Validation and application of a quantitative real-time PCR assay to detect common wheat adulteration of durum wheat for pasta production

Carloni, Elisa; Amagliani, Giulia; Omiccioli, Enrica; Ceppetelli, Veronica; Mastro, Michele Del; Rotundo, Luca; Brandi, Giorgio; Magnani, Mauro

doi:10.1016/j.foodchem.2016.12.053

Cited by 20 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Enzyme-linked immunosorbent assay (ELISA) [9], chromatographic methods [9][10][11][12][13], near-infrared spectroscopy (NIRS) [14,15] and real-time PCR [16][17][18][19][20][21][22] and other molecular techniques [23] were used for the specific detection of cereal species in food. Compared to other analytical parameters, such as proteins and fats, DNA is sufficiently stable, depending on the degree of processing, to be detected even in more processed foods [24].…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a method for quantification of common wheat, durum wheat, rye and barley by droplet digital PCR

Schulze

Geuthner

Mäde

2021

Eur Food Res Technol

View full text Add to dashboard Cite

Food fraud is becoming a prominent topic in the food industry. Thus, valid methods for detecting potential adulterations are necessary to identify instances of food fraud in cereal products, a significant component of human diet. In this work, primer–probe systems for real-time PCR and droplet digital PCR (ddPCR) for the detection of these cereal species: bread wheat (together with spelt), durum wheat, rye and barley for real-time PCR and ddPCR were established, optimized and validated. In addition, it was projected to validate a molecular system for differentiation of bread wheat and spelt; however, attempts for molecular differentiation between common wheat and spelt based on the gene GAG56D failed because of the genetic variability of the molecular target. Primer–probe systems were further developed and optimized on the basis of alignments of DNA sequences, as well as already developed PCR systems. The specificity of each system was demonstrated on 10 (spelt), 11 (durum wheat and rye) and 12 (bread wheat) reference samples. Specificity of the barley system was already proved in previous work. The calculated limits of detection (LOD95%) were between 2.43 and 4.07 single genome copies in real-time PCR. Based on the “three droplet rule”, the LOD95% in ddPCR was calculated to be 9.07–13.26 single genome copies. The systems were tested in mixtures of flours (rye and common wheat) and of semolina (durum and common wheat). The methods proved to be robust with regard to the tested conditions in the ddPCR. The developed primer–probe systems for ddPCR proved to be effective in quantitatively detecting the investigated cereal species rye and common wheat in mixtures by taking into account the haploid genome weight and the degree of milling of a flour. This method can correctly detect proportions of 50%, 60% and 90% wholemeal rye flour in a mixture of wholemeal common wheat flour. Quantitative results depend on the DNA content, on ploidy of cereal species and are also influenced by comminution. Hence, the proportion of less processed rye is overestimated in higher processed bread wheat and adulteration of durum wheat by common wheat by 1–5% resulted in underestimation of common wheat.

show abstract

Section: Introductionmentioning

confidence: 99%

Development and validation of a method for quantification of common wheat, durum wheat, rye and barley by droplet digital PCR

Schulze

Geuthner

Mäde

2021

Eur Food Res Technol

View full text Add to dashboard Cite

show abstract

“…Some of them are based on the detection of proteins like albumins, gliadin or friabilin by electrophoretic, immunological or chromatographic assays (Knödler, Most, Schieber, & Carle, 2010;Pasqualone, 2011). Another category of methods are based on DNA analysis that utilize the polymerase chain reaction (PCR) or real time PCR to quantify common wheat adulteration (Alary, Serin, Duviau, Joudrier, & Gautier, 2002;Carloni et al, 2017;Casazza et al, 2012;Ibrahim, Al-Hmoud, Al-Rousan, & Hayek, 2011;Pasqualone, 2011).…”

Section: Introductionmentioning

confidence: 99%

Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study

Girolamo

Arroyo

Cervellieri

et al. 2020

LWT

View full text Add to dashboard Cite

“…Carloni et al [3] used multiplex real-time PCR based on a dual-labeled probe strategy to detect normal wheat in durum wheat. Allmann [4] and Garcia et al [5] used manual detection methods for performing quantitative analysis of wheat in real-time.…”

Section: Introductionmentioning

confidence: 99%

Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging Technology

Jiang

Wang

et al. 2022

Agronomy

View full text Add to dashboard Cite

This paper offers a prospective solution to the poor quality and less prominent features of the original terahertz spectral images of unsound wheat grains caused due to the imaging system and background noise. In this paper, a CBDNet-V terahertz spectral image enhancement model is proposed. Compared with the traditional algorithms, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the obtained enhanced images using the proposed model show performance improvement. As validated by the ResNet-50 classification network, the proposed model processes images with an accuracy of 94.8%, and the recognition accuracy is improved by 3.7% and 1.9%, respectively, compared to the images with only denoising and feature extraction. The experimental results indicate that the deep learning-based terahertz spectral image technology for unsound wheat kernels has good prospects in the identification of unsound wheat kernels.

show abstract

Validation and application of a quantitative real-time PCR assay to detect common wheat adulteration of durum wheat for pasta production

Cited by 20 publications

References 30 publications

Development and validation of a method for quantification of common wheat, durum wheat, rye and barley by droplet digital PCR

Development and validation of a method for quantification of common wheat, durum wheat, rye and barley by droplet digital PCR

Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study

Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging Technology

Contact Info

Product

Resources

About