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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Article number5322
Pages (from-to)1-20
Number of pages20
JournalSensors (Switzerland)
Issue number18
Publication statusPublished - 2020 Sept 2


  • Classification
  • Convolutional neural network
  • Hyperspectral
  • Principal component analysis
  • Spatial‐spectral features

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'Deep spectral‐spatial features of near infrared hyperspectral images for pixel‐wise classification of food products'. Together they form a unique fingerprint.

Cite this