Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms

Pedro Sousa Sampaio, Andreia Soares, Ana Castanho, Ana Sofia Almeida, Jorge Oliveira, Carla Brites

Research output: Contribution to journalArticlepeer-review

176 Citations (Scopus)

Abstract

Determining amylose content in rice with near infrared (NIR) spectroscopy, associated with a suitable multivariate regression method, is both feasible and relevant for the rice business to enable Process Analytical Technology applications for this critical factor, but it has not been fully exploited. Due to it being time-consuming and prone to experimental errors, it is urgent to develop a low-cost, nondestructive and ‘on-line’ method able to provide high accuracy and reproducibility. Different rice varieties and specific chemometrics tools, such as partial least squares (PLS), interval-PLS, synergy interval-PLS and moving windows-PLS, were applied to develop an optimal regression model for rice amylose determination. The model performance was evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The high performance of the siPLS method (R = 0.94; RMSEP = 1.938; 8941–8194 cm−1; 5592–5045 cm−1; and 4683–4335 cm−1) shows the feasibility of NIR technology for determination of the amylose with high accuracy.

Original languageEnglish
Pages (from-to)196-204
Number of pages9
JournalFood Chemistry
Volume242
DOIs
Publication statusPublished - 1 Mar 2018

Bibliographical note

Publisher Copyright:
© 2017 Elsevier Ltd

Keywords

  • Multivariate models
  • PLS
  • Process Analytical Technologies
  • iPLS
  • mwPLS
  • siPLS

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