Feature engineering plays a critical role in handling hyperspectral data and is essential for identifying key wavelengths in food fraud detection. This study employs Bayesian Additive Regression Trees (BART), a flexible machine learning approach, to discriminate and classify samples of olive oil based on their level of purity. Leveraging its built-in variable selection mechanism, we employ BART to effectively identify the most representative spectral features and to capture the complex interactions among variables. We use network representation to illustrate our findings, highlighting the competitiveness of our proposed methodology. Results demonstrate that when principal component analysis is used for dimensionality reduction, BART outperforms state-of-the-art models, achieving a classification accuracy of 96.8\% under default settings, which further improves to 97.2\% after hyperparameter tuning. If we leverage a variable selection procedure within BART, the model achieves perfect classification performance on this dataset, improving upon previous optimal results both in terms of accuracy and interpretability. Our results demonstrate that three key wavelengths, 1160.71 nm, 1328.57 nm, and 1389.29 nm, play a central role in discriminating the olive oil samples, thus highlighting an application of our methodology in the context of food quality. Further analysis reveals that these variables do not function independently but rather interact synergistically to achieve accurate classification, and improved detection speed.

翻译：特征工程在处理高光谱数据中起着关键作用，对于识别食品欺诈检测中的关键波长至关重要。本研究采用贝叶斯加性回归树这一灵活的机器学习方法，根据橄榄油的纯度水平对样本进行区分和分类。利用其内置的变量选择机制，我们运用BART有效识别最具代表性的光谱特征，并捕捉变量间复杂的交互作用。我们采用网络表示法展示研究结果，突显了所提出方法的竞争力。结果表明，当使用主成分分析进行降维时，BART优于现有最先进模型，在默认设置下达到96.8%的分类准确率，经过超参数调优后进一步提升至97.2%。若在BART中采用变量选择程序，模型在该数据集上实现了完美的分类性能，在准确率和可解释性方面均超越了先前的最优结果。我们的研究证实，1160.71 nm、1328.57 nm和1389.29 nm这三个关键波长在区分橄榄油样本中发挥核心作用，从而凸显了该方法在食品质量背景下的应用价值。进一步分析表明，这些变量并非独立发挥作用，而是通过协同交互实现精确分类并提升检测速度。