McVey Claire, Gordon Una, Haughey Simon A, Elliott Christopher T
ASSET Technology Centre, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, Northern Ireland, UK.
Foods. 2021 Apr 27;10(5):956. doi: 10.3390/foods10050956.
The performance of three near-infrared spectroscopy (NIRS) instruments was compared through the investigation of coriander seed authenticity. The Thermo Fisher iS50 NIRS benchtop instrument, the portable Ocean Insights Flame-NIR and the Consumer Physics handheld SCiO device were assessed in conjunction with chemometric modelling in order to determine their predictive capabilities and use as quantitative tools through regression analysis. Two hundred authentic coriander seed samples and ninety adulterated samples were analysed on each device. Prediction models were developed and validated using SIMCA 15 chemometric software. All instruments correctly predicted 100% of the adulterated samples. The best models resulted in correct predictions of 100%, 98.5% and 95.6% for authentic coriander samples using spectra from the iS50, Flame-NIR and SCiO, respectively. The development of regression models highlighted the limitations of the Flame-NIR and SCiO for quantitative analysis, compared to the iS50. However, the results indicate their use as screening tools for on-site analysis of food, at various stages of the food supply chain.
通过对芫荽籽真实性的研究,比较了三种近红外光谱(NIRS)仪器的性能。对赛默飞世尔科技的iS50 NIRS台式仪器、便携式海洋光学公司的Flame-NIR以及消费者物理公司的手持式SCiO设备,结合化学计量学建模进行了评估,以确定它们的预测能力,并通过回归分析用作定量工具。在每台设备上分析了200个正宗芫荽籽样品和90个掺假样品。使用SIMCA 15化学计量学软件开发并验证了预测模型。所有仪器都正确预测了100%的掺假样品。使用iS50、Flame-NIR和SCiO的光谱,对正宗芫荽籽样品的最佳模型分别得出了100%、98.5%和95.6%的正确预测率。回归模型的建立突出了与iS50相比,Flame-NIR和SCiO在定量分析方面的局限性。然而,结果表明它们可作为食品供应链各个阶段食品现场分析的筛选工具。