulations were made in three independent batches: a control formulation without the addition of the by-product (F0) and a formulation containing 10% of the by-product (F1). The proximate composition of the cheeses was characterized on the first day after manufacturing them. The addition of the by-product led to an increase in ash, lipids, and carbohydrates and a reduction in moisture and protein contents. The physicochemical characterization and the texture profile analysis showed no changes throughout the product’s shelf life. The probiotic counts remained abundant (~eight log CFU/g) in both formulations with no changes seen throughout the shelf life period. Scanning electron microscopy images showed the added bacteria had typical structures. No differences were observed in the fatty acid profiles of the formulations, and both exhibited a total of 18 fatty acids, including saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs). Additionally, the by-product conferred antioxidant activity to the F1 formulation. The addition of the by-product in fresh cheese may be an interesting approach in regards to the processing technology used, its microbiological safety, and its nutritional value. The use of A2A2 milk and a probiotic culture thus enhanced the Petit Suisse cheese, resulting in a healthier product. Full article

Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals. Full article

Coastal aquaculture plays a crucial role in global food security and the economic development of coastal regions, but it also causes environmental degradation in coastal ecosystems. Therefore, the automation, accurate extraction, and monitoring of coastal aquaculture areas are crucial for the scientific management of coastal ecological zones. This study proposes a novel deep learning- and attention-based median adaptive fusion U-Net (MAFU-Net) procedure aimed at precisely extracting individually separable aquaculture ponds (ISAPs) from medium-resolution remote sensing imagery. Initially, this study analyzes the spectral differences between aquaculture ponds and interfering objects such as saltwater fields in four typical aquaculture areas along the coast of Liaoning Province, China. It innovatively introduces a difference index for saltwater field aquaculture zones (DIAS) and integrates this index as a new band into remote sensing imagery to increase the expressiveness of features. A median augmented adaptive fusion module (MEA-FM), which adaptively selects channel receptive fields at various scales, integrates the information between channels, and captures multiscale spatial information to achieve improved extraction accuracy, is subsequently designed. Experimental and comparative results reveal that the proposed MAFU-Net method achieves an F1 score of 90.67% and an intersection over union (IoU) of 83.93% on the CHN-LN4-ISAPS-9 dataset, outperforming advanced methods such as U-Net, DeepLabV3+, SegNet, PSPNet, SKNet, UPS-Net, and SegFormer. This study’s results provide accurate data support for the scientific management of aquaculture areas, and the proposed MAFU-Net method provides an effective method for semantic segmentation tasks based on medium-resolution remote sensing images. Full article

Globally, the agricultural industry has benefited from using pesticides to minimize crop losses. Nevertheless, the indiscriminate overuse of pesticides has led to significant risks associated with a detrimental impact on the environment and human health. Therefore, emerging concerns of pesticide residue found in crops, food, and livestock are a pressing issue. To address the above challenges, there have been many efforts made towards implementing machine learning to enable precision agricultural practices to reduce pesticide overuse. As of today, there are no guiding digital tools available for citrus growers to provide pesticide residue leaf coverage analysis after foliar applications. Herein, we are the first to report software assisted by lightweight machine learning (ML) to determine the Kocide 3000 and Oxytetracycline (OTC) residue coverage on citrus leaves based on image data analysis. This tool integrates a foundational Segment Anything Model (SAM) for image preprocessing to isolate the area of interest. In addition, Kocide 3000 and Oxytetracycline (OTC) residue coverage analysis was carried out using a specialized Mask Region-Based Convolutional Neural Network (CNN). This CNN was pre-trained on the MS COCO dataset and fine-tuned by training with acquired datasets in laboratory and field conditions. The developed software demonstrated excellent performance on both pesticides’ accuracy, precision, and recall, and F1 score metrics. In summary, this tool has the potential to assist growers with the decision-making process for controlling pesticide use rate and frequency, minimizing pesticide overuse. Full article

Phytopathogens are a significant challenge to agriculture and food security. In this regard, methods for the early diagnosis of plant diseases, including optical methods, are being actively developed. This review focuses on one of the optical diagnostic methods, chlorophyll fluorescence (ChlF) imaging. ChlF reflects the activity of photosynthetic processes and responds subtly to environmental factors, which makes it an excellent tool for the early detection of stressors, including the detection of pathogens at a pre-symptomatic stage of disease. In this review, we analyze the peculiarities of changes in ChlF parameters depending on the type of pathogen (viral, bacterial, or fungal infection), the terms of disease progression, and its severity. The main mechanisms responsible for the changes in ChlF parameters during the interaction between pathogen and host plant are also summarized. We discuss the advantages and limitations of ChlF imaging in pathogen detection compared to other optical methods and ways to improve the sensitivity of ChlF imaging in the early detection of pathogens. Full article