@article{CHEN2026, 
author = {Jia CHEN and Linfeng CHEN and Mengyu LI and Shaojia JIANG and Xiao XIAO},
title = {Quantitative Detection of Mold in Fermented Milk Based on Droplet Digital Polymerase Chain Reaction},
year = {2026},
journal = {Journal of Dairy Science and Technology},
volume = {49},
number = {1},
pages = {41-47},
keywords = {fermented milk, mold count, yeast, droplet digital polymerase chain reaction, quantitative detection},
url = {https://www.sciopen.com/article/10.7506/rykxyjs1671-5187-20251104-079},
doi = {10.7506/rykxyjs1671-5187-20251104-079},
abstract = {To address the issue of the long detection cycle and difficult quantitative analysis of molds in fermented milk, this study developed a systematic method for the detection of molds in commercial fermented milk by using droplet digital polymerase chain reaction (ddPCR) to absolutely quantify the copy number of target sequences in samples. The optimal ddPCR reaction system and amplification program were determined. The proposed method performed well in terms of specificity, sensitivity and quantitative accuracy. It specifically amplification target molds (Aspergillus niger, Penicillium citrinum, and Penicillium chrysogenum), with no cross-reactivity with yeasts, lactic acid bacteria, or pathogenic bacteria. Sensitivity tests indicated that at the minimum detectable concentration (0.625 ng/μL), the positive copy number was 9 copies/μL. In addition, this method established linear equations between bacterial suspension concentration and DNA concentration, as well as between DNA concentration and gene copy number, with correlation coefficients greater than 0.99, and based on them, a linear equation between bacterial suspension concentration and gene copy number was developed. The results of the ddPCR method for artificially contaminated samples were consistent with those of the national standard method (GB 4789.15-2016). Moreover, the method enables absolute quantification without the need for a standard curve, effectively shortening the detection cycle.}
}