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MLGIG VM 2024 data challenge submission

This repo provides code, slides and results for our winning entry in the VistaMilk 2024 International Data Challenge.

MLGIG Team – Milk Lactose Prediction Data Challenge,4th International Workshop on Spectroscopy and Chemometrics 2024, organised by the VistaMilk SFI Research Centre.

The methods and results will be described in an upcoming paper.

The data can be downloaded here

Citation

If you use this work, please cite as:

@incollection{mlgigvm2024,
  title={MLGIG Team Submission to the International Data Challenge on Spectroscopy and Chemometrics 2024},
  author={Ifrim, Georgiana and Le Nguyen, Thach and Aderinola, Timilehin and Serramazza, Davide},
  year={2024},
  booktitle = {International Workshop on Spectroscopy and Chemometrics 2024},
  publisher={}
}

The dataset used in this data challenge was collected and described in the paper below. 
Please cite as:

@article{CAPONIGRO2023109351,
title = {Single-drop technique for lactose prediction in dry milk on metallic surfaces: Comparison of Raman, FT – NIR, and FT – MIR spectral imaging},
journal = {Food Control},
volume = {144},
pages = {109351},
year = {2023},
issn = {0956-7135},
doi = {https://doi.org/10.1016/j.foodcont.2022.109351},
url = {https://www.sciencedirect.com/science/article/pii/S0956713522005448},
author = {Vicky Caponigro and Federico Marini and Amalia G.M. Scannell and Aoife A. Gowen},
keywords = {Milk, Lactose, Raman, FT, NIR, FT-MIR, Spectral, Hyperspectral, Imaging, Aluminium, Stainless steel, PLS},
abstract = {This study applies the single drop techniques to compare the efficacy of Raman, FT – NIR, and FT-MIR spectral imaging to quantify lactose concentration in dried whole milk on different metallic surfaces. Drying the samples avoids degradation problems such as water evaporation or oil degradation and scattering due to micelles. Spectral imaging techniques minimise sampling issues while also describing the sample spatial variation. The mean spectra of pre-processed images were used to build PLS regression models to predict lactose concentration. Raman, FT – NIR (5600–3730 cm−1), FT–MIR (3533–600 cm−1) models and the model obtained using the fusion of the three ranges were built independently and compared. This study confirms that is possible to quantify lactose rapidly using spectral imaging without adding standard references: the minimum RMSEP = 2.8 mg/mL (R2 = 0.98) was achieved with FT – MIR spectral imaging.}
}