Development and Certification of CPP-23: A Multi-Element Certified Reference Material for Cereal Plant Tissue from Semi-Arid Regions
Aziz Soulaimani, Mohamed El Gharous, Khalil El Mejahed, Mohamed Louay Metougui, Reda Oulfakir, Latifa Hajji, Said GmouhReliable determination of macro- and micronutrients in cereal plant tissues is essential for agronomic management, environmental monitoring, and interlaboratory data comparability. However, most existing plant certified reference materials (CRMs) are derived from temperate-region matrices and do not adequately represent cereals cultivated under semi-arid conditions, where differences in mineral composition may lead to matrix-related analytical bias. In this study, a new multi-element plant reference material, Cereal Plant Powder 2023 (CPP-23), was developed from composite wheat (Triticum aestivum and T. durum) samples collected across major Moroccan agro-ecological zones. The material was processed, homogenized, and evaluated for homogeneity and stability in accordance with ISO 33405:2024, with no significant short- or long-term variability observed. Elemental characterization was performed using microwave-assisted acid digestion followed by ICP-OES for major and trace elements, while total nitrogen was determined using the Kjeldahl method. Method validation demonstrated satisfactory linearity (R2 > 0.995), precision, and trueness against established reference materials. Certified values were assigned through an interlaboratory comparison involving eight ISO/IEC 17025-accredited laboratories using robust statistical estimators (ISO 13528:2022). Expanded uncertainties (k = 2) were below 15% for all analytes. While these results indicate acceptable internal consistency, the relatively limited number of participating laboratories and the absence of independent analytical validation techniques represent important constraints. CPP-23 provides a matrix-representative material suitable for quality control and method validation in semi-arid agricultural systems. Nevertheless, its ability to reduce analytical bias relative to existing CRMs and its applicability to specific use cases require further experimental validation.