The use of different food additives and their active metabolites has been found to cause serious problems to human health. Thus, considering the potential effects on human health, developing a sensitive and credible analytical method for different foods is important. Herein, the application of solvent-driven self-assembled Au nanoparticles (Au NPs) for the rapid and sensitive detection of food additives in different commercial products is reported. The assembled substrates are highly sensitive and exhibit excellent uniformity and reproducibility because of uniformly distributed and high-density hot spots. The sensitive analyses of ciprofloxacin (CF), diethylhexyl phthalate (DEHP), tartrazine and azodicarbonamide at the 0.1 ppm level using this surface-enhanced Raman spectroscopy (SERS) substrate are given, and the results show that Au NP arrays can serve as efficient SERS substrates for the detection of food additives. More importantly, SERS spectra of several commercial liquors and sweet drinks are obtained to evaluate the addition of illegal additives. This SERS active platform can be used as an effective strategy in the detection of prohibited additives in food.
Water retailer managed inventory is a classical and inevitable inventory management mode in present economic society. Stochastic models can more clearly explain demand uncertainty and are closely related to water supply chains. Risk preferences are widely valued in behavioral operation management. Related to the risk preferences in inventory management, the research on risk aversion is dominant, while risk-seeking is insufficient. Based on the model assumptions, the risk-seeking retailer’s optimal decision-making inventory model with stochastic demand in a water supply chain is studied. The risk-seeking retailer’s optimal inventory quantity, optimal inventory cost, supplier profit, retailer profit, and the profit of the entire water supply chain are derived. The validity of the equations is proved. The sensitivity analysis of the risk-seeking retailer’s optimal inventory decision-making is carried out. The risk level effects on the five dimensions, the retail price, wholesale price, unit shortage cost, unit inventory cost, and unit residual value, are displayed through numerical simulation. The optimal inventory quantity and optimal inventory cost of the risk-seeking retailer are obtained.
With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to reexamine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model.
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