# Harnessing Near-Infrared Spectroscopy and Machine Learning for Traceable Classification of Hanwoo and Holstein Beef

arXiv: https://arxiv.org/abs/2507.02903 · HTML: https://mlsanity.com/paper/2507.02903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Scalable Bayesian Optimization Using Deep Neural Networks](https://mlsanity.com/paper/1502.05700/) (2015)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [A Comparative Analysis of XGBoost](https://mlsanity.com/paper/1911.01914/) (2019)
- [The Shape of Learning Curves: a Review](https://mlsanity.com/paper/2103.10948/) (2021)
- [Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise](https://mlsanity.com/paper/2004.03406/) (2020)
- [SPlit: An Optimal Method for Data Splitting](https://mlsanity.com/paper/2012.10945/) (2020)
- [Is K-fold cross validation the best model selection method for Machine Learning?](https://mlsanity.com/paper/2401.16407/) (2024)
- [The Roles of Supervised Machine Learning in Systems Neuroscience](https://mlsanity.com/paper/1805.08239/) (2018)
- [Comparison of Machine Learning Models in Food Authentication Studies](https://mlsanity.com/paper/1905.07302/) (2019)

## Abstract (arXiv)

This study evaluates the use of Near-Infrared spectroscopy (NIRS) combined with advanced machine learning (ML) techniques to differentiate Hanwoo beef (HNB) and Holstein beef (HLB) to address food authenticity, mislabeling, and adulteration. Rapid and non-invasive spectral data were attained by a portable NIRS, recording absorbance data within the wavelength range of 700 to 1100 nm. A total of 40 Longissimus lumborum samples, evenly split between HNB and HLB, were obtained from a local hypermarket. Data analysis using Principal Component Analysis (PCA) demonstrated distinct spectral patterns associated with chemical changes, clearly separating the two beef varieties and accounting for 93.72% of the total variance. ML models, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest, Gradient Boosting (GB), K-Nearest Neighbors, Decision Tree (DT), Naive Bayes (NB), and Neural Networks (NN), were implemented, optimized through hyperparameter tuning, and validated by 5-fold cross-validation techniques to enhance model robustness and prevent overfitting. Random Forest provided the highest predictive accuracy with a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) of 0.8826, closely followed by the SVM model at 0.8747. Furthermore, GB and NN algorithms exhibited satisfactory performances, with cross-validation scores of 0.752. Notably, the NN model achieved the highest recall rate of 0.7804, highlighting its suitability in scenarios requiring heightened sensitivity. DT and NB exhibited comparatively lower predictive performance. The LR and SVM models emerged as optimal choices by effectively balancing high accuracy, precision, and recall. This study confirms that integrating NIRS with ML techniques offers a powerful and reliable method for meat authenticity, significantly contributing to detecting food fraud.