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Infrared spectroscopy for the quality assessment of Habanero chilli: a proof-of-concept study

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Version 2 2024-08-23, 01:59
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journal contribution
posted on 2024-08-23, 01:59 authored by Joel JohnsonJoel Johnson, Janice ManiJanice Mani, Kerry WalshKerry Walsh, Mani NaikerMani Naiker
Habanero chillies (Capsicum chinense cv Habanero) are a popular species of hot chilli in Australia, with production steadily increasing. However, there is limited research on this crop due to its relatively low levels of production at present. Rapid methods of assessing fruit quality could be greatly beneficial both for quality assurance purposes and for use in breeding programs or experimental growing trials. This work investigated the potential of using infrared spectroscopy for the quality assessment of 20 Australian Habanero chilli samples, including the prediction of dry matter content, total phenolic content, ferric reducing antioxidant potential and capsaicin/dihydrocapsaicin content. Near-infrared spectra (908–1676 nm) taken from the fresh fruit showed strong potential for the estimation of dry matter content, with a root mean standard error of cross-validation (RMSECV) of 0.50% FW. A moving-window partial least squares regression model was applied to optimise the spectral window used for dry matter content prediction, with the best-performing window being between 1224 and 1422 nm. However, the near-infrared spectra of fresh fruit could not be used to estimate the total phenolic content or capsaicin/dihydrocapsaicin content of the samples. Mid-infrared spectra (4000 − 400 cm− 1) collected from the dried, powdered material showed slightly more promise for the prediction of total phenolics and the ratio of capsaicin:dihydrocapsaicin, with a R2cv of 0.45 and RMSECV of 0.32 for the latter parameter. Although further refinement is required, this method may be able to detect samples with high/low contents of total phenolics or for the estimation of the capsaicin:dihydrocapsaicin ratio.

Funding

New Staff Grant (RSH/5343) awarded by CQUniversity

History

Volume

17

Issue

2

Start Page

1764

End Page

1774

Number of Pages

10

eISSN

2193-4134

ISSN

2193-4126

Publisher

Springer Science and Business Media LLC

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2022-12-04

Author Research Institute

  • Institute for Future Farming Systems

Era Eligible

  • Yes

Journal

Journal of Food Measurement and Characterization

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