Publication Type
Journal Article
Publication Date (Issue Year)
2025
Journal Name
Discover Applied Sciences
Abstract
Anti-nutritional factors can impact soybean nutrient bioavailability when consumed by monogastric animals. However, conventional methods available for quantifying anti-nutritional factors such as phytate and trypsin inhibitors in feeds are laboratory-intensive, time-consuming, expensive, and error-prone. This study developed near-infrared spectrophotometry (NIRS)-based models to quantify phytate and trypsin inhibitors in soybean. Thus, a set of 190 soybean genotypes assayed through conventional wet chemistry was used as a reference for model development and cross-validation. Using a benchtop NIR instrument (DS2500), spectra readings between 400 and 2500 nm were taken from each soybean sample. Mean values for phytate and total trypsin inhibitors (TTI) were 1.77 mg g−1 (SD = 1.23) and 0.89 mg g−1 (SD = 0.24), respectively. Predictive models were developed through partial least squares (PLS) and random forest (RF) regressions. The random forest models outperformed partial least squares regression with the best predictive performance of R2test = 0.97; RPD = 5.95 and R2test = 0.96; RPD = 3.62 for phytate and TTI, respectively. The high R2 and RPD values demonstrate the model's strong predictive capability and accuracy, suggesting that the NIRS-based models can effectively quantify phytate and TTI in soybean. Thus, breeders can efficiently select for low-anti-nutritional genotypes and accelerate the development of nutritionally beneficial legumes while reducing soybean processing costs. NIRS offers a promising alternative to traditional phenotyping methods due to its speed, simplicity, environmental friendliness, and cost-effectiveness. Its integration into breeding programs can streamline the screening process, especially in early selection stages.
Keywords
Soybeans, Phytate, Trypsin inhibitors, Near-infrared spectrophotometry (NIRS), Partial least squares, Random forest
Rsif Scholar Name
Noberto Jose Palange
Thematic Area
Food security and Agribusiness
Africa Host University (AHU)
Makerere University, Uganda
Recommended Citation
Palange, N. J. (2025). Artificial intelligence-driven near-infrared spectrophotometry model for rapid quantification of anti-nutritional factors in soybean (Glycine max.). Discover Applied Sciences, 7 (6), 1-13. https://doi.org/https://doi-org.uplib.idm.oclc.org/10.1007/s42452-025-07235-3