Rapid estimation of non-starch polysaccharides and lignin in monogastric feed ingredients using near-infrared spectroscopy

The fiber composition in protein meals and by-products is highly variable and is influenced by plant genetics, environmental conditions, and processing. Therefore, enabling the rapid analysis of fiber in various ingredients can support the inclusion of fiber-rich ingredients in monogastric diets. This study aimed to develop and validate near-infrared reflectance spectroscopy (NIRS) calibrations for predicting non-starch polysaccharides (NSP), lignin, and related components in feed ingredients. In this study, 628 samples of diverse feed ingredients and by-products were collected globally between 2015 and 2022. Chemical analyses were conducted to determine the NSP content, including total and insoluble NSP, as well as the monosaccharide sugars. Separate assays were also conducted to determine the cellulose and lignin contents. The same samples were screened using NIRS to predict the NSP, mono-component sugar (e.g., xylose, glucose, and mannose), and cellulose and lignin contents. The diversity in raw materials enabled the development of a global NIRS calibration for the NSP fractions, cellulose, lignin, and key monosaccharides. Global calibrations achieved strong predictive performance for total NSP [ R 2 = 0.96, ratio of standard error of performance to standard deviation (RPD) = 4.6], cellulose ( R 2 = 0.97, RPD = 5.1), and lignin ( R 2 = 0.90, RPD = 2.8), while the monosaccharides were predicted with robust accuracy ( R 2 typically ≥0.90, RPD ≥ 3.0). Ingredient-specific models for soybean meal showed good performance for the key fiber components, with insoluble NSP achieving R 2 = 0.85 (RPD = 4.5), cellulose with R 2 = 0.94 (RPD = 4.1), and the xylose and glucose calibrations demonstrating strong accuracy ( R 2 ≥ 0.9). The sunflower meal models also performed well, with total NSP achieving R 2 = 0.92 (RPD = 3.3) and lignin with R 2 = 0.90 (RPD = 3.2), while the total xylose and total glucose predictions showed strong accuracy ( R 2 ≥ 0.87, RPD ≥ 3.0). The rapeseed meal models showed strong performance for insoluble NSP ( R 2 = 0.93, RPD = 4.0) and insoluble arabinose ( R 2 = 0.94, RPD = 3.9), while the lignin and cellulose calibrations achieved moderate accuracy ( R 2 = 0.81 and 0.61, respectively). These findings demonstrate the potential of NIRS as a reliable, rapid method for fiber characterization in monogastric feed evaluation.