“Soybean meal processing impacts the quality of the material, and it is now possible for feed producers to check additional protein quality indicators that can be used as a guide to check impact of processing on soybean meal quality, including reactive lysine content and urease activity.”

Given current soybean meal price volatility, and environmental pressures, it is essential for feed producers to have a deep understanding of it´s nutritional composition to optimise its inclusion in the feed and compare different suppliers.

Historically feed producers have focussed on de- termining the macro-nutrient content of soybean meal (e.g. moisture, protein, oil, crude fibre, ash) in order to optimise the feed formula. More recently, companies can also check more advanced nutritional parameters, like amino acids, non-starch polysaccha- ride (NSP) content and phytate-P.

Soybean meal processing impacts the quality of the material, and it is now possible for feed producers to check additional protein quality indicators that can be used as a guide to check impact of processing on soybean meal quality, including reactive lysine con- tent and urease activity.

Rapid analysis of these additional parameters has become available thanks to recent advancements in near-infrared reflectance (NIR) technology, which is a cost effective alternative to traditional chemical testing to check the nutritional composition of feedstuffs.


Soybean meal production involves different steps with a range of pressures and temperatures applied in order to extract oils, remove anti-nutrients (e.g. trypsin inhibitors) and to increase digestibility, with the most sensitive step for quality control being the desolventizing-toasting (D-T) operation (Figure 1).

Figure 1: Typical soybean meal production process (1)

Detecting both undercooked and overcooked soy- bean meal is important for feed producers to dis- criminate low quality soybean meals. The American Soybean Meal Association compared the ileal digestibility of amino acids in raw soybean flake, properly cooked soybean meal and over-cooked soybean meal in caecectomised roosters (Figure 2). The study demonstrated that ileal digestibility of all amino ac- ids are compromised when soybean meals are either undercooked or overcooked.


Common tests for feed producers to check soybean meal quality include:

Trypsin inhibitor activity (TIA) the destruction of TIA during D-T operation is highly correlated with the destruction of urease activ- ity and can be used as a method to detect under-processed material.

Urease activity (UA) – the most common method to detect un- der-processed soybean meal, this test involves analysing the pH change due to the presence of urease. If the soy- bean meal is under-processed, there should be more urease enzyme pres- ent and there will be a greater pH rise. It is an indirect method com- monly used in the industry to rough- ly estimate the TIA, as detection of the TIA is difficult and expensive. UA correlates with TIA as both enzymes are similarly deactivated at a range of heat treatment. Typically UA is quot- ed in urease index units (U), and in- dustry expectation is 0.05-0.3U.

KOH solubility – used to detect over-cooked soybean meal by measur- ing nitrogen solubility in 0.2% KOH solution. Raw soybean meal is 100% soluble in 0.2% KOH but the opti- mum quality lies between 78% (over- cooked) and 84% (undercooked).

Figure 2: Ileal digestibility of soybean meal amino acids (raw flake, cooked and overcooked form) when fed to caecectomised roosters (2)

Protein dispersibility index (PDI) – The amount of soybean meal protein dispersed in water after blending a soybean meal sample in water with a high-speed blender. Optimum quality is PDI value of 40-45%.

Reactive lysine – total and reactive amino acid con- tent and digestibility are reduced during the toasting process. Lysine is a sensitive marker of overprocessing as the free amino group on its sidechain is exposed and reacts readily with sugars and fats if the temperature and moisture allow, making the lysine unusable by the animal. The reactive lysine content is a measure of the free amino groups remaining and thus the availability of the lysine for protein synthesis. Optimum values are above 90% reactive: total lysine ratio.

Table 1: A comparison summary of soybean meal quality parameters (3)
   TIA  UA  KOH  PDIReactive:Total Lysine
Under processingVery appropriateVery appropriateLow useAppropriateNot appropriate
Over processingNot appropriateNot appropriateAppropriateLow useVery appropriate
Target values<2.5 or <5 mg/g<0.05 or <0.30 pH rise78 – 84 %40 – 45 %>90%
Comment  DifficultMost commonMost commonSimple methodDifficult

Table 1 shows a comparison summary of these soy- bean meal quality parameters


Batal et al. (2000) (4) checked the impact of feed- ing processed soybean meal on broiler performance. Although KOH and PDI decreased with autoclav- ing time as expected, broiler performance was not affected, suggesting these methods of screening soybean meal quality may not be reliable tools to predict the true impact of soybean meal quality on animal performance.

The US soybean export council also stated that KOH protein solubility is not sensitive enough to gauge the level of heat processing that a soybean meal product has undergone (5), indicating that KOH sol- ubility only provides qualitative and not quantitative information about a degree of over-processing.

It is for these reasons that AB Vista has selected reactive lysine and UA as soybean meal quality in- dicators, which are available for customers to use on Feed Quality Service, AB Vista´s web-based NIR prediction service.


During the D-T process in soybean meal production, the carbonyl group of sugars (reducing sugars) can react with the amino group of lysine, creating a mixture of poorly characterised Maillard products. In addition, the lysine can also react with other nutrients such as fats, polyphenols, vitamins and other  food additives, creating unspecified compounds (Figure 3). The issue with this chemical reaction is that a part of lysine detected as total lysine in HPLC analysis are not bioavailable for lean tissue ananolism or for other biological function, as the heat damaged lysine is excreted through urine even if they are  digested and absorbed in the small intestine.

Figure 3: Formation of Maillard products and unspecified compounds during soybean meal toasting process. (6)

Based on AB Vista data collected worldwide, the reactive: total lysine ratio, and UA can be shown based on the country of sample origin.


Based on AB Vista´s global soy- bean meal database, it can be pre- dicted that ca. 15% of all soybean meal samples could be deemed over-toasted (reactive:total lysine ratio <90%). This means feed producers should consider supple- menting their feed containing this material with additional amino ac- ids to ensure the feed contains the correct amino acid levels.

In addition to total and reactive lysine content, AB Vista also pre- dicts standardised ileal digestible (SID) amino acid content in soy- bean meal. These results can be used in combination to create con- version factors in order to adjust expected SID AA content in the feed formulation system

Figure 4: Reactive:total lysine (black bars) content and variation from soybean meal Feed Quality Service sample data collected during 2020. Optimum values are above 90% reactive:total lysine ratio as shown by the horizontal red line.
Figure 5: UA and variation from soybean meal Feed Quality Service sample data collected during 2020. Industry expectation is between 0.05 and 0.3U.


The various steps in soybean meal production have an impact on the quality of the material. Tradi- tional methods to check the impact of soybean meal processing on quality may not be sensitive enough for use in practical situations and urease activity and reactive lysine are the most appropriate methods to detect under and over-processed soybean meal, respectively.

Near-Infrared Reflectance  (NIR) Technology  is a rapid, non-destructive and cost-effective alterna- tive to traditional wet chemistry analysis. Urease index and reactive lysine protein quality indicators are available for AB Vista clients to use via the web- based NIR prediction service, Feed Quality Service

For further information or references please contact

William Greenwood – Sales and Technical Services Manager EMEA – AB Vista

Jae Kim – Technical Manager ASPAC AB Vista