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This study aimed to obtain optimized mixture with three essential oils (EOs) for maximum antioxidant activity through the augmented simplex-centroid mixture design and evaluate the effect of this optimized blend on total aerobic psychrotrophic count (TAPC), lipid and protein oxidation, instrumental color parameters and texture profile in rainbow trout fillets at refrigerated storage for nine days. Considering the DPPH and FRAP assays, the ideal EO blend was 66% lemongrass and 34% oregano. During refrigerated storage, this blend at 2000 ppm was equally effective as BHT (100 ppm) (p > 0.05), mitigating the discoloration (a* and b*), lipid, and protein oxidation in 38.83%, 12.95%, 76.13%, and 35.13%, respectively, besides shows greater effectiveness for preserving texture changes (p < 0.05) and extending the shelf life in 13 h. The lemongrass + oregano EO blend reveals a promising natural alternative to enhance the quality of rainbow trout fillets under refrigerated storage. Furthermore, the multiresponse optimization showed to be a strong ally in enabling the use of these EOs by food industries.

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This systematic review aimed to investigate the occurrence of phthalates (phthalic acid esters [PAEs]) in different food matrices, as well as report the main sources of PAEs in food, the potential risks to the population, and the factors that influence its migration from food contact materials (FCMs) to food. Nineteen PAEs were identified, including di-(2-ehtylhexyl) phthalate (DEHP), dibutyl-phthalate (DBP), benzylbutyl phthalate (BBP), diisononyl phthalate (DINP), and diisodecyl phthalate (DIDP) in fruits and vegetables, milk and dairy products, cereals, meat, fish, fat and oils, snacks, condiments and sauces, miscellaneous, and baby food. Fifty-seven values of PAEs were above the legal limits of countries. DEHP is the PAE with the highest incidence, with maximum concentrations above the specific migration limit (SML) for milk and dairy products, oils and fats, fish, cereals, condiments and sauces, meat, and fruits and vegetables. The risk of exceeding the tolerable daily intake (TDI) was high for DEHP and DBP in fish, fat and oils, cereals, and milk and dairy products for children and adults. Fat and oils are the most critical food for DEHP, DBP, BBP, and DINP. Comparing the estimated daily intake (EDI) with the TDI, there was a risk for "milk and dairy products" in adults and for "cereal and cereal products" in children concerning DEHP. "Cereal and cereal products" presented a risk in children and adults concerning DBP. The "fat and oils" category presented a risk in children and adults about DBP and DINP. Temperature, contact time between food and the FCM, fat percent, and acidity positively correlate with the PAE's migration. The contamination occurs in many steps of the production chain.

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Escherichia coli harboring a transmissible locus of stress tolerance (tLST) and the ability to form biofilms represent a serious risk in dairy production. Thus, we aimed to evaluate the microbiological quality of pasteurized milk from two dairy producers in Mato Grosso, Brazil, with a focus on determining the possible presence of E. coli with heat resistance (60 °C/6 min), biofilm-forming potential phenotypes and genotypes, and antimicrobial susceptibility. For this, fifty pasteurized milk samples from producers named A and B were obtained for 5 weeks to investigate the presence of Enterobacteriaceae members, coliforms, and E. coli. For heat resistance, E. coli isolates were exposed to a water bath at 60 °C for 0 and 6 min. In antibiogram analysis, eight antibiotics belonging to six antimicrobial classes were analyzed. The potential to form biofilms was quantified at 570 nm, and curli expression by Congo Red was analyzed. To determine the genotypic profile, we performed PCR for the tLST and rpoS genes, and pulsed-field gel electrophoresis (PFGE) was used to investigate the clonal profile of the isolates. Thus, producer A presented unsatisfactory microbiological conditions regarding Enterobacteriaceae and coliforms for weeks 4 and 5, while all samples analyzed for producer B were contaminated at above-the-limit levels established by national and international legislation. These unsatisfactory conditions enabled us to isolate 31 E. coli from both producers (7 isolates from producer A and 24 isolates from producer B). In this way, 6 E. coli isolates (5 from producer A and 1 from producer B) were highly heat resistant. However, although only 6 E. coli showed a highly heat-resistant profile, 97% (30/31) of all E. coli were tLST-positive. In contrast, all isolates were sensitive to all antimicrobials tested. In addition, moderate or weak biofilm potential was verified in 51.6% (16/31), and the expression of curli and presence of rpoS was not always related to this biofilm potential. Therefore, the results emphasize the spreading of heat-resistant E. coli with tLST in both producers and indicate the biofilm as a possible source of contamination during milk pasteurization. However, the possibility of E. coli producing biofilm and surviving pasteurization temperatures cannot be ruled out, and this should be investigated.

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Brazil is the third largest exporter of fruits and vegetables in the world and, consequently, uses large amounts of pesticides. Food contamination with pesticide residues (PRs) is a serious concern, especially in developing countries. Several research reports revealed that some Brazilian farmers spray pesticides on fruits and vegetables in large quantities, generating PRs after harvest. Thus, ingestion of food contaminated with PRs can cause adverse health effects. Based on information obtained through a systematic review of essential information from 33 articles, we studied the assessment of potential health risks associated with fruit and vegetable consumption in children and adults from Brazilian states. This study identified 111 PRs belonging to different chemical groups, mainly organophosphates and organochlorines, in 26 fruit and vegetable samples consumed and exported by Brazil. Sixteen of these PRs were above the Maximum Residue Limit (MRL) established by local and international legislation. We did not identify severe acute and chronic dietary risks, but the highest risk values were observed in São Paulo and Santa Catarina, associated with the consumption of tomatoes and sweet peppers due to the high concentrations of organophosphates. A high long-term health risk is associated with the consumption of oranges in São Paulo and grapes in Bahia due to chlorothalonil and procymidone. We also identified that 26 PRs are considered carcinogenic by the United States Environmental Protection Agency (US EPA), and the carcinogenic risk analysis revealed no severe risk in any Brazilian state investigated due to the cumulative hazard index (HI) < 1. However, the highest HI values were in São Paulo due to acephate and carbaryl in sweet pepper and in Bahia due to dichlorvos. This information can help regulatory authorities define new guidelines for pesticide residue limits in fruits and vegetables commonly consumed and exported from Brazil and monitor the quality of commercial formulations.

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Vinegar is a versatile product used for food preservation, cooking, healthcare, and cleaning. In this study, 80 vinegar of different raw materials, aging time, and for the first time by the agronomic method of raw material cultivation were classified by spectralprint techniques with chemometrics. Datasets were obtained by proton nuclear magnetic resonance (1H NMR), Fourier transforms mid-infrared (FT-IR), near-infrared (NIR), and ultraviolet-visible (UV-vis); then evaluated by common dimension (ComDim) and partial least squares-discriminant analysis (PLS-DA). NMR with PLS-DA had the best prediction performance compared to other techniques, with accuracy values between 92.3 and 100 %, followed by FT-IR and UV-vis of 80.8 and 96.0 % and NIR between 69.2 and 84.0 %. The results indicated that the classification of vinegar according to the agronomic cultivation method is more complex than aging time or raw material. However, any of these spectralprint techniques have demonstrated that they can be used in the classification of vinegar.

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Brazil annually produces around 43 million tons of fruits and vegetables. Therefore, large amounts of pesticides are needed to grow these foods. The use of unauthorized or indiscriminate pesticides can lead to the adherence of residues of these compounds to the product in a concentration above the maximum residue limit (MRL). Pesticide residues (PRs) monitoring is a continuous challenge due to several factors influencing the detection of these compounds in the food matrix. Currently, several adaptations to conventional techniques have been developed to minimize these problems. This systematic review presents the main information obtained from 52 research articles, taken from five databases, on changes and advances in Brazil in sample preparation methods for determining PRs in fruits and vegetables in the last nine years. We cover the preexisting ones and some others that might be suitable alternatives approaches. In addition, we present a brief discussion on the monitoring of PRs in different Brazilian regions, and we found that residues belonging to the organophosphate and pyrethroid classes were detected more frequently. Approximately 67% of the residues detected are of irregular use in 28 types of fruits and vegetables commonly consumed and exported by Brazil.

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Analytical assurance of coffees' geographical indication (GI) authenticity is essential for producers and consumers. In this way, chemometric methods, electrochemical techniques, and 3D printed sensors become attractive to assure the coffee's quality. These sensors are low-cost, fast, and simple, with the possibility of miniaturization and portability. Therefore, 3D printed electrodes with chemometrics were used to classify-three Brazilian coffees from regions with GI. Further, Au/Gpt-PLA electrodes with partial least squares regression were used to detect the blending of GI coffee with traditional coffee. Soft independent modelling of class analogies coupled with cyclic voltammetry had the best performance, with 91-95% accuracy, specificity of 94-100%, and 80-83% sensitivity. Furthermore, the calibration models detected and quantified traditional coffee in all three coffees from regions with GI. The detection limits ranged from 1.4 to 10% (w/w), and quantification 4.6-32%, depending on the specific coffee.

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Among the extant beer types, Berliner Weisse is mainly characterized by its blend of barley and wheat malts, Saccharomyces cerevisiae for alcoholic fermentation, and Lactobacillus spp. for lactic fermentation. In this study, three formulations of Berliner Weisse were developed with various concentrations of barley, wheat malts, and hops. No variations were made in the concentrations of S. cerevisiae, Lacticaseibacillus casei, or cashew pulp. A L. casei-free formulation was used as a control. Physicochemical and sensory parameters were evaluated to characterize the formulations. The physicochemical data allowed for differentiation of the beverages in all evaluated parameters, except for the percentages of titratable acidity and diacetyl. From a sensory perspective, panelists classified the beer as acidic or fruity. The cashew peduncle pulp was proven to be a viable and attractive alternative for the production of Berliner Weisse-style beer with national characteristics and versatility in physicochemical and sensory parameters.(AU)

Dentre os tipos de cerveja existentes, a cerveja Berliner Weisse tem como principal característica ser produzida a partir de um blend de maltes de cevada e trigo, Saccharomyces cerevisiae para a fermentação alcoólica e Lactobacillus spp. para a fermentação láctica. Neste estudo, foram desenvolvidas três formulações de Berliner Weisse com variações na concentração de maltes de cevada e trigo e lúpulo. Não houve variação nas concentrações de S. cerevisiae, Lacticaseibacillus casei e polpa de caju. Uma formulação isenta de L. casei foi formulada como controle. Foram avaliados parâmetros físico-químicos e sensoriais para caracterização das formulações. Os dados físico-químicos permitiram diferenciar as bebidas em todos os parametros avaliados, exceto quanto a porcentagem de acidez tilulável e diacetil. Do ponto de vista sensorial, os avaliadores classificaram as cervejas como ácidas e frutadas. A utilização da polpa do pedúnculo de caju se mostrou uma alternativa viável e atraente para a produção de cerveja estilo Berliner Weisse com características nacionais e versatilidade em seus parâmetros físico-químicos e sensoriais.(AU)

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(1) Background: This study aimed to use the simplex-centroid mixture design methodology coupled with a microdilution assay to predict optimal essential oil (EO) formulations against three potential foodborne pathogens simultaneously through the desirability (D) function. (2) Methods: Oregano (ORE; Origanum vulgare), thyme (THY; Thymus vulgaris), and lemongrass (LG; Cymbopogon citratus) and their blends were evaluated concerning minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) for Salmonella enterica serotype Enteritidis, Escherichia coli and Staphylococcus aureus. (3) Results: THY combined with ORE or LG were the most promising EO formulations in inhibiting and killing each bacterium separately. Regarding the simultaneous effect, the optimal proportion for maximum inhibition was composed of 75% ORE, 15% THY, and 10% LG, while for maximum inactivation was 50% ORE, 40% THY, and 10% LG. (4) Conclusion: The multiresponse optimization allowed identifying an EO blend to simultaneously control three potential foodborne pathogens. This first report could be a helpful natural and green alternative for the industry to produce safer food products and mitigate public health risks.

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Biosensors are a simple, low-cost, and reliable way to detect pesticides in food matrices to ensure consumer food safety. This systematic review lists which nanomaterials, biorecognition materials, transduction methods, pesticides, and foods have recently been studied with biosensors associated with analytical performance. A systematic search was performed in the Scopus (n = 388), Web of Science (n = 790), and Science Direct (n = 181) databases over the period 2016-2021. After checking the eligibility criteria, 57 articles were considered in this study. The most common use of nanomaterials (NMs) in these selected studies is noble metals in isolation, such as gold and silver, with 8.47% and 6.68%, respectively, followed by carbon-based NMs, with 20.34%, and nanohybrids, with 47.45%, which combine two or more NMs, uniting unique properties of each material involved, especially the noble metals. Regarding the types of transducers, the most used were electrochemical, fluorescent, and colorimetric, representing 71.18%, 13.55%, and 8.47%, respectively. The sensitivity of the biosensor is directly connected to the choice of NM and transducer. All biosensors developed in the selected investigations had a limit of detection (LODs) lower than the Codex Alimentarius maximum residue limit and were efficient in detecting pesticides in food. The pesticides malathion, chlorpyrifos, and paraoxon have received the greatest attention for their effects on various food matrices, primarily fruits, vegetables, and their derivatives. Finally, we discuss studies that used biosensor detection systems devices and those that could detect multi-residues in the field as a low-cost and rapid technique, particularly in areas with limited resources.

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The growth of spoilage and pathogenic bacteria during storage represents significant losses in marketing raw milk cheeses. Thus, reducing NaCl in these products is challenging, as sodium has a critical antimicrobial role. Despite advances in non-thermal technologies, the short shelf life still limits the availability of raw goat cheese. Thus, combined preservation methods can be promising because their synergies can extend shelf life more effectively. In this context, Principal Component Analysis (PCA) was applied to variables to investigate the effect of pequi waste extract (PWE), a native Brazilian fruit, combined with UV-C radiation (CEU) and vacuum packaging (CEV) on the preservation of low-sodium raw goat cheese. CEV samples had lower loadings for Staphylococcus subsp. and Enterobacteriaceae than other treatments in PC2, having a count's reduction up to 3-fold (P < 0.05) compared to vacuum alone. In contrast, CEU showed an increase of up to 1.2-fold on staphylococcal count compared to UV-C alone. Still, the addition of PWE to UV-C-treated cheeses resulted in 8.5% protein loss. Furthermore, PWE, especially in CEV, delayed post-acidification during storage. It made CEV up to 4.5 and 1.6-fold more stable for color and texture, respectively than vacuum alone. These data strongly suggest that PWE may be a novel and promising synergistic agent in the microbial and physicochemical preservation of low-sodium raw milk cheese when combined with the vacuum.

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In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusive assignment ratio of 20%. On the other hand, SIMCA models had a 0 inconclusive rate but an sensitivity close to 85%. While the non-linear technique (SVM-DA) showed an accuracy of 98%, with 95% sensitivity and 98% specificity. The SPCE-SVM-DA technique was then used to distinguish at brand level two highly frauded beers. The SPCE coupled with SVM-DA performed with an accuracy of 97% for the classification of both brands. Therefore, the proposed electrochemicalsensor configuration has been deemed an appropriate tool for discrimination of American lager beers according to their producer and brands.

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Medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents.

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Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

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Essential oils (EOs) and their compounds have attracted particular attention for their reported beneficial properties, especially their antiviral potential. However, data regarding their anti-SARS-CoV-2 potential are scarce in the literature. Thus, this study aimed to identify the most promising EO compounds against SARS-CoV-2 based on their physicochemical, pharmacokinetic, and toxicity properties. A systematic literature search retrieved 1669 articles; 40 met the eligibility criteria, and 35 were eligible for analysis. These studies resulted in 465 EO compounds evaluated against 11 human and/or SARS-CoV-2 target proteins. Ninety-four EO compounds and seven reference drugs were clustered by the highest predicted binding affinity. Furthermore, 41 EO compounds showed suitable drug-likeness and bioactivity score indices (≥0.67). Among these EO compounds, 15 were considered the most promising against SARS-CoV-2 with the ADME/T index ranging from 0.86 to 0.81. Some plant species were identified as EO potential sources with anti-SARS-CoV-2 activity, such as Melissa officinalis Arcang, Zataria multiflora Boiss, Eugenia brasiliensis Cambess, Zingiber zerumbet Triboun & K.Larsen, Cedrus libani A.Rich, and Vetiveria zizanoides Nash. Our work can help fill the gap in the literature and guide further in vitro and in vivo studies, intending to optimize the finding of effective EOs against COVID-19.

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Design of Experiments (DoE) is a statistical tool used to plan and optimize experiments and is seen as a quality technology to achieve products excellence. Among the experimental designs (EDs), the mixture designs (MDs) stand out, being widely applied to improve conditions for processing, developing, or formulating novel products. This review aims to provide useful updated information on the capacity and diversity of MDs applications for the industry and scientific community in the areas of food, beverage, and pharmaceutical health. Recent works were selected following the Preferred Reporting Items for Systematic Review and Meta-Analyses statement (PRISMA) flow diagram. Data analysis was performed by self-organizing map (SOM) to check and understand which fields of application/countries/continents are using MDs. Overall, the SOM indicated that Brazil presented the largest number of works using MDs. Among the continents, America and Asia showed a predominance in applications with the same amount of work. Comparing the MDs application areas, the analysis indicated that works are prevalent in food and beverage science in the American continent, while in Asia, health science prevails. MDs were more used to develop functional/nutraceutical products and the formulation of drugs for several diseases. However, we briefly describe some promising research fields in that MDs can still be employed.

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The growing demand for authentic products that provide sensory characteristics combined with health benefits has been the focus of current studies. This study developed a Red Ale style craft beer with spices such as turmeric (T), black pepper (P) and aroma hops (H), used isolated or in mixtures. A mixture design was employed to evaluate the total phenolic compounds and the antioxidant activity in the green and aged beers formulations. The spice extracts influenced the product's shelf-life. The addition of spices into the beers did not affect the physicochemical parameters that classify the Red Ale style, according to the hierarchical cluster analysis, except for aroma hops. A multiresponse optimization approach simultaneously maximized the antioxidant activity and the phenolic compounds in beers. The ideal formulation obtained for green beers was 25% T and 37.5% P and H; for aged beers, the formulation was 50% T, 20% P and 30% H.

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Essential oils (EOs) are commercially important products, sources of compounds with antioxidant and antimicrobial activities considered indispensable for several fields, such as the food industry, cosmetics, perfumes, pharmaceuticals, sanitary and agricultural industries. In this context, this systematic review and meta-analysis, a novel approach will be presented using chemometric tools to verify and recognize patterns of antioxidant, antibacterial, and antifungal activities of EOs according to their geographic, botanical, chemical, and microbiological distribution. Scientific papers were selected following the Preferred Reporting Items for Systematic Review and Meta-Analyses statement flow diagram, and the data were evaluated by the self-organizing map and hierarchical cluster analysis. Overall, this novel approach allowed us to draw an overview of antioxidants and antimicrobials activities of EOs reported in 2019, through 585 articles evaluated, obtaining a dataset with more than 10,000 data, distributed in more than 80 countries, 290 plant genera, 150 chemical compounds, 30 genera of bacteria, and 10 genera of fungi. The networks for geographic, botanical, chemical, and microbiological distribution indicated that Brazil, Asia, the botanical genus Thymus, species Thymus vulgaris L. "thyme," the Lamiaceae family, limonene, and the oxygenated monoterpene class were the most representative in the dataset, while the species Escherichia coli and Candida albicans were the most used to assess the antimicrobial activity of EOs. This work can be seen as a guide for the processing of metadata using a novel approach with non-conventional statistical methods. However, this preliminary approach with EOs can be extended to other sources or areas of food science.

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Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country's measures, which were implemented to contain the virus' spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus' spread in these cities, states, and regions.

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Infinite factors can influence the spread of COVID-19. Evaluating factors related to the spread of the disease is essential to point out measures that take effect. In this study, the influence of 14 variables was assessed together by Artificial Neural Networks (ANN) of the type Self-Organizing Maps (SOM), to verify the relationship between numbers of cases and deaths from COVID-19 in Brazilian states for 110 days. The SOM analysis showed that the variables that presented a more significant relationship with the numbers of cases and deaths by COVID-19 were influenza vaccine applied, Intensive Care Unit (ICU), ventilators, physicians, nurses, and the Human Development Index (HDI). In general, Brazilian states with the highest rates of influenza vaccine applied, ICU beds, ventilators, physicians, and nurses, per 100,000 inhabitants, had the lowest number of cases and deaths from COVID-19, while the states with the lowest rates were most affected by the disease. According to the SOM analysis, other variables such as Personal Protective Equipment (PPE), tests, drugs, and Federal funds, did not have as significant effect as expected.

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