Food Quality Research Articles

Effects of mergers and acquisitions on food quality

We examine the impact of mergers on product quality in the U.S. agri-food industry. Our difference-in-differences analyses provide evidence that following a one standard deviation increase in merger activity there is a 48.1% decrease in pounds of food recalled and an 18.7% decrease in the total number of recalls. A significant implication of our analysis is that increased concentration increases product quality.

Development of multifunctional zein-based films engineered with gallic acid and Ag NPs loaded γ-CD-MOFs for pork preservation

The increasing emphasis on food quality and consumer safety has prompted researchers to explore green manufacturing techniques for packaging materials. In this study, γ-cyclodextrin metal-organic frameworks (γ-CD-MOFs) were prepared as promising nanocarriers for gallic acid (GA) and silver nanoparticles (Ag NPs). These nanocarriers (GA-Ag@CD-MOFs) were further incorporated into a zein matrix to fabricate multifunctional composite films, referred to as MOFs-Zein. Scanning electron microscopy (SEM) and atomic force microscope (AFM) analysis indicated that the fabricated composite film exhibited excellent biocompatibility, with GA-Ag@CD-MOFs uniformly dispersed within the film matrix. Fourier transform infrared spectroscopy (FT-IR) result revealed that the incorporation of GA-Ag@CD-MOFs facilitated the formation of intermolecular hydrogen bonds with the matrix polymer. The addition of GA-Ag@CD-MOFs enhanced the tensile strength of the composite film by 19.70 %, improved the water barrier performance by 57.76 %, and provided almost 100 % UV protection. The exceptional controlled release capability of γ-CD-MOFs for GA empowered the composite films to maintain their robust initial antioxidant capacity even after 100 days of storage. Moreover, the composite films performed high-efficiency antibacterial activity against Escherichia coli O157:H7 and Salmonella enterica. Acute toxicological studies showed no histopathology related toxicity was found in GA-Ag@CD-MOFs treated mice. When applied to fresh pork, the composite films significantly reduced the key quality indicators of raw meat, compared to the control film. Overall, this work presents a promising approach to fabricate the composite films as a sustainable and functional solution for green functional food packaging.

Assessment of a crustacean allergen in fishery products using a nanomodified label-free voltammetric immunosensing approach

In this work, a label-free immunosensor for the rapid analysis of the crustacean allergen tropomyosin (TPM) is presented. A screen-printed carbon electrode (SPCE) was (bio)modified with oxidized carbon nanotube/manganese(II) ferrite nanomaterial (CNT_O@MnFe2O4) and monoclonal antibodies. The immune interaction with the allergenic protein was detected using a redox probe ([Fe(CN)6]3−/4−) by differential pulse voltammetry. The CNT_O@MnFe2O4 led to a 4-fold increase of the analytical signal compared to the unmodified SPCE, indicating the usefulness of this material in TPM analysis. Moreover, the detection potential was reduced (i.e., closer to 0 V), which increased the selectivity of the sensor even further. The immunosensor enabled a fast analysis time (1 h), providing a limit of detection of 1.6 ng mL−1, and precise results (coefficient of variation = 6.5 %). The analysis of ten commercial foodstuffs was accomplished, and the results were validated with an ELISA assay. The highest TPM concentrations were quantified in shrimp species (M. affinis > P. latisulcatus > P. indicus) and low allergen levels were found in surimi lobster, clam, fresh calamari, and mayonnaise. The simplicity of the sensor makes it a good choice for quality control and food safety assessment in industrial settings.

PSLBII-5 Impacts of residual feed intake measured as a heifer in drylot on mature cows and calves grazing native pasture

Abstract Selection for feed efficiency is crucial for improving economic and environmental sustainability in beef cattle production. Residual feed intake (RFIfat) is a commonly used measure of feed efficiency and is determined in a drylot setting by calculating the difference between observed and expected dry matter intake (DMI), where expected feed intake is adjusted for metabolic body weight (BW), average daily gain (ADG) and body fatness. Lower RFIfat cattle are more efficient animals as they eat less than predicted for the same growth, body size and body fatness. Limited studies have been conducted evaluating the role of RFIfat on the subsequent performance of cow-calf pairs on native range. Kinsella Composite cows and calves (n = 120) grazed native pasture from July 11 to Sept 12 (summer) and from Sept 13 to Nov 2 (fall) of 2023 were included in this study. All dams had previously been evaluated for RFIfat as a heifer (8 to 12 mo of age) using the GrowSafe Feed Intake System (Vytelle, Canada). Cow and calf ADG were subjected to an analysis of covariance using lm function from stats R core package (R Core Team, 2024), with cow age (2 to 8 yr of age) as the fixed effect and RFIfat as the covariate, analyzing separately by season. An unbalanced Tukey HDS post-hoc were applied for age and p-values < 0.05 were considered significant. Weight gain was assessed on cows with an initial BW on pasture of 598.4 ± 68.4 kg, and 2 to 8 yr of age, and on calves having an initial on pasture BW of 106 ± 21.5 kg, and 65 ± 14 d of age. Cow age affected (P < 0.01) cow ADG during summer, with the youngest dams (2 yr old) gaining more weight than older animals (7 yr old; Figure 1A). No effect of RFIfat was found on cow weight gain during summer grazing. However, during fall grazing, RFIfat affected (P < 0.01) cow ADG (Figure 1B). Low-RFIfat cows (efficient) had increased weight gain during fall grazing as compared with high-RFIfat cows (inefficient: Figure 1B). Calf ADG was not affected (P > 0.1) by cow age, cow RFIfat and cow ADG during summer and fall. We conclude that cows with low RFIfat exhibited superior body weight gain, during fall grazing on native range when forage nutrient quality and quantity become limiting.

Combined effects of food quality and temperature on competitive interactions between small- and large-bodied cladoceran species

Competition is one of the main drivers of population dynamics of cladoceran species. According to the Size Efficiency Hypothesis, large-bodied cladocerans are superior competitors over small-bodied species because they are more effective filter-feeders and can consume a wider size spectrum of food particles. However, we hypothesized that food quality in terms of phosphorus (P) and/or polyunsaturated fatty acid (PUFA) content can alter competitive interactions, and these potential effects can be modified by temperature. Since large-bodied cladocerans are more vulnerable to poor food quality and are regarded as less adaptive to high temperatures, we hypothesized that large-bodied species would be superior competitors at low temperature and/or high food quality, while small-bodied cladocerans would be competitively superior at enhanced temperature and/or poor food quality. To verify this hypothesis, we conducted laboratory experiments with the small-bodied D. longispina and large-bodied D. magna fed with three different types of food: (1) P-poor and PUFA-rich green algae (low phosphorus or LP-treatment), (2) PUFA-poor-cyanobacteria treatment (CYANO) and (3) P-rich and PUFA-rich green algae (high phosphorus or HP-treatment) at 18°C and 24°C. We found that D. magna had a stronger suppressive competitive effect on D. longispina in all food quality and temperature treatments. Nevertheless, food quality and temperature modified competitive interactions between the small- and large-bodied Daphnia. In HP and CYANO, both Daphnia species were more strongly suppressed by competitor presence at high than low temperatures, whereas in LP mutual suppression was relatively weaker at higher temperatures. This phenomenon was attributed to higher requirement for P at enhanced temperature which created constraints for population growth thus decreasing competitive interactions at higher temperatures. Mutual competitive suppression in CYANO was weaker than in the HP treatment although cyanobacteria had the greatest negative impact on the abundance of both Daphnia species. In general our results show that the outcome of competition between large and small-bodied cladoceran species was not affected by food quality and/or temperature, yet, these factors markedly altered competitive interactions between species.

Innovation in Idli batter preservation through betel leaf essential oil-based nanoemulsion: Evaluating fermentation kinetics and physicochemical analysis

Extending shelf life while maintaining quality of traditional fermented foods is a major challenge. This study aimed to investigate betel leaf essential oil nanoemulsions (BLEONE) as a clean-label preservative for fermented Idli batter. BLEONE (1–10 %) was incorporated into Idli batter and evaluated for physicochemical properties, sensory quality, and shelf life during ambient (30 °C) and refrigerated (4 °C) storage. Kinetic modeling and analysis of variance (ANOVA) were used to quantify BLEONE's effects on fermentation dynamics and assess statistical significance. At 5 % concentration, BLEONE significantly (p < 0.05) maintained pH (4.5 vs 3.8 in control), restricted acidity increases (1.2 % vs 1.8 % in control), and limited viscosity loss (15 % vs 40 % in control) during fermentation compared to untreated and sodium benzoate-treated samples. Sensory analysis showed acceptable attributes up to 5 % BLEONE. Optimally fermented Idli batter with 5 % BLEONE extended shelf-life to 7 days at 30 °C and 25 days at 4 °C storage. The nanoemulsion's enhanced antimicrobial efficacy and controlled release of active compounds significantly improved safety and quality preservation during storage. This research demonstrates the potential of essential oil nanoemulsions as effective clean-label preservatives for traditional fermented foods, offering a promising solution to extend shelf life while maintaining product quality.

Barley disease recognition using deep neural networks

Plant disease negatively impacts food production and quality. It is crucial to detect and recognise plant diseases correctly. Traditional approaches do not offer a rapid and comprehensive management system for detecting plant diseases. Deep learning techniques (DL) have achieved encouraging results in discriminating patterns and anomalies in visual samples. This ability provides an effective method to diagnose any plant disease symptoms automatically. However, one of the limitations of recent studies is that in-field disease detection is underexplored, so developing a model that performs well for in-field samples is necessary. The objective of this study is to develop and investigate DL techniques for in-field disease detection of barley (Hordeum vulgare L.), one of the main crops in Australia, given visual samples captured at barley trials using a consumer-grade RGB camera. Consequently, A dataset was captured from test-bed trials across multiple paddocks infected with three diseases: net form net blotch (NFNB), spot form net blotch (SFNB), and scald, in various weather conditions. The collected data, 312 images (6000 × 4000 pixels), are divided into patches of 448 × 448 pixels, which are manually annotated into four classes: no-disease, scald, NFNB and SFNB. Finally, the data was augmented using random rotation and flip to increase the dataset size. The generated barley disease dataset is then applied to several well-known pre-trained DL networks such as DenseNet, ResNet, InceptionV3, Xception, and MobileNet as the network backbone. Given limited data, these methods can be trained to detect anomalies in visual samples. The results show that MobileNet, Xception, and InceptionV3 performed well in barley disease detection. On the other hand, ResNet showed poor classification ability. Moreover, Augmenting the data improves the performance of DL networks, particularly for underperforming backbones like ResNet, and mitigates the limited data access for these data-intensive networks. The augmentation step improved MobileNet performance by approximately 6 %. MobileNet achieved the highest accuracy of 98.63 % (the average of the three diseases) in binary classification and an accuracy of 93.50 % in multi-class classification. Even though classifying SFNB and NFNB is challenging in the early stages, MobileNet achieved the minimum misclassification rate among the two diseases. The results show the efficiency of this model in diagnosing barley diseases using complex data collected from the field environment. In addition, the model is lighter and comprises fewer trainable parameters. Consequently, MobileNet is suitable for small training datasets, reducing data acquisition costs.

Critical review on toxic contaminants in surface water ecosystem: sources, monitoring, and its impact on human health.

Surface water pollution is a critical and urgent global issue that demands immediate attention. Surface water plays a crucial role in supporting and sustaining life on the earth, but unfortunately, till now, we have less understanding of its spatial and temporal dynamics of discharge and storage variations at a global level. The contamination of surface water arises from various sources, classified into point and non-point sources. Point sources are specific, identifiable origins of pollution that release pollutants directly into water bodies through pipes or channels, allowing for easier identification and management, e.g., industrial discharges, sewage treatment plants, and landfills. However, non-point sources originate from widespread activities across expansive areas and present challenges due to its diffuse nature and multiple pathways of contamination, e.g., agricultural runoff, urban storm water runoff, and atmospheric deposition. Excessive accumulation of heavy metals, persistent organic pollutants, pesticides, chlorination by-products, pharmaceutical products in surface water through different pathways threatens food quality and safety. As a result, there is an urgent need for developing and designing new tools for identifying and quantifying various environmental contaminants. In this context, chemical and biological sensors emerge as fascinating devices well-suited for various environmental applications. Numerous chemical and biological sensors, encompassing electrochemical, magnetic, microfluidic, and biosensors, have recently been invented by hydrological scientists for the detection of water pollutants. Furthermore, surface water contaminants are monitored through different sensors, proving their harmful effects on human health.

Early life food-intake modulates effects of diet restriction on lifespan and fecundity in later life in a predatory mite (Acari: Phytoseiidae)

Abstract The nutritional status of an individual can significantly influence its life history traits, including development, growth, reproduction, and survival. In the predatory mite Phytoseiulus persimilis Athias-Henriot (Acari: Phytoseiidae), the plasticity of life history traits, such as developmental time and size at maturity, is influenced by the quality and quantity of food. This study aimed to investigate the effects of dietary restriction at different life stages (i.e., juvenile, early adulthood, and later adulthood) on the lifespan and fecundity of P. persimilis. We found that reduced dietary intake during early adulthood resulted in a shorter lifespan for both male and female P. persimilis. Furthermore, this study demonstrated a sex-specific response to dietary restriction: it extended the lifespan of males but reduced that of females during later adulthood. Diet restriction during the post-ovipositional period of females showed the most variable life history trait response. Our results showed that the impact of diet restriction at different life stages can have combined influences on the post-ovipositional duration of P. persimilis, where the individuals receiving diet restriction during immature development and early adulthood had a greater reduction in ovipositional duration as those experiencing diet restriction during late adulthood. Additionally, we observed a positive correlation between the lifespan and fecundity of females, with higher prey availability increasing both. The insights obtained from our research contribute to a better understanding of the aging process and dietary requirements of P. persimilis, which can facilitate the development of more effective biological control strategies using this predator for spider mites in agriculture.

Signal-enhanced electrochemical sensor employing MWCNTs/CMK-3/AuNPs and Au@Pd core-shell structure for sensitive determinationof AFB1 in complex matrix.

A sandwich electrochemical sensor was fabricated based on multi-walled carbon nanotubes/ordered mesoporous carbon/AuNP (MWCNTs/CMK-3/AuNP) nanocomposites and porous core-shell nanoparticles Au@PdNPs to achieve rapid and sensitive detection of AFB1 in complex matrices. MWCNTs/CMK-3/AuNP nanocomposite, which was prepared by self-assembly method, served as a substrate material to increase the aptamer loading and improve the conductivity and electrocatalytic activity of the electrode for the first signal amplification. Then, Au@PdNPs, which were synthesized by one-pot aqueous phase method, were applied as nanocarriers loaded with plenty of capture probe antibody (Ab) and signal molecule toluidine blue (Tb) to form the Au@PdNPs-Ab-Tb bioconjugates for secondary signal amplification. The sensing system could still significantly improve the signal output intensity even in the presence of ultra-low concentration target compounddue to the dual signal amplification of MWCNTs/CMK-3/AuNP nanocomposites and Au@PdNPs-Ab-Tb. The method exhibited high selectivity, low detection limit (9.13fg/mL), and strong stability to differentiate AFB1 from other mycotoxins. Furthermore, the sensor has been successfully applied to the quantitative determination of AFB1 in corn, malt, and six herbs, which has potential applications in food safety, quality control, and environmental monitoring.

Classification of kimchi using laser-induced breakdown spectroscopy and k-nearest neighbors modeling

This study introduces a novel application of Laser-Induced Breakdown Spectroscopy (LIBS) combined with k-nearest neighbors (KNN) modeling to classify the origin of kimchi. Using the spectral intensities of Mg II at 279 nm and K I at 766 nm, we achieved a classification accuracy of 92.8 %. This method effectively leverages regional differences in salt supply chains impacting kimchi's elemental composition. The innovation lies in applying the interclass distance method for variable selection in LIBS analysis, enhancing the interpretability and accuracy of food classification. Compared to traditional elemental analysis techniques, LIBS offers a practical, cost-effective solution for rapid field analysis with minimal sample preparation. This study not only demonstrates the potential of LIBS for food authenticity but also provides insights for developing accurate methods for detecting Mg and K in various food products, contributing to advancements in food quality control.

Research on model transfer strategies based on the fusion of NIR-MIR spectral data

This study investigated the impact of different data fusion strategies on the performance of soluble solids content (SSC) prediction models based on near-infrared and mid-infrared spectroscopic techniques. In the data-level fusion approach, we applied standard normal variate and multiplicative scatter correction for pre-processing the NIR and MIR data. For the feature-level fusion, we utilized successive projections algorithm and competitive adaptive reweighted sampling to select informative wavelengths, and then applied direct orthogonal projection (DOP) for model transfer. The study employed a dataset of 150 honey samples to evaluate the impact of different data fusion strategies on model performance. To effectively evaluate model performance, we utilized the coefficient of R2 and RMSEP as evaluation metrics. By comparing the results of data-level fusion, feature-level fusion and single-spectrum model transfer, the results showed that spectral data fusion improved the model transfer performance compared to the single-spectrum approach, with feature-level fusion exhibiting the most significant advantages. The effective variable selection techniques in feature-level fusion successfully removed a substantial amount of interfering data and significantly reduced noise influence, thereby improving the model accuracy. Specifically, the use of feature-level fusion improved the predictive model’s R2 from 0.319 to 0.878 and reduced the RMSEP from 1.974 to 0.613°Brix, demonstrating the significant advantages of this approach in enhancing model transfer performance. The research findings provide important reference and theoretical support for future studies in the field of food quality assessment and other near-infrared spectroscopic data applications. This not only validates the effectiveness of the feature-level fusion approach, but also lays the foundation for establishing efficient and reliable predictive models.

Genetic analysis of QTLs for lysine content in four maize DH populations

BackgroundLow levels of the essential amino acid lysine in maize endosperm is considered to be a major problem regarding the nutritional quality of food and feed. Increasing the lysine content of maize is important to improve the quality of food and feed nutrition. Although the genetic basis of quality protein maize (QPM) has been studied, the further exploration of the quantitative trait loci (QTL) underlying lysine content variation still needs more attention.ResultsEight maize inbred lines with increased lysine content were used to construct four double haploid (DH) populations for identification of QTLs related to lysine content. The lysine content in the four DH populations exhibited continuous and normal distribution. A total of 12 QTLs were identified in a range of 4.42–12.66% in term of individual phenotypic variation explained (PVE) which suggested the quantitative control of lysine content in maize. Five main genes involved in maize lysine biosynthesis pathways in the QTL regions were identified in this study.ConclusionsThe information presented will allow the exploration of candidate genes regulating lysine biosynthesis pathways and be useful for marker-assisted selection and gene pyramiding in high-lysine maize breeding programs.

A Novel Bioprotective Strain of Lactiplantibacillus plantarumF2-Physiological, Genetical, and Antimicrobial Characterization.

Lactiplantibacillus plantarum is a member of lactic acid bacteria that improves the quality of fermented foods while also having a positive impact on human health. In this study, L. plantarum F2 was studied for characteristics such as biochemical and genetic identification, metabolite production, antimicrobial activity, and plasmid content. This strain exerts antimicrobial activity against some Gram-positive and Gram-negative pathogens (Listeria monocytogenes, Staphylococcus aureus, Salmonella, and Escherichia coli) with inhibition zone diameters ranging between 17.0 and 29.0 mm; it can ferment glucose, arabinose, galactose, lactose, and demonstrated the ability to grow at high temperature (50°C). Another physiological specification of the strain was the morphology of the isolate in selective medium, the de Man, Rogosa, Sharpe medium (MRS medium containing triphenyl tetrazolium chloride), which exhibits a chromogenic colony (characterized as purple colonies) on the modified-MRS (mMRS) medium. Metabolites such as lactic acid and diacetyl production of the strain F2 were also investigated using chromatography and found to be 10.07 and 0.05 µg/mL, respectively. The peptides of the isolate's cell-free supernatant were determined to be ∼80 kDa, and finally, the plasmid isolated from the strain F2 was identified as L. plantarum strain KLDS1.0386 plasmid p4, which may be responsible for some characteristic properties, such as antimicrobial peptide production of the strain.

Using palm oil residue for food nutrition and quality: from palm fatty acid distillate to vitamin E toward sustainability.

Increasing global palm oil production yields a valuable palm fatty acid distillate (PFAD) - a rich vitamin E (Vit-E) source and multifunctional ingredient in the food agro-industry - that can be utilized to achieve sustainability. This article reviews trends in the use and role of PFAD and its Vit-E in the food sector and proposes an integrated agro-industrial concept toward sustainability. Vit-E can be separated from PFAD with diverse and impactful pharmaceutical activities, including antioxidant, anti-inflammatory, anticancer and anti-ultraviolet effects. Based on in vivo experimental tests, PFAD and Vit-E supplementation can enhance the productivity and quality of livestock-based food products. PFAD is a plasticizer and antistatic packaging material in food packaging systems, and its derivatives can be used as food additives. Meanwhile, the Vit-E molecule in packaging can extend food shelf life by maintaining color stability, reducing lipid oxidation and rancidity, adding antimicrobial properties, and influencing changes in packaging properties such as water vapor, tensile strength, melting point and other physical properties. Toward sustainability, an integrated agro-industrial design has been proposed to implement clean production, increase the added value of palm oil industry residues, minimize environmental risks and increase profits to achieve long-term social welfare. In conclusion, PFAD residues and their Vit-E content have shown broad benefits in the food sector and prospects toward sustainability. © 2024 Society of Chemical Industry.

H2S gas sensing based on CuNi MOFs derivative with TiO2 quantum dot decoration applied for meat freshness detection

As food safety issues have been widely revealed and exposed, the pursuit of food for people has shifted from food and clothing to fresh and healthy. Currently, there are many detection systems for detecting meat freshness on the market. However, most of the problems still exist are that they cannot be monitored in real time, cannot guarantee traceless monitoring, and the cost is high. To solve these problems, this work has built a portable meat freshness detection system based on copper oxide (CuO)/nickel oxide (NiO) doped titanium oxide (TiO2) quantum dot composites and added real-time monitoring and remote monitoring and finally packaged it into an intelligent food box. Real-time monitoring of the freshness of meat can be carried out in a specific range regardless of the space. Hydrogen sulfide (H2S) has always been an essential indicator of meat decay. Therefore, in this paper, an H2S gas sensor based on CuO/NiO doped TiO2 quantum dot composites material is designed and fabricated, which can effectively and quickly identify H2S. The gas sensor exhibits excellent gas sensing characteristics for H2S with a detection limit of 100 parts per billion (ppb) and has excellent selectivity and repeatability, which because of the presence of p-p type heterojunctions in CuO/NiO. In addition, the feasibility of the system and material for analyzing the meat deterioration process is demonstrated through the test of different meat samples. It is firmly believed that this new type of sensor mentioned in this paper can make contribution to food quality monitoring.

Will the decrease in food availability affect food tourism? A historical and contemporary perspective: horizon 2050 article

Purpose This study aims to discuss the possible consequences of food crises caused by unsustainable food production and distribution, as well as how the rapid depletion of food resources may affect human life, destinations and the food tourism industry. Design/methodology/approach To address the lack of research examining the impact of sustainability issues and food crises on food tourism, this study conducted a critical review of the literature and reports related to agriculture, food production and sustainability. Findings Global conflicts, climate change and supply disruptions result in the reduction of food availability and quality. The high price of food and energy in such circumstances will further exacerbate poverty and delay destination development, which will negatively impact food tourism's economic sustainability. Furthermore, local businesses will be limited in their ability to offer a variety of culinary experiences, which will negatively impact the overall experience of food tourism. In this context, there is a pressing need to investigate the experiential aspects of food tourism as well as today's challenges relating to food crises at multiple levels, sustainable approaches, food quality and food services. Research limitations/implications The United Nations, FAO, WHO, UNICEF and other regional and international organizations refer to unsustainable practices and food crises on a variety of levels. Several sectors, including the food tourism industry, are predicted to be negatively affected by the current situation. However, governments have not adopted an emergency code to address this issue, except in some regions experiencing extreme inflation and food shortages. Besides promoting sustainable practices, responding to global crises and enhancing culinary and cultural tourism experiences, food crisis management and sustainability efforts can have a significant impact on future research, practical applications and societal outcomes. Originality/value As one of the first studies conducted in this field, this study examines the effects of declines in food production and issues in the supply chain on the food tourism industry.

From Taste Buds to Loyalty: How Food and Service Quality Influence Customer Satisfaction and Repeat Intentions in Hotel Restaurants

Customer satisfaction with food and service quality in hotel restaurants is a crucial driver of repeat intentions, directly influencing guest loyalty and long-term business success in the hospitality industry. This study examines the factors that affect customer satisfaction with hotel restaurant offerings and the subsequent impact on customers' intentions to return. The researchers gathered the data for this study using a quantitative research design and customer questionnaires. A 95% response rate was collected and analyzed using SPSS software. Next, the researchers quantitatively analyzed the survey responses using statistical methods to measure customer satisfaction levels and identify key variables influencing repeat intentions. The results of this study have practical implications for hoteliers and restaurant managers looking to improve customer satisfaction and encourage repeat business. Emphasizing continuous staff training, maintaining high food safety standards, and creating a welcoming ambiance are key strategies to enhance customer experiences. Ultimately, understanding the dynamics of customer satisfaction and its direct link to repeat intention can assist hotel restaurants in building enduring guest relationships and establishing a competitive edge in the hospitality market

Highly sensitive detection of kojic acid in food samples using fluorescent carbon dots derived from pomegranate peel

Kojic acid (KA) has gained significant attention due to its widespread use in the food and cosmetics industries. However, concerns about its potential carcinogenic effects have heightened the need for sensitive detection methods. This study introduces a fluorescence-based optical sensor for the quantification of KA in food samples, utilizing fluorescent carbon dots (CDs) synthesized from pomegranate peel via a hydrothermal method. The Stern–Volmer plot demonstrated a linear response for KA in the range of 120 to 1200 µM, with a Pearson correlation coefficient (r) of 0.9999 and. The sensor exhibited a detection limit of 30 ± 0.04 µM and a limit of quantification (LOQ) of 90 ± 0.14 µM. Application of the developed method to soy sauce and vinegar samples yielded accurate KA determinations, with recoveries of 103.11 ± 0.96% and 104.45 ± 2.15%, respectively. These findings highlight the potential of the proposed sensor for practical applications in food quality and safety assessment, offering valuable insights into the presence of KA in food products.

Food waste reduction and its environmental consequences: a quasi-experimental study in a campus canteen

BackgroundFood waste is the third-largest contributor to greenhouse gas emissions, which has severe environmental and economic effects. This study presents a two-level intervention to estimate the quantity and environmental consequences of food waste at a campus canteen, offering innovative solutions to reduce food waste and its environmental footprint.MethodologyThis study involved 300 students and consisted of three main stages: initial food waste assessment, environmental and economic impact evaluation, and qualitative exploration of the causes of food waste through interviews with students. The assessment included direct measures and weighing of leftover food, and the environmental and economic impact was calculated. A two-level intervention was implemented for students and staff, and a re-assessment of food waste was conducted to evaluate the intervention’s impact. Statistical analysis was performed using SPSS.ResultsThe study monitored 26 meals, finding that the total amount of food waste in the university canteens was (mean = 60.65 g/person), and the intervention reduced food waste by 16.35% per meal (50.73 g/person). Moreover, after the intervention, the amount of food waste costs and total water waste were reduced by 30.14% and 16.66%, respectively. Grey water was reduced significantly by 12.5% (p = 0.033). Interviews with students identified low-quality meals, unpleasant taste, large portions, and a limited menu as the main causes of food waste.ConclusionsIt is possible to tackle food waste effectively with educational intervention, decreasing portion size, and improving the quality and variety of food.