Quality Control in Feed Plants: Analytical Parameters, Integration with Formulation, and Risk Management

Few functions within a feed mill have as cross-cutting an impact as quality control. It is present at each raw material lot receiving, traverses the production process, validates the finished product before dispatch, and feeds, with its data, formulation, purchasing, and supplier management decisions. When it works well, it is almost invisible: lots leave within specification, guarantee levels are met, and traceability allows identifying the origin of any deviation in minutes. When it fails, consequences cascade — below-expected animal performance, returns, rework costs, and regulatory liability.

This article addresses quality control as it works in the animal nutrition industry in practice: not as a compliance manual, but as a technical function that, when structured with the right parameters and integrated into the formulation process, becomes a concrete source of operational advantage.

Why ingredient variability is the central problem

The characteristic that makes quality control in animal nutrition particularly demanding is the inherent variability of raw materials. The same ingredient, purchased from the same supplier in different months, can show relevant variations in nutritional composition — and this variation has direct impact on the feed that reaches the animal and on the production cost per kilogram of protein.

Soybean meal is the most frequent example. Crude protein varying between 44% and 48%, actual lysine digestibility affected by thermal processing level, presence or absence of residual antinutritional factors. Corn, in turn, fluctuates in moisture (with direct reflection on energy value and fungal development risk) and can show mycotoxin contamination that varies substantially between crops and regions of origin. Fats and oils degrade based on acidity index and peroxide value, affecting energy density and oxidative stability of the final feed.

The problem is not the variability itself — it is intrinsic to agricultural commodities. The problem is formulating and producing as if it did not exist: using literature composition tables or supplier reports as fixed references, without confronting them with the actual values ingredients are delivering at the plant. When this happens, last month's feed may have different composition from today's, even using the same formula and the same suppliers.

Analytical parameters by ingredient category

An efficient quality control program does not analyze everything with the same depth. Analytical scope prioritization by ingredient should reflect the risk associated with quality deviations, frequency of formulation use, and observed historical variability. Below are the main parameters by category.

Energy grains: corn, sorghum, and by-products

For corn and sorghum, the minimum analytical package at receiving must include moisture, test weight (density), crude protein, ether extract, and ash. Moisture is the most immediate parameter: above 13%, the risk of filamentous fungal development during storage increases significantly, with potential for mycotoxin production in lots arriving within analytical limits. Ingredients with moisture between 12.5% and 13% should be prioritized for consumption, and lots above 13.5% should be rejected or receive specific treatment before incorporation into inventory.

Mycotoxin monitoring must be part of the regular grain receiving protocol, not only when there is visual suspicion. Aflatoxins (B1, B2, G1, G2), fumonisins (B1+B2), deoxynivalenol (DON), and zearalenone (ZEA) are the most relevant in Brazil, with maximum limits defined by MAPA that vary by destination species. For poultry feeds, the regulatory limit for total aflatoxins is 20 µg/kg (ppb), while for nursery swine the same limit applies — although effects of aflatoxin B1 on the broiler immune system begin to be clinically relevant at concentrations well below this limit with chronic exposure. For fumonisins, limits are 5,000 ppb for poultry and 1,000 ppb for swine in more sensitive phases. Zearalenone, with its estrogenic effects, is more problematic for swine, especially growing females and breeding dams, with limits ranging from 100 to 500 ppb depending on category.

Companies with in-house laboratories equipped with ELISA kits or calibrated NIRS systems can perform rapid mycotoxin screening at receiving and make the release or quarantine decision before unloading the truck. For quantitative confirmation of suspected lots, reference methodology (HPLC-MS/MS for aflatoxins, HPLC-FLD for fumonisins) should be performed in an accredited laboratory.

Soybean meal and plant protein sources

Soybean meal is the most used protein ingredient in monogastric feeds, and its quality shows relevant variation based on the extraction process and thermal treatment employed. The minimum analytical scope must include crude protein, ether extract, crude fiber, moisture, and two specific processing quality indicators: urease activity index (UAI) and protein dispersibility (PDI).

UAI reflects the degree of inactivation of thermolabile antinutritional factors, especially trypsin inhibitors and lectin. Adequately processed meal should show UAI between 0.05 and 0.20 pH units. Values below 0.05 indicate overheating, with damage to protein digestibility and compromised available lysine through Maillard reactions. Values above 0.20 indicate under-processing, with residual presence of antinutritional factors that reduce digestibility and compromise performance, especially in young poultry and swine.

PDI (Protein Dispersibility Index) complements this analysis by measuring the proportion of protein that disperses in water under standardized conditions. Well-processed meal shows PDI between 15% and 35%. PDI below 15% signals overheating; PDI above 35% indicates insufficient processing. For nursery piglet feeds, where sensitivity to antinutritional factors and protein digestibility are performance determinants, these two parameters should be monitored in 100% of received lots.

Reactive lysine (or bioavailable lysine), determined by the fluorodinitrobenzene (FDNB) method or calibrated NIRS spectroscopy, is the most direct indicator of the Maillard reaction's impact on the most limiting amino acid in monogastric feeds. A meal with adequate PDI but with high exposure to elevated temperatures in the presence of reducing sugars may show total lysine within specifications but with compromised available fraction — meaning formulation based only on crude protein or total lysine is overestimating this ingredient's contribution.

Animal-origin meals

Meat and bone meal, poultry viscera meal, and fish meal are high-variability ingredients with significant analytical risk. The minimum scope must include crude protein, ether extract, moisture, ash, calcium, phosphorus, and Ca:P ratio (which indicates bone proportion in the material and directly affects phosphorus availability). For fish meal, histamine is a mandatory parameter: levels above 300 ppm cause feed intake reduction and hepatic lesions in poultry, and the parameter is not detectable by visual or olfactory inspection of the finished product. Heavy metals (lead, cadmium, mercury) should be monitored periodically, especially in meals of unknown or imported origin.

Microbiological analysis for Salmonella spp. (absence in 25 g) is a critical control point for animal-origin meals, as discussed in detail in the HACCP article for feed mills. The protocol must include analysis of each received lot and periodic environmental monitoring in storage areas, given the risk of recontamination after thermal processing at the origin.

Fats, oils, and liquid ingredients

For animal fats and vegetable oils, the most relevant quality parameters are the acid value (AV), expressed as % free fatty acids or mg KOH/g, and the peroxide value (PV), expressed in meq O2/kg. AV reflects hydrolytic degradation, indicating presence of free fatty acids that reduce energy value and compromise feed palatability. PV reflects primary oxidation: elevated values indicate pro-inflammatory oxidation products that can compromise gastrointestinal tract health and productive performance. Fats with AV above 5–7% (depending on ingredient) and PV above 5–10 meq/kg should be critically evaluated before incorporation, especially in feeds for young or high-demand animals.

Production process control

Mix homogeneity: the most underestimated parameter

A feed may have average bromatological composition within specification in the finished product and still show unacceptable nutrient variability between samples from the same lot. This happens when the mix is not homogeneous — when micronutrients added in small quantities (vitamins, chelated minerals, synthetic amino acids, enzymes) are not uniformly distributed throughout the mixer mass.

The mixing uniformity test is performed by collecting samples from at least ten distinct lot points (over discharge time or at mixer points) and analyzing the coefficient of variation (CV) of an easily quantifiable tracer component — usually manganese or sodium. The resulting CV indicates homogeneity degree: values below 5% indicate excellent mixing; between 5% and 10%, acceptable for industrial production; above 10%, the mix is inadequate and animals will receive very different micronutrient quantities depending on which bag or which part of the silo they access.

The practical impact of a heterogeneous mix goes beyond analytical variation. In medicated feeds, variations in coccidiostat or growth promoter distribution imply both under-dosing (therapeutic inefficacy) and over-dosing (toxicity or residue risk). Periodic mixing uniformity checks — at each formula change, after mixer maintenance, and at minimum monthly frequency in regular production — are essential to ensure what was formulated is what the animal receives.

Pelleting, extrusion, and thermosensitive nutrient stability

Thermal processing improves feed hygiene and palatability, but imposes losses on the most heat-sensitive nutrients. B-complex vitamins (especially B1 and folic acid), vitamin C, and certain vitamin E forms show relevant degradation at pelleting temperatures (80–90°C in conditioner) and extrusion (130–180°C). Exogenous enzymes — phytases, xylanases, beta-glucanases — have variable thermal stability depending on formulation (conventional form vs. microencapsulated or protected form), and nutritional matrices should consider the percentage of residual activity after expected thermal processing.

Pelleting process monitoring must include regular records of conditioner temperature and moisture, pellet temperature at die exit, PDI (Pellet Durability Index, measured after standardized mechanical friction simulation), fines content, and yield. For lines processing feeds with high fat or moisture inclusion levels, PDI consistency is an indirect indicator of the conditioning process and pathogen inactivation efficacy.

Finished product control and validation of guarantee levels

Finished product analysis validates whether what was produced corresponds to what was formulated and what is declared on the label. For the minimum mandatory parameters established by MAPA (crude protein, ether extract, crude fiber, moisture, mineral matter, and calcium and phosphorus for feeds intended for species with these requirements), analysis frequency must be compatible with production volume and each product's historical variability.

A practice still not widely disseminated in the sector but of high diagnostic value is the systematic cross-referencing between finished product analytical results and values calculated by formulation. If feed was formulated for 18.5% crude protein and results from the last 20 finished product analyses show an average of 17.7% with standard deviation of 0.4%, there is a systematic deviation between formulated and produced that needs investigation. Causes may be varied: ingredients with composition below the matrix value used in formulation, dosing failure of some component, mixing segregation, or processing losses.

Without this cross-reference, the deviation remains invisible. The company believes it is delivering what it formulated, the customer does not formally complain, and the result appears only indirectly in animal performance — which in turn is attributed to management, health, or other factors. A LIMS integrating finished product analytical results with calculated formulation values allows this comparison to be generated automatically, with an alert when the deviation systematically exceeds a predefined limit.

Regulatory frameworks applicable to animal feed production

MAPA, product registration, and Self-Control Programs

The Ministry of Agriculture, Livestock, and Food Supply regulates animal feed production in Brazil through a set of rules covering product registration and guarantee level declaration to infrastructure and process control requirements for manufacturing establishments. MAPA Ordinance No. 46/1998 establishes the Procedures Manual for HACCP Implementation in the Animal Feed Production Chain, mandatory for registered establishments.

Self-Control Programs (PAC), required by MAPA Normative Instruction No. 4/2007, materialize Good Manufacturing Practices (GMP) into documented procedures and specific verification routines. A structured PAC covers supplier qualification, raw material receiving control, facility and equipment sanitation, pest control, lot traceability, and non-conformity monitoring. During MAPA inspections, PAC records are the primary evidence that the control system is functioning — not just documented on paper.

HACCP as a critical control points system

HACCP is not an additional program to quality control: it is the methodology that identifies, within the existing quality system, which steps require critical controls because failure in them can result in unacceptable risk to animal health or human health in the animal protein production chain. Typical Critical Control Points (CCPs) in feed mills include receiving animal-origin meals for Salmonella, receiving grains for mycotoxins, and the mixing step for drug cross-contamination. The article HACCP in Feed Mills: How a LIMS Supports the 7 Principles in Practice deepens each of these points with regulatory critical limits and the laboratory management system's role in monitoring.

GMP+ and certifications for the export market

For companies supplying ingredients or feeds to export chains — especially the European market — GMP+ (Good Manufacturing Practice for the Feed Sector), developed by the European animal nutrition sector, is the international reference framework. GMP+ goes beyond Brazilian regulatory requirements by requiring, among other elements, complete lot-to-lot traceability throughout the entire supply chain, documented risk analysis for each ingredient and supplier, and periodic audits by a certifying body. Integrators and slaughterhouses exporting to European markets frequently require GMP+ certification from their feed suppliers as a contractual prerequisite.

The laboratory as an analytical intelligence center

In a mature quality control program, the laboratory ceases to be merely a compliance verification sector and begins to function as an analytical intelligence center. The difference lies not in the number of analyses performed, but in how data is organized, interpreted, and made available for operational decisions.

A laboratory analyzing 20 soybean meal samples per month and recording results in individual spreadsheets is generating data. A laboratory that consolidates these 20 results, calculates mean and standard deviation by supplier, compares with the value declared on the entry report, builds Shewhart control charts to systematically identify trends and deviations outside control limits, and makes this history available to the formulator to update nutritional matrices is generating intelligence. The difference between the two approaches directly impacts the quality of decisions the company makes with each purchase, each formula adjustment, and each contract renewal with suppliers.

Statistical variability management: CV and control charts

The coefficient of variation (CV) of an analytical parameter over time is the most practical metric for quantifying ingredient or supplier variability. From a formulation standpoint, a high CV in a critical nutrient is a risk that needs to be managed — and the cost of this management frequently manifests as an additional safety margin in the formula, which increases cost without bringing real nutritional benefit.

Consider this concrete example: two soybean meal suppliers with identical average crude protein of 46%. Supplier A shows CV of 1.2% over the last 30 lots; Supplier B shows CV of 3.8%. To ensure feed does not deliver protein below the declared label value with acceptable probability, the formulator must insert a larger safety margin when using Supplier B's ingredients. This additional margin, depending on soybean meal's share in the formula, can represent R$15–25 per ton of feed — a cost the company pays for the cheaper-per-ton supplier that does not appear in the purchase negotiation because no one calculated the formulation cost of variability.

Shewhart control charts applied to analytical history of ingredients and finished products allow identifying: the actual parameter mean, control limits (±2s and ±3s), presence of out-of-control points indicating special events (problematic lots), and trends indicating systematic change over time — such as a supplier gradually reducing protein content without any individual lot being individually rejected. For laboratories accredited under ABNT NBR ISO/IEC 17025, internal analytical quality management (IQC) through Shewhart charts is a normative requirement to demonstrate statistical control of the analytical process.

Internal, outsourced, or hybrid laboratory

The decision to set up an internal laboratory, outsource analyses, or adopt a hybrid model depends on the volume of analyses needed, the required response speed, and the comparative cost of each model. For high-frequency screening analyses, such as basic receiving bromatology (protein, moisture, ether extract), a well-calibrated NIRS spectrometer provides results in under one minute per sample, with analysis cost far lower than an external laboratory. NIRS limitations are known: precision depends on calibration curve quality, which must be built and maintained with a robust sample database analyzed by reference methods. For ingredients with high variability or very heterogeneous composition (such as fermentation residues or ingredients from multiple origins), NIRS calibration may not be sufficiently precise for critical quantitative analyses.

For higher-complexity, lower-frequency analyses — amino acid determination by HPLC, fatty acid profile by gas chromatography, pesticide residue analyses, complete microbiological assays, quantitative mycotoxin confirmation — outsourcing to an accredited laboratory is more efficient than maintaining the analytical capacity internally. The hybrid model, combining internal laboratory agility for screenings with external laboratory analytical power for specialized assays, best serves most medium and large-scale plants. This model's efficiency depends on integrating results from both sources into a single management system with consolidated history.

From analysis to formulation: the flow that closes the cycle

The connection between the quality laboratory and the formulation process is where the most value can be created — and where the most value is lost when information flow does not work. The formulator needs reliable, current analytical data to make good decisions. The laboratory generates this data daily. But in many operations, these two worlds function separately: reports stay in the laboratory, formulation matrices stay in the software, and no one systematically updates one with the other.

Nutritional matrices are the convergence point of this relationship. They represent the expected composition values for each ingredient and are what formulation software uses to calculate formulas. If matrices are based on literature tables (Rostagno, NRC, FEDNA) without updating with the actual data of ingredients entering the plant, formulation is being done with assumptions that may not reflect operational reality.

The ideal flow works like this: the laboratory analyzes received lots of each ingredient and records results in the laboratory management system. Periodically — or automatically, when the system integrates with formulation software — accumulated results are consolidated by ingredient and supplier, generating mean values and confidence intervals that the formulator uses to update matrices. The formula is then calculated based on what the ingredient actually is, not what the table says it should be.

The practical impact of this integration is twofold. On one hand, it allows reducing artificial safety margins that increase formula cost without bringing real benefit to the animal, because uncertainty about actual ingredient composition is lower when there is in-house analytical history. On the other, it increases consistency of produced lots and reduces deviations between formulated and analyzed finished product — improving the company's technical credibility with customers and reducing regulatory risk of non-compliance with declared guarantee levels.

The cost of quality versus the cost of non-quality

A recurring objection to investment in more structured quality control is the cost: analytical equipment, reagents, qualified technicians, management systems. What this analysis generally does not include is the cost of non-quality — the cost of events that deficient quality control allows to happen.

A corn lot with aflatoxins above the limit that enters inventory and contaminates another 300 tons of stored grain represents a disposal or remediation cost far exceeding any screening kit. A feed lot with protein composition below declared values that compromises a broiler flock's feed conversion, detected only in the performance review three weeks later, represents losses difficult to quantify but easy to feel in results. A MAPA inspection identifying systematic discrepancy between declared label guarantee levels and analytical results can result in product interdiction, fine, and recall — with image impact beyond the immediate financial value.

The most solid technical argument for quality control as strategic investment is exactly this: the cost of a well-structured analytical program is predictable, recurring, and controllable. The cost of failures this program prevents is unpredictable, concentrated, and frequently much larger. Companies that have reached this conclusion and built quality control programs with real data, efficient traceability, and formulation integration do not return to the previous model — not because they became more risk-averse, but because they discovered that data-based decisions are systematically better, faster, and cheaper than those based on supplier reports and generous safety margins.