Handheld spectroscopy for dairy quality control: towards a real time milk spoilage classification model

James Gillespie*, Jordan Vincent, Omar Dib, Matthias Heiden, Joan Condell

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In response to efforts to reduce food waste, some leading UK supermarkets have removed expiration dates from a variety of dairy products, such as milk, encouraging consumers to rely on the subjective ‘sniff test’ for assessing freshness. While this approach aims to extend the shelf life of dairy items, it inadvertently creates uncertainty among consumers about the freshness of their products in the absence of quantifiable information from producers. To address this issue, this work explores a proof of principle experiment leveraging machine learning techniques on spectral data recorded by the consumer-friendly FreshDetect BFD-100 3D Fluorescence spectrometer to perform quantitative, on-the-fly assessment of milk quality. A spectral dataset of twelve milk samples deteriorating over 48 h was collected and correlated with an increase in acidity, an indicator of milk spoilage. Seven classification algorithms were tested to classify the milk samples as fresh or spoiled based on a defined pH cut-off. Among these, a Naïve Bayes classifier was the best performing model, achieving an average accuracy of 92.2% using 10-fold cross-validation for training and 95.4% on an independent test set. These findings highlight the potential of using machine learning and spectral data to provide consumers with reliable, real-time assessments of milk freshness.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024)
EditorsJosé Bravo, Chris Nugent, Ian Cleland
PublisherSpringer Science and Business Media Deutschland GmbH
Pages829-840
Number of pages12
ISBN (Electronic)9783031775710
ISBN (Print)9783031775703
DOIs
Publication statusPublished - 21 Dec 2024
Externally publishedYes
Event16th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2024 - Belfast, United Kingdom
Duration: 27 Nov 202429 Nov 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1212 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference16th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2024
Country/TerritoryUnited Kingdom
CityBelfast
Period27/11/202429/11/2024

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • Classification
  • Fluorescence spectroscopy
  • Handheld spectroscopy
  • Machine Learning
  • Milk spoilage

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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