Deep spectral‐spatial features of near infrared hyperspectral images for pixel‐wise classification of food products

Hongyan Zhu, Aoife Gowen, Hailin Feng, Keping Yu, Jun Li Xu

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Hyperspectral imaging (HSI) emerges as a non‐destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel‐wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three‐dimensional convolution neural network (3‐D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2‐D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral‐spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre‐processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.

本文言語English
論文番号5322
ページ(範囲)1-20
ページ数20
ジャーナルSensors (Switzerland)
20
18
DOI
出版ステータスPublished - 2020 9 2

ASJC Scopus subject areas

  • 分析化学
  • 生化学
  • 原子分子物理学および光学
  • 器械工学
  • 電子工学および電気工学

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