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Avocado ripeness classification using handheld Raman spectroscopy: addressing data imbalance with machine learning

In-Hwan Lee1, Zhengao Li2, Luyao Ma3

  • 1Department of Food Science and Technology, College of Agricultural Sciences, Oregon State University, Corvallis, OR 97331, USA.

Food Chemistry
|January 27, 2026
PubMed
Summary
This summary is machine-generated.

This study uses machine learning and Raman spectroscopy to non-destructively assess avocado ripeness, helping reduce food waste. The developed method accurately classifies ripeness, offering a practical in-field solution.

Keywords:
Data imbalanceFood qualityFood wasteMachine learningRaman spectroscopyResampling

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Computer Science

Background:

  • Food waste is a significant global issue, exacerbated by inefficient quality assessment methods.
  • Current methods for determining fruit ripeness, such as destructive testing and visual inspection, are often inaccurate and labor-intensive.

Purpose of the Study:

  • To develop a non-destructive, machine learning-assisted method for classifying avocado ripeness using handheld Raman spectroscopy.
  • To provide a rapid and accurate in-field assessment tool to minimize food waste.

Main Methods:

  • Collected 1274 Raman spectra from avocados, correlating them with firmness and internal quality data.
  • Developed and compared machine learning models, including a two-layer one-dimensional convolutional neural network (1D-CNN).
  • Investigated the impact of the synthetic minority oversampling technique on imbalanced datasets.

Main Results:

  • The two-layer 1D-CNN achieved a high performance (ROC-AUC of 0.831) on the original imbalanced dataset.
  • Raman spectra showed characteristic changes related to chlorophyll and anthocyanin levels during avocado ripening.
  • Oversampling techniques improved traditional models (SVM, Random Forest) but the 1D-CNN remained superior on the raw data.

Conclusions:

  • Machine learning-assisted Raman spectroscopy offers a viable non-destructive method for assessing avocado ripeness.
  • This technology has the potential to significantly reduce food waste by enabling accurate, in-field quality control.
  • The study demonstrates the effectiveness of 1D-CNN for ripeness classification, even with imbalanced sample sizes.