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Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation

机译:振动光谱与阿根廷蜂蜜出处确认的多变量分析方法

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摘要

In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a “year-by-year” validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context.
机译:在目前的工作中,使用阿根廷蜂蜜的出处鉴别作为案例研究,比较三种光谱技术的能力作为蜂蜜认证目的的快速筛选平台。在阿根廷的三个主要蜂蜜产物区收集了多氯蜜醇,超过四个收获季节。每个样品由FT-MIR,NIR和FT-Raman光谱指示。基于受监督的化学计量方法实现的分类性能进行比较光谱平台。此外,尝试了低中级数据融合以增强分类结果。最后,以SIMCA建模的最佳性能解决方案,目的是再现食品认证方案。所有发达的分类模型都完成了“一年逐年”的验证策略,从而进行了合理的评估,他们的长期稳健性并不包括模型过度装备的任何问题。通过FT-MIR提供了良好的分类分数,所有技术都实现了几乎完美的分类。所有数据融合策略都提供了令人满意的结果,其中高水平的方法优于低级数据融合。然而,获得单独对FT-MIR的显着优势。当使用更多的收获季节来实现模型校准时,实现了FT-MIR数据的SIMCA建模,并实现了高敏感的模型和整体预测能力改进(敏感度为86.7%和91.1%的特异性)。本作者获得的结果表明FT-MIR用于指纹识别的蜂蜜认证的主要潜力,并证明可以达到可商购用的精度水平。另一方面,多个振动光谱指纹的组合代表了应从工业背景中的成本/益处的角度仔细评估的选择。

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