Search

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Milk and whey are subjected to quick deterioration due to bacterial growth. Turning them into powder allows for extended shelf life, easier storage, and more effective transport. Monitoring mineral elements in dairy powders is crucial for both technological and nutritional implications. Current analysis of minerals in food matrixes is time consuming and requires high costs and labor. Energy-dispersive X-ray fluorescence (ED-XRF) may represent a potential alternative technique to rapidly determine mineral content of food matrixes. The present study aimed to investigate the effectiveness of the ED-XRF technique for the prediction of Na, Mg, P, S, K, Ca, Cu, and Zn in skim milk and whey powders. Reference analysis for mineral concentration in dairy powders was inductively coupled plasma-optical emission spectrometry. The accuracy of ED-XRF for the prediction of mineral elements was assessed on all available samples (global calibration set; n = 23) and after splitting the whole data into a training set (n = 16) and a testing set (n = 7), comprising 70% and 30% of the available samples, respectively. The accuracy of prediction was moderate to low for Na and Mg, whereas robust predictions were obtained for P, S, K, Ca, Cu, and Zn, with coefficients of determination in global calibration from 0.88 to 0.99, and ratios performance to deviation in testing from 2.14 to 24.74. Results suggest that ED-XRF can be considered for accurate prediction of the main mineral elements in dairy powders. [ABSTRACT FROM AUTHOR]

Five varieties of beans from Laibin, Guangxi, China, including mung beans, soybeans, black beans, red beans, and adzuki beans, were chosen as study subjects for multi-index determination and full assessment. Data on the fat, ash, crude fiber, trace element, thermogravimetric analysis and combustion heat of five different types of beans were gathered. The results showed that the order of combustion heat of the 5 kinds of beans was black beans>soybeans>mung beans>adzuki beans>red beans, the order of combustion stability was soybeans>mung beans>black beans>red beans>adzuki beans, the order of fat content was soybeans>red beans>adzuki beans>black beans>mung beans, the order of ash content was black beans>soybeans>adzuki beans>mung beans>red beans, the order of crude fiber content was red beans>soybeans>adzuki beans, and the order of multi-index stoichiometric analysis was soybeans>red beans>black beans>mung beans>adzuki beans. The quality of the food was assessed and classified using the stoichiometric approach, while the food nutrition was assessed from the perspectives of fat, ash, crude fiber, trace elements, thermogravimetric analysis and combustion heat. This work provides a solid scientific foundation for the assessment and investigation of the thermo gravimetric analysis of food combustion stability. This study's multi-index comprehensive evaluation approach offers a novel concept for food nutrition. [ABSTRACT FROM AUTHOR]

The heterogeneity in East African small-holder agricultural systems influences the farm households' welfare outcomes. We examine the heterogeneity of rural farm households in Eastern Africa and the influence of such diversity on household food security and how these relate to intra-household decision-making dynamics. Using data from the Rural Household Multi-Indicator Survey (RHoMIS) 1,199 households in Ethiopia, Kenya, Malawi and Uganda were typologized using partition-based cluster analysis. These were grouped into relatively homogenous classes reflecting their socio-demographic characteristics, resource endowment and farm orientation based at a country level to reflect the unique institutional conditions within these countries. Additional analysis was then used to characterise the differences in food insecurity and intra-household decision-making of these clusters. Five distinct farm household clusters were identified in Ethiopia and Kenya, four in Malawi and three in Uganda. The main factors differentiating the farm clusters were educational attainment of the household head, levels of resource endowment, market orientation and off-farm income. Market orientated clusters and clusters comprising more educated household heads were generally more food secure across all countries. In Ethiopia, Kenya and Uganda, women had greater decision-making control in clusters comprising least educated household heads. Female decision-making control was negatively associated with the resource endowment of clusters in Kenya, Malawi and Uganda. This emphasises the importance of understanding rural farm household heterogeneity when tailoring interventions to target household food insecurity and gender inequity in decision-making in Eastern Africa. [ABSTRACT FROM AUTHOR]

The efficient extraction of various analytes from a wide spectrum of matrices with organic solvents is still a great challenge in analytical chemistry. Especially polar and charged compounds are hard to extract in combination with neutral analytes of intermediate to low polarity. The QuEChERS method is often chosen and has been adapted not only to the analysis of food samples, but also to environmental matrices (soil, wastewater) or biota. In this study, we overcome major drawbacks of QuEChERS such as low recoveries of charged analytes and impairment of downstream analysis by high salt loads. The new extraction method, applicable to liquid and solid samples, is called SWIEET (sugar water isopropanol ethyl nitrile extraction technique). Phase separation of the otherwise miscible extraction solvents water and acetonitrile is achieved by sugaring-out instead of salting-out. Extraction efficiencies were greatly improved by adding isopropanol to the acetonitrile phase. The concentrations of the additives glucose and isopropanol, as well as temperature, were optimized by a design of experiment. Further improvement was achieved through electro- or double-extractions. For all sample types tested (surface water, wastewater treatment plant effluent, tomato, soil, and oats), recoveries and precision were higher with SWIEET than with the established QuEChERS method. From wastewater treatment plant effluent, 75% recovery on average were achieved with our SWIEET method compared to 37% with QuEChERS for a model analyte mixture with polarities of logDpH7 = − 5.7 − 3.5. Higher recoveries and lower standard deviations compared to QuEChERS were achieved especially for polar and charged analytes such as metformin. Handling proved to be easy, since there was no additional solid phase and no tedious weighing of salts. [ABSTRACT FROM AUTHOR]

Hydrolytic nanozyme–based visual colorimetry has emerged as a promising strategy for the detection of aluminum ions. However, most studies focus on simulating the structure of natural enzymes while neglecting to regulate the rate of hydrolysis-related steps, leading to low enzyme-like activity for hydrolytic nanozymes. Herein, we constructed a ruthenium dioxide (RuO2) in situ embedded cerium oxide (CeO2) nanozyme (RuO2/CeO2) with a Lewis acid–base pair (Ce–O-Ru-OH), which can simulate the catalytic behavior of phosphatase (PPase) and can be quantitatively quenched by Al3+ to achieve accurate and sensitive Al3+ colorimetric sensing detection. The incorporation of Ru into CeO2 nanorods accelerates the dissociation of H2O, followed by subsequent combination of hydroxide species to Lewis acidic Ce–O sites. This synergistic effect facilitates substrate activation and significantly enhances the hydrolysis activity of the nanozyme. The results show that the RuO2/CeO2 nanozyme exhibits a limit of detection as low as 0.5 ng/mL. We also demonstrate their efficacy in detecting Al3+ in various practical food samples. This study offers novel insights into the advancement of highly sensitive hydrolytic nanozyme engineering for sensing applications. [ABSTRACT FROM AUTHOR]

As a representative gas of food spoilage, the development of rapid hydrogen sulfide (H2S) analysis strategies for food safety control is in great demand. Despite traditional methods for H2S detection possessing great achievements, they are still incapable of meeting the requirement of portability and quantitative detection at the same time. Herein, a nanozyme catalysis pressure-powered sensing platform that enables visual quantification with the naked eye is proposed. In this methodology, Pt nanozyme inherits the catalase-like activity to facilitate the decomposition of H2O2 to O2, which can significantly improve the pressure in the closed container, further pushing the movement of indicator dye. Furthermore, H2S was found to effectively inhibit the catalytic activity of Pt nanozyme, indicating that the catalase-like activity of PtNPs may be regulated by varying concentrations of H2S. Therefore, by utilizing a self-designed pressure-powered microchannel device, the concentration of H2S was successfully converted into a distinct signal variation in distance. The effectiveness of the as-designed sensor in assessing the spoilage of red wine by H2S determination has been demonstrated. It exhibits a strong correlation between the change in dye distance and H2S concentration within the range of 1–250 μM, with a detection limit of 0.17 μM. This method is advantageous as it enhances the quantitative detection of H2S with the naked eye based on the portable pressure-powered sensing platform, as compared to traditional H2S biosensors. Such a pressure-powered distance variation platform would greatly broaden the application of H2S-based detection in food spoilage management. [ABSTRACT FROM AUTHOR]

The identification and evaluation of barriers to artificial intelligence (AI) adoption in food supply chain finance (FSCF) can be addressed as a multiattribute decision-making problem. However, only a few studies have reported the application of decision models for evaluating barriers to the implementation of AI in FSCF, especially within an uncertain context. Hence, this work explores the evaluation issue of implementation barriers via an integrated decision model. In this model, the conventional additive ratio assessment (ARAS) model integrated with the Choquet integral and criteria importance through intercriteria correlation (CRITIC) is extended into the interval-valued Fermatean fuzzy (IVFF) setting for ranking the barriers. The IVFF weighted average operator based on the Choquet integral is introduced to form a group decision matrix. Then, the developed ARAS model with the IVFF-CRITIC method is proposed to evaluate the implementation barriers for AI in FSCF, which can depict the interactions between the barriers. Finally, a case of an FSCF, including four participants, is presented to illustrate the application of the reported model and demonstrate its reliability. The result shows that "Data privacy" () is the main barrier impeding AI adoption in FSCF, and the participant "small and medium-sized processing enterprises" () has the highest barrier level to AI adoption. [ABSTRACT FROM AUTHOR]

Food public opinion is characterized by its low ignition point, high diffusibility, persistence, and strong negativity, which significantly impact food safety and consumer trust. This paper introduces the Food Public Opinion Prevention and Control (FPOPC) model driven by deep learning and personalized recommendation algorithms, rigorously tested and analyzed through experimentation. Initially, based on an analysis of food public opinion development, a comprehensive FPOPC framework addressing all stages of food public opinion was established. Subsequently, a sentiment prediction model for food news based on user comments was developed using a Stacked Autoencoder (SAE), enabling predictions about consumer sentiments toward food news. The sentiment values of the food news were then quantified, and improvements were made in allocating Pearson correlation coefficient weights, leading to the design of a collaborative filtering-based personalized food news recommendation mechanism. Furthermore, an enhanced Bloom filter integrated with HDFS technology devised a rapid recommendation mechanism for food public opinion. Finally, the designed FPOPC model and its associated mechanisms were validated through experimental verification and simulation analysis. The results demonstrate that the FPOPC model can accurately predict and control the development of food public opinion and the entire food supply chain, providing regulatory agencies with effective tools for managing food public sentiment. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background : Global food systems are essential to sustain life; however, unhealthy diets are the leading cause of poor health and death worldwide. Natural food environments are a critical source of healthful food such as fresh fruit and vegetables, lean meat and aquatic foods, particularly in low- and middle-income countries (LMICs) and Indigenous food systems. Understanding and monitoring natural food environments is critical to protecting the supply of healthy food, and the land and water it comes from, for current and future generations. Methods: A scoping review was applied to classify and summarise empirical methods and measures used for characterising natural food environments. The ways in which food environment characteristics are conceptualised and measured for natural (wild and cultivated) food environments were explored. Data were extracted from the included studies using the following fields: study country, aim/objective(s), type of natural food environment (wild vs. cultivated), method or measure used to measure the food environment, key food environment characteristic/s (up to four) measured and key findings in relation to the food environment measurement. Results: One hundred forty seven studies were found to be relevant in this review. Most studies investigated food environments in LMICs, with almost two-thirds of all studies focussing on middle-income countries (n = 89, 61%). There was a strong focus on food security and home-based agriculture from studies that measured the cultivated food environment in LMICs, while the majority of studies on the cultivated food environment from high-income countries focussed on urban and community gardening. In addition to the most common survey-based methodological approaches, our review yielded a broad range of both qualitative and quantitative methods for measuring natural food environments, such as geospatial analysis; biochemical analysis of food, soil and water; citizen science; photovoice and food availability calendars. Conclusions: Our review demonstrated that the concept and characteristics of the food environment are a promising conceptualization for measuring natural food environments, particularly in relation to food availability, healthiness and food security. Mapping the currently used methods and measures to assess natural food environments is important to help identify critical leverage points for strengthening policy interventions, and monitoring and evaluation of progress. [ABSTRACT FROM AUTHOR]

Aquaculture of the silver carp (Hypophthalmichthys molitrix) is one of the most important species for ensuring food security. This analysis study aimed to comprehend the conceptual framework of the recent research for it, based on 959 Scopus-indexed core collection papers from 446 publications produced by 139 authors from 57 countries between 2003 and 2023. VOSViewer was used to conduct this bibliometric analysis, which looked into the fundamental profile of the chosen fields. Of the ten authors, Luo Yongkang emerged as the most productive author in the field, with an h-index of 58, 268 articles, and 268 documents. China and the USA were found to have the strongest teamwork and the most productive nations, producing 437 and 262 papers, respectively. The most frequently mentioned journals that addressed the research topic were Food Hydrocolloids (411) and Food Chemistry (391, 341, 321, and 196, respectively), which dealt with the research topic the most. As a result, this bibliometric analysis offers thorough details on the field of study and research landscape around silver carp and indicates interest in the subject matter. In addition, it identifies crucial areas for further investigation and makes it easier to map possible authors, organizations, and nations for partnerships. [ABSTRACT FROM AUTHOR]

Macrobenthic communities in a lake are affected by the type of bottom cover such as macrophytes or algae. In the southern basin of Lake Biwa, mats of the benthic cyanobacteria (BC) Microseira wollei widely cover the lake bottom and are interspersed with submerged macrophytes (SMs). Because different macrobenthos species appear to occur at those bottoms, we investigated the composition of the communities. Furthermore, as M. wollei is supposed to be inedible to consumer organisms owing to its hard sheath and toxins, the food possibility of the cyanobacteria and macrophytes for macrobenthos was analyzed. In this study, macrobenthos were collected, identified molecularly, counted in the BC and SM zones, and analyzed for carbon and nitrogen stable‐isotopic compositions. In the BC zone, asellids and amphipods dominated the macrobenthic community, while chironomids dominated the SM zone. The stable‐isotope analysis revealed that M. wollei was a minor food source for macrobenthos and other higher‐level consumers, with some macrophytes, particulate organic matter and bottom sediment potentially being a major source. The dominance of crustacean macrobenthos in M. wollei mats suggested that they provided a refuge from predation for crustaceans, whereas SMs were not sufficiently abundant to achieve this. Although different macrobenthic communities in the BC and SM zones were likely supported by common food sources, with the exeption of M. wollei, the present study was conducted over a short period and lacked advanced methods for gut content analysis. Therefore, further monitoring and food web analysis are required. [ABSTRACT FROM AUTHOR]

The coupling of Solid-Phase Microextraction (SPME) technology with gas chromatography (GC) has a well-established and successful history. Traditionally, SPME fibers have been the most popular form thanks to their versatility and the ease with which they can be fully automated. However, alternative geometries for SPME have been developed over the years, beginning with Stir Bar Sorptive Extraction (SBSE) and later evolving into Thin-Film SPME (TF-SPME) devices. Each of these formats offers distinct advantages and disadvantages, which are explored in this study. The primary objective of this study was to comprehensively compare available forms of SPME devices, with a special focus on the advantages of TF-SPME, a novel microextraction method particularly suited for the analysis of odorants in food. The study involved analyzing a standard mixture of 11 key food odorants, representing a range of polarities, to evaluate the efficiency of TF-SPME devices in terms of the number of analytes extracted. Furthermore, four types of TF-SPME devices were compared against each other in both standard mixtures and actual food samples. The final stage of the study employed GCxGC-ToFMS analysis to showcase the potential of the most efficient HLB-TF-SPME device in the non-targeted analysis of complex samples, exemplified by unfiltered wheat beer. This analysis demonstrated the significant capability of HLB-TF-SPME in capturing and identifying a wide range of volatile compounds in complex matrices. [ABSTRACT FROM AUTHOR]

Children who do not consume enough iron have decreased growth, reduced immunity, and poor cognitive development. This study aimed to assess the pooled iron-rich food consumption, trends, and predictors among children aged 6–59 months old in Ethiopia. This study used Ethiopian demographic and health survey (EDHS-2005–2016) data with a total weighted sample size of 26,773 among children aged 6–59 months old. A multilevel mixed-effects logistic regression analysis was used to identify predictors of good iron-rich food consumption. The pooled proportion of iron-rich food consumed among children aged 6–59 months in Ethiopia was 11.77% (10.71, 12.92%). In Ethiopia, the proportion of iron-rich food intake by children aged 6–59 months was slightly increased from 9.25% (8.04, 10.62%) in EDHS 2005 to 10.86% (9.06, 12.95%) in EDHS 2011, and sharply increased to 17.49% (15.04, 20.24%) in EDHS 2016. The findings indicated that being children born to mothers aged 20 to 34 years old [AOR = 0.56, 95% CI: 0.35, 0.89], mothers aged ≥ 35 years old [AOR = 0.47, 95% CI: 0.28, 0.82], married parents [AOR = 1.68, 95% CI: 1.14, 2.48], completed primary education [AOR = 1.32, 95% CI: 1.02, 1.73], secondary education [AOR = 2.66, 95% CI: 1.53, 4.59], a higher education status of mother [AOR = 3.98, 95% CI: 1.92, 8.23], medium family [AOR = 1.39, 95% CI: 1.03, 1.89], rich family [AOR = 1.45, 95% CI: 1.09, 1.95], media exposure [AOR = 1.35, 95% CI: 1.06, 1.72], residents of the Tigray [AOR = 2.69, 95% CI: 1.51, 4.79], Oromia [AOR = 3.30, 95% CI: 1.30, 4.02], Benishangul [AOR = 2.12, 95% CI: 1.15, 3.88], Gambela [AOR = 5.85, 95% CI: 2.97, 11.50], and Dire Dawa region [AOR = 3.38, 95% CI: 1.76, 6.47], were significantly associated with good iron-rich food consumption among children aged 6–59 months old. The findings revealed that iron-rich food intake was considerably low among Ethiopian children aged 6–59 months. Increasing women's literacy and economic empowerment improves iron-rich food consumption among children aged 6–59 months old. [ABSTRACT FROM AUTHOR]

Background: Peroxisome-proliferator-activated receptors (PPARs) constitute nuclear transcription factors controlling gene expression associated with cell growth and proliferation, diverse proteins, lipids, and glucose metabolism, being related to several other pathophysiological states such as metabolic disorders, atherogenesis, carcinogenesis, etc. The present survey aims to analyze the natural compounds that can act as agonists for the PPAR-α, PPAR-β/δ, and PPAR-γ system targeting, highlighting how the amazing biochemical diversity of natural compounds can yield new insights into this "hotspot" of the scientific field. Methods: A narrative review was performed by searching the recent international literature for the last two decades in the most authoritative scientific databases, like PubMed, Scopus, Web of Science, and Embase, using appropriate keywords. Results: Several natural compounds and/or their synthetic derivatives can act as ligands of PPARs, stimulating their transcriptional activity and enabling their use as preventive and/or therapeutic agents for several disease states, such as inflammation, oxidative stress, metabolic disturbances, atherogenesis, and carcinogenesis. Although synthetic compounds are increasingly used as drugs to manage health problems, serious side effects have been observed, while their natural analogues exhibit only few minor side effects. Conclusions: Further clinical studies on natural compounds such as ligands of PPARs and the evaluation of the related molecular mechanisms are needed to implement an effective strategy concerning the pharmaco-technology, food chemistry, and nutrition to introduce them as part of clinical and dietary practice. [ABSTRACT FROM AUTHOR]

Global fish and seafood consumption is increasing annually, frequently leading to the emergence of food fraud, mainly related to mislabeling and adulteration like, for example, the use of illicit/unauthorized food additives to mask or delay fish spoilage. Among the available diagnostic tools for control purposes, spectroscopic techniques have often been proposed to identify these kinds of illicit practices in fish and seafood products. The presented study aims to test two cheap and portable near infrared (NIR) spectrometers, a handheld MicroNIR and a pocket-sized SCiO, to uncover use of the illicit food additive Cafodos, a mixture of sodium citrate and hydrogen peroxide used to preserve some fish characteristics (like smell, color, na dtexture). The NIR spectroscopy in combination with chemometric approaches, allowed the successfully classification of (81–100%) samples of sea bass (Dicentrarchus labrax) treated with Cafodos. The study highlights the potential of this technique that, by not requiring pre-treatment of samples with further reagents, is cheaper and safer for the environment. In conclusion, the study confirmed the potential of portable devices for rapid NIR spectroscopy analysis to identify food fraud and ensure consumer safety. [ABSTRACT FROM AUTHOR]

Eating a diet high in fruits and vegetables is associated with a reduction in the risk of chronic diseases such as obesity, cardiovascular disease, Type 2 diabetes, hypertension, asthma, depression, and mental illness, yet few low-income adults meet these recommendations. Nutrition incentive programs have emerged over the last decade to offer incentives to food-insecure households to purchase more fruits and vegetables. Although the goal of the program is to increase the consumption of fresh fruits and vegetables, defenders of nutrition incentive programs increasingly want to document economic impacts of their programs but often lack the resources to do so. Rather than these programs spending their scarce resources on conducting studies, this research created a rigorous, yet easy to use tool that U.S.-based practitioners operating nutrition incentive programs can use to estimate the economic impact of their programs, including incentive redemptions at both farm-direct and brick-and-mortar businesses. [ABSTRACT FROM AUTHOR]

Introduction: Evidence suggests an increasing misuse of veterinary medicines by humans. This study aims to analyse Adverse Events (AEs) associated with selected veterinary products using the Food and Drug Administration Adverse Events Reporting System (FAERS). Methods: A descriptive pharmacovigilance analysis was conducted on AEs related to 21 drugs approved for human and/or animal use. Results: A total of 38,756 AEs, including 9566 fatalities, were identified. The United States reported the highest number of cases (13,532), followed by Canada (2869) and the United Kingdom (1400). Among the eight drugs licenced exclusively for animals, levamisole, pentobarbital, and xylazine were most frequently reported. Reports predominantly involved males (57%) from the 18–64 age group, with incidents related mainly to overdose, dependence, and multi-agent toxicities. Unmasking techniques revealed 'intentional overdose' as the primary reaction. Polysubstance use was evident in 90% of the drugs, with benzodiazepines/Z-drugs and opioids as common co-used classes. Conclusions: Veterinary medications are increasingly infiltrating the illicit drug market due to their pharmacological properties. This trend highlights the need for heightened vigilance and awareness to prevent further public health risks associated with the adulteration of illicit substances with veterinary products like xylazine and pentobarbital. [ABSTRACT FROM AUTHOR]

The significance of food nutrients and bioactive compounds in human health has driven the development of many methods for their determination in different matrices. Among these, electroanalysis has gained popularity due to its cost-effectiveness, rapidity, and, in many cases, portability and minimal sample treatment. This review highlights key advances in electrochemical sensors and biosensors from 2019 to the present. Given the variability and the challenges of managing food matrices, the focus is limited to methods that have been thoroughly assessed for their applicability to real samples. The technical characteristics and analytical performance of the proposed sensors are discussed, along with breakthrough features and future trends. [ABSTRACT FROM AUTHOR]

We present a novel, systematic method for evaluating design printability in 3D food printing using a scoring system based on the Design for Additive Manufacturing (DfAM) guidelines. This study addresses a gap in the current literature by proposing a structured approach to assess and enhance the printability of 3D food designs. Our framework consists of a set of nine critical questions derived from the multi-level DfAM guidelines, focusing on key printability factors including unsupported features, geometric accuracy, and surface finish. The evaluation process converts qualitative assessments into numerical values, resulting in a comprehensive printability score that categorizes designs into high, moderate, or low printability levels. To validate the effectiveness of this method, we conducted a case study involving five different designs. The scoring system successfully explores the design space and maximizes the printability of 3D food products. This method alleviates the challenges in design evaluation compared with traditional trial-and-error approaches. The results demonstrate the practicality and efficiency of our framework's output. The proposed methodology provides a structured approach to design evaluation, offering practical insights and a valuable tool for improving the success rate of 3D printed food products. This research contributes to the field by offering a systematic framework for assessing and enhancing the printability of 3D food designs, potentially accelerating the adoption and effectiveness of 3D food printing technology in various applications. [ABSTRACT FROM AUTHOR]

Dark chocolate is a confectionery product traditionally made from cocoa beans, sugar, and vanilla essence. The aim of the study was to investigate the thermal properties of dark chocolates and fats extracted from these chocolates using thermal methods of food analysis, such as differential scanning calorimetry (DSC) and thermogravimetry (TG). The profile of fatty acids in the fat extracted from the chocolates was also determined. The presence of three fatty acids (palmitic P, stearic S, and oleic O) constituting triacylglycerols—SOS, POP, POS, POO, and SOO—was observed in all the samples. The presence of linoleic acid (L) was also found, which forms triacylglycerols such as PLP and PLS. The researched chocolates were characterized by a diverse composition of fatty acids. In all the obtained DSC melting curves of fats, the presence of endothermic peaks was observed. The peaks, appearing at negative temperatures, may be caused by the transition of low-melting triacylglycerols. The differences between the melting curves for the obtained dark chocolate fats may have resulted from the presence of less stable polymorphic forms of cocoa butter. Based on the shape of the TG and DTG curves, it could be possible to indicate the adulteration of chocolates. [ABSTRACT FROM AUTHOR]

Food safety is a major public health issue of global concern. In recent years, the CRISPR/Cas system has shown promise in the field of molecular detection. The system has been coupled with various nucleic acid amplification methods and combined with different signal output systems to develop a new generation of CRISPR/Cas-based nucleic acid biosensor technology. This review describes the design concept of the CRISPR/Cas-based nucleic acid biosensor and its application in food analysis. A detailed overview of different CRISPR/Cas systems, signal amplification methods, and signal output strategies is provided. CRISPR/Cas-based nucleic acid biosensors have the advantages of high sensitivity, strong specificity, and timeliness, achieving fast analysis of a variety of targets, including bacteria, toxins, metal ions, pesticides, veterinary drugs, and adulteration, promoting the development of rapid food safety detection technology. At the end, we also provide our outlook for the future development of CRISPR/Cas-based nucleic acid biosensors. [ABSTRACT FROM AUTHOR]

The use of antimicrobials is essential for the aquaculture sector; however, their misuse is cause for concern because it could adversely affect the environment and human health. Irresponsible use of antimicrobials can lead to antimicrobial resistance and result in the presence of unwanted residues in aquaculture products. The establishment of maximum residue limits (MRLs) for antimicrobials is of great importance for monitoring their correct use, but the lack of harmonized standards as well as the regulatory gaps for the aquatic products create a challenging situation, as it is reflected in import notifications related to food safety. This paper collects information about current MRLs for antimicrobials in aquatic commodities in the main importing and exporting countries of aquatic products and compares them with each other and with MRLs set by Codex Alimentarius. This analysis highlighted the need for Codex Alimentarius guidance regarding MRLs for nine antimicrobials in aquaculture products (chlortetracycline, doxycycline, enrofloxacin, erythromycin, florfenicol, sulfadimethoxine + ormetoprim, sulfamerazine, tetracycline, and trimethoprim). This paper shows that there is still work to do to implement exhaustive science‐based international standards for residues permitted in aquatic commodities and promote harmonization. [ABSTRACT FROM AUTHOR]

Dispersive solid-phase extraction (dSPE) is a crucial step for multiresidue analysis used to remove matrix components from extracts. This purification prevents contamination of instrumental equipment and improves method selectivity, sensitivity, and reproducibility. Therefore, a clean-up step is recommended, but an over-purified extract can lead to analyte loss due to adsorption to the sorbent. This study provides a systematic comparison of the advantages and disadvantages of the well-established dSPE sorbents PSA, GCB, and C18 and the novel dSPE sorbents chitin, chitosan, multi-walled carbon nanotube (MWCNT), and Z-Sep® (zirconium-based sorbent). They were tested regarding their clean-up capacity by visual inspection, UV, and GC-MS measurements. The recovery rates of 98 analytes, including pesticides, active pharmaceutical ingredients, and emerging environmental pollutants with a broad range of physicochemical properties, were determined by GC-MS/MS. Experiments were performed with five different matrices, commonly used in food analysis (spinach, orange, avocado, salmon, and bovine liver). Overall, Z-Sep® was the best sorbent regarding clean-up capacity, reducing matrix components to the greatest extent with a median of 50% in UV and GC-MS measurements, while MWCNTs had the largest impact on analyte recovery, with 14 analytes showing recoveries below 70%. PSA showed the best performance overall. [ABSTRACT FROM AUTHOR]

Ensuring accurate classification of rice as either cooked or dry is vital for food safety, as improperly stored or cooked rice contain harmful bacteria, emphasizing the importance of maintaining and monitoring food safety standards. In the field of image analysis and food classification, classifying dry and cooked rice samples using photographs is an interesting but difficult task. The main challenge stems from the minute visual variations between cooked and dry rice, which has not always displayed distinct traits that are easily observable by machines. Hence, various machine learning algorithms were implemented to effectively mitigate this issue. However, the existing works have not analysed the physicochemical characteristics due to non-destructive type of experimentation method with image processing. To overcome this issue, this work develops the Classification and Regression Tree (CART) of Decision Tree Learning method for classifying the rice grain samples as dry or cooked based on the physicochemical characteristics such as morphological, texture and color features, which in turn gain an exhaustive facts of rice quality in diverse state. Initially, the images are captured, pre-processed and the features are extracted. From the extracted features, the rice samples are classified as dry and cooked using DT and the results are compared with the existing algorithms like K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). The comparative analysis of these classification algorithms infers the outperformance of the DT learning model under morphological, texture and color features in terms of accuracy, error, precision, recall and F-score. [ABSTRACT FROM AUTHOR]

Food loss and waste (FLW) is a primary focus topic related to all human activity. This phenomenon has a great deal of importance due to its effect on the economic and social aspects of human systems. The most integrated approach to food waste analysis is based on the study of FLW alongside the agrifood chain, which has also been performed in previous studies by the authors. This paper presents a modality of determination of food loss and waste effects with an emphasis on consumer segments in agrifood chains in the form of a predictive model based on statistical data collected based on specific methods in Romania. The determination is made comparatively, using two predictive machine learning-based methods and separate instruments (software), in order to establish the best model that fits the collected data structure. In this matter, a Decision Tree Approach (DTA) and a Neural Network Approach (NNA) will be developed, and common methodologies of the approaches will be applied. The results will determine predictive outcomes for a specific food waste (FW) agent (e.g., consumer) based on pattern recognition of the collected data. The results showed relatively high-accuracy predictions, especially for the NN approach, with lower performances using the DTA. The effects of the application of this predictive model will be expected to improve the food loss prevention measures within economic contexts when applied to real-life scenarios. [ABSTRACT FROM AUTHOR]

Purpose: Assessing the efficiency of fresh food cold chain logistics as accurately as possible is essential for industry development planning. This study was designed to analyze the efficiency of fresh food cold chain logistics in China. Design/methodology/approach: A three-stage data envelopment analysis (DEA) model was used to analyze the efficiency of fresh food cold chain logistics in 30 provinces of China from 2013 to 2019. The stochastic frontier analysis (SFA) model in the second stage was used to eliminate the influence of external environmental factors and random disturbances on efficiency analysis results. Findings: (1) The overall actual efficiency of fresh food cold chain logistics in China is unsatisfactory, with an average technical efficiency of 0.382 over the 7-year period. (2) The national average technical efficiency and average scale efficiency were overestimated by 29.9% and 40.0%, respectively, compared with the actual values. (3) The efficiency of fresh food cold chain logistics does not align with the level of regional economic development. (4) Distinct regional variations exist in the efficiency of fresh food cold chain logistics in China, with higher efficiencies observed in Northwest China and the Central Yangtze River regions, and the lowest efficiencies in the northeast regions. Originality/value: This study applies a three-stage DEA model to assess the development and regional differences of fresh food cold chain logistics in China, enriching the application of models and empirical analysis in this field. By analyzing the situation in China, it provides ideas and references for other developing countries to develop cold chain logistics. [ABSTRACT FROM AUTHOR]

Adulteration of food products is a serious problem in the current economy. Honey has become the third most counterfeit food product in the world and requires effective authentication methods. This article presents a new approach to the differentiation of bee pollen, which can support the development of a methodology to test honey quality based on the analysis of bee pollen. The proposed method is built on applying the Hjorth descriptors—Activity, Mobility, and Complexity—known from electroencephalography (EEG) analysis, for 2D bee pollen images. The sources for extracting the bee pollen images were the photos of honey samples, which were taken using a digital camera with a resolution of 5 megapixels connected to the tube of an optical microscope. The honey samples used were prepared according to the Polish standard PN-88/A-77626 (related to the European standard CELEX-32001L0110-PL-TXT). The effectiveness of the proposed method was positively verified for three selected groups of bee pollen—Brassica napus, Helianthus, and Phacelia—containing 35 images. Statistical analysis confirms the ability of the Hjorth descriptors to differentiate the indicated bee pollen groups. Based on the results obtained, there is a significant difference between the bee pollen groups under consideration regarding Activity p < 0.00001 , Mobility p < 0.0001 , and Complexity p < 0.00001 . [ABSTRACT FROM AUTHOR]

Public health authorities are undertaking initiatives aimed at reducing sugar intake because it is linked to detrimental health outcomes. Individuals' attitudes and intentions toward sugar can be significant factors affecting intake. Therefore, we here aimed to evaluate individuals' attitudes and intentions toward sugar intake by combining the theory of planned behavior with different frameworks. Accordingly, we developed an online questionnaire and reached 940 participants. We observed that subjective norms (β = −0.140; p < 0.001) and perceived behavioral control (β = −0.138; p < 0.001) as defined in the theory of planned behavior significantly affected attitude. Subsequently, examining the effect of attitude (β = −0.209; p = 0.018) and intention (β = 0.717; p < 0.001) on sugar intake showed that intention had a positive effect, whereas attitude had no significant effect. Mediation analysis showed that attitude influenced sugar intake through intention (β = −0.286; p = 0.001). Furthermore, we determined that components including habits, perceived understanding, perceived nonautonomy, negativity, and apathy can affect attitude toward sugar intake (p < 0.001). Analyzing the effects of sociodemographic factors revealed that sugar intake was lower in individuals with food allergies (p < 0.05). In conclusion, these findings suggest that the theory of planned behavior, when combined with different frameworks, can be effective in predicting sugar intake and individuals' intentions. [ABSTRACT FROM AUTHOR]

The human diet is a factor for disease prevention and the extension of life expectancy. Loss of teeth can adversely affect chewing capacity, which can lead patients to modify their diet and subsequently result in a poor dietary intake. This work is conducted within the framework of an ongoing research project in the Dentistry School of Universidad de la Frontera aimed at designing a diet for patients with complete removable dental prostheses (CRDP). This study aimed to evaluate the hardness of foods in a diet designed for patients using CRDP, using texture profile analysis (TPA). TPA was used to measure the hardness of 43 foods, categorized into seven groups, dairy, animal protein, fruits, vegetables, cereals and grains, high-lipid foods, and vegetable protein, to understand their impact on masticatory performance in CRDP wearers. TPA consists of two compression cycles where the food sample is compressed until it reaches a pre-established deformation. The first force peak achieved in the first cycle is used as a measure of sample hardness. Significant differences in hardness were identified within each food group, indicating a wide spectrum of textural properties that could influence chewing behavior. These findings suggest that assessing food hardness can help tailor dietary recommendations to improve masticatory efficiency in patients with dental prostheses. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is crucial to realize the point‐of‐care (POC) testing of harmful analytes, capable of saving limited agricultural resources, assisting environmental remediation, ensuring food safety, and enabling early disease diagnosis. Compared with other conventional POC sensing strategies, aggregation‐based analytical chemistry facilitates the practical‐oriented development of POC nanosensors by altering the aggregation status of nanoprobes through the action of multiple aggregation‐induced "forces" originating from the targets. Herein, we have proceeded with a comprehensive review focusing on the aggregation‐based analytical chemistry in POC nanosensors, covering aggregation‐induced "forces", aggregation‐induced signal transductions, aggregation‐induced POC nanosensing strategies, and their applications in biomolecular monitoring, food safety analysis, and environmental monitoring. Finally, challenges existing in practical applications have been further proposed to improve their sensing applications, and we expect our review can speed up the development of cost‐effective, readily deployable, and time‐efficient nanosensors through aggregation‐based analytical chemistry. [ABSTRACT FROM AUTHOR]

Based on the bibliometric method, relevant research literature in the field of cranberry food included in the CNKI database from 2013 to 2023 was searched and further analyzed. A total of 444 Chinese and English literatures were obtained through screening, and CiteSpace software was used to analyze the current research status and hotspots in this field from the perspectives of annual publication volume, issuing journals, countries, authors and their institutions, as well as cooccurrences, clustering, and emergent words of keywords. The results showed that the overall trend of the number of publications from 2013 to 2023 was stable, with an average of about 40 publications per year. The journal with the highest number of publications was Food Chemistry (25), which accounted for 6% of the total literature, followed by Food & Function (22) and Journal of Agricultural and Food Chemistry (20). The top 3 countries in terms of the number of articles published were the United States, Poland, and Canada. The author with the most postings was Christina Khoo (17), a researcher from Ocean Spray, USA. The keyword analysis in the CNKI database showed that the keywords with the highest frequency were cranberry, proanthocyanidins, cranberry juice, bioactive components, urinary tract infection, antioxidant properties, intestinal microbiota and processing, health functions, and so on. The hotspots of the literature research in the field of cranberry food were mostly focused on the study of cranberry composition and function, and the exploration of the application of new processed products such as cranberry juice in the food industry. By comprehensively analyzing the literature in the CNKI database, this article can point out the direction for researchers in China cranberry food industry to engage in related research, and provide data references and help in predicting the future development trend of the industry. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Gas Chromatography Tandem Mass Spectrometry (GC-MS/MS) with modified QuEChERS sample preparation has been applied to the high-throughput screening of pesticide residuals in rice collected from Bangladesh and Saudi Arabia markets. Both countries consume high volumes of rice, which is a fundamental food for their populations. We report optimized sample preparation and mass spectrometry analysis protocols, which can be rapidly deployed in analytical laboratories. The screening of four groups (organophosphorus, synthetic pyrethroid, organonitrogen, and organochlorine) of a total of 115 pesticides can be performed within ~10 min using a matrix-matched calibration. For most compounds, the limits of detection and quantification (LOD/LOQ) are well below the maximum residue levels (MRLs) of the main regulators. The method generally demonstrates acceptable recovery values (91 compounds 75–125% and 10 compounds 30–75%). Out of 55 rice samples analyzed, 16 samples (29%) contained pesticide residues above LOQ. Four samples contained chlorpyrifos with concentrations ranging from 21.3 to 71.9 µg/kg, ten samples contained tebuconazole (34.7–69.0 µg/kg), and three samples contained pirimiphos methyl (10.7–20.7 µg/kg). The concentrations of the pesticide residues detected in these samples are well below MRL of FAO/WHO (chlorpyrifos, 500 µg/kg; tebuconazole, 1500 µg/kg; pirimiphos methyl, 7000 µg/kg). [ABSTRACT FROM AUTHOR]

The assessment of global food security is imperative for sustainable development worldwide. However, limited data availability has impeded a comprehensive evaluation on a global scale. This study addresses this challenge by conducting a thorough food security analysis spanning 169 countries from 2008 to 2020. To overcome data limitations, an imputed database is generated using four machine learning models to estimate missing data. The results uncover significant disparities in food security across regions. Europe, Oceania, and North America exhibit higher levels, while East Asia and South America fall in the middle, and Africa and South Asia experience lower levels. Notably, six African countries have made considerable progress in food security, potentially attributed to support from nations such as France, the United States, China, and Japan. Policy recommendations include humanitarian relief, expanding social safety nets, developing transportation networks, supplying fertilizers in vulnerable areas, and stabilizing food prices through national fiscal policies are also proposed for economies with low food security performance. The study provides an imputed database for 44 indicators from 2008 to 2020, offering insights into food security and potential solutions for governments and international organizations. [ABSTRACT FROM AUTHOR]

Food allergens in processed foods are affected by heating, processing, and the food matrix. To conduct highly reliable tests, extracting allergens into test solutions is necessary for appropriate detection. In addition to the commonly used enzyme-linked immunosorbent assay (ELISA), liquid chromatography-mass spectrometry (LC–MS), which has the advantage of simultaneously detecting multiple allergens in foods, is being increasingly used. When managing food allergens at food manufacturing sites, obtaining the same measured values is desirable, regardless of the analytical method used. Therefore, in this study, we focused on the importance of pretreatment steps for LC–MS when examining food allergens in processed foods, which can be difficult to analyze. The ELISA method uses food extracts optimized for analyzing allergens in processed foods. We developed a high-resolution accurate mass spectrometry (HRAM)-LC–MS/MS method using the same food extract used in the ELISA method and an MS sample preparation kit. Multiple food allergen analysis was performed using 1, 5, 10, and 20 ppm of allergen-incurred processed foods. Overall, a strong correlation was observed between the measured values of HRAM-LC–MS/MS and ELISA, demonstrating the applicability of multi-allergen analysis using LC–MS. [ABSTRACT FROM AUTHOR]

Plant pathogens, including viruses, bacteria, and fungi, cause massive crop losses around the world. Abiotic stresses, such as drought, salinity and nutritional deficiencies are even more detrimental. Timely diagnostics of plant diseases and abiotic stresses can be used to provide site- and doze-specific treatment of plants. In addition to the direct economic impact, this "smart agriculture" can help minimizing the effect of farming on the environment. Mounting evidence demonstrates that vibrational spectroscopy, which includes Raman (RS) and infrared spectroscopies (IR), can be used to detect and identify biotic and abiotic stresses in plants. These findings indicate that RS and IR can be used for in-field surveillance of the plant health. Surface-enhanced RS (SERS) has also been used for direct detection of plant stressors, offering advantages over traditional spectroscopies. Finally, all three of these technologies have applications in phenotyping and studying composition of crops. Such noninvasive, non-destructive, and chemical-free diagnostics is set to revolutionize crop agriculture globally. This review critically discusses the most recent findings of RS-based sensing of biotic and abiotic stresses, as well as the use of RS for nutritional analysis of foods. [ABSTRACT FROM AUTHOR]

Non-targeted NMR is widely accepted as a powerful and robust analytical tool for food control. Nevertheless, standardized procedures based on validated methods are still needed when a non-targeted approach is adopted. Interlaboratory comparisons carried out in recent years have demonstrated the statistical equivalence of spectra generated by different instruments when the sample was prepared by the same operator. The present study focused on assessing the reproducibility of NMR spectra of the same matrix when different operators performed individually both the sample preparation and the measurements using their spectrometer. For this purpose, two independent laboratories prepared 63 tomato samples according to a previously optimized procedure and recorded the corresponding 1D 1H NMR spectra. A classification model was built using the spectroscopic fingerprint data delivered by the two laboratories to assess the geographical origin of the tomato samples. The performance of the optimized statistical model was satisfactory, with a 97.62% correct sample classification rate. The results of this work support the suitability of NMR techniques in food control routines even when samples are prepared by different operators by using their equipment in independent laboratories. [ABSTRACT FROM AUTHOR]

Rhyzopertha dominica causes significant economic losses in stored cereals. Insects' digestive tract microbiome is crucial for their development, metabolism, resistance, and digestion. This work aimed to test whether the different chemical properties of different wheat and barley grain cultivars cause disturbances in insect foraging and rearrangements of the structure of the R. dominica microbiome. The results indicated that grain cultivars significantly influence the microbiome, metabolism, and insect foraging. Most observed traits and microbiome structures were not correlated at the species level, as confirmed by ANOSIM (p = 0.441). However, the PLS-PM analysis revealed significant patterns within barley cultivars. The study found associations between C18:2 fatty acids, entomopathogenic bacteria, an impaired nitrogen cycle, lysine production of bacterial origin, and insect feeding. The antioxidant effects also showed trends towards impacting the microbiome and insect development. The findings suggest that manipulating grain chemical properties (increasing C18:2 and antioxidant levels) can influence the R. dominica microbiome, disrupting their foraging behaviours and adaptation to storage environments. This research supports the potential for breeding resistant cereals, offering an effective pest control strategy and reducing pesticide use in food production. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision‐language models (VLMs) with chemical analysis techniques. A cutting‐edge VLM is unveiled, utilizing the expansive UMDFood‐90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro‐AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high‐throughput solution for nutritional analysis. [ABSTRACT FROM AUTHOR]

Extreme climatic events, such as droughts, hinder progress toward achieving the sustainable development goal of food security. South Africa is vulnerable to drought‐related agricultural losses, which have led to food insecurity. However, few studies have focused on the long‐term impacts of drought on crop production at a regional scale. Therefore, we aimed to examine the intensity, magnitude, and trend of rainfall‐based short‐term agricultural drought at the provincial scale in South Africa based on the Standardized Precipitation Index (SPI). Additionally, we analyzed the impact of agricultural drought on maize yield by calculating the Standardized Yield Residual Series (SYRS) and Crop Drought‐Resilience Factor (CDRF). To this end, we collected rainfall data from 29 stations across nine provinces along with maize yield data for the period of 1993–2022. Agricultural drought analyses based on the three‐month (SPI‐3) and six‐month (SPI‐6) SPIs demonstrated dynamic variations in occurrence, with Sen's slope indicating that 10 stations exhibited a significant increase in drought events across South Africa. Notably, SPI‐6 analysis showed that Gauteng, Free State, and North West provinces experienced the highest percentages of severe to extreme drought events during the study period, at 4.17%, 3.89%, and 3.61%, respectively. Furthermore, the majority of provinces in South Africa experienced an extreme SPI‐6 magnitude ranging from −46.03 in Western Cape Province to −61.6 in Free State Province. The dynamic effects of agricultural drought on maize yield revealed that the maximum yield loss of 13% occurred in 1993 in Eastern Cape Province, while some provinces experienced no yield loss during certain years. However, CDRF analyses identified Western Cape (CDRF [SPI‐3] = 0.52, CDRF [SPI‐6] = 0.62) and Mpumalanga (CDRF [SPI‐6] = 0.7) provinces as the most vulnerable to food insecurity due to the severe non‐resilience of maize to drought in these regions. This study reveals the complex interplay between climatic extremes and maize yield variability, providing valuable insights for managing regional food production systems and ensuring future food security in South Africa. [ABSTRACT FROM AUTHOR]

The growing production and use of plastics significantly contribute to microplastics (MPs) contamination in the environment. Humans are exposed to MPs primarily through the gastrointestinal route, as these particles are present in beverages and food, e.g., sugar. Effective isolation and identification of MPs from food is essential for their elimination. This study aimed to evaluate factors influencing the isolation of MPs from sucrose solutions to determine optimal conditions for the process. Polyethylene particles were used to test separation methods involving chemical digestion with acids and filtration through membrane filters made of nylon, mixed cellulose ester, and cellulose acetate with pore sizes of 0.8 and 10 µm. The effects of temperature and acid type and its concentration on plastic particles were examined using scanning electron microscopy and µ-Raman spectroscopy. The results indicate that increased temperature reduces solution viscosity and sucrose adherence to MPs' particles, while higher acid concentrations accelerate sucrose hydrolysis. The optimal conditions for MPs' isolation were found to be 5% HCl at 70 °C for 5 min, followed by filtration using an efficient membrane system. These conditions ensure a high recovery and fast filtration without altering MPs' surface properties, providing a reliable basis for further analysis of MPs in food. [ABSTRACT FROM AUTHOR]

Milk has a high nutritional value and is also one of the most versatile products in the food industry. Unfortunately, fraud involving the product is constant and needs to be combated. This work presents a measurement methodology to perform the electrical impedance analysis in food. The influence of temperature on the measurements has been evaluated through mathematical models specially developed for identifying the fraudulent activity of adding water to milk. The bioimpedance measurements have been performed in a sample container with a four-electrode measurement system and a digital oscilloscope. Samples of 150 mL were analysed, prepared using whole UHT milk adulterated with distilled water in different % by volume (V/V), at temperatures of 16, 28, and 37 °C. The proposed measurement methodology has been validated and showed sensitivity for analysis in milk, revealing the influence of temperature on the impedance values; from the results mathematical models were proposed for investigating adulteration in milk. This work deals with the design of a device capable of measuring bioimpedance in milk with a view to its application in physicochemical analyses. The influence of temperature on the impedance values was also evaluated to propose mathematical models to facilitate analysis with complex factors and matrices. [ABSTRACT FROM AUTHOR]

This study investigates the socio-spatial distribution of food deserts in Santiago de Chile, aiming to understand how urban planning and socioeconomic factors influence access to nutritious food. Employing geospatial analysis techniques with data from OpenStreetMap and the 2017 Census, the research identifies areas within Santiago where access to healthy food is limited. The novelty of this study lies in its application of spatial autocorrelation methods, specifically Local Indicators of Spatial Association (LISA), to reveal clusters of nutritional inequality. The findings indicate significant concentrations of food deserts in both lower socioeconomic peripheral areas and, surprisingly, in some high-income central areas. These results suggest that both poverty and urban infrastructure, including car dependency, play critical roles in shaping access to healthy food. By highlighting over two million residents affected by food deserts, the study underscores the urgent need for integrated urban planning and public health strategies. This research contributes to the understanding of urban nutritional inequality and provides a replicable methodological framework for other cities. The implications extend beyond Santiago, offering insights into how urban design can be leveraged to improve public health outcomes through better access to nutritious food. [ABSTRACT FROM AUTHOR]

Foodstuffs, particularly perishable ones such as meat, are frequently discarded once the best-before date has been reached, despite the possibility of their continued suitability for human consumption. The implementation of intelligent packaging has the potential to contribute to a reduction in food wastage by enabling the monitoring of meat freshness during storage time independently of the best-before date. The process of meat spoilage is associated with the formation of specific degradation products, some of which can be potentially utilized as spoilage indicators in intelligent packaging. The aim of the review is to identify degradation products whose concentration correlates with meat shelf life and to evaluate their potential use as spoilage indicators in intelligent packaging. To this end, a comprehensive literature research was conducted to identify the factors influencing meat spoilage and the eight key degradation products (carboxylic acids, biogenic amines, total volatile basic nitrogen, aldehydes, alcohols, ketones, sulfur compounds, and esters) associated with this process. These degradation products were analyzed for their correlation with meat shelf life at different temperatures, atmospheres, and meat types and for their applicability in intelligent packaging. The review provides an overview of these degradation products, comparing their potential to indicate spoilage across different meat types and storage conditions. The findings suggest that while no single degradation product universally indicates spoilage across all meat types and conditions, compounds like carboxylic acids, biogenic amines, and volatile basic nitrogen warrant further investigation. The review elucidates the intricacies inherent in identifying a singular spoilage indicator but underscores the potential of combining specific degradation products to expand the scope of applications in intelligent packaging. Further research (e.g., storage tests in which the concentrations of these substances are specifically examined or research on which indicator substance responds to these degradation products) is recommended to explore these combinations with a view to broadening their applicability. [ABSTRACT FROM AUTHOR]

Highlights: Previous studies have addressed the difficulty of complying with the Good Manufacturing Practices of Cachaça producers based on the high number of samples that fall outside the parameters required by legislation; Improving the chemical and sensory quality of Cachaça is a crucial factor in increasing exports of this distillate; The current study demonstrates the levels of contaminants found in 531 samples of Cachaça between the years 2021 and 2023. The objective of this study was to determine the chemical composition of sugarcane spirits and commercial Cachaças, comparing them with the limits established by national legislation and with studies conducted in previous periods. Previous studies have shown that 50% of the samples of this distillate were above the contaminant limits allowed by national legislation, constituting one of the main factors responsible for the low volume of exports. In this research, 531 Cachaça samples were analyzed in order to verify whether they complied with the limits of contaminants and volatile compounds required by Brazilian legislation. The results obtained indicate that Brazilian producers have adapted to the use of good manufacturing practices during the production process, ensuring the standardization of this distilled beverage and consequent compliance with legislation. [ABSTRACT FROM AUTHOR]