Accurate and cost‐effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor

2019 ◽  
Vol 66 (4) ◽  
pp. 681-689 ◽  
Author(s):  
Seyed Nezamedin Hosseini ◽  
Amin Javidanbardan ◽  
Maryam Khatami
Keyword(s):  
Neural Network ◽  
Pichia Pastoris ◽  
Fermentation Process ◽  
Cost Effective ◽  
Soft Sensor ◽  
Industrial Scale ◽  
Recombinant Pichia Pastoris ◽  
Hbsag Titer

Application of recurrent neural network for online prediction of cell density of recombinant Pichia pastoris producing HBsAg

2019 ◽  
Vol 49 (4) ◽  
pp. 352-359 ◽  
Author(s):  
Ahmad Beiroti ◽  
Mohammad Reza Aghasadeghi ◽  
Seyed Nezamedin Hosseini ◽  
Dariush Norouzian
Keyword(s):  
Neural Network ◽  
Pichia Pastoris ◽  
Cell Density ◽  
Recurrent Neural Network ◽  
Recombinant Pichia Pastoris ◽  
Online Prediction

A robust soft sensor to monitor 1,3‐propanediol fermentation process by Clostridium butyricum based on artificial neural network

2020 ◽  
Vol 117 (11) ◽  
pp. 3345-3355
Author(s):  
Ai‐Hui Zhang ◽  
Kai‐Yi Zhu ◽  
Xiao‐Yan Zhuang ◽  
Lang‐Xing Liao ◽  
Shi‐Yang Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fermentation Process ◽  
Soft Sensor ◽  
Clostridium Butyricum ◽  
Artificial Neural

scholarly journals Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7635
Author(s):  
Bo Wang ◽  
Xingyu Wang ◽  
Mengyi He ◽  
Xianglin Zhu
Keyword(s):  
Pichia Pastoris ◽  
Least Squares ◽  
Fermentation Process ◽  
Model Simulation ◽  
Weighted Least Squares ◽  
Soft Sensor ◽  
Modeling Method ◽  
Local Model ◽  
Support Vector ◽  
Sensor Modeling

The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.

Dual Regulation of Cytoplasm and Peroxisomes for Improved Α-Farnesene Production in Recombinant Pichia pastoris

2021 ◽  
Author(s):  
Hui Liu ◽  
Sheng-Ling Chen ◽  
Jian-Zhong Xu ◽  
Wei-Guo Zhang
Keyword(s):  
Pichia Pastoris ◽  
Recombinant Pichia Pastoris

scholarly journals Detection of Strawberry Diseases Using a Convolutional Neural Network

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Jia-Rong Xiao ◽  
Pei-Che Chung ◽  
Hung-Yi Wu ◽  
Quoc-Hung Phan ◽  
Jer-Liang Andrew Yeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Powdery Mildew ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Cost Effective ◽  
Gray Mold ◽  
Leaf Blight ◽  
Crown Rot ◽  
Accuracy Rate ◽  
Fruit Disease

The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

Sign in / Sign up

Export Citation Format

Share Document