Development of Near Infrared Calibrations for Determination of Non-Starch Polysaccharide Content in Feedstuff

Antibiotic growth promoters have been successfully utilized in the past to control gut dysbiosis (Dibner and Richards, 2005). However, poultry production is changing because of consumer and governmental pressure to reduce the use of antibiotics (Cervantes, 2015). Little or no attention is given by nutritionists to understanding the fibre composition in poultry diets and their role in gut health and litter quality. Crude fibre method is largely employed to describe dietary fibre content in poultry nutrition, but this method only captures around 25% of what is considered “true” fibre (Graham et al., 1991; Choct, 2015a). Better knowledge of fibre composition is needed since the intestinal microbiota will largely utilize these components as substrates for fermentation. The fermentation products such as volatile fatty acids can play an important role in gut health and overall metabolism of the host (Józefiak et al., 2004; den Besten et al., 2013; Hervik and Svihus, 2019). As opposed to the term “dietary fibre”, the term “analytical dietary fibre” (Kerr and Shurson, 2013; Choct, 2015b) comprises the analysis of the structural carbohydrates by their individual sugars and solubility and gives a much better understanding of the dietary fibre composition of feedstuff. However, the methods used to analyse these fibre components are quite laborious and expensive and, as a result, very few data are available in the literature (Knudsen, 2014; Choct, 2015b; Rostagno et al., 2017). The only table that has reported the non-starch polysaccharides (NSP) contents in feedstuff is the Brazilian Tables for Poultry and Swine (Rostagno et al., 2017). It should be noted that there is a lot of variability in NSP content within the same raw material. Near-infrared reflectance spectroscopy (NIRS) is a well-established method for analysis and quality control of ingredients providing fast and inexpensive analysis of organic compounds (e.g. moisture, protein, fat, starch, crude fibre, amino acids, etc), serving as an analytical tool to reduce excess of nutrients while formulating diets (Van Kempen & Simmins, 1997), thereby reducing feed cost and environmental impact of animal production (Ferket et al., 2002). Despite that, very few attempts have been made to create calibrations for NSP determination in feedstuff (Archibald and Kays, 2000; Blakeney and Flinn, 2005; Hollung et al., 2005). The objective of this paper is to summarize the results of NSP contents of feedstuff samples collected worldwide and provide with the calibration statistics for prediction of total insoluble NSP and total NSP (sum of soluble and insoluble NSP).

 

Approximately 1,700 feedstuff samples from 24 different countries were collected over 5 years and analysed for NSP at Englyst Carbohydrate Ltd laboratory (Southampton, UK, SO16 7NP). The samples were analysed according to the method proposed by Englyst et al. (1994). Samples were split into 3 categories which comprised fibrous materials (DDGs, wheat bran, rice bran, soy hulls, cassava meal, sunflower meal, etc), protein materials (soybean meal, full fat soy, canola meal, peanut meal, corn gluten meal, etc) and cereals (barley, corn, oat, millet, quinoa, rice, rye and sorghum).

All samples were ground (1mm particle size) and scanned using a benchtop NIR machine (FOSS DS2500) monochromator spectrometer (FOSS A/S Hillerød, Denmark), covering the spectral range 400-2500 nm, with a spectral interval of 2 nm, and running Foss Mosaic software. Six pre-treatments were investigated: raw absorbance spectra, first derivative, and second derivative, each tried without and with Standard Normal Variate (SNV) pre-processing. In the case of two treatments, the SNV was applied after the derivative. The numbers of factors were chosen based on the plot of RMSECV versus the number of factors, observing where the curve starts to flatten out, giving the best RMSEV for the optimum number of factors.

The accuracy of the equations was evaluated through the coefficient of determination (R²), standard error of cross validation (SECV), standard error of prediction (SEP) and RPD (ratio of standard deviation of analysed data by the SEP).

 

Non-starch polysaccharide results are summarized in Tables 1 and 2. Only ingredients with 10 or more analysis are reported. The coefficient of variation of soluble total NSP ranged from 16 to 101% and that of insoluble total NSP ranged from 6 to 129%.

After removing outliers, the models for fibrous, protein and cereal feedstuff contained 130, 364 and 1053 samples, respectively for NIR calibrations. Overall equations for insoluble NSP showed very good accuracy, with R ² ranging from 86 to 97%, with SECV being close to SEP values, and RPD between 3 to 6, all of which indicates good models for predicting insoluble NSP. The same scenario was observed for total NSP calibration, with R² ranging from 91 to 97% and RPD between 3 to 6.

 

Results of the current study demonstrate the degree of variation in NSP composition between and within ingredients. Fibre has anti-nutritive characteristics but can also improve gut function in monogastrics (De Vries, 2015). Therefore, a better understanding of fibre composition would allow the use of nutritional strategies (e.g. carbohydrase enzymes) to boost fibre fermentation by gut microbiota, thereby influencing the host metabolism positively. However, care should be taken when we refer to fibre hydrolysis and release of simple sugars since this may not be beneficial for the host. As an example, Schutte (1990) showed that, when xylose and arabinose were supplemented to broiler diets, these created performance and litter quality issues. The generation of oligosaccharides is therefore preferable, and can stimulate microbiota fermentative capacity (Broekaert et al., 2011).

The use of NIR to predict NSP content in feed ingredients is possible, as previously demonstrated (Archibald and Kays, 2000; Blakeney and Flinn, 2005; Hollung et al., 2005). Our database covered more ingredients and variation than the previous studies, resulting in robust calibrations that can help nutritionists to better formulate poultry diets.