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Hyperspectral imaging predicts free fatty acid levels, peroxide values, and linoleic acid and oleic acid concentrations in tree nut kernels

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Gama, T., Farrar, M. B., Tootoonchy, M., Wallace, H. M., Trueman, S. J., Tahmasbian, I. and Hosseini Bai, S. (2024) Hyperspectral imaging predicts free fatty acid levels, peroxide values, and linoleic acid and oleic acid concentrations in tree nut kernels. LWT . p. 116068. ISSN 0023-6438

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Article Link: https://doi.org/10.1016/j.lwt.2024.116068

Publisher URL: https://www.sciencedirect.com/science/article/pii/S0023643824003475

Abstract

Imaging technologies are advancing rapidly in food processing systems to reduce food waste. However, modelling techniques, sample sizes and dataset proportioning methods significantly affect the performance of models in predicting food quality variables. This study examined the potential of hyperspectral imaging to predict peroxide values (PV), free fatty acid (FFA) levels and fatty acid concentrations, including oleic and linoleic acid, in two tree nuts, canarium and macadamia. The effectiveness of artificial neural network (ANN) regression and partial least squares regression (PLSR) techniques to make these predictions was examined. Additionally, the importance of the dataset size on prediction accuracy and the dataset-proportioning method for developing predictive models were assessed. Both ANN and PLSR models predicted FFA levels and oleic acid concentrations with high accuracy, but PV and linoleic acid concentrations were predicted poorly. Changing the test-dataset proportioning method for the small dataset led to comparable R2test values by both ANN and PLSR in predicting FFA levels. Successful prediction of FFA levels could be explained partly by high variability, even in the dataset with the small number of samples. The study highlights the significance of factors like dataset size, test-dataset proportioning method, and model selection when predicting the quality attributes.

Item Type:Article
Corporate Creators:Department of Agriculture and Fisheries, Queensland
Business groups:Animal Science
Keywords:Hyperspectral Non-destructive Nuts
Subjects:Agriculture > Agriculture (General) > Agricultural chemistry. Agricultural chemicals
Plant culture > Tree crops
Plant culture > Fruit and fruit culture > Nuts
Technology > Technology (General) > Spectroscopy
Live Archive:15 Apr 2024 03:37
Last Modified:15 Apr 2024 03:37

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