## 1 Introduction  
With the advancement of the Food 4.0 framework, the food industry has widely adopted the systematic verification of raw materials, finished products, and manufacturing workflows. This shift reflects a growing emphasis on integrating digital technologies to enable full-spectrum monitoring and validation across the production lifecycle [1, 2]. This approach emphasizes the integration of advanced digital technologies, including the Internet of Things (IoT), artificial intelligence (AI), and diverse sensing systems, into the food industry to address critical challenges such as safety and security [3, 4, 5]. Building effective traceability frameworks and authenticity measures requires a comprehensive and methodical grasp of food-related processes, logistics, and storage conditions throughout the supply chain and value network. Moreover, beyond the core focus on traceability and authenticity, the adoption of analytical processing techniques plays a pivotal role in shaping a forward-thinking, sustainable, and adaptive food manufacturing sector [6]. Food fingerprinting has emerged as a critical approach for verifying authenticity, ensuring consistency, and maintaining the traceability of food products. This technique significantly contributes to fostering transparency and supporting sustainable practices within the food sector, ultimately benefiting consumers [7, 8, 9]. For example, [PERSON]'s group developed high-performance MoO\({}_{3}\) nanosheet gas sensors for triethylamine detection to assess fish freshness [10]. Moreover, machine learning models were applied to classify and estimate non-refrigeration time with different quantities of variables to ensure the quality and safety of hydrobiological species for human consumption [11]. Importantly, the implementation of food safety, security, authentication, and traceability systems requires the involvement of multiple stakeholders. Typically, these efforts are supported by a collaborative network that includes food producers, regulatory bodies, technology developers, and academic researchers. Such cooperation, which is rooted in multidisciplinary engagement, facilitates the creation and refinement of innovative applications for the industry [12; 13]. The food manufacturing sector faces numerous challenges and risks concerning food authenticity, including cross-contamination, mislabeling, unintentional ingredient errors, and deliberate economic food fraud. These issues are prevalent across various stages of the food supply and value chains, posing substantial disruptions to the industry [14; 15]. To address these concerns, there is a growing need for early problem detection and the continuous advancement of real-time monitoring and verification methods to ensure food authenticity from production to consumption. To verify the authenticity of food products, numerous analytical strategies have been employed, ranging from conventional proximate methods used to quantify constituents like moisture, proteins, and fats to sophisticated instrumental platforms. These include techniques such as gas and liquid chromatography (GC and LC), mass spectrometry (MS), high-performance liquid chromatography (HPLC), integrated or hyphenated analytical methods, and molecular approaches based on DNA profiling [8; 16; 17; 18; 19]. While these sophisticated methods provide high-resolution insights capable of identifying specific molecules and compounds, they are often criticized for their drawbacks, including high costs, lengthy processing times, and operational inefficiency. These limitations can hinder their practicality within the fast-paced, dynamic frameworks required by modern food supply and value chain systems for effective traceability and fingerprinting. Techniques based on vibrational spectroscopy, including mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy, have gained recognition as reliable tools for assessing food authenticity in both research settings and industrial or commercial operations.