Department of Physical and Analytical Chemistry, Voronezh State University of Engineering Technologies, Revolution Avenue 19, 394000 Voronezh, Russia.
Department of Technology of Animal Products, Voronezh State University of Engineering Technologies, Revolution Avenue 19, 394036 Voronezh, Russia.
Sensors (Basel). 2024 Jun 4;24(11):3634. doi: 10.3390/s24113634.
Milk and dairy products are included in the list of the Food Security Doctrine and are of paramount importance in the diet of the human population. At the same time, the presence of many macro- and microcomponents in milk, as available sources of carbon and energy, as well as the high activity of water, cause the rapid development of native and pathogen microorganisms in it. The goal of the work was to assess the possibility of using an array of gas chemical sensors based on piezoquartz microbalances with polycomposite coatings to assess the microbiological indicators of milk quality and to compare the microflora of milk samples. Piezosensors with polycomposite coatings with high sensitivity to volatile compounds were obtained. The gas phase of raw milk was analyzed using the sensors; in parallel, the physicochemical and microbiological parameters were determined for these samples, and species identification of the microorganisms was carried out for the isolated microorganisms in milk. The most informative output data of the sensor array for the assessment of microbiological indicators were established. Regression models were constructed to predict the quantity of microorganisms in milk samples based on the informative sensors' data with an error of no more than 17%. The limit of determination of QMAFAnM in milk was 243 ± 174 CFU/cm. Ways to improve the accuracy and specificity of the determination of microorganisms in milk samples were proposed.
奶类及其制品被列入《食品安全学说》，在人类饮食中具有至关重要的地位。与此同时，奶类中存在许多宏量和微量成分，作为碳和能源的可用来源，以及高水活性，导致其中的天然和病原体微生物迅速生长。本工作的目的是评估基于压电石英微天平的多复合涂层气体化学传感器阵列评估牛奶质量微生物指标的可能性，并比较牛奶样品的微生物菌群。获得了对挥发性化合物具有高灵敏度的多复合涂层压电传感器。使用传感器分析生牛乳的气相，同时为这些样品确定理化和微生物参数，并对牛奶中分离的微生物进行物种鉴定。为评估微生物指标，确定了传感器阵列最具信息量的输出数据。基于信息传感器的数据，构建了用于预测牛奶样品中微生物数量的回归模型，误差不超过 17%。在牛奶中定量测定 QMAFAnM 的检出限为 243±174 CFU/cm。提出了提高牛奶样品中微生物测定的准确性和特异性的方法。
