With high nutritive value, providing macronutrients (proteins, fat and minerals) and micronutrients (vitamins and trace elements), cow milk is among the recognized contributor to a balanced diet of many populations. Due to the high nutritional composition, a high rate of milk consumption with an increasing demand exists worldwide (Handford et al., 2016). Despite the role of milk in food and nutrition security, the increase in demand has amplified fraudulent activities, subsequently making milk the second most vulnerable product to adulteration (Moore et al., 2012).
Milk adulteration could be dilution with water with the intention to increase economic gain or addition of substances (e.g., Sucrose, sodium chloride, vegetable oil and surfactants) that improve the physicochemical and visual characteristics of milk (Poonia et al., 2017). Besides, the addition of substances that extend the shelf life of milk, such as formaldehyde, hydrogen peroxide and hypochlorite is becoming a serious issue of adulteration in the dairy industry (Das et al., 2016; Handford et al., 2016; Nascimento et al., 2017).
Assessments on the prevalence of milk adulteration in several countries found water as the most frequently added adulterants (Faraz et al., 2013; Kandpal et al., 2012; Shabir Barham, 2014; Soomro et al., 2014). Water is added to grow economic gain by increasing the volume of milk through dilution. However, the addition of water to milk dilutes the constituents in milk and could cause potential public health risk of acute malnutrition (stunting, wasting and underweight) which leads to nutrition-related child mortality (Handford et al., 2016; Park et al., 2013; Shabir Barham, 2014). According to experts, next to educating farmers about the consequences of milk fraud, the need for improved detection is key to address the prevailing risk of fraud in milk (Handford et al., 2016).
Several studies have shown the possibility of determining the presence of water as an adulterant in milk samples using different techniques. Newly developing techniques that are robust, green, simple and cost-effective are gaining increasing importance in food quality monitoring. Digital image-based procedures that use the power of machine learning algorithms are increasingly used to assess adulteration in agro-food products including milk. In recent years, several studies were conducted to develop digital image-based techniques for the determination of adulterants in milk. However, the newly developed techniques lack representative sampling during imaging of milk samples. In addition, indicator chemicals were used to bring the desired classification result before the imaging process (Dos Santos & Pereira-Filho, 2013; Kobek, 2017). This brings limitations in the utilization of those techniques since users of such methods are required to have technical knowledge of the procedure.
Considering the limitation in the existing methods, this paper proposed a clean method based on digital image processing coupled with a machine-learning algorithm to test milk adulteration with water. The proposed technique is fast, robust and doesn’t require sample preparation and use of any chemicals.