## 1 Introduction  
Milk is a crucial nutritional source consumed globally, yet the dairy industry faces significant challenges due to prevalent adulteration practices such as the addition of water, starch, and other harmful substances. These adulterations compromise milk's nutritional value and pose health risks to consumers, highlighting a critical gap in current detection methods, which often lack speed, accuracy, and cost-effectiveness. This study specifically addresses the gap by proposing a novel detection method using artificial neural network (ANN) models and multispectral sensor technology to identify starch adulteration in milk. This approach is expected to enhance the rapidity and accuracy of tests compared to traditional laboratory methods. The objective of this research is to develop and validate an ANN model that can reliably detect starch adulteration in milk samples using spectral data analysis. By integrating multispectral sensing and advanced computational models, this study aims to provide a cost-effective and efficient solution to a widespread problem affecting the dairy industry. Milk is a vital nutritional source consumed by a wide population, ranging from infants to the elderly. It boasts a robust nutritional profile, containing high-quality proteins, lipids, carbohydrates, vitamins, amino acids, and minerals [1]. However, the global milk market is prone to adulteration with low-cost, substandard, and potentially harmful components, driven by motives such as increasing volume, concealing inferior products, or manipulating consumer purchasing power [2]. This situation is evolving into a serious issue, posing a significant threat to global food safety, particularly within small rural farms that face greater challenges in maintaining regulatory compliance due to limited resources [3]. Ensuring food safety in the dairy industry, especially on small-scale farms, is critical for protecting consumer health and sustaining the integrity of short food supply chains. With the world population expected to reach 10 billion by 2050 [4], the nutritional richness and widespread consumption of dairy products indicate that the dairy market is becoming increasingly vulnerable to fraudulent activities. Therefore, the detection of milk adulteration is of critical importance for consumer health and food safety [5]. Recent studies support this assertion [6]. Milk adulteration is a global concern, carried out with various additives. The most common forms of adulteration include the addition of water, starch, urea, detergent, melamine, formalin, hydrogen peroxide, and sucrose [7], while the simplest and most prevalent form of adulteration is the addition of water to increase the volume of milk [8]. This form of adulteration has been extensively documented in various studies [9]. Sometimes, water-diluted milk is further adulterated with melamine to increase the nitrogen content [10]. Starch is used to increase the non-fat solid content of milk [11]. Fraudsters continuously devise new methods of adulteration to evade inspections, posing serious challenges for the food industry and safety authorities. Milk-based adulterations hold a significant place among all types of food fraud [12]. Increasing cases of adulteration and contamination in dairy products have become a major concern for food safety [13]. In this context, the detection and prevention of starch adulteration are crucial, as starch is a commonly used adulterant that negatively impacts consumer health. Milk with excessive starch content cannot be fully digested by the digestive system and may lead to severe health problems, such as diarrhea [14]. In diabetics, the accumulation of high levels of starch in the body can lead to fatal outcomes. Milk adulterated with starch not only lacks essential nutrients but also leads to health issues like headaches, visual disturbances, hypertension, and kidney stones [15].