## 1 Introduction
Starch can mask the effects of diluting and partially removing natural macromolecules in milk (such as fats), making it an advantageous adulterant for economic gain. Accurately detecting and preventing starch adulteration in milk is vital for upholding global health and nutrition standards. Various methods are available for determining the concentration of starch in milk [16], yet most of these methods either provide qualitative results or require the use of sensitive reagents, limiting their application. Most traditional methods necessitate the transportation of samples to a laboratory, which is time-consuming and costly. Additionally, samples can deteriorate during this process, potentially affecting the results. Recently, portable and cost-effective devices have been developed to determine starch concentrations in milk [17]. These devices are notable for their user-friendliness and ability to produce rapid results. However, these devices have limitations; some are manually operated or require specific types of hardware [18], and the necessary software for data collection and analysis in field conditions is not always available. Therefore, there is a continual need for new methods that can quickly, accurately, and cost-effectively determine starch concentrations in milk. Recent research has identified several potential approaches for achieving this goal [6]. For example, one study focused on developing cost-effective portable devices for starch detection in milk [8]. Another study emphasized the importance of field-ready solutions that can provide accurate results without requiring laboratory-based equipment [2]. This study aims to fill this gap by proposing the use of multispectral sensor technology to detect starch concentrations in milk accurately and efficiently. Mid and near-infrared spectroscopy techniques are commonly used for analyzing milk and its components [19], particularly effective in determining the main components in milk powder (carbohydrates, lipids, and proteins) as well as quantifying whey, starch, and sucrose content. The enhancement of these techniques with quantitative analysis and artificial intelligence techniques for assessing the quality and authenticity of dairy products is noteworthy. This study explores the use of artificial neural networks (ANNs) and multispectral sensors for detecting starch adulteration in dairy products, offering a more advanced approach than current methods. In the current work, a developed MSANNdevice, utilizing multispectral sensor technology and artificial neural networks, enables rapid and portable detection of starch adulteration. This represents a significant advantage over laboratory-based methods, as it allows for swift analyses under field conditions, introducing a major innovation in food safety inspections. The model developed can detect various concentrations of starch with high accuracy, enhancing its generalization capability to a level of precision and generalization not achieved in prior studies. This study demonstrates that the ANN-based model can successfully differentiate between different levels of starch, thereby yielding more reliable results. The cost of the devices and sensors used is considerably lower compared to traditional laboratory equipment, providing a significant cost advantage in large-scale applications. Particularly in developing countries, such low-cost solutions can be extensively utilized in food safety inspections. This study offers an innovative approach to data analysis using artificial intelligence and machine learning techniques, beyond traditional methods. Especially, the techniques employed for data preprocessing and model optimization enhance the model's performance and provide a higher accuracy rate compared to previous studies. In conclusion, this study presents a portable, rapid, cost-effective, and highly accurate solution for detecting starch adulteration in dairy products. These features significantly contribute to the existing literature and have the potential to set a new standard in the field of food safety.