共查询到19条相似文献,搜索用时 171 毫秒
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采用Box-Behnken试验设计对薏苡仁酒的发酵条件进行优化,并对Box-Behnken(BB)试验结果分别进行响应面法(RSM)和人工神经网络(ANN)分析。结果表明,RSM、ANN优化发酵条件分别为薏苡仁∶糯米为1∶2(g∶g)、酵母A1接种量为4.7%、温度为31.7 ℃、初始pH为3.0;薏苡仁∶糯米为1∶1.9(g∶g)、酵母A1接种量为4.2%、温度为28.1 ℃、初始pH为3.0,ANN、RSM分别在其最优条件下的实际值和预测值都基本一致。ANN、RSM拟合模型的相关系数(R)、决定系数(R2)、均方误差(MSE)、均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)分别为0.994 5、0.988 9、0.011 7、0.108 4、0.072 2、0.486 3%和0.983 6、0.967 5、0.028 9、0.170 1、0.143 7、0.985 7%。ANN具有更高拟合能力和准确性,拟合效果更好,更适合应用于薏苡仁酒发酵条件优化。 相似文献
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以草鱼作为原料,通过分析pH、微生物、挥发性盐基氮(TVB-N值)、氨基态氮(ANN)、硫代巴比妥酸(TBARS)等指标,并结合色泽、质构以及感官评定,探究了不同糟制温度(4、10、15、20℃)对糟鱼肉品质特性的影响。结果表明:随着时间的变化,各糟制温度下的鱼肉pH呈逐渐下降趋势,而酸度、TVB-N、TBARS和ANN值则呈上升趋势,且温度越高变化趋势越明显。乳酸菌和酵母菌作为糟鱼发酵过程中的优势菌种,呈现先上升后稳定的趋势,而葡萄球菌和肠道菌的生长在发酵中后期被抑制,微生物的生长速度和温度呈正相关关系。结合色泽、质构以及感官评定结果综合分析,15℃下糟制发酵成熟的糟鱼鱼肉在产品品质及消费偏好方面表现较优。 相似文献
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以微生物、pH值、挥发性盐基氮(TVBN)、氨基态氮(ANN)、硫代巴比妥酸(TBARS)和挥发性风味成分为指标,研究了混合发酵菌种和盐含量对鲢鱼发酵品质的影响。结果表明,在发酵过程中乳酸菌快速生长,在发酵48 h内鱼肉pH值快速下降至4.23,有效抑制了葡萄球菌、肠杆菌、假单胞菌的生长繁殖以及挥发性盐基氮、硫代巴比妥酸的积累。在3%~5%范围内盐含量对微生物生长和发酵鱼肉品质特性没有显著影响。利用混合接种发酵可以改善发酵鱼肉风味,提高产品安全性。 相似文献
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电子舌技术对香醋发酵过程的监控研究 总被引:1,自引:0,他引:1
采用电子舌对香醋发酵过程中总酸、不挥发酸、还原糖、氨幕酸态氮进行定量分析.对比了偏最小二乘法(PLS)和人工神经网络(ANN)的不同算法,结果显示基于非线性映射的人工神经网络算法具有较好的定量精度,预测值和实测值的相关系数分别为0.8439、0.9382、0.8322和0.8558.预测标准偏差(RMSEP)分别为0.8240、0.0963、0.1482和0.5557.研究表明:电子舌能对香醋发酵产物进行定量预测,并对食醋发酵过程的监控有良好的应用前景. 相似文献
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人工神经网络(ANN)智能技术与热处理 总被引:4,自引:0,他引:4
本文着重介绍了目前国内外人工神经网络(ANN)技术在材料热处理领域的应用情况,论述了ANN技术在构造专家系统方面的优越性,简要介绍了作者开发的热处理性能预测专家系统。同时对人工神经网络(ANN)技术在纺织器材的热处理中的应用提出了自己的看法。 相似文献
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为实现对鳓鱼固态发酵过程的监测,用常规分析方法和电子舌技术分别测定了鳓鱼发酵过程中水分、p H、总酸、氨基态氮(ANN)、挥发性盐基氮(TVB-N)和味觉指纹的变化;基于电子舌数据,采用主成分分析(PCA)和判别分析(DA)对不同发酵时间的鳓鱼样品进行识别;采用偏最小二乘回归分析(PLSR)建立电子舌数据与相关理化指标之间的预测模型,并对模型进行评价。结果表明:发酵过程中鳓鱼理化指标和滋味特征均有显著变化;主成分分析提取的3个主成分的累积贡献率可达94.49%,判别分析的判别符合率为100%,不同发酵时间的鳓鱼能被有效识别;基于电子舌响应信号建立的5种理化指标预测模型中,水分和ANN模型的相对分析误差(RPD)均为1.80,可用于定性分析。TVB-N模型的RPD为2.47,具有一定定量检测分析能力。p H和总酸的PLSR预测模型的RPD大于5,定量效果良好,稳定性优良,预测精度高。因此,利用电子舌结合相关化学计量方法对鳓鱼固态发酵过程进行识别和监控可行。 相似文献
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基于神经网络的大生产规模啤酒发酵过程建模 总被引:3,自引:1,他引:3
应用神经网络建立了啤酒发酵过程发酵度及主要风味物质产生量的预测模型。建立的发酵度模型有很好的仿真能力和较好的预测能力。在建立的4个风味物质模型中,异戊醇和乙酸乙酯模型的仿真能力和预测能力都比较强,双乙酰和乙醛模型的仿真能力和预测能力都比较差,需要进一步研究。 相似文献
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Vajihe Mozafary 《纺织学会志》2013,104(1):100-108
Today’s industry gives first priority to information technology. Since understanding the structures and relationships dominated of data can help industrial managers to attend in competitive market successfully, a special mechanism must be developed to process data stored in a system. Hence, the focus on widespread use of data mining gains increasing attention. The purpose of this paper is using data-mining technique in textile industry. More than 150,000 data includes testing of raw materials, manufacturing process parameters and yarn quality parameters, during one year in worsted spinning factory were collected. Next, yarn quality was predicted by using data-mining methods containing clustering and artificial neural network (ANN). In order to evaluate the proposed method, the results obtained were compared with conventional methods based on ANN. The results showed that the performance of data-mining technique is more accurate than that of ANN. 相似文献
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建立了用于在线估计高密度重组毕赤酵母培养过程中处于表达阶段的菌体密度软测量模型。分别对比了基于遗传算法(GA)的动力学软测量模型以及基于人工神经网络(ANN)的软测量模型,并对神经网络软测量模型的拓扑结构以及训练参数进行了初步探讨。当采用基于遗传算法(GA)的动力学模型,模型拟舍值的最大误差为7.63%;在采用神经网络软测量技术时,选取合适的模型结构和输入参数,最大误差为3.12%,而且软测量模型可以很好地反映菌体浓度实时变化趋势。该研究结果表明,在酵母细胞的高密度培养过程中采用基于神经网络的软测量模型具有较高的准确度,可以较好地实时反映发酵过程中菌体浓度的变化。 相似文献
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纺织工业中的虚拟加工技术与模式 总被引:6,自引:0,他引:6
简要介绍了纺织品加工过程、人工神经网络(ANN)及其相关算法的特征。通过ANN技术建立的原料、纺纱、织造和后整理预测/反演模型,能够优化生产工艺,预测与控制产品质量,是纺织设计与虚拟加工的基础。采用主因子、聚类、案例模板和ANN等算法完成对输入参数的归纳、筛选与增补,是提高预测/反演模型精度和效率的有效步骤。以此构建的模块组合式虚拟加工系统,对纺织工业的快速、准确和理性加工,纺织品的低成本和高质量实现,具有重要意义。 相似文献
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VURAL GÖKMEN ÖZGE ÇETNKAYA AÇAR ARDA SERPEN DRS SÜÜT 《Journal of food process engineering》2009,32(2):248-264
An artificial neural network (ANN) was developed to model the dead-end ultrafiltration process of apple juice. Molecular weight cutoff, transmembrane pressure, gelatin–bentonite concentration and time were the input variables, while filtrate flux and filtrate volume were the output variables of the ultrafiltration process. According to error results and correlation values for two types of network (one or two hidden layer configurations), configurations with two hidden layers had comparatively better performance. The highest correlation coefficient with the minimum prediction error was calculated for two hidden layers with 6-5 nodes configuration. Trained ANN (4-6-5-2) predicted filtrate flux and filtrate volume with 2.33 and 1.38% mean relative error, respectively. The results suggest that the ANN modeling can be effectively used to optimize filtration process.
Membrane separation processes including ultrafiltration have gained importance in the food industry. Today, fruit juices are widely clarified by means of ultrafiltration process instead of tedious and laborious conventional clarification treatments. Membrane fouling which results in flux decline is the main problem associated with the ultrafiltration of fruit juices. In order to perform an efficient ultrafiltration process, optimization is required to obtain maximum filtrate volume per unit time. Artificial neural network (ANN) modeling offers great advantage on improving the performance of ultrafiltration process by accounting the effects of different variables, i.e., feed properties, transmembrane pressure and membrane pore size on filtrate volume as the main output of the filtration process. ANN modeling of ultrafiltration may be an alternative to previously proposed empirical and semiempirical models. 相似文献
PRACTICAL APPLICATION
Membrane separation processes including ultrafiltration have gained importance in the food industry. Today, fruit juices are widely clarified by means of ultrafiltration process instead of tedious and laborious conventional clarification treatments. Membrane fouling which results in flux decline is the main problem associated with the ultrafiltration of fruit juices. In order to perform an efficient ultrafiltration process, optimization is required to obtain maximum filtrate volume per unit time. Artificial neural network (ANN) modeling offers great advantage on improving the performance of ultrafiltration process by accounting the effects of different variables, i.e., feed properties, transmembrane pressure and membrane pore size on filtrate volume as the main output of the filtration process. ANN modeling of ultrafiltration may be an alternative to previously proposed empirical and semiempirical models. 相似文献
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Margareta Schmid 《Holz als Roh- und Werkstoff》2008,66(1):71-73
Aspects of data processing are discussed with particular emphasis on data division into sets for network training and network testing. The close relationship between the data used in artificial neural network (ANN) development and model applicability is pointed out. Based on the data also used in the original paper a neural network is developed and ANN testing output is discussed and compared with the results described in the original paper. Conclusions are drawn with respect to the applicability of the model. 相似文献
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Ubonrat Siripatrawan & Pantipa Jantawat 《International Journal of Food Science & Technology》2009,44(1):42-49
Actual storage shelf life test by storing a packaged product under typical storage conditions is costly and time consuming. A new approach using an artificial neural network (ANN) algorithm for shelf life prediction of two varieties of moisture-sensitive rice snacks packaged in polyethylene and polypropylene bags and stored at various storage conditions was established. The ANN used to predict the shelf life was based on multilayer perceptron with back propagation algorithm. The ANN algorithm employed the data of product characteristics, package properties and storage conditions. The neural network comprised an input, one hidden and one output layers. The network was trained using Bayesian regularisation. The performance of ANN was measured using regression coefficient ( R 2 = 0.23–0.28) and root mean square error (RMSE = 0.96–0.99). The ANN-predicted shelf lives agreed very well with actual shelf life data. ANN could be used as an alternative method for shelf life prediction of moisture-sensitive food products as well as product/package optimisation. 相似文献