首页> 外文期刊>Talanta: The International Journal of Pure and Applied Analytical Chemistry >A rapid food chain approach for authenticity screening: The development, validation and transferability of a chemometric model using two handheld near infrared spectroscopy (NIRS) devices
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A rapid food chain approach for authenticity screening: The development, validation and transferability of a chemometric model using two handheld near infrared spectroscopy (NIRS) devices

机译:真实性筛选的快速食品链方法:化学计量模型使用两种手持式近红外光谱(NIRS)器件的开发,验证和转移性

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摘要

This study assesses the application of a handheld, near infrared spectroscopy (NIRS) device, namely the Neo-Spectra Micro, for the determination of oregano authenticity. Utilising a large sample set of oregano (n = 295) and potential adulterants of oregano (n = 109), models were developed and validated using SIMCA 15 software. The models demonstrated excellent predictability for the determination of authentic oregano and adulterant samples. The optimal model resulted in a 93.0% and 97.5% correct prediction for oregano and adulterants, respectively. Different standardisation approaches were assessed to determine model transferability to a second NIRS device. In the case of the second device, the best predictions were achieved with data that had not undergone any spectral standardisation (raw). Subsequently, the optimal model was able to correctly predict 90% of authentic oregano samples and 100% of the adulterant samples on the second device. This study demonstrates the potential of the device to be used as a simple, cost effective, reliable and handheld screening tool for the determination of oregano authenticity, at various stages of the food supply chain. It is believed that such forms of monitoring could be highly beneficial in other areas of food authenticity analysis to help combat the negative economical and health implications of food fraud.
机译:本研究评估了手持式近红外光谱(NIRS)设备,即Neo Spectrum Micro在牛至真伪测定中的应用。利用牛至(n=295)的大样本集和牛至(n=109)的潜在掺假品,使用SIMCA 15软件开发和验证模型。该模型对正宗牛至和掺假样品的测定具有良好的可预测性。优化模型对牛至和掺假品的预测正确率分别为93.0%和97.5%。评估了不同的标准化方法,以确定模型可转移到第二个NIRS设备。在第二台设备的情况下,最好的预测是用没有经过任何光谱标准化(raw)的数据实现的。随后,优化模型能够在第二台设备上正确预测90%的正宗牛至样品和100%的掺假样品。这项研究表明,在食品供应链的各个阶段,该设备有望成为一种简单、经济、可靠的手持筛选工具,用于确定牛至的真实性。人们相信,这种形式的监测在食品真实性分析的其他领域可能非常有益,有助于打击食品欺诈的负面经济和健康影响。

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