The Significance of Meat Quality in a Growing Global Market
Meat has become an essential component of diets around the world, sparking an ongoing increase in production volumes to meet growing consumer demand (OECD/FAO, 2022). However, alongside this demand comes a critical necessity: ensuring the quality of meat products. Factors such as freshness and safety are major concerns within the food industry, as they significantly impact consumer health and satisfaction. Research indicates that improper storage and handling can lead to faster microbial growth and the degradation of vital components like proteins and lipids, directly affecting sensory attributes such as odor, color, and texture (Zhu et al., 2024; J. Chen et al., 2024; Shao et al., 2021).
Measuring Meat Spoilage: Analytic Methods and Innovations
To determine the quality of meat products, various physicochemical parameters can be quantified through traditional and innovative methods. Conventional analytical techniques assess spoilage by measuring attributes such as pH levels, water holding capacity, drip loss, and total volatile basic nitrogen (Bekhit et al., 2021; Cai et al., 2024). However, the evolution of food science has introduced foodomics—the study of food at a molecular level. This integrative approach employs techniques such as proteomics, metabolomics, lipidomics, and transcriptomics, employing high-throughput technologies like LC-MS, GC-MS, and RNA-Seq. Foodomics enables scientists to capture complex biochemical signatures revealing spoilage dynamics and sensory deterioration with unprecedented specificity (Bevilacqua et al., 2017; Valdés et al., 2022).
The Rise of Sensor-Based Technologies for Quality Assessment
Alongside conventional methods, sensor-based technologies have revolutionized food quality assessment. Electronic noses (E-noses) and electronic tongues (E-tongues) exemplify innovative approaches that emulate human sensory perception through arrays of gas and electrochemical sensors (Putri et al., 2023; Wojnowski et al., 2017). Beyond these technologies, optical sensor-based methods encompass techniques such as visible (VIS), near-infrared (NIR), and Raman spectroscopy, which have diversified food quality analysis and enhanced accuracy (Schreuders et al., 2021; X. Wu et al., 2022).
Challenges in Routine Quality Monitoring
Despite these technological advancements, the application of analytical and foodomics techniques in routine meat quality monitoring faces significant challenges. High costs, complex instrumentation, and the necessity for skilled personnel limit their accessibility (Cajka, 2024; Shi et al., 2024). Additionally, the time-consuming nature of sample preparation increases variability and poses scalability issues, making it difficult to implement consistent freshness assessments throughout the supply chain (Shi et al., 2024; Valdés et al., 2022).
Harnessing Computer Vision for Real-Time Quality Assessment
An exciting alternative lies in computer vision technology, which utilizes RGB images to analyze visual cues related to meat freshness. This method incorporates advanced image processing and machine learning techniques, enabling the assessment of attributes such as color, marbling, and texture (D. Wu & Sun, 2013; Liao et al., 2025; Modzelewska-Kapituła & Jun, 2022). Computer vision presents a scalable, non-destructive means for real-time quality assessment, proving advantageous in industrial environments (Kaushal et al., 2024; Saha et al., 2025).
The Role of Deep Learning in Visual Assessment
Deep convolutional neural networks (DCNNs) have garnered significant attention for their prowess in computer vision tasks such as object recognition and visual quality assessment (Kaushal et al., 2024; Zhao et al., 2025). However, as the complexity of DCNN architectures increases, so too do the demands for training data and computational resources. This reality often translates into costly and time-consuming optimization processes (Lee et al., 2022). While advances in hardware somewhat alleviate performance issues, reliance on large labeled datasets remains a hurdle in applications like food quality monitoring (Luo et al., 2024; Rex et al., 2022; Saha et al., 2025).
Exploring Alternatives: RADAM and Its Advantages
An innovative approach to training DCNNs involves extracting high-level representations from pre-trained models, circumventing the need for extensive retraining (Filus & Domańska, 2023). The Random encoding of Aggregated Deep Activation Maps (RADAM) is one such technique, capitalizing on the rich texture information embedded within intermediate network layers. This method transforms complex texture patterns into compact numerical vectors, suitable for traditional machine learning classifiers (Scabini et al., 2023). By sidestepping large-scale training requirements, RADAM enables the development of accurate and efficient models that can function effectively with smaller datasets and simpler hardware setups (G. Bin Huang et al., 2006; Kasun et al., 2013).
Technical Insight into RADAM
The texture features utilized in RADAM derive from the activation maps of a pre-trained DCNN, which capture spatial patterns across various semantic levels. When aggregating these activation maps, each one is resized using bilinear interpolation to maintain uniform spatial dimensions, allowing for effective consolidation into one comprehensive structure known as the aggregated activation map. This process prioritizes the texture information captured from the deeper layers of the network, which generally retain coarser structures that convey essential details (Scabini et al., 2023).
Practical Applications of RADAM in Meat Freshness Classification
The aggregated activation maps are then processed through a randomized autoencoder (RAE), facilitating the transformation of high-dimensional data into a lower-dimensional latent space. This method employs fixed linear projections followed by nonlinear activations, enabling the encoding of important texture information in a compact vector. The resultant low-dimensional feature vectors are primed for use in classification or retrieval tasks relevant to meat freshness assessment.
The potential for RADAM to streamline the training process sheds light on its effectiveness in classifying meat freshness from image data. This innovative approach aims to evaluate the classification performance of RADAM when coupled with various pre-trained DCNN backbones and traditional machine learning classifiers. Central to this study is the goal of assessing whether outer visual features serve as reliable indicators of meat freshness in non-destructive and rapid inspection scenarios.