Reactive Lysine: A Better Determinant for Soybean Meal Quality?

I. INTRODUCTION

In the 1990s, researchers from the American Soybean Association (ASA) showed that soybean meal with similar proximate analysis can perform very differently in poultry and swine feeding. Subsequently, Creswell and Swick (2008) carried out feeding trials, AME and digestible amino acid analysis of 4 soybean meals. They found that there was a large difference between the soybean meals in BWG and FCR from the broiler feeding trials as well as in AME and digestible lysine. They also quantified the financial value of each soybean meal which exceeded USD70/MT between certain soybean meals. Our analysis of the data from that trial showed that there is excellent correlation between digestible lysine of the soybean meals with BWG and FCR of broilers (Neoh and Ng, 2006). Interestingly, the digestible lysine in soybean meal was almost perfectly correlated with AME of soybean meal. This suggests that processing conditions that give high digestible lysine will also increase the AME of the soybean meal.

In recent years, there has been much research done to find a more accurate but simple to measure determinant or determinants that can differentiate between soybean meals that can give different performances in animal feeding. The KOHPS method was suggested by Araba and Dale (1990) to be a good determinant for protein digestibility of soybean meal. Generally, it is accepted by the industry that soybean meal with KOHPS over 75% and with TIA less than 4 mg/g can be considered to be of good quality and will perform satisfactory for animal feeding. Unfortunately, Araba and Dale (1990) used an autoclave to simulate processing condition in the solvent extraction plant. Their conclusion was that soybean meals with KOHPS exceeding 85% will be under toasted. However, in our opinion this is not applicable for soybean meal samples from solvent extraction plants due to the presence of hexane during desolventising and toasting which has a big effect on protein solubility. Regrettably, most of the research done on soybean meal processing conditions and protein solubility was done with autoclaves in the laboratory. These results do not truly reflect the processing conditions in the solvent extraction plants.

There are numerous research studies on reactive lysine of soybean meal using homoarginine, FDNB and furosine methods. In our opinion, the furosine method is suitable for soybean meal as it has normally undergone only early Maillard reactions producing mainly Amadori compounds. In general, it is accepted that reactive lysine is correlated with digestible lysine in animal nutrition. Kim et al. (2015) demonstrated there was an excellent correlation between reactive lysine with apparent ileal digestible (AID), standardised ileal digestible (SID) and true ileal digestible (TID) lysine with R² = 0.99 in pigs. Kim and Mullan (2013) also managed to produce a workable NIR calibration for reactive lysine using a mixture of soybean meals obtained from the market as well as samples produced in an autoclave at their laboratory.

For this study, we collected 98 samples of soybean meal from our 2 crushing plants together with some soybean meals imported from Argentina. These samples were not subjected to further heat treatment and were directly analysed for reactive lysine, KOHPS, PDI, TIA, colour L and a. The reactive lysine contents were then correlated with their corresponding KOHPS, PDI, TIA, colour L and a results. The reactive lysine data were also used for calibrating our online NIR with the intention of using it for online real time control of reactive lysine content in our soybean meal.

II. METHOD

The soybean meal samples were analyzed for TIA (AOCS Ba 12-75), PDI (AOCS Ba 10-65) and KOHPS (Arada and Dale, 1990). These samples were also analyzed for color L and a using a Konica Minolta Chroma Meter CR-400/ DP-400.

Amino acids were determined by using Waters AccQ•Fluor™ reagent for post column derivatization and L-α-Aminobutyric acid as the internal standard. The derivatives are analyzed by HPLC with FLR detector. Before analysis, samples were hydrolyzed with 6 N HCl for 24 h at 110°C (AOAC 982.30 E). Methionine and Cys were analyzed as methionine sulfone and cysteic acid, respectively, after cold performic acid oxidation overnight before hydrolysis. Tryptophan was determined after NaOH hydrolysis for 16 h at 120°C.

For quantification of furosine content (Dong et al., 2019; Pahm et al., 2008), samples were hydrolyzed with 10.6 N HCl for 24 h at 110 °C and further processed using reversed-phase solidphase extraction (SPE). 0.5 mL of the filtrate was eluted with 3 mL of 3 N HCl and evaporated by nitrogen stream. The dried samples were reconstituted in 3 mL of a mixture of water, acetonitrile, and formic acid and filtered through a 0.45-μm filter before HPLC injection. Mobile phases gradient (0.1% trifluoroacetic acid in deionised water for mobile phase A and Methanol for mobile phase B), and samples were detected by HPLC with UV/VIS detector.

III. RESULTS AND DISCUSSION

The reactive lysine of the soybean meals ranged from 2.5 to 3.2% which is a variation of 28%. This is considered to be a very large variation and would have a very big impact on the growth performances of animals. The KOHPS of these samples ranged from 53 to 90% and there is a reasonable correlation with reactive lysine of R² = 0.70; P < 0.001 (Figure 1). The PDI of soybean meal samples from our plant ranged from 7.5 to 49% and is correlated with reactive lysine of R² = 0.60; P < 0.001 (Figure 2). This PDI measurement was done within 3 days of production as we have previously published showing that PDI will deteriorate quickly with time and is not a reliable measurement unless we know the storage time and condition of the soybean meal (Neoh, 2008). Therefore, we excluded all the imported soybean meal samples from the PDI correlation study with the reactive lysine. There was also reasonable correlation between reactive lysine and colour L of R² = 0.55; P < 0.001 (Figure 4). However, there was no correlation between TIA and colour a with reactive lysine as shown in Figure 3 and Figure 5.

From these results, we can propose that reactive lysine may be a good determinant of soybean meal quality. The correlations between reactive lysine and KOHPS as well as PDI would suggest that reactive lysine content is able to differentiate between soybean meals with different protein digestibility over a wide range. However, in order to establish that reactive lysine content of feed ingredients can be a good predictor of animal performance we need to correlate reactive lysine content of feed ingredients with growth rate and FCR of animals. In the near future, we will be carrying out broiler feeding trials using our soybean meals with reactive lysine content of 2.6, 2.7, 2.8, 2.9, 3.0, 3.1 and 3.2% to determine if there is a correlation between reactive lysine, BWG and FCR.

We are also able to calibrate our NIR online using the reactive lysine data to obtain a robust calibration with R2 = 0.956, SEC = 0.030, SECV = 0.046 and SEP = 0.052 (Figure 6). This will enable us to use real time online NIR to optimize the level of reactive lysine in soybean meal produced from our crushing plants.

Figure 1 - Correlation between KOHPS and reactive lysine.