Standardizing Analytical Results in Animal Nutrition: Methods, Expression Bases, and Laboratory Consistency

Analytical standardization is often treated as synonymous with using the same units of measurement. In animal nutrition lab practice, it's far broader: it covers the basis of expression (as-fed or dry matter), the reference analytical method and its relationship with fast methods like NIRS, the distinction between total and digestible or available fractions for amino acids and phosphorus, the limits that define compliance at each decision stage, and the procedures that ensure a result generated on the morning shift is comparable to one generated on the night shift. When any of these dimensions isn't standardized and documented, analytical results can be technically correct and operationally useless — or worse, a source of silent errors in formulation.

This article walks through the concrete dimensions of analytical standardization that most impact the quality of decisions at feed plants, premix plants, and animal nutrition quality-control laboratories.

Basis of expression: as-fed versus dry matter

The distinction between a result expressed on an as-fed basis (AF, also called "as received") and on a dry-matter basis (DM) is the most frequent source of confusion at the interface between laboratory and formulation — and, surprisingly, the one that gets the least explicit attention in many operations.

The same soybean meal with 12% moisture and 46.2% crude protein on an as-fed basis shows 52.5% crude protein on a dry-matter basis (calculated as 46.2 ÷ (1 - 0.12)). If the formulator uses table values expressed on an as-fed basis and the lab reports on a dry-matter basis, or vice versa, the error in that ingredient's protein contribution to the formula is more than six percentage points — enough to significantly compromise the feed's protein requirements.

The Rostagno table, the main ingredient-composition reference for poultry and swine in Brazil, presents values on an as-fed basis. Many NIRS calibrations are also configured to report on an as-fed basis. However, in situations comparing ingredients with very different moisture levels, or analyzing compositional variability between batches from different times of year, comparing on a dry-matter basis eliminates the moisture effect and is analytically more informative. Standardization requires the laboratory to explicitly define, for each parameter and each type of result, on which basis the result is recorded and reported — and for that information to be visible on the report.

In operations that run both NIRS screening analyses (generally calibrated on an as-fed basis) and wet-chemistry reference analyses (which may or may not include a dry-matter correction), it's especially important for the recording system to distinguish these cases and for historical comparisons to always be made on the same basis.

The nitrogen-to-protein conversion factor

Crude protein determined by the Kjeldahl method (or any total-nitrogen-based method) is calculated by multiplying the determined nitrogen content by a conversion factor. The universal standard factor is 6.25, derived from the assumption that proteins contain, on average, 16% nitrogen. This factor is adequate for most commonly used ingredients in animal nutrition — grains, oilseeds, animal-origin meals.

However, applying 6.25 indiscriminately to all ingredients creates inconsistencies in specific cases. For wheat and its byproducts, the more appropriate factor is 5.83, since wheat proteins (mainly gliadins and glutenins) have a slightly higher nitrogen proportion. For milk proteins (powdered milk, whey), the factor is 6.38. If the laboratory consistently uses 6.25 for wheat and the formulator applies the value without considering this difference, the crude protein of wheat bran or rice bran will be slightly overestimated in the matrices.

In precision nutrition for high-performance poultry and swine, where formulation operates on tight margins, this difference has an impact. Standardization requires that the conversion factor used for each ingredient be documented in the analytical procedure and made explicit in the result or the corresponding composition matrix.

Total versus digestible: amino acids and phosphorus

The distinction between total and digestible (or available) fraction is perhaps the most consequential for monogastric feed formulation, and it's where standardization errors have the greatest impact on animal performance.

Total versus digestible SID amino acids

HPLC amino acid analysis determines an ingredient's total amino acid composition. But what a chicken or a pig absorbs isn't the total amino acid — it's the digestible amino acid, the fraction that survives digestion and is absorbed in the small intestine. Digestibility varies by amino acid and by ingredient, and is expressed as the standardized ileal digestibility coefficient (SID, from the English term Standardized Ileal Digestibility).

Soybean meal with 2.85% total lysine and an SID lysine coefficient of 0.88 contributes 2.51% digestible lysine to the formula. Meat and bone meal with 1.4% total lysine and an SID of 0.57 contributes only 0.80% digestible lysine — less than half the total value. If the formulator uses the lab's total amino acid values without applying the correct SID coefficients for each ingredient, ideal-protein-based formulation will be wrong from the start, risking either underestimating actual requirement fulfillment or wasting synthetic amino acids to correct a deficit that doesn't exist.

Standardization in this context means defining, at the lab-formulation interface, whether the values recorded in matrices are total or digestible SID, which bibliographic source the SID coefficients are drawn from (Rostagno, NRC, CVB), and how HPLC total-amino-acid results from specific batches are converted into digestible values when used to update matrices. This conversion isn't automatic — it involves a methodological decision that needs to be documented and applied consistently.

Total phosphorus, available phosphorus, and phytase's role

In plant-origin ingredients, on average 60–70% of phosphorus is in phytate (phytic acid) form, which is practically unavailable to poultry and swine without added exogenous phytase. The laboratory generally determines total phosphorus by spectrophotometry or ICP-OES — a value that doesn't directly correspond to what the animal will absorb.

Historically, monogastric feed formulation used the concept of "available phosphorus" (non-phytate phosphorus), estimated as the proportion of total phosphorus in the form of inorganic phosphates and non-phytate organic phosphates — typically 30–40% of the total in grains and oilseeds. With the widespread use of exogenous phytases, part of the usable fraction of phytate phosphorus can be released, and the phytase "matrix" — the phosphorus, calcium, and other nutrient values credited to the enzyme in formulation — needs to be integrated coherently into the system.

When the laboratory reports only total phosphorus and formulation uses availability coefficients that depend on the type and dosage of phytase used, standardization requires the system to record not just the total P analytical result, but also the methodology and availability factors applied to arrive at the usable P value used in the formula. Without this, a change of phytase supplier can silently change the P efficiency in the formula without the lab or formulator noticing.

Reference method and rapid method: which prevails?

NIRS is a secondary analytical method: it doesn't directly measure an ingredient's composition, but predicts the value from a calibration built on results from primary methods (Kjeldahl for protein, Soxhlet for ether extract, among others). This has a direct implication for standardization: the NIRS result and the Kjeldahl result for the same sample almost always differ slightly, and the lab needs to explicitly define which one prevails for each decision.

The most common and technically sound practice is to use NIRS as a screening method at receiving — fast decision, result in under a minute — and the reference method as the arbiter when the NIRS result is close to the specification limit, when there's a discrepancy between NIRS and the supplier's report, or when the result will be permanently recorded as a basis for updating matrices. The acceptable difference between NIRS and Kjeldahl for crude protein in soybean meal is typically defined at ±0.5 percentage points. If the difference is greater, the protocol should require confirmation by the reference method before batch release.

Without this explicit definition, each analyst individually decides when to confirm and when to accept the NIRS result, generating historical inconsistency that undermines data comparability. This is especially problematic when the lab uses analytical history to calculate average values and CVs by supplier to update matrices: if some records are from NIRS and others from Kjeldahl without distinction, the statistics mix populations with different biases.

Specification limits, control limits, and action limits

Another aspect of standardization that directly impacts operations is the distinction between the different types of limits that guide lab decisions. Confusing them — or not explicitly defining them — results in unnecessary rejections, excessive tolerances, or uncertainty about what to do when a result is "close to the limit".

The specification limit is the contractual or regulatory criterion: the boundary below (or above) which the ingredient is formally non-compliant. For corn, a moisture specification limit of 14% means batches above that must be rejected or treated before use. For soybean meal crude protein, a minimum of 44% may be the limit contracted with the supplier.

The control limit is an internal, more restrictive criterion that the lab uses to flag the need for attention before the specification limit is reached. Corn moisture of 13.5% is still within specification, but in a well-structured system it can trigger consumption priority before long-term storage. Protein below 45% in soybean meal still passes the 44% specification, but it can trigger a matrix check in the formulation software.

The warning limit, normally defined at ±2s on Shewhart control charts, indicates the process is operating near the edge of expected behavior — not necessarily a problem, but worth investigating. The action limit (±3s) indicates the process has gone out of statistical control and requires intervention before continuing.

These limits need to be defined per parameter and per ingredient, documented in the laboratory management system, and applied consistently by all analysts. When each analyst intuitively decides what's "close to the limit" and what warrants confirmation, the operation loses predictability and traceability.

Consistency across analysts, shifts, and units

One of the most common forms of non-systematic analytical variability in industrial laboratories is inter-analyst variability: the same method, the same equipment, and the same sample producing different results depending on who ran the analysis. The most common causes are differences in sample preparation (grinding time, prior drying temperature), variations in procedure execution (digestion time, extraction temperature), and different interpretations of ambiguously written procedures.

The ABNT NBR ISO/IEC 17025 standard requires documented standard operating procedures (SOPs) for each analytical method, including sample preparation, equipment operation, result calculation, and acceptance criteria. These SOPs need to be detailed enough that two trained analysts, following the same document, produce results within the expected repeatability range for the method.

The tool for verifying whether this is happening is internal analytical quality control (IQC): periodic analysis of a certified reference material (CRM) or an internal control sample with a known value, whose results are recorded on a Shewhart control chart. When the CRM result starts drifting systematically from the expected value, there's a method drift — outdated calibration, deteriorated reagent, equipment maintenance issue — that needs to be identified and corrected before customer results are compromised. For laboratories accredited by INMETRO, this demonstration of statistical control of the analytical process is a normative ISO/IEC 17025 requirement, not an optional best practice.

In companies with multiple production units, standardization needs to go beyond shift and plant. When each unit has its own laboratory with its own procedures, comparability of results between plants depends on all of them using the same reference methods, the same conversion factors, the same bases of expression, and the same acceptance limits. Without this, an ingredient rejected at Unit A might be accepted at Unit B with the same analytical result, because the decision criteria are different.

Comparability between in-house and external laboratories

A frequent situation at feed plants using a hybrid lab model (in-house for screening, external for confirmatory or specialized analyses) is a discrepancy between in-house and external lab results for the same sample. When this happens without a defined resolution protocol, it creates an uncertain decision situation: the supplier was rejected in-house but approved externally, or vice versa.

Standardizing this workflow requires predefining: which lab is the arbiter for each type of analysis; what the maximum acceptable difference between the two results is before the discrepancy is investigated; what the protocol is when the difference exceeds that threshold (third analysis, analysis at an accredited lab, measurement-uncertainty analysis); and what happens to the batch while the discrepancy is being resolved.

This standardization is especially important for parameters with regulatory impact, such as microbiological analyses for Salmonella in products subject to MAPA's PAC program, where the analytical method and the responsible laboratory need to be traceable and defensible in the event of an audit.

Rounding, decimal places, and precision consistent with the method

The granularity with which results are expressed has practical formulation implications that are rarely discussed. Reporting soybean meal crude protein as 46.23%, 46.2%, or 46% isn't indifferent when that value is used to calculate the protein contribution of 300 kg/ton of that ingredient in the formula: the difference between 46.0% and 46.2% produces a variation of 0.6 kg of crude protein per ton of feed — not negligible in precision formulations for high-performance poultry or swine.

Rounding policy should be consistent with the analytical method's actual precision. NIRS for crude protein in soybean meal has typical repeatability of ±0.2 to ±0.3 percentage points — so reporting to three decimal places communicates a precision the method doesn't have. One decimal place is generally adequate for most proximate-analysis parameters expressed as a percentage. For parameters expressed in mg/kg (like vitamins in premix, or mycotoxins), the relevant number of decimal places depends on the magnitude of the values and the method's analytical limit.

The lab recording system should allow reporting precision to be configured per parameter and applied automatically, without relying on manual formatting on each report. When rounding is done manually by each analyst, historical data consistency is compromised and comparisons between periods or analytical sources become less reliable.

Standardization as quality-system infrastructure

All the dimensions described above share one trait: they need to be documented, implemented, and systematically verified. It's not enough to decide that the standard expression basis is as-fed and communicate that verbally in a team meeting. If the definition isn't recorded in the analytical procedure, isn't accessible when the analyst is reporting the result, and isn't verifiable in an audit, it isn't standardization — it's an intention.

Laboratory management systems that support configuring parameters with their expression bases, conversion factors, limits by ingredient and intended use, reference methodologies linked to the result, and automatic IQC controls turn standardization from a set of individual decisions into operational infrastructure. When these elements are configured in the system, standardization is applied consistently by all analysts, on every shift, without depending on everyone having memorized every convention.

For the formulator using this data, trust in the analytical result is inseparable from trust in the process that generated it. A history of results produced with the same bases, the same methods, and the same criteria over time is a solid foundation for updating matrices, calculating CVs by supplier, and making formulation decisions close to maximum efficiency. A history produced with varying conventions is a collection of numbers that need to be interpreted with caution before any analytical use.