利益多元化语境下的食品安全规制研究 ———以利益博弈为视角

利益是社会发展的动力，利益机制理论是研究社会问题的一种重要方法。在食品安全领域中存在相关利益群体的利益对立和冲突，由此产生了生产者与消费者、监管者与生产者、生产者与销售者之间的利益博弈现象。我国既有食品安全治理模式是建立在一种单元化利益观主导基础上的政府主导型模式，这种模式存在忽略食品安全领域相关利益群体利益冲突和协调机制、淡化其利益产生和分配机制、缺乏利益表达和保障机制的局限，应当重构一种以多元利益观为导向的新型食品安全治理模式。     

社会共治视角下食品安全风险交流机制研究

日本食品安全风险交流以双向沟通机制、风险素养培养机制和国际合作机制为核心,形成了政府主导下多元主体参与的社会共治格局。但在核泄漏事故中仍呈现出交流手段陈旧,交流方案滞后,专家与公众的风险认知差等问题。我国应在总结日本经验的基础上,以“社会共治”理念为引导,通过明确风险交流主体的定位,搭建多样化信息沟通平台,建立风险交流公众参与机制,加强风险交流的国际合作来推进食品安全风险交流机制的建立。     

《食品集》考辨

在明代中叶大量的养生著作中,吴江县医学训科吴禄的《食品集》是一部较有代表性的食疗医书。他以《食物本草》和《饮膳正要》为蓝本,根据自身医学实践的经验进行增删改定,指导吴中百姓日常生活中的安全饮食、健康养生。     

试论中国盐业垄断经营的法律规范问题

目前 ,中国盐业面临严重的行业垄断问题 ,政企不分 ,不规范的食盐与工业盐二元管理模式是这一问题症结所在。问题的解决不仅需要中国盐业在管理模式上进行创新 ,也需要相关法律制度予以保障和规范     

试析长征时期的粮食供应问题

长征时期由于红军远离根据地,沿途许多地区又非常贫困落后等原因,粮食供应极其困难。为了解决长征途中的粮食供应问题,红军制定了打土豪、在战争中缴获粮食、向沿途群众购买、留条(留牌)借粮以及动员群众主动捐献等一些积极有效的措施,从而保证了部队战略转移的顺利进行。     

氧弹燃烧-离子选择电极法测定食品中的氟

介绍了一种测定大米、黄豆、面粉等食品中氟含量的快速方法。首先利用氧弹燃烧技术对样品进行预处理,将氟转化为氟化氢,用碱液吸收燃烧产物,吸收液加入TISAB消除Al3+、Fe3+等离子干扰,调节溶液pH值在5～8后进行定容,然后用氟离子选择性电极用标准加入法测定溶液的电势,从而测得有机物中氟的含量,线性范围在1～10-6　mol·L-1时,回收率96.4%～102.5%。     

Modeling Growth and Reduction of Microorganisms in Foods as Functions of Temperature and Time

Food safety objectives (FSOs) are established in order to minimize the risk of foodborne illnesses to consumers, but these have not yet been incorporated into regulatory policy. An FSO states the maximum frequency and/or concentration of a microbiological hazard in a food at the time of consumption that provides an acceptable level of protection to the public and leads to a performance criterion for industry. However, in order to be implemented as a regulation, this criterion has to be achievable by the affected industry. In order to determine an FSO, the steps to produce and store that food need to be known, especially where they have an impact on contamination, growth, and destruction. This article uses existing models for growth of Listeria monocytogenes in conjunction with calculations of FSOs to approximate the outcome of more than one introduction of the foodborne organism throughout the food-processing path from the farm to the consumer. Most models for the growth and reduction of foodborne illnesses are logarithmic in nature, which fits the nature of the growth of microorganisms, spanning many orders of magnitude. However, these logarithmic models are normally limited to a single introduction step and a single reduction step. The model presented as part of this research addresses more than one introduction of food contamination, each of which can be separated by a substantial amount of time. The advantage of treating the problem this way is the accommodation of multiple introductions of foodborne pathogens over a range of time durations and conditions.     

Using unlabelled data to update classification rules with applications in food authenticity studies

Summary.  An authentic food is one that is what it purports to be. Food processors and consumers need to be assured that, when they pay for a specific product or ingredient, they are receiving exactly what they pay for. Classification methods are an important tool in food authenticity studies where they are used to assign food samples of unknown type to known types. A classification method is developed where the classification rule is estimated by using both the labelled and the unlabelled data, in contrast with many classical methods which use only the labelled data for estimation. This methodology models the data as arising from a Gaussian mixture model with parsimonious covariance structure, as is done in model-based clustering. A missing data formulation of the mixture model is used and the models are fitted by using the EM and classification EM algorithms. The methods are applied to the analysis of spectra of food-stuffs recorded over the visible and near infra-red wavelength range in food authenticity studies. A comparison of the performance of model-based discriminant analysis and the method of classification proposed is given. The classification method proposed is shown to yield very good misclassification rates. The correct classification rate was observed to be as much as 15% higher than the correct classification rate for model-based discriminant analysis.     

New Prediction Interval and Band in the Nonlinear Regression Model: Application to Predictive Modeling in Foods

This article is concerned with the proposal of a new prediction interval and band for the nonlinear regression model. The construction principle of this interval and band is based on an exact (the meaning of the term “exact” will be given later) confidence region for parameters of the nonlinear regression model. This region, fully described in Vila and Gauchi (2007 Vila , J.-P. , Gauchi , J.-P. ( 2007 ). Optimal designs based on exact confidence regions for parameter estimation of a nonlinear regression model . Journal of Statistical Planning and Inference 137 ( 9 ): 2935 – 2953 .[Crossref], [Web of Science ®] , [Google Scholar]), provides a rigorous justification for the new prediction interval and band that we propose. This new band is then compared to the classical bands (which are asymptotic and thus approximate for small n), and also to the band based on the bootstrap resampling method. The comparison of these bands is undertaken with simulated and real data from predictive modeling in food science.     

Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization

Decision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health-related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision-making process.