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Towards Online Near-Infrared Spectroscopy to Optimise Food Product Mixing

Lookup NU author(s): Angela Barone, Professor Jarka Glassey, Professor Gary Montague



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Elsevier Ltd, 2019.

For re-use rights please refer to the publisher's terms and conditions.


This paper advances the use of in-situ Near-Infrared (NIR) spectroscopy as the basis for an in-line control system to optimise mixing time of food powder blends. A non-contact NIR fibre-optic probe installed in a conical screw mixer was used to scan three powder mixtures characterised by different particle size distribution and component distribution. The current state of the art is extended by comparing Conformity Index and Standard deviation of the Moving Block Standard Deviation (MBSD), establishing the optimal pre-treatment combination and investigating the effects of the mixture properties on the results. Products with a broad particle size distribution were more accurately represented using derivatives rather than SNV and Detrending, while products with a broad component distribution showed good results with all pre-treatments.This study evaluated the effect of data pre-treatments on mixing time for different physical properties of powder blends and provided a general guidance on the most appropriate pre-treatment.

Publication metadata

Author(s): Barone A, Glassey J, Montague G

Publication type: Article

Publication status: Published

Journal: Journal of Food Engineering

Year: 2019

Volume: 263

Pages: 227-236

Print publication date: 01/12/2019

Online publication date: 05/07/2019

Acceptance date: 04/07/2019

Date deposited: 11/07/2019

ISSN (print): 0260-8774

ISSN (electronic): 1873-5770

Publisher: Elsevier Ltd


DOI: 10.1016/j.jfoodeng.2019.07.003


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Funder referenceFunder name
EP/G037620/1Alcyomics Ltd
EP/G037620/1BOC Group Limited