Identification of Panax Notoginseng Powder in Different Parts Based on the Electronic Nose and Time-Domain Feature Extraction

Lin, Yuhao; Zhang, Fujie; Li, Lixia; Shi, Lei; Zhu, Yinlong; Wang, Jun

doi:10.1149/1945-7111/ac62bf

Cited by 8 publications

(2 citation statements)

References 39 publications

(36 reference statements)

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“…To eliminate the redundant information between features, IRIV, VISSA, VISSA‐IRIV were used as the feature selection methods to optimize 825 sound signal feature variables of walnut to improve the generalization ability and recognition accuracy of the model, thus enhancing the model's robustness (Lin et al, 2022).…”

Section: Results and Analysismentioning

confidence: 99%

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

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

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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.

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Section: Results and Analysismentioning

confidence: 99%

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

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Principal component analysis (PCA) is a traditional linear feature extraction method and is widely used in data analysis (Uddin et al, 2021). Cevoli et al (2021) tionally, some studies have showed that the modeling performance is influenced by the model the parameters, but there is no fixed selection method for the optimal parameters (Lin et al, 2022;Ying et al, 2017). Equilibrium slime mold algorithm (ESMA) is a novel metaheuristic optimization approach that has not been used for the task of parameter searching.…”

Section: Introductionmentioning

confidence: 99%

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

Zhong,

Sun,

Yao

et al. 2024

Journal of Food Safety

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Moisture content detection has guiding significance for the storage and quality detection of rice. To detect moisture content rapidly and non‐destructively, hyperspectral imaging technology (400‐1000 nm) was employed to analyze rice with different moisture content, and Savitzky–Golay mixed standard normalized variable algorithm (SG‐SNV) was used for spectral data pretreatment. Furthermore, a modified supervised locality preserving projections (MSLPP) method was proposed to extract spectral features. The modeling results showed that MSLPP had better spectral feature extraction performance. Finally, to improve prediction accuracy, the equilibrium slime mold algorithm (ESMA) was introduced to obtain the optimal parameters (c, g) of the support vector regression (SVR) model. And MSLPP–ESMA–SVR model had higher prediction accuracy and stronger robustness, with R2p reaching 0.9755 and root mean square error of prediction reaching 0.8597%. Therefore, hyperspectral imaging technology combined with MSLPP–ESMA–SVR model is feasible to detect rice moisture content.

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Electronic nose and its application in the food industry: a review

Wang,

Chen

2023

Eur Food Res Technol

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Identification of Panax Notoginseng Powder in Different Parts Based on the Electronic Nose and Time-Domain Feature Extraction

Cited by 8 publications

References 39 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

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

Electronic nose and its application in the food industry: a review

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