Exploration of Foods and Foodomics

Table 2. Comparison table for the different analytical techniques with potential application for organic seeds evaluation.

Techniques	Analytical method	Analytical principle	Advantages	Limitations	Sensitivity	Specificity	Applicability for organic seed authentication
Physicochemical analysis/physiological seed quality	Physicochemical and seed testing methods (germination, seed mass, moisture, purity tests)	Physical tests (visual and/or other organoleptic characterization) and physiological seed parameters assessment	Simple (almost no sample preparation), standardized, low cost; widely used in seed certification	Limited discriminatory power for production systems; influenced by environmental and varietal factors	↑	○	Useful for evaluating seed quality and viability but insufficient alone for distinguishing organic vs. conventional seeds
	Imaging and optical sensing techniques (X-ray, thermal imaging, hyperspectral imaging, machine vision)	Spatial and spectral images formed from electromagnetic radiation incidence over seed tissues, reflecting structural and compositional features	Rapid, non-destructive; capable of detecting internal defects and seed structure; compatible with machine learning classification	Requires specialized instrumentation and calibration models; classification often indirect	↑↑	◎	Promising screening tools; hyperspectral imaging has shown potential for distinguishing organic seeds in some crops
	Electronic nose technologies	Detection of volatile organic compounds using sensor arrays	Rapid detection of volatile fingerprints; minimal sample preparation	Sensor drift and environmental sensitivity; requires calibration models	↑↑	◎	Potential complementary tool for seed classification based on volatile signatures
Isotopic and elemental analysis	Stable isotope analysis (IRMS)	Measurement of stable isotope ratios (eg, ¹⁵N/¹⁴N) based on mass-to-charge separation of ionized molecules	Strong link of isotopic signatures to agronomic practices (eg, fertilization); high reproducibility	Influenced by geographic and climatic factors; requires expensive instrumentation	↑↑↑	◉	One of the most promising approaches for distinguishing organic and conventional production systems
Isotopic and elemental analysis	Elemental profiling (ICP-MS, ICP-OES, AAS)	Quantification of macro- and trace elements based on the interaction of atoms/electromagnetic energy in atomic spectroscopy techniques	Multi-element capability; high sensitivity; suitable for fingerprinting approaches reflecting soil composition, fertilization, and environmental inputs	Elemental composition influenced by soil type and geography; requires specialized use	↑↑↑	◎	Useful as complementary markers for classification of farming systems
Spectroscopic methods	Spectroscopic techniques (NIR, MIR, Raman, NMR)	Measurement of molecular vibrational or magnetic properties generating chemical fingerprints of food matrices	Rapid, non-destructive; minimal sample preparation; suitable for high-throughput screening	Requires chemometric modelling; spectral overlap may reduce specificity	↑↑↑	◎	Effective for fingerprint-based classification and preliminary authentication screening
Spectroscopic methods	Mass spectrometry techniques (MALDI-TOF, DART-MS)	Ionization and detection of molecules based on mass-to-charge ratio to generate molecular profiles	High molecular specificity; suitable for proteomic and metabolomic profiling	Instrument cost and complexity; often requires advanced data processing	↑↑↑↑	◉	Useful for identifying molecular markers associated with production systems
Chromatographic and omics approaches	LC-MS, GC-MS, metabolomics, proteomics	Separation and identification of metabolites or proteins generating comprehensive molecular fingerprints	High analytical resolution; enables biomarker discovery and pathway analysis	Expensive instrumentation; complex data interpretation	↑↑↑↑	◉	Highly suitable for discovering discriminative markers of organic production systems

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Sensitivity scale: ↑ low, ↑↑ moderate, ↑↑↑ high, ↑↑↑↑ very high. Specificity scale: ○ low, ◎ moderate, ◉ high.

Declarations

Author contributions

DAR: Conceptualization, Investigation, Writing—original draft, Writing—review & editing, Funding acquisition. FPA: Conceptualization, Writing—review & editing. RFB: Writing—review & editing. ABC: Validation, Writing—review & editing, Supervision, Funding acquisition. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

The authors would like to thank the Universidad Nacional de Cuyo, Mendoza Argentina (Grant SIIP UNCuyo 2022 06/A007-T1; Grant SIIP UNCuyo 2025 Investigadores Noveles 80020240200036UN; Grant Fortalecimiento en Cs y Técnica UNCuyo 2025 06/80020240400061UN), and the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET Argentina) (Grant PIP 11220200101692CO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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