4.7 Article

Precise Authenticity of Quinoa, Coix Seed, Wild Rice and Chickpea Components Using Optimized TaqMan Real-Time PCR

Journal

FOODS
Volume 12, Issue 4, Pages -

Publisher

MDPI
DOI: 10.3390/foods12040852

Keywords

food authenticity; quinoa; coix seed; wild rice; TaqMan real-time quantitative polymerase chain reaction

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In this study, a real-time quantitative polymerase chain reaction (qPCR) method was developed for rapid detection of quinoa, coix seed, wild rice, and chickpea in food to authenticate the presence of these components. The qPCR method specifically identified the four source components and had a detection limit of 0.96, 1.14, 1.04, and 0.97 pg/μL for quinoa, coix seed, wild rice, and chickpea, respectively. The method was applicable to different food matrices and could be used for authenticity verification in deeply processed food.
Functional food such as, quinoa, coix seed, wild rice and chickpea have experienced rapidly increasing demand globally and exhibit high economic values. Nevertheless, a method for rapid yet accurate detection of these source components is absent, making it difficult to identify commercially available food with labels indicating the presence of relevant components. In this study, we constructed a real-time quantitative polymerase chain reaction (qPCR) method for rapid detection of quinoa, coix seed, wild rice and chickpea in food to identify the authenticity of such food. Specific primers and probes were designed with 2S albumin genes of quinoa, SAD genes of coix seed, ITS genes of wild rice and CIA-2 genes of chickpea as the target genes. The qPCR method could specifically identify the four wild rice strains, yielding, LODs of 0.96, 1.14, 1.04 and 0.97 pg/mu L quinoa, coix seed, wild rice and chickpea source components, respectively. Particularly, the method allowed the identification of the target component with content below 0.01%. A total of 24 commercially available food samples of different types were detected by using the method and the results indicate that the developed method is applicable to the detection of different food matrices, as well as authenticity verification in deeply processed food.

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