Chemometric Tools for Optimizing an Analytical Method and Evaluating the Mineral Composition of Industrialized Fruit Juices

Introduction: Industrialized fruit juices are popular for their taste and nutritional value. However, prolonged use of this product may lead to exposure to potentially toxic elements, resulting in cumulative adverse health effects. Therefore, monitoring mineral content during quality control is essential to ensure product safety and nutritional compliance. Method: An analytical method for simultaneous determination of twenty-one inorganic constituents (Al, B, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Sr, Ti, V, and Zn) in industrialized fruit juice samples employing Inductively Coupled Plasma Optical Emission Spectrometry (ICPOES) was developed. A two-level full factorial design and a 32 factorial design with two variables were employed to optimize the digestion of fruit juice samples and the spectrometer's operating conditions, respectively. The accuracy of the analytical method was confirmed by analyzing two Certified Reference Materials (CRMs), Peach leaves (NIST 1547) and Tea (NCS DC 73351), as well as addition and recovery tests. Result: In this work, sixteen samples of processed fruit juices from four different brands were acquired from supermarkets in Aracaju, Sergipe, Brazil. The limit of quantification (LOQ) ranged between 0.006 mg L-1 (Li) and 12 mg L-1 (Na). Precision was expressed as the Relative Standard Deviation (RSD), which was <4.6% (n = 3). Concentrations in the industrialized fruit juice ranged between 0.037 (± 0.002) mg L-1 (Mn) and 411.5 (± 12) mg L-1 (K). Discussion: The optimized method was sensitive and accurate in determining multiple elements in processed juices by ICP-OES, with several concentrations below the LOQ. Multivariate data analysis (PCA and HCA) highlighted the elements influencing sample separation by brand. Thus, the method ensured data reliability and optimized the experimental design, facilitating the assessment of product composition and authenticity. Conclusion: Multivariate data analysis revealed that the samples tended to group by brand. Still, the manufacturing process was responsible for subdividing samples of the same brand.