Arndt Maike, Drees Alissa, Ahlers Christian, Fischer Markus
Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany.
Center for Hybrid Nanostructures (CHyN), Department of Physics, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany.
Foods. 2020 Dec 13;9(12):1860. doi: 10.3390/foods9121860.
The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this study, a method to distinguish between seven geographical origins of walnuts using Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics as a fast, versatile, and easy to handle analytical tool was developed. NIR spectra of 212 ground and afterwards freeze-dried walnut samples, harvested in three consecutive years (2017-2019), were collected. We optimized the data pre-processing by applying and evaluating 50,545 different pre-processing combinations, followed by linear discriminant analysis (LDA) which was confirmed by nested cross-validation. The results show that in the scope of our research minimal pre-processing led to the best results: By applying just multiplicative scatter correction (MSC) and median centering, a classification accuracy of 77.00% ± 1.60% was achieved. Consequently, this complex model can be used to answer economically relevant questions e.g., to distinguish between European and Chinese walnuts. Furthermore, the great influence of the applied pre-processing methods, e.g., the selected wavenumber range, on the achieved classification accuracy is shown which underlines the importance of optimization of the pre-processing strategy.
核桃的价格因其地理来源而异，因此存在掺假的经济诱因。需要一种可靠的分析方法来快速检测可能的误报，从而防止食品欺诈。在本研究中，开发了一种使用傅里叶变换近红外（FT-NIR）光谱结合化学计量学作为快速、通用且易于操作的分析工具来区分核桃七个地理来源的方法。收集了连续三年（2017 - 2019年）收获的212个磨碎并冻干的核桃样品的近红外光谱。我们通过应用和评估50545种不同的预处理组合来优化数据预处理，随后进行线性判别分析（LDA），并通过嵌套交叉验证进行确认。结果表明，在我们的研究范围内，最小程度的预处理产生了最佳结果：仅通过应用乘法散射校正（MSC）和中位数中心化，就实现了77.00% ± 1.60%的分类准确率。因此，这个复杂的模型可用于回答与经济相关的问题，例如区分欧洲核桃和中国核桃。此外，还展示了所应用的预处理方法（如所选波数范围）对所实现的分类准确率的重大影响，这突出了优化预处理策略的重要性。
