Development of Decision Trees for Refrigerated Warehouse Working Practice Analysis

• Main Authors: Wong, Chee-Chuen, 黃志泉
• Other Authors: Cheng-Ming, Chang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/urxmfn

碩士 === 國立臺灣海洋大學 === 食品科學系 === 102 === The purpose of this study is to establish a series of decision trees for cold chain management of low temperature warehouse working practices. It is done by collecting temperature history of 3 refrigerated and 5 frozen storage facilities over a period from 1 to 7 days. Investigated food sectors included raw meat, fresh vegetable and processed seafood products, and service types covered B2B and B2C. Decision trees were developed through in-depth analyzing index defined from various temperature history, i.e. average temperature, maximum and minimum temperatures, p-chart profiling and on-site confirmation with experienced experts. Finally, worse case scenario was adopted based on the maximum growth rate of Pseudomonas spp. from literature to estimate the cumulative growth in food by using constant temperature 15、7 and 2 ℃ as the boundaries, and divided into four categories. Frozen and refrigerated determine the overall outcome of the decision tree found, in the industry operating problems are the most frequent problems arise, in addition, overall refrigeration equipment were also found in the ideal equipment for each one. For hardware problems in freezing equipment, problems caused by poor defrost will lead equipment temperature unable to reach the set temperature, as well as the insulation capacity of the equipment doors (equipment should be checked regularly to ensure that their hardware performance to maintain in good condition, but also can be installed temperature buffer device to reduce the differences of inside and outside in order to enhance the temperature control around the door). By using microbial growth rate predict as a grade boundaries in temperature management, the higher the average temperature for the device is more unfavorable. Therefore, the grade method focuses on storage temperature of the target product for grading. . different temperatures of industry equipment (Follow-up will be referred the temperature history), establish decision tree of refrigeration equipment performance and quality using, but also by the temperature history of each grading equipment so, thus achieving effective management, in addition to the industry in exceptional cases may be the result of the decision tree and classification as a reference set of buyers and sellers protocol.