Detection of peach soluble solids based on near‐infrared spectroscopy with High Order Spatial Interaction network

Background Due to the scalability of deep learning technology, researchers have applied it to the non‐destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC...

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Veröffentlicht in:Journal of the science of food and agriculture Jg. 104; H. 7; S. 4309 - 4319
Hauptverfasser: Qi, Hengnian, Luo, Jiahao, Chen, Gang, Zhang, Jianyi, Chen, Fengnong, Li, Hongyang, Shen, Cong, Zhang, Chu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Chichester, UK John Wiley & Sons, Ltd 01.05.2024
John Wiley and Sons, Limited
Schlagworte:
agriculture
Algorithms
attention module
Brix value
Clustering
data collection
Deep learning
Destructive testing
Fruits
High Order Spatial Interaction Network
Infrared spectroscopy
Learning algorithms
Machine learning
markets
Model accuracy
Modules
Near infrared radiation
near-infrared spectroscopy
Neural networks
Peaches
Performance prediction
permutation algorithm
Permutations
prediction
Predictions
soluble solids content
Spectral bands
spectral clustering
taste
texture
Wavelengths
deep learning
permutation algorithm
High Order Spatial Interaction Network
soluble solids content
attention module
spectral clustering
ISSN:0022-5142, 1097-0010, 1097-0010
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Zusammenfassung:Background Due to the scalability of deep learning technology, researchers have applied it to the non‐destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. Results This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group‐based Clustering Subspace Representation (GCSR‐C) is used to establish the Position and Channel Attention Module‐High Order Spatial Interaction (PC‐HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC‐HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. Conclusion The optimal model, PC‐HOSI model, performed well with an order of 3 (PC‐HOSI‐3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.
Bibliographie:These authors contributed equally to this work.
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ISSN:0022-5142
1097-0010
1097-0010
DOI:10.1002/jsfa.13316