Optimised extraction and HPLC–DAD determination of steroidal glycoalkaloids in potato tubers: Analytical validation and environmental sustainability evaluation

Steroidal glycoalkaloids (α-solanine and α-chaconine) are major potato toxins whose accurate quantification is often challenged by matrix effects and chromatographic selectivity. This study reports the optimisation and validation of a robust reversed-phase HPLC-DAD method for the determination of glycoalkaloids in potato tissues, combined with a systematic assessment of the environmental sustainability of the analytical workflow. Extraction conditions were optimized by integrating systematic solvent screening with a factorial experimental design to evaluate the effect of key operational variables on glycoalkaloid recovery and refine extraction performance The resulting extraction medium improved extract cleanliness and chromatographic stability. A subsequent C18 solid-phase extraction SPE step further reduced matrix effect and enhanced analytical selectivity. Chromatographic separation was achieved on a conventional C18 column using an acetonitrile-water gradient, providing baseline resolution (Rs > 1.5) of α-solanine and α-chaconine with stable diode-array detection (DAD) at 205 nm. The method was validated according to ICH Q2(R2) and AOAC guidelines, demonstrating excellent linearity (R 2 > 0.995), low limits of detection (0.462–0.767 µg/mL) and quantification (1.540–2.558 µg/mL), high precision (≤ 5 % RSD), and recoveries of 90–105 %. Robustness testing confirmed minimal sensitivity to routine variations in chromatographic parameters. Application to potato breeding lines demonstrated reliable quantification and revealed genotype-dependent variation in glycoalkaloid content. The environmental assessment of the analytical procedure was evaluated using Analytical Eco-scale, GAPI and AGREE metrics, demonstrating reduced solvent consumption, avoidance of strongly acidic extraction conditions, and a simplified sample preparation workflow. The proposed HPLC-DAD workflow provides a validated, high-performance, and environmentally conscious approach for routine GA quantification, suitable for food-safety monitoring, regulatory compliance and breeding programme applications.