Journal of Food Quality
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 Journal metrics
Acceptance rate33%
Submission to final decision79 days
Acceptance to publication43 days
CiteScore3.600
Journal Citation Indicator0.480
Impact Factor2.450

Investigation and Strategy Research on Dietary Nutrition Knowledge, Attitude, and Behavior of Athletes

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 Journal profile

Journal of Food Quality publishes original research on issues of food quality, including the handling of food from a quality and sensory perspective and covers both medical and functional foods.

 Editor spotlight

Chief Editor, Anet Režek Jambrak, is a professor at the University of Zagreb. Her fields of research include food physics, food processing, food chemistry, sustainability, nonthermal processing, and advanced thermal processing.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

Application of Artificial Neural Network in the Baking Process of Salmon

The global production of farmed Atlantic salmon amounts to over 2 million tons per year. Consumed all over the world, salmon is not only delicious but also nutritious. This paper deals with the relationship between moisture content, low-field nuclear magnetic resonance (LF-NMR), scanning electron microscope (SEM), and sensory evaluation in the baking process of salmon. An artificial neural network (ANN) model has been established to simulate the change of moisture content and energy consumed in the baking process. Through the study of LF-NMR, SEM, and sensory evaluation, it was found that the change of sensory indexes was consistent with the results observed by LF-NMR and SEM. With the increase of temperature, muscle fibers contracted, the interstices increased, the rate of water loss increased, and the sensory score decreased. Initial moisture content, baking time, baking temperature, baking humidity, and baking air velocity were employed as the baking control parameters for the ANN. ANN can be used to determine the moisture content and energy consumed of baking salmon. The best network topology occurred with 5 input layer neurons, 17 hidden layer neurons, and 2 output layer neurons, and the MSE was 0.00153, and Rall was 0.99661. According to the experiment, it was demonstrated that the ANN is a reliable software-based method.

Research Article

Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

When analyzing agriculture’s total factor productivity, traditional measurement approaches do not take into account the inefficiency value. The production functions are assumed to be analyzed on basis of the random boundaries, which makes the analysis results inaccurate and unreliable. As a result, this paper proposes an analytical approach for agricultural total factor productivity based on the stochastic block model (SBM), which combines the benefits of statistics and machine learning. It uses the SBM direction distance function and the Luenberger productivity index to measure the agricultural efficiency, total factor productivity, and their components. The research study considers the data from 31 provinces from 2006 to 2018 years. First, one output indicator and six input indicators are established. The time-varying variations of the national agricultural inefficiency value and its source decomposition under variable scale returns are then determined using the SBM-based algorithm of agricultural total factor productivity and the obtained sample data. The changes of the agricultural total factor productivity and its components are comprehensively analyzed. Following an examination of the elements impacting agricultural efficiency and productivity, the socioeconomic development of the agricultural total factor productivity is examined in order to achieve efficient growth.

Research Article

The Effect of Upcycled Brewers’ Spent Grain on Consumer Acceptance and Predictors of Overall Liking in Muffins

The brewing industry generates large amounts of food waste including brewers’ spent grain (BSG) and leftover malted grains from beer production. BSG compositions can vary but consistently include high levels of protein and fiber. The potential nutritional and health benefits of BSG have sparked recent interest for food fortification. However, the challenges associated with BSG addition can impact food quality due to increases in fiber and protein content and reduction in starch content. Consumer testing was conducted to evaluate muffins containing varying levels of BSG (0, 20, 30% wt:wt flour) to determine the highest acceptable concentration on overall likeability, appearance, texture, moistness, sponginess, and taste attributes. Significant differences were found within appearance (F = 7.728, P = .001) and taste (F = 4.947, P = .008) ratings across all muffins. Control and 20% BSG muffins were rated significantly higher for appearance (6.74 ± 0.18; 6.64 ± 0.18) than 30% BSG muffins (6.11 ± 0.18). Muffins containing 20% BSG (7.15 ± 0.17) received significantly higher taste ratings than 30% BSG muffins (6.56 ± 0.22) and control muffins (6.49 ± 0.19). However, 30% BSG muffins maintained acceptance for all attributes showing higher allowable BSG substitutions than previously reported. Bivariate correlation analyses found that all attributes across each muffin variation were strongly, positively correlated (r > 0.6) with overall likeability excluding appearance (r = 0.359, P < 0.001) and moistness (r = .466, P < 0.001) in control muffins. Significant predictors of overall likeability were appearance (β = 0.088, P = 0.005), texture (β = 0.181, P < 0.001), sponginess (β = 0.226, P < 0.001), and taste (β = 0.494, P < 0.001). Brewers’ spent grain consumer acceptance results will guide the development of test food products for future human diet intervention compliance.

Research Article

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Several industries have recently seen the replacement of human labor by automated machinery and equipment. Across the globe, farmers’ attitudes on the use of technology in agriculture are divergent. However, although some people are excited and ready to embrace technology, others are cautious and wary of trying new technologies for the first time. The third category is particularly prevalent in underdeveloped nations such as India, owing to a lack of competence, a lack of effective translation, and most crucially, a lack of financial resources. It is fruitless for the government to attempt to resolve these difficulties due to the fact that they do not take into consideration the changing circumstances and input needs of each agricultural group. Smart Tillage is a cutting-edge framework that was developed to solve the challenges listed above. In India, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment. The option is entirely reliant on a variety of input variables, including crop kind, harvest time/month, crop equipment needed, harvest type, and the amount of money available for rental. Additionally, an ideal recommendation engine driven by content and collaborative-based filtering will provide the farmer’s requirements depending on their requirements. In terms of escalation, the proposals would be cost-effective and excellent since they would need little changes in training, technique improvements, and resource management via a new rent-share model similar to that used by Uber. In this work, demand and supply algorithms are used to define market equilibrium, and the results are shown in graphs. This includes discussion of a variety of demand and supply parameters, their impact on market equilibrium prices and quantities, and their effect on shifting demand and supply curves. The many sorts of elasticities (demand, cross-price, supply, income, and so on) are examined, as well as the ramifications for pricing systems that may result from these elasticities.

Research Article

Effect of the Addition of Different Levels of Chard on the Dough Properties and Physicochemical and Sensory Characteristics of Pan Breads

Background. Chard is a valuable vegetable and is considered a beneficial functional food. Fortification of bread with chard could increase the nutraceutical and functional food consumption. Objective. In this study, we performed a chemical analysis of chard and performed rheological analyses and sensory attribute evaluations of pan breads fortified with 5% and 10% chard powder. Design. The gross chemical composition of chard, some minerals, vitamin C, and total phenolic and flavonoid compounds were estimated. The rheological properties of doughs fortified with 5% and 10% chard powder and the chemical composition and sensory attributes of control, 5% chard and 10% chard pan bread samples were determined. Results. Chard contains carbohydrate, protein, and ash in addition to essential minerals and antioxidants such as vitamin C, phenols, and flavonoids. The chemical composition of 5% chard pan bread was significantly higher in ash and fiber, while the chemical composition of 10% chard pan bread was significantly higher in protein, ash, fiber, and moisture and significantly lower in fat, carbohydrate, and energy level than that of control pan breads. Compared with the control pan bread, the pan bread with increased chard powder content (10%) had significantly increased water absorption percentage, arrival time, dough development, elasticity, and proportional number ratio but significantly decreased stability time, softening degree, and extensibility. Pan bread fortified with 10% chard had the lowest specific volume among the tested breads. Sensory attribute evaluation further showed that increasing the amount of chard to 10% in the bread dough formulation produced lower overall acceptability scores. Conclusions. Pan bread containing 5% chard had better rheological scores and sensory attributes than the other formulations, in addition to good nutritional quality values.

Research Article

Prevalence of Multidrug-Resistant Listeria monocytogenes in Dairy Products with Reduction Trials Using Rosmarinic Acid, Ascorbic Acid, Clove, and Thyme Essential Oils

Continuous monitoring of Listeria spp., particularly Listeria monocytogenes, in foods is a mandatory task for food safety and microbiology sectors. This study aimed to determine the prevalence and antimicrobial resistance patterns of L. monocytogenes in milk and dairy products retailed in Egypt. Furthermore, an experimental trial was conducted to investigate the antilisterial effects of some phytochemicals. A total of 200 samples (market raw milk, Kareish cheese, Damietta cheese, and plain yoghurt, 50 each) were collected and examined for detection of Listeria spp. The results revealed that 8, 12, 1, and 0 samples of market raw milk, Damietta cheese, Kareish cheese, and plain yoghurt were contaminated with Listeria spp., respectively. Antimicrobial sensitivity testing revealed that all L. monocytogenes isolates (15) were resistant to streptomycin and erythromycin. Molecular analysis revealed that 86.67% of L. monocytogenes harbored hylA virulent gene. Use of rosmarinic acid, ascorbic acid, thyme, and clove essential oils significantly () reduced L. monocytogenes growth in soft cheese—artificially contaminated with L. monocytogenes throughout a 4-week incubation period. In conclusion, strict hygienic conditions should be adopted during the preparation and distribution of dairy products. In addition, rosmarinic acid, ascorbic acid, clove, and thyme essential oils are good candidates as food preservatives with antilisterial activities.

Journal of Food Quality
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
Acceptance rate33%
Submission to final decision79 days
Acceptance to publication43 days
CiteScore3.600
Journal Citation Indicator0.480
Impact Factor2.450
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.