Recently, growing consumer awareness of food quality and sustainability has led to a rising demand for effective food authentication methods. Vibrational spectroscopy techniques have emerged as a promising tool for collecting large volumes of data to detect food adulteration. However, spectroscopic data pose significant challenges from a statistical viewpoint, highlighting the need for more sophisticated modeling strategies. To address these challenges, in this work we propose a latent variable model specifically tailored for food adulterant detection, while accommodating the features of spectral data. Our proposal offers greater granularity with respect to existing approaches, since it does not only identify adulterated samples but also estimates the level of adulteration, and detects the spectral regions most affected by the adulterant. Consequently, the methodology offers deeper insights, and could facilitate the development of portable and faster instruments for efficient data collection in food authenticity studies. The method is applied to both synthetic and real honey mid-infrared spectroscopy data, delivering precise estimates of the adulteration level and accurately identifying which portions of the spectra are most impacted by the adulterant.

翻译：近年来，消费者对食品质量和可持续性的日益关注，推动了对有效食品真实性鉴定方法的需求不断增长。振动光谱技术已成为一种有前景的工具，可用于收集大量数据以检测食品掺假。然而，从统计学角度来看，光谱数据带来了显著挑战，凸显了对更复杂建模策略的需求。为应对这些挑战，本研究提出了一种专门针对食品掺假检测设计的潜变量模型，同时适应光谱数据的特性。相较于现有方法，我们的方案提供了更高的粒度，因为它不仅能识别掺假样本，还能估计掺假水平，并检测受掺假物影响最大的光谱区域。因此，该方法提供了更深入的见解，并可能促进便携式和更快速仪器的开发，以支持食品真实性研究中高效的数据收集。该方法应用于合成和真实的蜂蜜中红外光谱数据，能够精确估计掺假水平，并准确识别光谱中受掺假物影响最大的部分。