Department of Electrical Engineering, Sao Paulo State University, Bauru 17033-360, Brazil.
Department of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago de Chile 8940000, Chile.
Sensors (Basel). 2021 Mar 17;21(6):2101. doi: 10.3390/s21062101.
Milk is an important dietary requirement for many populations due to its high nutritional value. However, increased demand has also made it prone to fraudulent activity. In this sense, scientists have sought to develop simple, low-cost, and portable techniques to achieve quality control of milk in industry and farms as well. This work proposes a new instrumentation system based on acoustic propagation and advanced signal processing techniques to identify milk adulteration by industrial contaminants. A pair of transmitter-receiver low-cost piezoelectric transducers, configured in a pitch-catch mode, propagated acoustic waves in the bovine milk samples contaminated with 0.5% of sodium bicarbonate, urea, and hydrogen peroxide. Signal processing approaches such as chromatic technique and statistical indexes like the correlation coefficient, Euclidian norm and cross-correlation square difference were applied to identify the contaminants. According to the presented results, CCSD and RMSD metrics presented more effectiveness to perform the identification of milk contaminants. However, CCSD was 2.28 × 10 more sensitivity to distinguish adulteration in relation to RMSD. For chromatic clustering technique, the major selectivity was observed between the contamination performed by sodium bicarbonate and urea. Therefore, results indicate that the proposed approach can be an effective and quick alternative to assess the milk condition and classify its contaminants.
牛奶因其高营养价值而成为许多人群的重要饮食需求。然而，需求的增加也使其容易受到欺诈活动的影响。从这个意义上说，科学家们一直在寻求开发简单、低成本和便携式的技术，以实现工业和农场的牛奶质量控制。这项工作提出了一种基于声波传播和先进信号处理技术的新仪器系统，用于识别工业污染物对牛奶的掺假。一对低成本的压电换能器收发器，采用收发模式配置，在被 0.5%的碳酸氢钠、尿素和过氧化氢污染的牛牛奶样品中传播声波。应用了色度技术和相关系数、欧几里得范数和互相关平方差等统计指标等信号处理方法来识别污染物。根据所提出的结果，CCSD 和 RMSD 指标在识别牛奶污染物方面表现出更高的有效性。然而，CCSD 在区分掺假方面的灵敏度比 RMSD 高 2.28×10。对于色度聚类技术，在碳酸氢钠和尿素引起的污染之间观察到了最大的选择性。因此，结果表明，所提出的方法可以成为评估牛奶状况和分类其污染物的有效和快速替代方法。
