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A novel constrained optimization-based parameter-free model updating strategy for enhancing fruit quality evaluation

Penghui Liu1, Yingjie Zheng1, Hao Tian2

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, PR China; Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture, Hangzhou 310058, PR China; The National Key Laboratory of Agricultural Equipment Technology, Beijing 100083, PR China.

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Summary

This study introduces a new calibration method, modified semi-supervised parameter-free calibration enhancement (MSS-PFCE), to significantly improve fruit quality assessment. The approach enhances prediction accuracy with minimal new data, ensuring reliable on-site applications.

Keywords:
Biological variabilityConstrained optimizationFruit quality assessmentMSS-PFCEModel updating

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Calibration models degrade over time due to variations in samples and measurement conditions.
  • Accurate fruit quality assessment is crucial for agricultural applications and requires robust calibration techniques.

Purpose of the Study:

  • To introduce and evaluate a modified semi-supervised parameter-free calibration enhancement (MSS-PFCE) method.
  • To improve the accuracy and reliability of fruit quality assessment models across diverse biological variability.
  • To assess the performance of MSS-PFCE against existing calibration methods using limited data.

Main Methods:

  • Developed a modified semi-supervised parameter-free calibration enhancement (MSS-PFCE) approach.
  • Tested MSS-PFCE on six diverse fruit datasets (varying seasons, origins, cultivars).
  • Compared MSS-PFCE with four benchmark methods: SS-PFCE, global model, recalibration, and slope/bias correction (SBC).

Main Results:

  • MSS-PFCE significantly reduced Root Mean Square Error of Prediction (RMSEP) for slave spectra by over 50.63%, 92.66%, and 76.47%.
  • The method outperformed all benchmark methods.
  • Model updating was effective using only 5% of slave samples, demonstrating low sample dependence.
  • Maintained robust reliability and stable fitting across varying sample proportions and cost thresholds.

Conclusions:

  • MSS-PFCE offers a highly accurate and scalable solution for on-site fruit quality assessment.
  • The technique requires minimal data for model updating, reducing costs and effort.
  • This novel updating technique enhances prediction performance and model stability in real-world agricultural settings.