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Analysis of critical control points in deviant thermal processes using artificial neural networks

机译:使用人工神经网络分析异常热过程中的关键控制点

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The successful implementation of a scheduled thermal process is of paramount importance in ensuring the safety of heat processed foods. It is therefore important to consider all associated critical factors and their relative importance in process calculations. The objectives of this study were (ⅰ) to evaluate the relative order of importance of different critical control variables with respect to process calculations and (ⅱ) to develop predictive models to compensate for their deviations. The critical variables studied were: retort temperature (RT), initial temperature (T_i), cooling water temperature (T_w), heating rate index (f_h), heating lag factor (j_h) and cooling lag factor (j_c). Their ranges of deviation from a set point were selected as -2 to 2℃ for RT, -5 to 5℃ for both T_w and T_i, -2 to 2 min for f_h, and -0.2 to 0.2 for both j_c and j_h,. Artificial neural network (ANN) models were developed and used for analysis of different critical variables with respect to their importance on the accumulated lethality (F), process time (PT), cooling time (CT), and total time (TT) under the given processing conditions. The results indicated that within the deviation ranges, the relative order of importance of critical variables were as follows: for F, RT > f_h > j_h > T_iT_w > T_i > T_if_h > RT T_i; >j_c > RT j_h; for PT, RT>f_hj_h > T_i >j_c >T_iT_w; for CT, j_c > T_w >f_h; for TT, RT >f_h >j_h > j_c > T_w > T_i > T_ij_c > T_iT_w. When the desired F value was set at 6 +- 0.5 min, the maximum acceptable deviation ranges of different variables were: +-0.3℃ for RT, +-4℃ for T_i, +-0.1 for j_h, +-0.8, +-1, +-1.2 min for f_h at f_h = 20, 40, 60 min, respectively, and +-0.4 for j_c. Finally, the combination effect of deviation of multiple variables on F, PT and CT were analyzed. ANN models could be effectively used for identification of critical control points, and for correcting process deviations, both important from the point of view of implementation of HACCP approach in thermal processing.
机译:计划中的热处理过程的成功实施对于确保热处理食品的安全至关重要。因此,重要的是要考虑所有相关的关键因素及其在过程计算中的相对重要性。这项研究的目的是(ⅰ)评估不同关键控制变量相对于过程计算的重要性的相对顺序,以及(ⅱ)开发预测模型以补偿其偏差。研究的关键变量是:蒸馏温度(RT),初始温度(T_i),冷却水温度(T_w),加热速率指数(f_h),加热滞后系数(j_h)和冷却滞后系数(j_c)。它们的相对于设定点的偏差范围对于RT选择为-2至2℃,对于T_w和T_i选择为-5至5℃,对于f_h选择为-2至2 min,对于j_c和j_h选择为-0.2至0.2。开发了人工神经网络(ANN)模型,并将其用于分析不同关键变量在累积杀伤力(F),处理时间(PT),冷却时间(CT)和总时间(TT)的重要性下的重要性。给定加工条件。结果表明,在偏差范围内,关键变量的重要性相对顺序如下:对于F,RT> f_h> j_h> T_iT_w> T_i> T_if_h> RT T_i; > j_c> RT j_h;对于PT,RT> f_h j_h> T_i> j_c> T_iT_w;对于CT,j_c> T_w> f_h;对于TT,RT> f_h> j_h> j_c> T_w> T_i> T_ij_c> T_iT_w。当所需的F值设置为6 +-0.5分钟时,不同变量的最大可接受偏差范围为:RT为+ -0.3℃,T_i为+ -4℃,j_h为+ -0.1,+-0.8,+- 1,f_h在f_h分别为+ -1.2分钟,40、60分钟,j_c为+ -0.4分钟。最后,分析了多个变量的偏差对F,PT和CT的组合影响。从热处理中实施HACCP方法的观点来看,人工神经网络模型可以有效地用于关键控制点的识别和过程偏差的校正。

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