A deep learning approach to improving spectral analysis of fruit quality under interseason variation

Model updating for developed calibrations is critical for robust spectral analysis in fruit quality control. Existing methods have limitations that usually need sufficient samples for model recalibration and are mainly designed for conventional linear models. This study proposes a model fine-tuning...

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Detalhes bibliográficos
Autor principal: Yang, Jie (author)
Outros Autores: Luo, Xuan (author), Zhang, Xiaolei (author), Passos, Dário (author), Xie, Lijuan (author), Rao, Xiuqin (author), Xu, Huirong (author), Ting, K.C. (author), Lin, Tao (author), Ying, Yibin (author)
Formato: article
Idioma:eng
Publicado em: 2022
Assuntos:
Biological variability
Visible/near-infrared spectroscopy
Deep learning
Convolutional neural network
Model updating
Fruit quality
Texto completo:http://hdl.handle.net/10400.1/18594
País:Portugal
Oai:oai:sapientia.ualg.pt:10400.1/18594

Texto Completo

http://hdl.handle.net/10400.1/18594

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