食品中兴奋剂污染研究现状

随着食品和膳食补充剂的市场变得越来越全球化,食品和膳食补充剂的安全性、质量和功效引起人们的高度关注。近年来,食品和膳食补充剂中被检测出兴奋剂阳性的事件屡见不鲜。运动员在误服误用被兴奋剂污染的食品和膳食补充剂后,会导致兴奋剂检测呈阳性,这对运动员和国家都造成了重大损失。由于摄入受污染的食品或膳食补充剂会导致严重的健康损害或意外违反反兴奋剂规定,因此准确了解食品和膳食补充剂中兴奋剂污染种类是十分有必要的。本文主要从食品和膳食补充剂中兴奋剂污染的来源和种类以及常用的检测方法等方面进行简要概述,以提高运动员对高风险食品的警惕和防范,避免因误服被兴奋剂污染的食品、膳食补充剂而导致的不良分析结果。     

Formation of acrylamide in microwave-roasted sorghum and associated dietary risk

The effect of microwave roasting parameters (300, 450 and 600 W; 5, 10 and 15 min) on acrylamide content in sorghum grain was determined using High Pressure Liquid Chromatography (HPLC)-photo diode array (PDA) detector coupled with C-18 column. Samples roasted at 300 and 450 W did not possess acrylamide, whereas 600 W (15 min) favoured formation of 2740.19 µg/kg of acrylamide, levels far exceeding the defined European Union (EU) limits. The chronic daily intake (CDI) for acrylamide through consumption of such grain flour was 3.25–9.5-fold higher to Joint FAO/WHO Expert Committee on Food Additives (JECFA) defined high exposure limits. The margin of exposure (MOE) values ranged from 4.3 to 12.76 and from 11.07 to 32.27 for neoplastic and neurological effects, respectively, demonstrating high exposure and serious health concerns associated with dietary intake of this toxicant. This study assesses the risk for the Indian population and highlights the importance of optimising process parameters for food product to minimise such exposure risks.     

基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

Thiol-Functionalized Covalent Organic Frameworks as Thermal History Indictor for Temperature and Time History Monitoring

Various products, including foods and pharmaceuticals, are sensitive to temperature fluctuations. Thus, temperature monitoring during production, transportation, and storage is critical. Facile indicators are required to monitor temperature conditions via color changes in real time. This study aimed to prepare and apply thiol-functionalized covalent organic frameworks (COFs) as a novel indicator for monitoring thermal history and temperature abuse. The COFs underwent obvious color changes from bright yellow to purple after exposure to different temperatures for varying durations. The reaction kinetics are analyzed under isothermal conditions, which reveal that the order of reaction rates is k_−20°C < k_4°C < k_20°C < k_35°C < k_55°C. The activation energy (E_a) of the COFs is calculated using the Arrhenius equation as 50.71 kJ moL⁻¹. The COFs are capable of sensitive color changes and offer a broad temperature tracking range, thereby demonstrating their application potential for the monitoring of temperature and time exposure history during production, transportation, and storage. This excellent performance thermal history indicator also shows promise for expanding the application field of COFs.     

Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Novel approaches to purifying bacteriocin: A review

ABSTRACT

Bacteriocin is a proteinaceous biomolecule produced by bacteria (both Gram-positive and Gram-negative) that exhibits antimicrobial activity against closely related species, and food-borne pathogens. It has recently gained importance and attracted the attention of several researchers looking to produce it from various substrates and bacterial strains. This ushers in a new era of food preservation where the use of bacteriocin in food products will be an alternative to chemical preservatives, and heat treatment which are understood to cause unwanted side effects, and reduce sensory and nutritional quality. However, this new market depends on the success of novel downstream separation schemes from various types of crude feedstocks which are both effective and economic. This review focuses on the downstream separation of bacteriocin from various sources using both conventional and novel techniques. Finally, recommendations for future interesting areas of research that need to be pursued are highlighted.     

A red code triggers an unintended approach motivation toward sweet ultra-processed foods: Possible implications for front-of-pack labels

Front-of-package labels (FOPL) are recommended to reduce consumer intake of ultra-processed food products (UPP). The multiple traffic-light label is one example of FOPL that indicates the content of target nutrients in products by displaying red (high), amber (medium), and/or green (low) color-coding. The red code may implicitly enhance sweetness perception and approach dispositions toward sweet UPP via cross-modal visual-taste interactions. We conducted two experiments to examine the possibility of contradictory influence of explicitly learned and implicit cross-modal associations on the emotional responses evoked by UPP pictures. In both experiments, we first explicitly associated the color codes with health-related meanings. In Experiment I (n = 78), a psychometric tool estimated the emotional responses (pleasantness and arousal ratings) evoked by UPP pictures when preceded by red, amber, or green color-codes. In Experiment II (n = 24), we recorded participants’ electrocortical brain activity to assess the early posterior negativity (EPN) component as an index of the emotional responses to UPP. The reported pleasantness (Experiment I) and the EPN amplitude (Experiment II) were greater for sweet UPP relative to salty UPP when primed with red codes but not when primed with green or amber. A red code increased positive emotions toward sweet UPP despite its explicit association with increased health-risks. Thus, the use of multiple traffic-lights might lead to an unintended implicit approach behavior toward sweet UPP. Designers, researchers, and policy makers may consider color-taste cross-modal associations when designing, testing, and applying FOPL.     

Consumer perception of extruded snacks containing brown rice and dried mushroom

Effects of mushroom type, seasoning and health benefit information (HBI) on consumers’ saltiness expectation, sensory liking, elicited emotions and purchase intent (PI) of extruded snacks were investigated. Five snacks were evaluated: straw mushroom (Volvariella volvacea) extrudates without (SME) or with seasoning (SMES), phoenix mushroom (Pleurotus pulmonarius) extrudates without (PME) or with seasoning (PMES), and the control without mushroom and seasoning. Hedonic scores and positive emotions were generally higher for seasoned mushroom-containing snacks (SMES and PMES) with 65% and 75.83% of consumers reporting willingness to purchase, respectively, after receiving HBI. Bored, interested and satisfied were identified as significant emotional predictors for PI odds. Flavour, saltiness, overall liking, bored, good and interested were critical attributes, differentiating snacks. This study demonstrated that sensory liking and PI of extruded brown rice-based snacks containing mushroom could be improved through savoury seasoning addition, which also allowed saltiness expectations to be met.     

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.