Publications by authors named "Zunongjiang Abula"
There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration.
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- The study addresses the issue of milk adulteration, where higher priced milks like buffalo, goat, and camel are mixed with cheaper cow milk or water, impacting consumer health and market dynamics.
- Using mid-infrared spectroscopy (MIRS) along with advanced statistical machine learning techniques, particularly support vector machines (SVM), the study successfully detected and quantified the levels of cow milk and water adulteration in various milk types.
- The research found that modern machine learning methods significantly outperformed traditional techniques like partial least squares (PLS), achieving high accuracy rates in classification and regression models for identifying and quantifying milk adulteration.
Microsatellite markers, also known as short tandem repeats (STRs), are important for marker-assisted selection to detect genetic polymorphism, and they are uniformly distributed in eukaryotic genomes. To analyze the relationship between microsatellite loci and lactation traits of Holstein cows in Xinjiang, 175 lactating cows with similar birth dates, the same parity, and similar calving dates were selected, and 10 STR loci closely linked to quantitative trait loci were used to analyze the correlation between each STR locus and four lactation traits (daily milk yield, milk fat percentage, milk protein percentage, and lactose percentage). All loci showed different degrees of genetic polymorphism.
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