Food Processing Center, Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln NE 68588, USA.
Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
Food Chem. 2022 Mar 30;373(Pt B):131471. doi: 10.1016/j.foodchem.2021.131471. Epub 2021 Oct 26.
Raman spectroscopy is an emerging technique for the rapid detection of oil qualities. But the spectral analysis is time-consuming and low-throughput, which has limited the broad adoption. To address this issue, nine supervised machine learning (ML) algorithms were integrated into a Raman spectroscopy protocol for achieving the rapid analysis. Raman spectra were obtained for ten commercial edible oils from a variety of brands and the resulting spectral dataset was analyzed with supervised ML algorithms and compared against a principal component analysis (PCA) model. A ML-derived model obtained an accuracy of 96.7% in detecting oil type and an adulteration prediction of 0.984 (R). Several ML algorithms also were superior than PCA in classifying edible oils based on fatty acid compositions by gas chromatography, with a faster readout and 100% accuracy. This study provided an exemplar for combining conventional Raman spectroscopy or gas chromatography with ML for the rapid food analysis.
拉曼光谱是一种新兴的快速检测油品质量的技术。但是，光谱分析耗时且通量低，这限制了它的广泛应用。为了解决这个问题，我们将九种有监督的机器学习（ML）算法集成到拉曼光谱协议中，以实现快速分析。我们从各种品牌的十种商业食用油中获得拉曼光谱，并使用有监督的 ML 算法对得到的光谱数据集进行分析，并与主成分分析（PCA）模型进行比较。基于脂肪酸组成的气相色谱法，ML 衍生模型在检测油类和掺假预测方面的准确率分别达到了 96.7%和 0.984（R）。几种 ML 算法在基于脂肪酸组成的食用油分类方面也优于 PCA，其读取速度更快，准确率达到 100%。本研究为将常规拉曼光谱或气相色谱与 ML 结合用于快速食品分析提供了一个范例。
