How does sample composition affect the analysis of plant-based foods?

Sample composition directly affects plant-based food analysis because the matrix itself introduces variability that can distort measurement accuracy, interfere with analytical signals, and compromise reproducibility. Plant-based foods contain a highly diverse mix of moisture, fibre, starch, minerals, and bioactive compounds, each of which interacts differently with analytical instruments and methods. Understanding these matrix effects is essential for any quality control or R&D laboratory working with plant-derived products.

The sections below address the most common analytical challenges labs encounter when working with plant-based food matrices, from sample preparation through to method selection and batch-to-batch consistency.

What makes plant-based food matrices analytically complex?

Plant-based food matrices are analytically complex because they combine high biological variability with chemically heterogeneous compositions that change depending on species, growing conditions, processing methods, and storage. Unlike simpler matrices, plant-based foods present multiple interfering components simultaneously, making it difficult to isolate and measure target analytes with precision.

Several structural and chemical characteristics drive this complexity:

High and variable moisture content that affects sample weight, homogeneity, and instrument response
Diverse fibre and starch fractions that bind analytes and resist complete digestion or extraction
Naturally occurring mineral and organic acid profiles that create spectral interferences in elemental analysis
Lipid and protein co-extractants that contaminate extracts and foul instrument components
Pigments and polyphenols that absorb across broad wavelength ranges and interfere with optical detection

For food quality control laboratories in the Benelux region, this complexity is compounded by the rapid expansion of plant-based product categories, each with its own matrix profile. A pea protein isolate behaves very differently from a whole-grain flour or a fermented legume product, even though all three fall under the plant-based food umbrella.

How does moisture content affect measurement accuracy in plant-based foods?

Moisture content in plant-based foods affects measurement accuracy by altering sample mass, diluting analyte concentrations, and introducing inconsistency between replicate measurements. Because moisture levels vary significantly across raw, processed, and dried plant materials, results expressed on a fresh-weight basis can be misleading unless moisture is controlled or corrected for.

When moisture is not accounted for, a laboratory may report elemental or chemical concentrations that appear to differ between batches simply because the water content of those batches differed. This creates false variability that obscures genuine quality differences. Expressing results on a dry-weight basis is widely adopted in plant food analysis for exactly this reason.

Moisture also affects sample homogenisation. High-moisture plant materials such as fresh leafy vegetables or fruit pulps are difficult to grind to a uniform particle size without caking or smearing. This leads to subsampling errors, where the portion taken for analysis is not representative of the whole. Freeze-drying or controlled oven-drying before grinding is commonly used to stabilise moisture content prior to analysis.

Which sample preparation steps matter most for plant-based matrices?

The most critical sample preparation steps for plant-based food matrices are drying and homogenisation, followed by the appropriate extraction or digestion method matched to the target analyte. Poor preparation at any of these stages propagates error through the entire analytical workflow, making even a high-performance instrument unreliable.

A well-structured preparation sequence typically addresses these priorities in order:

Moisture stabilisation through freeze-drying or controlled drying to achieve a consistent, workable sample form
Size reduction and homogenisation to produce a representative, uniform subsample free of particle-size bias
Extraction or digestion using a method appropriate to the matrix, such as acid digestion for elemental work or solvent extraction for organic analytes
Cleanup or separation to remove co-extractants like lipids, pigments, or proteins that would otherwise interfere downstream
Dilution and matrix matching to bring the prepared sample into the calibration range of the instrument and minimise matrix-induced signal suppression or enhancement

The choice of digestion approach is particularly consequential for plant-based matrices. Incomplete digestion of fibrous materials leaves analytes trapped in the residue, leading to systematic underestimation of elemental concentrations. Microwave-assisted acid digestion is widely adopted for plant food analysis because it achieves more complete dissolution of resistant organic structures than open-vessel techniques.

How do fibre and starch fractions interfere with elemental and chemical analysis?

Fibre and starch fractions interfere with elemental and chemical analysis by binding metal ions, resisting complete digestion, and forming viscous solutions that affect instrument uptake rates and signal stability. In plant-based foods with high dietary fibre content, these interferences can be substantial enough to cause significant underreporting of mineral concentrations if not addressed.

Cellulose and hemicellulose structures in plant cell walls are particularly resistant to standard acid digestion. If digestion is incomplete, trace elements such as iron, zinc, calcium, and magnesium remain associated with the organic residue rather than entering solution, which depresses measured concentrations. Starch fractions present a different challenge: when gelatinised during sample preparation, they increase solution viscosity, which alters the nebulisation efficiency of instruments that use pneumatic sample introduction.

For chemical analysis beyond elemental work, high fibre content complicates extraction efficiency. Polyphenols and other bioactive compounds can adsorb onto fibre surfaces, reducing their recovery in standard solvent extractions. Analytical methods developed for lower-fibre matrices often need adaptation when applied to whole-grain, legume, or seed-based products.

What analytical methods are best suited to plant-based food composition analysis?

The analytical methods best suited to plant-based food composition analysis depend on the target parameter, but inductively coupled plasma techniques are widely adopted for elemental profiling, while near-infrared spectroscopy and automated titration are commonly used for moisture, protein, fat, and acidity determinations. No single method covers all compositional parameters in a plant-based food matrix.

For elemental analysis of plant foods, ICP-OES and ICP-MS are the most widely adopted approaches because they offer multi-element capability, broad dynamic range, and sufficient sensitivity for both major minerals and trace elements. These techniques are well-suited to the complex inorganic profiles of plant materials, though matrix-matched calibration and internal standardisation are typically required to manage interference effects.

For proximate composition, automated titration methods are commonly applied to acidity, moisture-related parameters, and certain nitrogen determinations. Near-infrared methods offer rapid, non-destructive screening for moisture, protein, and fat content, making them practical for high-throughput quality control in food production environments.

For food quality control applications where regulatory compliance is a driver, method selection should align with recognised food analysis standards. Laboratories should consult the applicable regulatory guidance or food safety standards for the specific parameters and product categories they are working with, as requirements vary by market and product type.

How can labs ensure reproducible results across different plant-based product batches?

Reproducible results across plant-based product batches require standardised sample preparation protocols, consistent use of certified reference materials, and regular instrument calibration combined with matrix-matched quality controls. Because plant-based raw materials are inherently variable, procedural consistency in the laboratory is the primary tool for managing batch-to-batch analytical variation.

Several practices support reproducibility in this context:

Using certified reference materials with a plant-based matrix composition similar to the product being analysed, to verify method performance across runs
Establishing fixed drying and grinding parameters so that sample physical form is consistent before any chemical preparation begins
Running procedural blanks and spike recovery checks with each analytical batch to detect matrix-induced suppression or enhancement
Documenting and controlling digestion or extraction conditions, including reagent grades, temperatures, and times, to minimise run-to-run variation
Applying statistical process control to monitor analytical performance over time and identify drift before it affects reported results

Instrument qualification and regular calibration verification are equally important. In regulated environments, instrument performance must be documented in line with GMP or GLP requirements. For laboratories operating under these frameworks, instrument qualification records and calibration traceability are part of the reproducibility assurance system, not optional add-ons.

How SciSPX supports plant-based food analysis in Benelux laboratories

Analytical challenges in plant-based food matrices require more than capable instruments. They require application-level expertise to configure methods correctly, prepare samples appropriately, and validate performance against the matrix in question. SciSPX supports quality control, R&D, and analytical laboratories across the Benelux with exactly this combination.

Instrument selection and application advice from Sales Engineers and Product Specialists with hands-on experience in food matrix analysis
IQ/OQ installation and validation services aligned with GMP, GLP, and 21 CFR Part 11 standards, delivered by certified Service & Support engineers
Ongoing calibration, preventive maintenance, and training to keep instruments performing reliably and staff equipped to interpret results confidently
Application-level support for elemental analysis, automated titration, and food quality control instrumentation relevant to plant-based product testing

If your laboratory is working with plant-based food matrices and needs guidance on method development, instrument qualification, or sample preparation workflows, contact the SciSPX team to discuss your specific analytical requirements.

Frequently Asked Questions

How do I know if my current analytical method is suitable for a new plant-based product I'm testing?

Start by characterising the matrix composition of the new product — particularly its moisture, fibre, fat, and mineral profile — and compare it against the matrix your current method was validated for. If the new product falls outside that validated scope, you will need to perform method verification or full revalidation, including spike recovery tests and certified reference material checks. A method that performs well on a pea protein isolate, for example, may not transfer directly to a fermented legume product or a high-fibre seed blend without adjustment.

What are the most common mistakes labs make when preparing plant-based food samples for analysis?

The most frequent mistakes are skipping or inconsistently applying the drying step before grinding, using a digestion method that is not aggressive enough for fibrous matrices, and failing to matrix-match calibration standards to the sample type. Each of these errors can introduce systematic bias that is difficult to detect without reference material checks. Running procedural blanks and spike recoveries with every batch is the most reliable way to catch preparation-related errors before they affect reported results.

Can near-infrared (NIR) spectroscopy replace wet chemistry methods for plant-based food analysis?

NIR is a powerful screening tool for proximate parameters such as moisture, protein, and fat, but it cannot fully replace wet chemistry methods for regulatory compliance or trace element analysis. NIR calibration models are matrix-specific and must be built and validated against reference data generated by primary wet chemistry methods, meaning the two approaches are complementary rather than interchangeable. For high-throughput quality control where speed matters, NIR is highly practical, but confirmatory wet chemistry should remain part of the analytical workflow, particularly when results are used for labelling or regulatory submissions.

How should I handle plant-based samples with extremely high or variable fibre content during digestion?

For high-fibre plant matrices, microwave-assisted acid digestion using a combination of concentrated nitric acid and hydrogen peroxide is the most reliable approach, as it achieves more complete breakdown of resistant cellulosic structures than open-vessel or hotplate methods. Extending digestion time or running a two-stage digestion programme can further improve dissolution completeness. Always verify digestion efficiency by checking spike recoveries against a certified reference material with a comparable fibre level, and adjust your programme if recoveries fall outside your accepted range.

What certified reference materials (CRMs) are available for plant-based food matrix validation?

Several internationally recognised CRMs are available for plant food matrices, including materials from NIST (such as SRM 1570a Spinach Leaves and SRM 1515 Apple Leaves), the European Commission’s Joint Research Centre (JRC), and IRMM. These materials cover a range of plant-derived matrices and certified parameters including major and trace elements, moisture, and selected organic analytes. When selecting a CRM, choose one whose matrix composition and certified analyte concentrations are as close as possible to the product you are routinely analysing, as matrix-matched validation gives the most meaningful performance data.

How do polyphenols and pigments in plant-based foods specifically affect ICP-OES or ICP-MS measurements?

Polyphenols and pigments are organic compounds that, if not fully destroyed during digestion, can cause carbon-based spectral interferences and deposit residues on instrument cones, torch components, and spray chambers, leading to signal drift and instrument fouling over time. Incomplete digestion is the primary cause — ensuring full oxidation of organic matter through adequate acid and oxidant volume is the most effective preventive measure. Running a carbon blank check after digestion and monitoring internal standard recoveries throughout a run will help detect any residual organic interference before it compromises your elemental data.

At what point in method development should a lab involve an instrument specialist or application expert?

Ideally, involve an application specialist before finalising your sample preparation protocol and method parameters, not after you have already encountered reproducibility problems. Early-stage input on digestion conditions, calibration strategy, and instrument configuration for your specific matrix can prevent significant rework and wasted reagent and sample costs. If you are working with a matrix type that is new to your laboratory — such as a novel plant protein source or a complex fermented plant product — application-level support during method development is particularly valuable for shortening the validation timeline.

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