Food Quality Assessment Research Articles

Nutritional, Microbiological, and Toxicological Quality Assessment of Foods Sold in Urban and Suburban Markets in Burkina Faso.

Food safety risks are becoming a public health problem with important socioeconomic consequences for human wellbeing, especially for pregnant women and infants. In this article, we describe findings from microbiological, toxicological, and nutritional quality assessments of foods from 5 localities in Burkina Faso, with the aim to provide baseline data on the quality of food and the risks to mothers and children. Samples for assessment included food sold in markets, stores, and restaurants (eg, cereals, oilseeds, vegetables, edible oils, powdered milk, dried fish, packaged water, ready-to-eat meals). The research team selected the samples using the random route method and analyzed them at the National Public Health Laboratory in Ouagadougou between January and December 2020. A total of 443 food samples were collected, of which 101 were analyzed for microbial contamination, 360 were analyzed for the presence of toxins, and 59 were analyzed for their nutritional value. The microbiological quality of 11.88% of the food samples was unsatisfactory, and 41.50% were contaminated with aflatoxins. At least 1 pesticide residue and cyfluthrin were detected in 58.10% of samples. The most detected contaminant (cyfluthrin) was found in 79.10% of the analyzed samples. A peroxide index higher than the normal value (10 mEq/kg) was found in 3.38% of the oil samples and 76.27% of the oil samples had a vitamin A content lower than the recommended limit of 11 mg/kg. This study is the first in Burkina Faso that provides baseline data on the quality of food and potential health risks to mothers and children in Burkina Faso. Considering the level of contaminants reported in this article, it is imperative to enhance routine monitoring of foods in the country.

Development of an Integrated Soft E-Nose for Food Quality Assessment

Food is one of the basic essential elements for human beings. Its quality is directly related to our physical, emotional, and social well-being. An electronic nose is a non-destructive instrument and is an effective solution for quick and easy determination of food quality. In this paper, an integrated soft E-nose methodology focusing on performance enhancement and complexity reduction is proposed and implemented for the effective classification of beef quality and prediction of microbial population in beef. The soft E-nose methodology is tested on 18 available datasets. Ours is a four-step approach that includes validated data collection, signal processing with a moving average filter and principal component analysis for feature reduction, efficient pattern recognition using machine learning and deep learning techniques like support vector machine, k-nearest neighbors, artificial neural network, extreme learning machine, and deep neural network for classification and regression, and an output stage for classification and prediction of the microbial population of beef. This methodology could provide accuracy greater than 98% using k-nearest neighbors, artificial neural network methods, and a correlation coefficient of greater than 0.98 using support vector machine and artificial neural network methods. The proposed methodology is validated using 10-fold cross-validation.

Assessment of Food Quality, Perceived Value, and Brand Image as Determinants of Brand Switching among Dinners in Food Service Establishments in Umuahia Metropolis, Abia State, Nigeria

Assessment of Food Quality, Perceived Value, and Brand Image as Determinants of Brand Switching among Dinners in Food Service Establishments in Umuahia Metropolis, Abia State, Nigeria

E-Senses, Panel Tests and Wearable Sensors: A Teamwork for Food Quality Assessment and Prediction of Consumer’s Choices

At present, food quality is of utmost importance, not only to comply with commercial regulations, but also to meet the expectations of consumers; this aspect includes sensory features capable of triggering emotions through the citizen’s perception. To date, key parameters for food quality assessment have been sought through analytical methods alone or in combination with a panel test, but the evaluation of panelists’ reactions via psychophysiological markers is now becoming increasingly popular. As such, the present review investigates recent applications of traditional and novel methods to the specific field. These include electronic senses (e-nose, e-tongue, and e-eye), sensory analysis, and wearables for emotion recognition. Given the advantages and limitations highlighted throughout the review for each approach (both traditional and innovative ones), it was possible to conclude that a synergy between traditional and innovative approaches could be the best way to optimally manage the trade-off between the accuracy of the information and feasibility of the investigation. This evidence could help in better planning future investigations in the field of food sciences, providing more reliable, objective, and unbiased results, but it also has important implications in the field of neuromarketing related to edible compounds.

Artificial senses and their fusion as a booming technique in food quality assessment

Sensory science has witnessed increased adoption of technological advancements in recent years. Food analysis using human senses severely impacted the evaluation responses due to errors and the complexity of the assessment methods. Hence, the adoption of tools capable of mimicking human senses is considered a more viable approach. This article provides a critical demonstration of the developments in sensory science detailing the technology behind the construction and working of the electronic tongue, electronic nose, and electronic eye. The paper also attempts to brief the industrial applications of artificial senses and the fusion technique in monitoring as well as assessment of food quality.

Dietary discrimination using a dual-choice self-feeding system in seabream (Sparus aurata)

Self-feeding systems have been extensively used to study animal behaviour and food preferences in vertebrates. In this study, we used a dual-choice self-feeding system to explore feeding behaviour and dietary discrimination based on organoleptic cues. Experimental tanks were provided with 2 self-feeders activated by string sensors and connected to a computer system. In the first experiment (E1) [6 tanks, 10 animals/tank, body weight (BW) 266 g ± 19.4 g], both feeders delivered the same control standard diet to evaluate the potential preference for one feeder. E1 had 3 phases: 1st, animals were allowed to demand from both feeders; 2nd, from only one feeder and 3rd from both feeders again. The second experiment (E2) (11 tanks, 15 animals/tank, BW range 25–50 g) examined whether seabream can discriminate between 3 isoenergetic diets: the control diet; a positive/palatable diet containing high levels of fish, squid and krill meal; and a negative diet in which 1.5% quinine hydrochloride was added to the control diet. E2 had 3 phases: 1st acclimation with control diet; 2nd dual-choice; 3rd dual-choice with reversal of feeders. The third experiment (E3) (8 tanks, 10 animals/tank, BW range 25-50 g) was performed using a single self-feeder per tank, with each tank being fed either control or quinine diet, to corroborate the aversive response to quinine in the absence of dietary choice. E1 demonstrated that animals demanded significantly less when only one feeder/sensor was available. E2 shows that seabream can easily discriminate a bitter compound (quinine) in the diet, showing negative preference (P < 0.05) in both phases, 2nd and 3rd. When no choice was available (E3), seabream equally demanded control or quinine diet, presumably driven by its energy requirements. However, the presence of the bitter substance increased feed refusals, 14.3% rejection of demanded feed in quinine diet, versus 2.6% in control diet, indicating a role of sensory systems in assessing food quality. A positive conditioning with a palatable diet was not effective and no positive choices were measured over a basal diet, although results were possibly influenced by the previous familiarization with the basal diet and by the experimental conditions (fast growing juveniles at high temperature). This poor dietary selection during active feeding periods could partly explain the easy adaptation of this species to substitution diets. Finally, results suggest that food intake is conditioned by agonistic behaviour in an age-dependent manner. The information will be useful for diet and feeding strategy design in seabream.

In-depth profiling, nontargeted metabolomic and selective ion monitoring of eight chemical markers for simultaneous identification of different part of Eucommia ulmoides in 12 commercial products by UPLC/QDa

Despite Eucommia ulmoides to be served as a crucial source for food additives and healthcare products, unclarity of the metabolomic differences among four different plant parts (bark, leaf, flower, seed) seriously restricts the comprehensive quality control of the herb and its related commercial products. In this study, a strategy integrating in-depth profiling, nontargeted metabolomics, and selected detection was established and exemplarily applied to authenticate E. ulmoides related products. UPLC/Q-TOF Fast DDA approach was utilized for in-depth profiling among four parts to characterize 222 compounds. In addition, 38 batches of different parts of E. ulmoides were compared by metabolomics method, and multi-step chemometrics analysis to unveil 25 chemical markers. Finally, an UPLC/QDa-SIM method was established based 8 most important diagnosis components to identify 12 commercial products. This study provides a novel and comprehensive strategy for the quality assessment of foods and dietary supplements.

Recent development of modified fluorescent carbon quantum dots-based fluorescence sensors for food quality assessment

Recent development of modified fluorescent carbon quantum dots-based fluorescence sensors for food quality assessment

The Increasing Popularity and Service Quality Assessment of Online Food Ordering Applications in the COVID-19 Pandemic. A Study of Uber Eats in Poland

Theoretical background: The COVID-19 pandemic has a significant impact on every aspect of the economy and society. It forced changes in the functioning of many enterprises, also in the field of food production and delivery, and widely understood supply chains. The situation in the world has also contributed to changes in consumer behavior, which translated into, for example, forms of food delivery and payment. Methods: The aim of the study is to determine the level of satisfaction of Uber Eats consumers in Poland, as well as customer behavior and habits during the COVID-19 pandemic. Another aim of this study is to identify problems and causes of customer dissatisfaction at every stage of the delivery chain. A survey of Uber Eats users is conducted to collect data on the level of satisfaction with the services offered by this company. The collected data was analyzed by means of a comparative analysis covering the two periods in which the study was conducted. Results and conclusions: Researching customer satisfaction in various areas has long been of interest to scientists. Particular interesting conclusions in this regard can be observed during the restrictions related to the COVID-19 pandemic, which introduced many restrictions in the everyday life of consumers, e.g. restaurants and eateries were closed, it was only possible to order take-out or with delivery. Research carried out at that particular time could change the behavior of customers, influence their opinion, and change the requirements and preferences. The study enables a new look at food ordering services in times of epidemics and evaluates the functioning of the Uber Eats supply chain in Poland.

IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry

Internet of Things (IoT) results in a massive amount of streaming data, often referred to as &#x201C;big data,&#x201D; which brings new opportunities to monitor agricultural and food processes. Besides sensors, big data from social media is also becoming important for the food industry. In this review, we present an overview of IoT, big data, and artificial intelligence (AI), and their disruptive role in shaping the future of agri-food systems. Following an introduction to the fields of IoT, big data, and AI, we discuss the role of IoT and big data analysis in agriculture (including greenhouse monitoring, intelligent farm machines, and drone-based crop imaging), supply chain modernization, social media (for open innovation and sentiment analysis) in food industry, food quality assessment (using spectral methods and sensor fusion), and finally, food safety (using gene sequencing and blockchain-based digital traceability). A special emphasis is laid on the commercial status of applications and translational research outcomes.

Stainless steel electrochemical capacitive microneedle sensors for multiplexed simultaneous measurement of pH, nitrates, and phosphates.

Concerns for agri-food safety and environmental management require development of simple to use and cost- and time-effective multiplex sensors for point-of-need (PON) chemical analytics by public end-user. Simultaneous detection of nitrates, phosphates, and pH is of importance in soil and water analysis, agriculture, and food quality assessment. This article demonstrates a suite of stainless steel microneedle electrochemical sensors for multiplexed measurement of pH, nitrate, and phosphate using faradaic capacitance derived from cyclic voltammetry as the mode of detection. The multi-target microneedle sensors were fabricated by layer-by-layer (LbL) assembly in a stainless steel hypodermic microneedle substrate. For nitrate sensing, the stainless steel was coated with carbon nanotube/cellulose nanocrystal (CNT)/CNC) decorated with silver nanoparticles (Ag). For pH measurement, the polyaniline (pANI) was coated onto the CNT/CNC@Ag film, while for phosphate detection, the CNT/CNC/Ag @pANI microneedle was further decorated with ammonium molybdenum tetrahydrate (AMT). The microelectrode platforms were characterized by FTIR, Raman, and microscopic techniques. The nitrate- and phosphate-based microneedle electrochemical sensors had excellent selectivity and sensitivity, with a determined limit of detection (LOD) of 0.008mM and 0.007mM, respectively. The pH microneedle sensor was responsive to pH in the linear range of 3-10. The three microneedle sensors yielded repeatable results, with a precision ranging from 4.0 to 7.5% RSD over the concentration ranges tested. The inexpensive (~ 1 $ CAD) microneedle sensors were successfully verified for use in quantification of nitrate, pH, and phosphate in brewed black coffee as a real sample. As such, the microneedle sensors are economical devices and show great promise as robust platforms for PON precision chemical analytics.

Recent technology for food and beverage quality assessment: a review.

Food and beverage assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. These assessments had become crucial, especially in the issues of adulteration, replacement, and contamination that happened in artificial adjustment relating to the quality, weight and volume. Thus, this review will examine and describe features recently applied in image, odour, taste and electromagnetic, relevant to the food and beverages assessment. This review will also compare and discuss each technique and provides suggestions based on the current technology. This review will deliberate technology integration and the involvement of deep learning to enable several types of current technologies, such as imaging, odour and taste senses, and electromagnetic sensing, to be used in food evaluation applications for inspection and packaging.

Storage of extruded cereal and legume grain bases in ion-ozone medium

The modern food industry seeks to produce ecologically healthy and clean food products. However, finding effective means of storing raw materials without losing their quality is still a major problem faced by the industry. One of the promising methods on long-term storage of grain bases for good quality and safety is the ion-ozone cavitation treatment. Therefore, this study aims to determine the influence of various ion-ozone treatment parameters on the quality and safety factors of millet, buckwheat, chickpea, and lentil grain bases at the Food Quality and Safety Assessment Laboratory, Almaty University of Technology, Almaty, Kazakhstan. With ozonation, the buckwheat grain bases showed a significant increase in fatty acids, the millet and chickpea grains showed a slight increase, while the lentil seeds exhibited a slight decrease in fatty acids compared to the control. Overall, by treating with a low concentration of ion-ozone, the buckwheat showed fewer microorganisms; in chickpea grains the molds and yeast did not develop; in millet grains the yeast was absent and molds were not higher than control; and in lentil grain bases, the permissible yeast growth with no molds was observed. The storage life of extruded grain bases after ion-ozone cavitation treatment increased by a minimum of 60 days (millet) and to more than 240 days (buckwheat). The optimum basic parameters were determined, ensuring a maximum shelf life of up to eight months without losing the quality of the cereals and legume grain products. The said procedure was found as an effective measure for long-term storage of the extruded grain bases with sustainable safety and quality.

Low-Cost, Real-Time Polymerase Chain Reaction System for Point-of-Care Medical Diagnosis.

Global health crises due to the prevailing Coronavirus Disease 2019 (COVID-19) pandemic have placed significant strain on health care facilities such as hospitals and clinics around the world. Further, foodborne and waterborne diseases are not only spreading faster, but also appear to be emerging more rapidly than ever before and are able to circumvent conventional control measures. The Polymerase Chain Reaction (PCR) system is a well-known diagnostic tool for many applications in medical diagnostics, environmental monitoring, and food and water quality assessment. Here, we describe the design, development, and testing of a portable, low-cost, and real-time PCR system that can be used in emergency health crises and resource-poor situations. The described PCR system incorporates real-time reaction monitoring using fluorescence as an alternative to gel electrophoresis for reaction analysis, further decreasing the need of multiple reagents, reducing sample testing cost, and reducing sample analysis time. The bill of materials cost of the described system is approximately $340. The described PCR system utilizes a novel progressive selective proportional–integral–derivative controller that helps in reducing sample analysis time. In addition, the system employs a novel primer-based approach to quantify the initial target amplicon concentration, making it well-suited for food and water quality assessment. The developed PCR system performed DNA amplification at a level and speed comparable to larger and more expensive commercial table-top systems. The fluorescence detection sensitivity was also tested to be at the same level as commercially available multi-mode optical readers, thus making the PCR system an attractive solution for medical point-of-care and food and water quality assessment.

Attitudes and Expectations of Clinical Research Participants Toward Digital Health and Mobile Dietary Assessment Tools: Cross-Sectional Survey Study.

BackgroundThe adoption of health technologies is key to empower research participants and collect quality data. However, the acceptance of health technologies is usually evaluated in patients or healthcare practitioners, but not in clinical research participants.MethodsA 27-item online questionnaire was provided to the 11,695 members of a nutrition clinical research participant database from the Nantes area (France), to assess (1) participants' social and demography parameters, (2) equipment and usage of health apps and devices, (3) expectations in research setting and (4) opinion about the future of clinical research. Each item was described using frequency and percentage overall and by age classes. A global proportion comparison was performed using chi-square or Fisher-exact tests.ResultsA total of 1529 respondents (81.0% women, 19.0% men) completed the survey. Main uses of health apps included physical activity tracking (54.7%, age-related group difference, p < 0.001) and food quality assessment (45.7%, unrelated to age groups). Overall, 20.4% of respondents declared owning a connected wristband or watch. Most participants (93.8%) expected the use of connected devices in research. However, protection of personal data (37.5%), reliability (35.5%) and skilled use of devices (28.5%) were perceived as the main barriers. Most participants (93.3%) would agree to track their food intake using a mobile app, and 80.5% would complete it for at least a week while taking part in a clinical study. Only 13.2% would devote more than 10 min per meal to such record. A majority (60.4%) of respondents would accept to share their social media posts in an anonymous way and most (82.2%) of them would accept to interact with a chatbot for research purposes.ConclusionsOur cross-sectional study suggests that clinical study participants are enthusiastic about all forms of digital health technologies and participant-centered studies but remain concerned about the use of personal data. Repeated assessments are suggested to evaluate the research participant's interest in technologies following the increase in use and demand for innovative health services during the pandemic of COVID-19.

Foods and beverages provided in out of school hours care services: an observational study

IntroductionOut of school hours care (OSHC) is a fast-growing childcare setting in Australia, however the types of foods and beverages offered are relatively unknown. This study describes the food and beverages offered and investigates sector-level and setting-level factors which may impact OSHC in meeting the Australian Dietary Guidelines (ADG).MethodsThis cross-sectional, observational study was conducted in 89 OSHC services (between 2018 and 2019). Food and beverages offered, kitchen facilities and menus were captured via direct observation. Foods were categorised into five food groups or discretionary foods, based on the ADG, and frequencies determined. Short interviews with OSHC directors ascertained healthy eating policies, staff training, food quality assessment methods and food budgets. Fisher’s exact test explored the influence of sector-level and setting-level factors on food provision behaviours.ResultsDiscretionary foods (1.5 ± 0.68) were offered more frequently than vegetables (0.82 ± 0.80) (p < .001), dairy (0.97 ± 0.81) (p = .013) and lean meats (0.22 ± 0.54) (p < .001). OSHC associated with long day care and reported using valid food quality assessment methods offered more lean meats (p= .002, and p= .004). Larger organisations offered more vegetables (p = .015) and discretionary foods (p= .007). Menus with clearly worded instructions to provide fruits and vegetables daily offered more fruit (p= .009), vegetables (p < .001) and whole grains (p= .003). No other sector or setting-level factors were associated with services aligning with the ADG.ConclusionFuture interventions could benefit from trialling menu planning training and tools to assist OSHC services in NSW meet the ADG requirements.

A comparative analysis of hybrid SVM and LS‐SVM classification algorithms to identify dried wolfberry fruits quality based on hyperspectral imaging technology

A very common preservation method used in food processing is the drying method. Hence it is necessary to properly examine and ascertain how the fruit's quality is affected after drying. Hyperspectral imaging (HSI) technology is one of the advanced technologies adopted for assessing food quality. This study explored the impact of standard normalization variate (SNV) and Savitsky–Golay (SG) data preprocessing and optimization methods on spectral data of dried wolfberry fruits’ quality taken in a range of 400.680–1001.612 nm wavelength. Thus, this research demonstrated that using HSI technology and integrating SG-SVN preprocessing methods to the least square-support vector machine (LS-SVM) model could accurately predict dried wolfberry fruit quality. The prediction accuracy of the LS-SVM algorithm coupled with SG-SVN achieved 96.66%, which was the highest classification accuracy. The study results demonstrated that HSI technology combined with the LS-SVM model is feasible for dried wolfberry fruit quality classification. Novelty impact statement Conventional methods of classifying wolfberry fruit quality rely mainly on appearance and human sensory, which is time consuming and lacks accuracy. In this study, hyperspectral imaging technology with the LS-SVM algorithm is used for precise rapid nondestructive classification of wolfberry fruit dried under different temperatures and time frames, respectively.

Metallic Nanoparticles in the Food Sector: A Mini-Review.

Nanomaterials, and in particular metallic nanoparticles (MNPs), have significantly contributed to the production of healthier, safer, and higher-quality foods and food packaging with special properties, such as greater mechanical strength, improved gas barrier capacity, increased water repellency and ability to inhibit microbial contamination, ensuring higher quality and longer product shelf life. MNPs can also be incorporated into chemical and biological sensors, enabling the design of fast and sensitive monitoring devices to assess food quality, from freshness to detection of allergens, food-borne pathogens or toxins. This review summarizes recent developments in the use of MNPs in the field of food science and technology. Additionally, a brief overview of MNP synthesis and characterization techniques is provided, as well as of the toxicity, biosafety and regulatory issues of MNPs in the agricultural, feed and food sectors.

Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes.

Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the “smell”, “taste”, and “color” of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review’s purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied.

Hospitals’ Food Services Quality and Factors Associated with Patients’ Satisfaction in University Hospitals in the North of Iran

This cross-sectional study evaluated patients’ satisfaction with hospital meals in seven urban public hospitals using a validated questionnaire. The quality of food services based on the hospital standards was assessed using a checklist prepared by Iran’s ministry of health. Of 800 subjects, the quality and quantity aspects of hospital meals, in general, were nearly satisfactory to the patients (mean score: 2.13, range: 1–3, SD: 0.39, 40% good and 36% moderate). The mean score of hospitals’ food service quality based on hospital standards was 2.17 (range: 0–4, SD: 0.59). Females, younger patients and those with higher education level reported to be less pleased with hospital meal services. No relationship between patients’ satisfaction and hospital food quality assessment based on hospital standards was found.