scholarly journals Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil

Molecules ◽  
2021 ◽  
Vol 26 (21) ◽  
pp. 6717
Author(s):  
Shengquan Huang ◽  
Ying Liu ◽  
Xuyuan Sun ◽  
Jinwei Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Free Radicals ◽  
Linseed Oil ◽  
Evaluation Model ◽  
Heating Time ◽  
Gas Chromatography Mass Spectrometry ◽  
Oxidized Oil ◽  
Artificial Neural ◽  
Signal Intensities

In this study, electron paramagnetic resonance (EPR) and gas chromatography-mass spectrometry (GC-MS) techniques were applied to reveal the variation of lipid free radicals and oxidized volatile products of four oils in the thermal process. The EPR results showed the signal intensities of linseed oil (LO) were the highest, followed by sunflower oil (SO), rapeseed oil (RO), and palm oil (PO). Moreover, the signal intensities of the four oils increased with heating time. GC-MS results showed that (E)-2-decenal, (E,E)-2,4-decadienal, and 2-undecenal were the main volatile compounds of oxidized oil. Besides, the oxidized PO and LO contained the highest and lowest contents of volatiles, respectively. According to the oil characteristics, an artificial neural network (ANN) intelligent evaluation model of free radicals was established. The coefficients of determination (R2) of ANN models were more than 0.97, and the difference between the true and predicted values was small, which indicated that oil profiles combined with chemometrics can accurately predict the free radical of thermal oxidized oil.

Hybrid Strategy for Product Quality Credit Evaluation Based on Statistics and Artificial Neural Network

2011 ◽  
Vol 361-363 ◽  
pp. 1499-1505 ◽  
Author(s):  
Li Mei Liu ◽  
Heng Qian ◽  
Yong Chao Gao ◽  
Ding Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Product Quality ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Credit System ◽  
Hybrid Strategy ◽  
Credit Evaluation ◽  
Social Credit ◽  
Artificial Neural

In China, quality credit is an important part of the social credit system, and evaluation of quality credit is the key to the construction of quality credit system. In this paper, on the basis of product quality credit factor analysis and evaluation index construction, a hybrid strategy of three stages is proposed according to the different nature of indicators. The emphasis is put on intelligent evaluation model based on statistics and artificial neural network. According to the results of experimental verification, this credit evaluation method shows a high accuracy for the evaluation of quality credit.

scholarly journals Evaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network

2021 ◽  
Vol 16 (08) ◽  
pp. 188
Author(s):  
Shu Ji ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Evaluation Model ◽  
Intelligent Manufacturing ◽  
Evaluation Index System ◽  
Vocational Schools ◽  
Participation Motivation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

During the reform of talent training mode, higher vocational schools must promote and apply modern apprenticeship to meet the needs of intelligent manufacturing. However, most enterprises and schools differ greatly in the participation enthusiasm and implementation motivation for modern apprenticeship. To enhance the participation motivation, it is critical to correctly evaluate the motivation status of enterprises and schools participating in modern apprenticeship, and analyze its key influencing factors. For this reason, this paper employs the Artificial Neural Network (ANN) to evaluate such motivation status. Firstly, a Modern Apprenticeship Motivation Status (MAMS) evaluation model was established, along with its evaluation index system (EIS). Then, differences in the motivation status were compared from seven aspects. After that, an improved backpropagation (BP) neural network was built to construct and optimize the MAMS prediction model. Finally, the constructed model was proved valid through experiments.

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