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Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits.

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  • 1Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, P61 P302 Fermoy, Ireland.

Foods (Basel, Switzerland)
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Summary

A new local changepoint approach for predicting milk traits from mid-infrared spectra showed improved performance, especially for outlier spectra, outperforming other methods in specific cases. Objective neighbor selection enhanced prediction accuracy compared to fixed neighbor selection.

Keywords:
local changepoint analysismidinfrared spectroscopyneighbours

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Dairy Science

Background:

  • Predicting milk technological traits from mid-infrared (MIR) spectra is crucial for dairy quality control.
  • Global modeling approaches using all available spectral data may be suboptimal for spectra with poor representation.
  • Outlier spectra can significantly impact the accuracy of predictive models.

Purpose of the Study:

  • To develop and evaluate an alternative local changepoint approach for predicting six milk technological traits using MIR spectra.
  • To compare the performance of the local changepoint approach against global partial least square regression (PLSR) and a fixed-neighbor LOCAL approach.
  • To assess the impact of objective neighbor selection on prediction accuracy, particularly for outlier spectra.

Main Methods:

  • A local changepoint approach was developed to predict milk technological traits from MIR spectra.
  • Objective identification of neighboring spectra was performed using Mahalanobis distances between spectral principal components.
  • Partial least square regression (PLSR) was employed, with comparisons made to global PLSR and a fixed-neighbor LOCAL approach.

Main Results:

  • Global PLSR achieved the lowest root mean square error of cross-validation (RMSEV) for five out of six traits.
  • The local changepoint approach outperformed the LOCAL approach for four traits and showed the lowest RMSEV for one trait.
  • When analyzing outlier spectra (top 5% Mahalanobis distance), the local changepoint approach outperformed global PLSR and the LOCAL approach for two and five traits, respectively.

Conclusions:

  • Objective neighbor selection in the local changepoint approach improved prediction performance over fixed neighbor selection.
  • While generally not outperforming global PLSR, the local changepoint approach demonstrated advantages in handling spectral variations and outliers.
  • The local changepoint approach offers a valuable alternative for milk trait prediction from MIR spectra, especially when dealing with heterogeneous spectral datasets.