The development of real-time digital PCR technology using an improved data classification method

2021 ◽  
pp. 113873
Author(s):  
Jia Yao ◽  
Yuanyuan Luo ◽  
Zhiqi Zhang ◽  
Jinze Li ◽  
Chuanyu Li ◽  
...  
Author(s):  
Christian Schulze ◽  
Anne-Catrin Geuthner ◽  
Dietrich Mäde

AbstractFood 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.


Food Control ◽  
2019 ◽  
Vol 98 ◽  
pp. 380-388 ◽  
Author(s):  
Xiaofu Wang ◽  
Ting Tang ◽  
Qingmei Miao ◽  
Shilong Xie ◽  
Xiaoyun Chen ◽  
...  

2013 ◽  
Vol 443 ◽  
pp. 741-745
Author(s):  
Hu Li ◽  
Peng Zou ◽  
Wei Hong Han ◽  
Rong Ze Xia

Many real world data is imbalanced, i.e. one category contains significantly more samples than other categories. Traditional classification methods take different categories equally and are often ineffective. Based on the comprehensive analysis of existing researches, we propose a new imbalanced data classification method based on clustering. The method clusters both majority class and minority class at first. Then, clustered minority class will be over-sampled by SMOTE while clustered majority class be under-sampled randomly. Through clustering, the proposed method can avoid the loss of useful information while resampling. Experiments on several UCI datasets show that the proposed method can effectively improve the classification results on imbalanced data.


2021 ◽  
Vol 32 ◽  
pp. S1358
Author(s):  
I.M. Lambrescu ◽  
V.S. Ionescu ◽  
G. Gaina ◽  
A. Popa ◽  
C. Niculite ◽  
...  

Plant Disease ◽  
2021 ◽  
Author(s):  
Li Wang ◽  
Tian Qian ◽  
Pei Zhou ◽  
Wenjun Zhao ◽  
Xianchao Sun

Clavibacter michiganensis subsp. michiganensis (Cmm), the cause of bacterial canker disease, is one of the most destructive pathogens in greenhouse and field tomato. The pathogen is now present in all main production areas of tomato and is quite widely distributed in the EPPO(European and Mediterranean Plant Protection Organization)region. The inspection and quarantine of the plant pathogens relies heavily on accurate detection tools. Primers and probes reported in previous studies do not distinguish the Cmm pathogen from other closely related subspecies of C. michiganensis, especially the non-pathogenic subspecies that were identified from tomato seeds recently. Here, we have developed a droplet digital polymerase chain reaction (ddPCR) method for the identification of this specific bacterium with primers/TaqMan probe set designed based on the pat-1 gene of Cmm. This new primers/probe set has been evaluated by qPCRthe real time PCR(qPCR) and ddPCR. The detection results suggest that the ddPCR method established in this study was highly specific for the target strains. The result showed the positive amplification for all 5 Cmm strains,and no amplification was observed for the other 43 tested bacteria, including the closely related C. michiganensis strains. The detection threshold of ddPCR was 10.8 CFU/mL for both pure Cmm cell suspensions and infected tomato seed, which was 100 times-fold more sensitive than that of the real-time PCR (qPCR ) performed using the same primers and probe. The data obtained suggest that our established ddPCR could detect Cmm even with low bacteria load, which could facilitate both Cmm inspection for pathogen quarantine and the routine pathogen detection for disease control of black canker in tomato.


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