A multi-source data integration for soybean differentiation through multiblock data analysis using a novel adaptation of ComDim

Abstract

Soybean is a major global commodity. Given its importance, ensuring traceability becomes essential. Genetic, climatic, and soil-related factors influence its chemical composition. Integrating multi-source data using a multiblock analysis represents a powerful approach to differentiating soybeans and monitoring their traceability. This study employed an extension of the ComDim method (also known as Common Components and Specific Weights Analysis, CCSWA) to simultaneously differentiate 20 Brazilian soybean varieties, conventional and transgenic, based on cultivation region and cultivation type. The extension replaced the PCA (Principal Components Analysis) used in classical ComDim by CCA (Common Components Analysis). Forty samples cultivated in Londrina and Ponta Grossa (Paraná, Brazil) were analyzed for their fatty acid, amino acid, isoflavone, and mineral profiles using GC-FID, IEC, HPLC-DAD, and ICP-OES. The CCA-based ComDim results revealed that Common Component 2 (CC2) was primarily responsible for distinguishing the geographical regions of Londrina and Ponta Grossa. The global loadings of CC2 indicated that zinc (Zn), manganese (Mn), oleic acid, arginine, and malonyl genistin were the most influential variables in this component. In contrast, CC3 was associated with differentiating conventional and transgenic cultivars. The global loadings highlighted linoleic acid, oleic acid, α-linolenic acid, malonyl glycitin, malonyl genistin, Fe, Zn, and Mn as the most relevant contributors. The combined CC2 and CC3 plots indicated tendencies toward differentiation of soybean samples by cultivation region and cultivation type. This result highlights the potential of CCA-based ComDim as an effective tool for soybean traceability.