Identification of red jujube varieties based on hyperspectral imaging technology combined with <scp>CARS‐IRIV</scp> and <scp>SSA‐SVM</scp>

Wang, Simin; Sun, Jun; Fu, Lvhui; Xu, Min; Tang, Ningqiu; Cao, Yan; Yao, Kunshan; Jing, Jianpeng

doi:10.1111/jfpe.14137

Cited by 13 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Features selected by VISSA were extracted by IRIV for the second time, and the number of features was reduced to 33, which not only reduced the complexity of the model, but also achieved the best accuracy of the classification model. This indicates that the combination of the two feature variable selection methods is better (Fuxiang et al, 2022; Simin et al, 2022). In summary, this paper will use VISSA‐IRIV as the feature extraction method for the original data.…”

Section: Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

Cracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time‐domain features, 13 frequency‐domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray‐scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and of the classification model. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.Practical applicationsCracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identification combine with feature fusion. The features of time domain, frequency domain and Mel spectrogram were extracted from the effective sound signal, and the features were fused. The influence of three feature selection methods, three models and three optimization algorithms on walnut sound signal recognition was analyzed and compared. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, and the method proposed in this study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.

show abstract

Section: Results and Analysismentioning

confidence: 99%

“…In this study, 257 feature variables were selected at the minimum RMSECV (at the black arrow in Figure 9). (Fuxiang et al, 2022;Simin et al, 2022). In summary, this paper will use VISSA-IRIV as the feature extraction method for the original data.…”

Section: Feature Selection Based On Vissamentioning

confidence: 99%

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…In order to compare the excellent performance of CNN model based on two‐dimensional gray image data in sorghum variety recognition, this study established other traditional classification models based on one‐dimensional average spectral data. SVMs are widely used to overcome issues with the classification of spectral data because of their excellent generalization ability 29 . In general, SVMs use the kernel function to map the nonlinear problem in the low‐dimensional space to the high‐dimensional space and construct a linear decision function in the high‐dimensional space to allow nonlinear decision‐making in the original space.…”

Section: Methodsmentioning

confidence: 99%

“…SVMs are widely used to overcome issues with the classification of spectral data because of their excellent generalization ability. 29 In general, SVMs use the kernel function to map the nonlinear problem in the low-dimensional space to the high-dimensional space and construct a linear decision function in the high-dimensional space to allow nonlinear decision-making in the original space. They also use the theory of structural risk minimization to establish a separation hyperplane in a high-dimensional space to maximize the distance between different categories and achieve a small classification error.…”

Section: Support Vector Machines (Svms) and The K-nearest Neighbor (K...mentioning

confidence: 99%

Rapid nondestructive detecting of sorghum varieties based on hyperspectral imaging and convolutional neural network

Jiang

Tian

et al. 2022

J Sci Food Agric

View full text Add to dashboard Cite

BACKGROUND The purity of sorghum varieties is an important indicator of the quality of raw materials used in the distillation of liquors. Different varieties of sorghum may be mixed during the acquisition process, which will affect the flavor and quality of liquor. To facilitate the rapid identification of sorghum varieties, this study proposes a sorghum variety identification model using hyperspectral imaging (HSI) technology combined with convolutional neural network (AlexNet). RESULTS First, the watershed algorithm, which was modified with the extended‐maxim transform, was used to segment the hyperspectral images of a single sorghum grain. The isolated forest algorithm was used to eliminate abnormal spectral data from the complete spectral data. Secondly, the AlexNet model of sorghum variety identification was established based on the two‐dimensional gray image data of sorghum grain in group 1. The effects of different preprocessing methods and different convolution kernel sizes on the performance of the AlexNet model were discussed. The eigenvalues of the last layer of the AlexNet model were visualized using the t‐distributed random neighborhood embedding method, which is used to evaluate the separability of features extracted by the AlexNet model. The performance differences between the optimal AlexNet model and traditional machine learning models for sorghum variety identification were compared. Finally, the varieties of sorghum grains in groups 2 and 3 were identified based on the optimal AlexNet model, and the average accuracy values of the test set reached 95.62% and 95.91% respectively. CONCLUSION The results in this study demonstrated that HSI combined with the AlexNet model could provide a feasible technical approach for the detection of sorghum varieties. © 2022 Society of Chemical Industry.

show abstract

“…The sparrow search algorithm (SSA) has better global exploration ability and local development ability. There are jujube varieties identification and apple mycosis detection supported by SSA (Wang, Sun, et al., 2022; Zhao et al., 2022). The SSA is used to optimize the initial weight and threshold, and also the BP neural network prediction model is established, it can effectively overcome the problem that BP neural network tend to fall into local optimum.…”

Section: Introductionmentioning

confidence: 99%

Drying kinetics prediction and quality effect of ultrasonic synergy vacuum far‐infrared drying of Codonopsis pilosula

Wang,

Ying,

Zhang

et al. 2023

Journal of Food Science

View full text Add to dashboard Cite

By using ultrasonic synergy vacuum far‐infrared drying (US‐VFID), the effects of different conditions on the drying kinetics, functional properties, and microstructure of Codonopsis pilosula slices were studied. The sparrow search algorithm (SSA) was used to optimize the back‐propagation (BP) neural network to predict the moisture ratio during drying. With the increase of ultrasonic frequency, power and radiation temperature, the drying time of C. pilosula was shortened. The drying time of US‐VFID was 25% shorter than VFID, when radiation temperature was 50°C, ultrasonic power was 48 W, and frequency was 28 kHz. The SSA‐BP neural network, the average absolute error prediction was 0.0067. Compared with hot air drying (HAD), the total phenolic content and antioxidant activity of C. pilosula by US‐VFID were increased by 29.47% and 8.67%, respectively, and a reduction in color contrast of 16.19%. The dilation and generation of microcapillary of C. pilosula were more obvious. The study revealed US‐VFID could be used for the selection and process control of agro‐processing methods for C. pilosula products.

show abstract

Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

Cited by 13 publications

References 40 publications

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Rapid nondestructive detecting of sorghum varieties based on hyperspectral imaging and convolutional neural network

Drying kinetics prediction and quality effect of ultrasonic synergy vacuum far‐infrared drying of Codonopsis pilosula

Contact Info

Product

Resources

About