An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples

Iman Tahmasbian; Helen Wallace; Tsvakai Gama; Shahla Hosseini Bai

doi:10.1016/j.lwt.2021.110893

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An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples

Journal article

Peer reviewed

An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples

Iman Tahmasbian, Helen Wallace, Tsvakai Gama and Shahla Hosseini Bai

LWT - Food Science and Technology, Vol.143, pp.1-9

2021

DOI: https://doi.org/10.1016/j.lwt.2021.110893

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Abstract

food quality control

free fatty acid

hyperspectral imaging

oleic acid

peroxide value

SVM

This study aimed to develop an automated technique, which is rapid, non-destructive and inexpensive, to test for rancidity of nuts. A visible to near infrared benchtop hyperspectral camera was used to capture images from blanched canarium, unblanched canarium and macadamia samples. Support vector machine classification (SVC) and PLSR models were developed to segregate the pooled spectra of the nuts and predict their peroxide values (PV) and free fatty acid (FFA) concentrations. The SVC and PLSR models were then used in a hierarchical model to develop an automated system for predicting PV and FFA. The automated model was then tested using a test set providing classification accuracy of 87% and R2 between 0.60 and 0.76 and RPD between 1.6 and 2.7 for PV and FFA prediction. Overall, the automated system has the potential commercial application in nut processing to detect rancidity of mixed nut samples non-destructively and in real-time. It is suggested to train other machine learning models with more samples to improve the accuracy of predictions.

Details

Title: An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples
Authors: Iman Tahmasbian (Author) - Griffith University
Helen Wallace (Author) - Griffith University
Tsvakai Gama (Author) - University of the Sunshine Coast, Queensland, GeneCology Research Centre - Legacy
Shahla Hosseini Bai (Author) - Griffith University
Publication details: LWT - Food Science and Technology, Vol.143, pp.1-9
Publisher: Academic Press
DOI: 10.1016/j.lwt.2021.110893
ISSN: 1096-1127
Organisation Unit: University of the Sunshine Coast, Queensland; GeneCology Research Centre - Legacy; School of Science and Engineering - Legacy
Language: English
Record Identifier: 99501508802621
Output Type: Journal article

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Collaboration types: Domestic collaboration
Web Of Science research areas: Food Science & Technology

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