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

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Tahmasbian, I., Wallace, H. M., Gama, T. and Bai, S. H. (2021) An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples. LWT, 143 , 110893. ISSN 0023-6438


Article Link: https://doi.org/10.1016/j.lwt.2021.110893

Publisher URL: https://www.sciencedirect.com/science/article/pii/S0023643821000463


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.

Item Type:Article
Business groups:Animal Science
Additional Information:Crown Copyright © 2021 Published by Elsevier Ltd
Keywords:Food quality control free fatty acid hyperspectral imaging oleic acid peroxide value SVM
Subjects:Agriculture > Agriculture (General) > Agricultural chemistry. Agricultural chemicals
Plant culture > Food crops
Plant culture > Fruit and fruit culture > Nuts
Live Archive:22 Feb 2021 03:01
Last Modified:03 Sep 2021 16:46

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