## 4 Discussion  
In this study, the effectiveness of an artificial neural network (ANN)-based model for detecting starch adulteration was evaluated. The findings indicate that the model is capable of distinguishing milk samples with different starch concentrations with high accuracy. However, to better understand the model's performance and enhance its general applicability, it is crucial to compare it with other detection methods reported in the literature. While this model has been tested primarily on cow's milk, it can potentially be adapted for use with other types of milk, provided that adjustments are made to accommodate the specific spectral characteristics of each milk type [27]. Previous studies have demonstrated that techniques like FTIR and NIR spectroscopy, which are integral to our method, can be effectively applied across different dairy matrices with minimal adjustments, ensuring the method's broader applicability [28]. Spectroscopic analysis methods are widely used for detecting starch adulteration in dairy products [29]. These methods, while effective, often require significant sample preprocessing and are constrained by the high cost of the necessary equipment. Our approach using mid-infrared spectroscopy coupled with an ANN allows for the rapid and quantitative detection of starch without the need for extensive sample preparation [25]. Techniques such as Fourier transform infrared spectroscopy (FTIR) and near-infrared spectroscopy (NIR) enable non-invasive analysis of milk components and can detect the presence of additives like starch [30]. FTIR, in particular, allows for the rapid identification of components such as starch, sugars, and melamine in milk powders [10]. However, these methods have certain limitations, including the need for sample preprocessing and the requirement for expensive equipment [17]. The ANN-based model developed in our study offers a more portable and economical solution compared to spectroscopic methods like FTIR and NIR [18]. Moreover, the environmental impact of our method is minimal, making it a 'greener' alternative for milk adulteration detection [18]. The model's ability to successfully detect starch adulteration in raw milk samples, along with its capacity for real-time analysis in field conditions, represents a significant advantage. However, considering the level of detail and high accuracy rates provided by spectroscopic methods, it can be argued that these techniques perform better in laboratory settings. Therefore, the ANN-based model and spectroscopic methods could be used complementarily, depending on the specific needs of the application. Chemical tests have long been widely used methods for detecting additives such as starch in dairy products. Specifically, the iodine test using [PERSON]'s solution offers a simple and rapid method for identifying the presence of starch [7]. However, the main disadvantage of these tests is that they can only detect the presence of starch without providing information about the quantity or detecting other types of adulteration. Additionally, chemical tests are typically conducted in laboratory settings, making them unsuitable for field use. The ANN-based model overcomes these limitations of chemical tests by not only detecting the presence of starch but also determining the concentration of starch adulteration at different levels. By utilizing spectral data, the model provides a more quantitative analysis and offers users more detailed information. Furthermore, due to its portability, the model can be used in real-time in the field, unlike chemical tests. This demonstrates the effectiveness of the model in practical applications. In recent years, radiographic methods such as X-ray and Raman spectroscopy have been employed for the detection of adulteration in dairy products. Raman spectroscopy stands out for its ability to detect chemical changes in milk with high accuracy [9]. However, the high cost of equipment and the complexity of analysis processes make these methods impractical for widespread and field use.