cff-version: 1.2.0
abstract: "<p>The overall aim of this research is to explore the feasibility of sensing technology to measure non-destructively fruit quality properties on a batch and an individual product level.</p><p><br></p><p>The specific objective for this dataset was to utilize various sensor data inputs for AI models aimed at quantifying the brix (sugar content) and firmness of mangoes. AI models were developed using data from individual sensors, as well as models based on the fusion of all sensor data. The brix and firmness were modeled using a multi-sensor approach, incorporating ultrasound and spectroscopy signals. Refractometer measurements for brix and puncture measurements for firmness served as the reference standards.</p>"
authors:
  - family-names: Mishra
    given-names: P.
    orcid: "https://orcid.org/0000-0001-8895-798x"
  - family-names: Offermans
    given-names: P.
    orcid: "https://orcid.org/0000-0001-5823-270X"
  - family-names: Mensink
    given-names: M.G.J.
    orcid: "https://orcid.org/0009-0002-9948-4481"
  - family-names: van de Zedde
    given-names: H.J.
    orcid: "https://orcid.org/0000-0002-8394-4538"
  - family-names: Chauhan
    given-names: A.
    orcid: "https://orcid.org/0000-0001-9012-0070"
  - family-names: Meesters
    given-names: L.M.J.
    orcid: "https://orcid.org/0000-0002-1917-7119"
title: "Dataset: Sensing Potential in the Food Supply Chain - Mango"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/fb26fd3f-ba3c-4cf0-8926-14768a256933.v1
license: CC BY-SA 4.0
date-released: 2025-03-04