Omics as a predictive tool for food quality: can protein biomarkers replace traditional freshness and spoilage indices?

The evaluation of food freshness and spoilage has traditionally relied on sensory assessment, microbiological enumeration, and physicochemical indices, including pH, total volatile basic nitrogen (TVB-N), K-value, thiobarbituric acid reactive substances (TBARS), and peroxide value. These approaches are well-established, operationally simple, and widely accepted by both industry and regulatory agencies. However, their fundamental limitation lies in their retrospective nature, as they identify quality deterioration only after perceptible sensory, chemical, or microbial changes have occurred. In modern food systems characterized by extended supply chains, dynamic storage conditions, and growing consumer demand for minimally processed products, reactive quality assessment strategies are increasingly inadequate. Small deviations in temperature or handling early in the distribution process can irreversibly compromise product quality long before conventional indices detect a meaningful change. Against this backdrop, omics technologies, particularly proteomics and peptidomics, offer a paradigm shift, enabling the detection of molecular events that precede visible spoilage. This viewpoint focuses on whether protein biomarkers can realistically replace conventional freshness and spoilage indices and whether they are poised to redefine food quality assessment through predictive, mechanistically informed complementarity.

Proteins and peptides play a central role in the biochemical pathways governing post-harvest deterioration, as structural proteins, metabolic enzymes, and stress-response proteins respond rapidly to endogenous enzymatic activity, oxidative stress, and microbial colonization (Table 1)^[1]. As a result, protein modifications and degradation products often emerge at the earliest stages of quality loss, preceding the accumulation of secondary metabolites or the manifestation of perceptible sensory defects^[2,3]. Across diverse food matrices, including seafood, meat, dairy products, fruit, and vegetables, proteomic investigations have consistently identified early appearing molecular signals, such as myofibrillar degradation peptides, microbial exoproteases, oxidatively modified proteins, heat-shock and other stress-response proteins, and enzyme inactivation patterns associated with autolytic processes^[4]. These molecular signatures may arise hours to days before detectable changes in odour, texture, or conventional physicochemical indices, conferring a clear temporal advantage for early quality assessment. This predictive capacity positions protein biomarkers as particularly valuable tools for monitoring perishable and high-value foods, where timely intervention can substantially reduce economic losses and food waste (Fig. 1). Also, the molecular resolution offered by proteomics aligns with the ongoing evolution of food quality science toward precision and systems-level frameworks, in which freshness is understood not as a binary sensory attribute but as a dynamic biochemical trajectory^[5].

Figure 1. 

Integrating omics and conventional indices for predictive shelf-life assessment (created in Biorender).

Despite their considerable promise, protein biomarkers are not yet capable of replacing established indicators of freshness and spoilage^[6]. The right replacement would require broad applicability across food matrices, regulatory acceptance, economic feasibility, and operational simplicity, criteria that current proteomic approaches have yet to fully satisfy. Although molecular biomarkers provide unparalleled mechanistic insight, their translation into routine quality assessment frameworks remains constrained by biological variability, limited validation, and technological complexity. Food matrices are intrinsically heterogeneous, and spoilage kinetics are influenced by multiple interacting factors, including species or cultivar, diet, seasonality, post-harvest handling, packaging atmosphere, storage temperature, and microbiome composition^[7]. Consequently, a peptide biomarker that reliably predicts spoilage in salmon under chilled aerobic storage may perform poorly in other species, such as cod, shrimp, or vacuum-packaged products^[8,9]. Even within a single species, biomarker behavior can vary substantially across processing and storage conditions, underscoring the difficulty of identifying universal protein indicators applicable across diverse supply chains. This lack of generalizability remains a critical obstacle to biomarker replacement of conventional indices.

Additionally, most protein biomarker discovery studies are conducted under controlled laboratory conditions, using limited sample sizes and narrowly defined storage conditions^[10]. While these approaches are essential for mechanistic understanding, they do not adequately capture the complexity of commercial food systems, where temperature fluctuations, handling variability, and dynamic microbial succession strongly shape deterioration pathways. Regulatory authorities require consistent performance under real-world conditions, supported by large-scale, multi-site validation, which protein biomarkers have not yet demonstrated at the necessary scale. Technological constraints further limit the feasibility of full replacement. High-resolution proteomic platforms, such as liquid chromatography–tandem mass spectrometry (LC–MS/MS), provide rich molecular information but are costly, time-intensive, and require specialized expertise and infrastructure^[11]. As a result, routine quality control laboratories are unable to readily integrate such techniques into day-to-day operations. Although significant progress has been made in translating biomarkers into rapid and cost-effective formats, including immunoassays, aptamer-based sensors, microfluidic devices, and portable mass spectrometry, these technologies remain under development and currently lack the simplicity, robustness, and accessibility of conventional assessments such as sensory evaluation or TVB-N measurements^[12].

A more scientifically grounded and constructive perspective is that protein biomarkers will complement, rather than replace, traditional freshness indices in the foreseeable future^[13]. Their greatest strength lies in early detection and mechanistic insight, while conventional methods retain advantages in simplicity, standardization, and regulatory familiarity. In an integrated framework, proteomic markers can serve as early warning signals, identifying incipient quality loss before irreversible sensory deterioration occurs. Traditional indices then confirm end-stage spoilage and ensure compliance with regulatory thresholds. This hybrid approach is particularly valuable for products with short shelf lives, high economic value, or vulnerability to temperature fluctuations, where even modest predictive gains translate into substantial reductions in waste and financial loss^[13,14]. Importantly, such integration aligns with the broader transition toward data-driven food quality systems. Machine-learning models can combine proteomic features with temperature history, microbial counts, sensory scores, and physicochemical parameters to predict remaining shelf life with greater accuracy than any single metric^[15]. Within this context, protein biomarkers function not as standalone replacements but as informative variables within predictive decision-support systems. Over the next few years, the field should focus on more biomarker discovery, validation of existing biomarkers, and their simplification. The most realistic progress will come from targeted biomarker panels using advanced targeted LC-MS/MS rather than untargeted whole-proteome screening. Candidate markers should be prioritized only when they meet three criteria: biological relevance to spoilage pathways, statistical association with conventional indices, and reproducibility across independent sample sets.

For proteomics to have a meaningful impact on food quality evaluation, efforts must extend beyond biomarker discovery toward standardization, large-scale validation, and practical implementation. The right analytical workflows and multi-site studies are essential to establish biomarker robustness under commercial conditions, while expanded food-specific proteomic databases will support broader applicability. Early engagement with regulatory agencies and continued development of cost-effective, field-deployable analytical tools will be critical for translating molecular insights into routine quality assessment. Protein biomarkers provide predictive insight into early molecular events preceding conventional spoilage indicators; however, variability across food matrices, analytical complexity, and regulatory constraints currently limit their capacity to replace established methods. The most realistic and productive path forward lies in the integration and validation of protein biomarkers, where omics-derived protein markers complement traditional sensory, chemical, and microbiological indices. As validation and technology advance, proteomics will become an essential component of modern, predictive food quality systems, not as a replacement, but as a transformative enhancement.