Artificial neural network model for prediction of cold spot temperature in retort sterilization of starch-based foods

Llave, Yvan; Hagiwara, Takaaki; Sakiyama, Takaharu

doi:10.1016/j.jfoodeng.2011.10.024

Cited by 36 publications

(18 citation statements)

References 18 publications

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“…When comparing the whole profile, the mean of the absolute values of the relative error ( RE ) was 1.02%, with a standard deviation (SD RE ) of 0.43%. The method for evaluating the error was described in detail by Llave et al (2012). This led to the conclusion that the surface temperature profile of the grilled fish could be determined accurately using the IR thermographic camera.…”

Section: Construction Of the Surface Temperature Distributionmentioning

confidence: 99%

Visualization of Browning Color Formation on Grilled Fish Through a 2D Simulation

Llave

Wakisaka

et al. 2014

FSTR

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Section: Construction Of the Surface Temperature Distributionmentioning

confidence: 99%

Visualization of Browning Color Formation on Grilled Fish Through a 2D Simulation

Llave

Wakisaka

et al. 2014

FSTR

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Section: Feed-forward Neural Networkmentioning

confidence: 99%

“…In this paper, we employed the feedforward neural network (FNN), because it did not need information related to the probability distribution [42], nor the a priori probabilities of different classes [43]. To build the FNN to be equal to train the weights/biases of all neurons in the FNN, which is treated as an optimization problem, i.e., we need to obtain the optimal weights/biases in order to make minimal the mean-squared error (MSE) between real outputs and target outputs.…”

Section: Feed-forward Neural Networkmentioning

confidence: 99%

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

Zhang

Yang

Cattani

et al. 2016

Entropy

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This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10ˆ10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification.

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“…The reason we chose FNN was that (1) it has been widely used in pattern classification; (2) it does not need any a priori information about the probability distribution [25]. The common model of one-hidden-layer FNN is shown in Figure 3.…”

Section: Feed-forward Neural Networkmentioning

confidence: 99%

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Wang

Zhang

et al. 2015

Entropy

137

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Abstract:Fruit classification is quite difficult because of the various categories and similar shapes and features of fruit. In this work, we proposed two novel machine-learning based classification methods. The developed system consists of wavelet entropy (WE), principal component analysis (PCA), feedforward neural network (FNN) trained by fitness-scaled chaotic artificial bee colony (FSCABC) and biogeography-based optimization (BBO), respectively. The K-fold stratified cross validation (SCV) was utilized for statistical analysis. The classification performance for 1653 fruit images from 18 categories showed that the proposed "WE + PCA + FSCABC-FNN" and "WE + PCA + BBO-FNN" methods achieve the same accuracy of 89.5%, higher than state-of-the-art approaches: "(CH + MP + US) + PCA + GA-FNN " of 84.8%, "(CH + MP + US) + PCA + PSO-FNN" of 87.9%, "(CH + MP + US) + PCA + ABC-FNN" of 85.4%, "(CH + MP + US) + PCA + kSVM" of 88.2%, and "(CH + MP + US) + PCA + FSCABC-FNN" of 89.1%. Besides, our methods used only 12 features, less than the number of features used by other methods. Therefore, the proposed methods are effective for fruit classification. OPEN ACCESSEntropy 2015, 17 5712

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Artificial neural network model for prediction of cold spot temperature in retort sterilization of starch-based foods

Cited by 36 publications

References 18 publications

Visualization of Browning Color Formation on Grilled Fish Through a 2D Simulation

Visualization of Browning Color Formation on Grilled Fish Through a 2D Simulation

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

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