Assessing avocado firmness at different dehydration levels in a multi-sensor framework

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Abstract

This study aims to utilize non-destructive sensing based on Vis-NIR spectroscopy and acoustic to predict firmness of avocado fruit. The study has three aims, the first aim was to find the best reference firmness measurement technique for calibrating Vis-NIR spectroscopy data related to avocado ripening i.e., acoustic firmness (AF), limited compression (LC) and penetrometer max force (Fmax). The second aim was to study the generalizability of Vis-NIR models with respect to the dehydration level of avocado fruits. Dehydration of outer skin during storage is common and may cause model failure as the Vis-NIR signal is dominated by signal corresponding to high moisture in fresh fruit. The third aim was to fuse the Vis-NIR spectroscopy and acoustic information to improve the prediction of the LC and Fmax, otherwise unattainable with a single technique. The results showed that the best models for firmness prediction were obtained with LC as the reference. The avocado skin dehydration negatively affected the performance of Vis-NIR models to predict firmness. Further, a fusion of Vis-NIR spectroscopy and acoustic information improved prediction (reduced error by 21%) of firmness in avocado. Assessing avocado firmness in a multi-sensor framework can allow to precisely access the ripeness stage of avocados.

Original languageEnglish
Article number103901
JournalInfrared Physics and Technology
Volume118
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Chemometrics
  • Fruit quality
  • Sensor-fusion
  • Variable selection

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