Weight interpretation of artificial neural network model for analysis of rice (Oryza sativa L.) with near-infrared spectroscopy

Seungwoo Son, Donghwi Kim, Myoung Choul Choi, Joonhee Lee, Byungjoo Kim, Chang Min Choi, Sunghwan Kim

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Prediction models for major nutrients of rice were built using near-infrared (NIR) spectral data based on the artificial neural network (ANN). Scientific interpretation of the weight values was proposed and performed to understand the wavenumbers contributing to the prediction of nutrients. NIR spectra were acquired from 110 rice samples. Carbohydrate and moisture contents were predicted with values for the determination coefficient, relative root mean square error, range error ratio, and residual prediction deviation of 0.98, 0.11 %, 44, and 7.3, and 0.97, 0.80 %, 27, and 5.8, respectively. The results agreed well with ones reported in the previous studies and acquired by the conventional partial least squares (PLS)-variable importance in projection method. This study demonstrates that the combination of NIR and ANN is a powerful and accurate tool to monitor nutrients of rice and scientific interpretation of weights can be performed to overcome black box nature of the ANN.

Original languageEnglish
Article number100430
JournalFood Chemistry: X
Volume15
DOIs
StatePublished - 30 Oct 2022

Keywords

  • Artificial neural network
  • Near-infrared spectroscopy
  • Nutrients
  • Partial least squares
  • Prediction model
  • Rice

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