Quality changes and predictive models of radial basis function neural networks for brined common carp (Cyprinus carpio) fillets during frozen storage

Kong, Chunli; Wang, Huiyi; Li, Dapeng; Zhang, Yuemei; Pan, Jinfeng; Zhu, Beiwei; Luo, Yongkang

doi:10.1016/j.foodchem.2016.01.088

Cited by 51 publications

(35 citation statements)

References 39 publications

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“…These results were different from Wang et al () that the RBFNNs predicted changes of TAC, K value, TBA, TVB‐N, SA and HxR of brined fillets during storage with relative error all within ±5%, and the result of Kong et al () that RBFNNs were developed to predicted quality (FFA, SEP, SH and Ca 2+ ‐ATPase activity) of brined carp fillets during frozen storage with relative error all within ±5%. Dutta et al () used a RBFNNs to achieve up to 100% accuracy in the flavour classification of five different tea samples.…”

Section: Resultscontrasting

confidence: 82%

Nondestructive determination of salmon fillet freshness during storage at different temperatures by electronic nose system combined with radial basis function neural networks

Jia

Shi

Wang

et al. 2020

Int J of Food Sci Tech

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This study develops a predictive model for determining freshness of salmon fillets during cold storage at different temperatures using electronic nose combined with principal component analysis (PCA) and radial basis function neural networks (RBFNNs). The electronic nose sensed ammonia/amines, hydrocarbons, solvents and aromatics that increased during storage. The concentrations of the volatiles were compared with the increased thiobarbituric acid (TBA), total volatile basic nitrogen (TVB-N), total aerobic bacteria count (TAC) and decreased of sensory assessments (SA). Gas chromatograph-ion mobility spectrometry analysis confirmed the changes in gas species. RBFNNs and PCA were used to establish predictive models and the relative errors of TBA, TVB-N and TAC by the PCA-RBFNNs model were all within AE10% and SA was within AE15%. These results suggest that the PCA-RBFNNs model can be used to predict changes in the freshness of salmon fillets stored at À2 to 10°C.Keywords Cold storage, electronic nose, freshness index prediction, gas chromatograph -ion mobility spectrometry, radial basis function neural networks, salmon fillets.

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Section: Resultscontrasting

confidence: 82%

Nondestructive determination of salmon fillet freshness during storage at different temperatures by electronic nose system combined with radial basis function neural networks

Jia

Shi

Wang

et al. 2020

Int J of Food Sci Tech

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“…Xu et al [12] used the RBFNN model for predicting the quality of thawed shrimp stored at different temperatures (−3, 0, 3, and 6°C) and reported the accurate prediction. Wang et al [13] and Kong et al [14] applied RBFNN model to predict the quality changes of bream and common carp fillets during storage. However, the studies are scanty on the application of RBFNN to predict the quality changes of protein and lipid in shrimp during frozen storage.…”

Section: Introductionmentioning

confidence: 99%

Protein and lipid changes of mud shrimp (Solenocera melantho) during frozen storage: chemical properties and their prediction

Shi

Zhang

Han

et al. 2017

International Journal of Food Properties

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In this study, changes of protein and lipid in mud shrimp during frozen storage were investigated. Protein changes were evaluated through sulfhydryl content, disulfide bond, carbonyls, surface hydrophobicity, and intrinsic fluorescence intensity, whereas lipid oxidation was measured by free fatty acids, thiobarbituric acid reactive substances, peroxide value, and fluorescent compounds. In addition, radial basis function neural network (RBFNN) model was established to predict the quality of shrimp. Results indicated that the lipid changes of frozen mud shrimp were dominant. Mud shrimp stored at −20°C exhibited more lipid oxidative damage compared to those stored at −30 and −40°C. Protein denaturation occurred gradually with the prolongation of frozen storage time and protein oxidation mainly occurred after 12 weeks. RBFNN model can accurately predict and evaluate the protein and lipid qualities of shrimp stored at −20 to −40°C.

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“…A common type of ANN is the radial basis function ANN (RBF-ANN), which is used to classify features into different classes by finding common characteristics among the samples of the known feature class. In this type of network, nonlinearity is embedded within the transfer functions of the hidden layer neurons, making the optimization of tunable parameters a linear search Dash et al (2000); Kong et al (2016). Figure 4(a) shows a schematic representation of the RBF-ANN, which was proposed by Beale et al Beale et al (2012).…”

Section: Modelingmentioning

confidence: 99%

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Castro

Oblitas

De-la-Torre

et al. 2019

Preprint

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The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques to automate this process. Machine learning techniques such as artificial neural networks, support vector machines, decision trees, and K-nearest neighbor algorithms have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a 5-fold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of an increased complexity.

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Quality changes and predictive models of radial basis function neural networks for brined common carp (Cyprinus carpio) fillets during frozen storage

Cited by 51 publications

References 39 publications

Nondestructive determination of salmon fillet freshness during storage at different temperatures by electronic nose system combined with radial basis function neural networks

Nondestructive determination of salmon fillet freshness during storage at different temperatures by electronic nose system combined with radial basis function neural networks

Protein and lipid changes of mud shrimp (Solenocera melantho) during frozen storage: chemical properties and their prediction

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

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