Artificial intelligence‑driven near‑infrared spectrophotometry model for rapid quantification of anti‑nutritional factors in soybean (Glycine max.)
| dc.contributor.author | Palange, Norberto Jose | |
| dc.contributor.author | Obua, Tonny | |
| dc.contributor.author | Sserumaga, Julius Pyton | |
| dc.contributor.author | Ochwo-Ssemakula, Mildred | |
| dc.contributor.author | Edema, Richard | |
| dc.contributor.author | Tukamuhabwa, Phinehas | |
| dc.date.accessioned | 2025-10-22T05:29:29Z | |
| dc.date.available | 2025-10-22T05:29:29Z | |
| dc.date.issued | 2025-06-15 | |
| dc.description.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. | |
| dc.identifier.citation | Palange, N. J., Obua, T., Sserumaga, J. P., Wembabazi, E., Ochwo-Ssemakula, M., Nuwamanya, E., ... & Tukamuhabwa, P. (2025). Artificial intelligence-driven near-infrared spectrophotometry model for rapid quantification of anti-nutritional factors in soybean (Glycine max.). Discover Applied Sciences, 7(6), 647. | |
| dc.identifier.other | https://doi.org/10.1007/s42452-025-07235-3 | |
| dc.identifier.uri | https://nru.uncst.go.ug/handle/123456789/12158 | |
| dc.language.iso | en | |
| dc.publisher | Discover Applied Sciences | |
| dc.title | Artificial intelligence‑driven near‑infrared spectrophotometry model for rapid quantification of anti‑nutritional factors in soybean (Glycine max.) | |
| dc.type | Article |