番茄干颜色特性敏感性分析及软计算预测

Q3 Engineering
J. Hussein, M. Oke, F.F. Agboola, E. Oke
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引用次数: 0

摘要

用热风烘干机过度加热会造成番茄干质量的相当大的损失,特别是在感官和颜色特性方面。因此,需要优化工艺参数,以尽量减少使用复杂的颜色检测设备可能不容易实现的有害颜色质量变化。虽然对番茄的干燥进行了大量的研究,但利用自适应神经模糊推理系统(ANFIS)和人工神经网络(ANN)对番茄在对流热风干燥过程中的颜色特征进行软计算建模和敏感性分析仍未见报道。因此,这项工作提出了番茄在对流热风干燥过程中颜色特征的软计算预测。番茄经过预处理,切片,并在40、50和60℃下干燥。确定前后的颜色特征(L*, a*, b*, a*/b*颜色变化,褐变指数,色调,色度),并使用数据训练ANN和ANFIS模型。通过计算预测结果与实验结果之间的决定系数(R2)、均方根误差(RMSE)和平均绝对误差(MAE)来确定模型的预测性能。结果表明,其颜色特征范围分别为26.83 ~ 43.27、22.79 ~ 42.10、16.99 ~ 33.72、1.11 ~ 1.34、16.70 ~ 42.71、16.94 ~ 62.37、28.43 ~ 53.94和0.84 ~ 0.93。ANFIS模型显示了颜色变化与干燥条件之间有意义的关系,其R2(0.9999)较高,RMSE(0.0452)和MAE(0.0312)低于ANN。因此,ANFIS模型预测可靠,可进一步用于基于模糊的控制器过程设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensitivity analysis and soft-computaional prediction of colour characteristics of dried tomatoes
Excessive heating with hot-air oven dryers produces considerable losses in the quality of dried tomatoes, particularly in the organoleptic and colour characteristics. Thus, process parameters need to be optimised to minimise detrimental colour quality changes that might not be easily achieved using sophisticated colour detection devices. While a sizable number of studies on the drying of tomatoes, soft-computational modelling and sensitivity analysis of tomatoes' colour characteristics during convective hot-air drying using Adaptive Neuro-fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) are still unreported. Therefore, this work presents a soft-computing prediction of tomatoes' colour characteristics during convective hot-air drying. The tomatoes were pretreated, sliced, and dried at 40, 50, and 60?C. The colour characteristics (L*, a*, b*, a*/b* change in colour, browning index, hue, and chroma) before and after were determined, and the data was used to train ANN and ANFIS models. The model's predictive performance was determined by calculating the coefficient of determination (R2), Root Means Squared Error (RMSE), and Mean Absolute Error (MAE) between predicted and experimental results. The results showed a range of 26.83 - 43.27, 22.79 - 42.10, 16.99 - 33.72, 1.11 - 1.34, 16.70 - 42.71, 16.94 - 62.37, 28.43 - 53.94, and 0.84 - 0.93, respectively, for the colour characteristics. The ANFIS model demonstrates a meaningful relationship between colour changes and drying conditions with a higher R2 (0.9999) and lower RMSE (0.0452) and MAE (0.0312) than ANN. Thus, the ANFIS model is reliable for prediction and can be further used for fuzzy-based controller process design.
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来源期刊
Acta Periodica Technologica
Acta Periodica Technologica Engineering-Engineering (all)
CiteScore
0.60
自引率
0.00%
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0
审稿时长
8 weeks
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