Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

NIRS Methodology for Quantifying Dry Matter Variability in Hass Avocado Fruit

Version 1 : Received: 31 March 2023 / Approved: 3 April 2023 / Online: 3 April 2023 (10:22:00 CEST)

How to cite: Rodríguez, P.; Villamizar, J.; Londoño, L.; Tran, T.; Davrieux, F. NIRS Methodology for Quantifying Dry Matter Variability in Hass Avocado Fruit. Preprints 2023, 2023040021. https://doi.org/10.20944/preprints202304.0021.v1 Rodríguez, P.; Villamizar, J.; Londoño, L.; Tran, T.; Davrieux, F. NIRS Methodology for Quantifying Dry Matter Variability in Hass Avocado Fruit. Preprints 2023, 2023040021. https://doi.org/10.20944/preprints202304.0021.v1

Abstract

Knowing, with reasonable accuracy, the dry matter (DM) content of Hass avocado fruit will help determine when the fruit must be harvested. The reliability of predictive models based on near infrared spectra for DM quantification depends on the ability of the spectra to be representative of the DM gradient within a whole fruit. The aim of this work was to develop a methodology to determine the optimum number of spectra to develop a robust model for DM content quantification. Three spectra were recorded for each zone of the intact fruit: peduncle, equator, and base. Each scanning point was sampled, and the DM content was determined using oven drying. Two-way ANOVA confirmed the DM gradient within the whole fruit. This gradient was observed within spectra using the RMS (root mean square) criterion and PCA. The PLS models showed that at least one spectrum per zone could be enough to construct an efficient and robust model for dry matter quantification.

Keywords

Hass avocado dry matter gradient; near infrared spectroscopy; Hass avocado harvest; fruit quality; multivariate data analysis; root mean squares

Subject

Chemistry and Materials Science, Food Chemistry

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