Roberto Gonçalves Junqueira's research while affiliated with Federal University of Minas Gerais and other places

Neutralization with alkaline compounds is one of the most common adulterations in milk. The rosolic acid method is a classical test widely used in different countries for detection of neutralizers in milk. The official and a modified version were validated in a single laboratory validation process considering four adulterants: sodium bicarbonate(BI), sodium carbonate(CA), sodium hydroxide(HY) and sodium citrate(CI). The modified version, which presented better performance was selected for interlaboratory validation. In this process, samples of raw milk with acidities of 0.19% were neutralized with different concentrations of BI, CA, HY and C and tested for homogeneity and stability. Eight laboratories, which represented different sectors of the milk production chain, received and analysed these samples. The collaborative trial results confirmed the method performance, although sensitivity and precision were inferior to those obtained in the intralaboratory process, demonstrating its applications and limitations

O objetivo do presente trabalho foi realizar uma análise histórica do perfil de conformidade de 2.580 amostras de queijos industriais sob fiscalização no estado de Minas Gerais, em relação aos parâmetros físico-químicos regulamentados Umidade, Matéria Gorda no Extrato Seco, Atividade da Enzima Fosfatase Alcalina Residual e Nitrato, no período de sete anos. Os parâmetros com maior prevalência de não conformidades foram Matéria Gorda no Extrato Seco e Atividade da Enzima Fosfatase Alcalina, alavancados pelas contribuições dos queijos Minas Frescal e Muçarela, respectivamente. Estabilidade no perfil de não conformidades foi evidenciada no período considerado. Concluiu-se que o investimento em ações voltadas para aqueles parâmetros identificados como críticos seria uma importante tática para promoção de uma mudança no patamar de qualidade e conformidade de queijos no estado. Ainda, observou-se que a aplicação de ferramentas estatísticas sobre as séries históricas de análises fiscais permite um melhor entendimento e monitoramento do sistema, com direcionamento das ações de melhoria, visando à promoção da segurança dos alimentos.

The presence of dibenzo-p-dioxins, polychlorinated dibenzofurans, and polychlorinated biphenyls in food is an important consideration for food safety, especially in fish matrices that are susceptible to bioaccumulation of those compounds from marine environments. In this context, the production of a reference material (RM) for this analytical scope is of great importance for ensuring the reliability of the results generated by monitoring programs. A lyophilized material was produced by spiking tilapia fillets with 29 compounds, and the material was evaluated based on the potential for using it as a certified reference material (CRM) or in proficiency testing (PT) schemes. A pilot experiment was performed to evaluate the incorporation of the analytes in tilapia fillets using standards prepared in nonane and in fish oil. The process yield was approximately 16% for both conditions, but higher mean recoveries were achieved with fish oil. The tilapia fillets were processed, and a portion of this bulk was spiked and then homogenized. The spiked and unspiked portions were frozen prior to lyophilization. Each freeze-dried portion was homogenized and sieved, and then, the portions were mixed, packed, and stored under refrigeration. The material was considered homogeneous for all analytes based on the criteria established for PT. Of the tested analytes, 27 were found to be stable in the short-term design (p > 0.05) when the material was kept at 45 ± 2 °C for 9 days. Long-term stability was evaluated at 2, 4, 6, and 8 months, and 22 of the analytes were found to be stable through up to 4 months of storage based on the criteria defined in references related to both the production of CRM and PT. The contributions of the homogeneity and stability in the short- and long-term uncertainties were estimated and indicated the adequacy of the material for use as a reference for trueness experiments.

A procedure to evaluate matrix effects was systematized and applied to the analysis of polycyclic aromatic hydrocarbons in roasted coffee and cocoa beans. The analytical method relied on gas chromatography coupled to mass spectrometry with isotope dilution (GC-IDMS). Once the regression assumptions were confirmed, the slopes and intercepts obtained for traditional and matrix-matched calibration curves were compared by the t test, with or without combined variances, depending on the profile of the residual variances. Matrix effects were considered when the slopes and/or intercepts differed significantly. The occurrence of matrix effects diverged for the studied matrices. No matrix effects were detected for roasted coffee using GC-IDMS, whereas significant effects were observed for cocoa beans. An original discussion related to these findings was presented, based on the differences in the chemical compositions of the matrices and the instrumental conditions. These results serve as a warning to current practices in validation of methods that involve IDMS and highlight the importance of the matrix effects evaluation, even when using an isotope dilution approach.

A sequential strategy was proposed to detect adulterants in milk using a mid-infrared spectroscopy and soft independent modelling of class analogy technique. Models were set with low target levels of adulterations including formaldehyde (0.074 g.L-1), hydrogen peroxide (21.0 g.L-1), bicarbonate (4.0 g.L-1), carbonate (4.0 g.L-1), chloride (5.0 g.L-1), citrate (6.5 g.L-1), hydroxide (4.0 g.L-1), hypochlorite (0.2 g.L-1), starch (5.0 g.L-1), sucrose (5.4 g.L-1) and water (150 g.L-1). In the first step, a one-class model was developed with unadulterated samples, providing 93.1% sensitivity. Four poorly assigned adulterants were discarded for the following step (multi-class modelling). Then, in the second step, a multi-class model, which considered unadulterated and formaldehyde-, hydrogen peroxide-, citrate-, hydroxide- and starch-adulterated samples was implemented, providing 82% correct classifications, 17% inconclusive classifications and 1% misclassifications. The proposed strategy was considered efficient as a screening approach since it would reduce the number of samples subjected to confirmatory analysis, time, costs and errors.

An accelerated solvent extraction (ASE) procedure for use with gas chromatography-mass spectrometry (GC-MS) was optimized for the determination of eight polycyclic aromatic hydrocarbons (PAHs) in cocoa beans. Plackett-Burman and rotatable central composite design (RCCD) indicated that three variables affected the recoveries of PAHs during the extraction and purification steps: agitation time in the second liquid-liquid partition, weight of silica gel in the column, and volume of hexane for PAH elution from the column. After obtaining the optimal conditions, a single laboratory method validation was performed. Linearity was demonstrated for benzo[a]pyrene in the concentration range from 0.5 to 8.0 mg.kg⁻¹ of sample, corresponding to 1.25 to 20.0 μg.kg⁻¹ of cocoa on a fat basis. For the other analytes, linearity was observed from 0.75 to 8.0 μg.kg⁻¹ of sample (1.88 to 20.0 μg.kg⁻¹ of cocoa on a fat basis). Significant matrix effects were found for chrysene and benzo[b]fluoranthene. The precision of the method was verified with relative standard deviations (RSDs) ranging from 2.57 to 14.13 % and from 4.36 to 19.77 % under repeatability and intermediate precision conditions, respectively. The average recoveries of the eight PAHs ranged from 74.99 to 109.73 %. These parameters, limits and measurement uncertainties met the performance criteria established by European Union regulations, except for the theoretical limit of detection for chrysene. The method was applied to the analysis of samples of Brazilian cocoa beans, and only one sample was found to have a PAH content above the maximum limit defined by the European Union legislation. This optimized and validated method is intended to be used as part of the official Brazilian monitoring programs investigating contaminants and residues in food.

A strategy for determining performance parameters of two–class multivariate qualitative methods was proposed. As case study, multivariate classification methods based on mid-infrared (MIR) spectroscopy coupled with the soft independent modelling of class analogy (SIMCA) technique for detection of hydrogen peroxide and formaldehyde in milk were developed. From the outputs (positive/negative/inconclusive) of the samples, which were unadulterated and adulterated at target value, the main performance parameters were obtained. Sensitivity and specificity values for the unadulterated and adulterated classes were satisfactory. Inconclusive ratios 12% and 21%, respectively, for hydrogen peroxide and formaldehyde were obtained. To evaluate the performance parameters related to concentration, Probability of Detection (POD) curves were established, estimating the decision limit, the capacity of detection and the unreliability region. When inconclusive outputs were obtained, two additional concentration limits were defined: the decision limit with inconclusive outputs and the detection capability with inconclusive outputs. The POD curves showed that for concentrations below 3.7 g L⁻¹ of hydrogen peroxide and close to zero of formaldehyde, the chance of giving a positive output (adulterated sample) was lower than 5%. For concentrations at or above 11.3 g L⁻¹ of hydrogen peroxide and 10 mg L⁻¹ of formaldehyde, the probability of giving a negative output was also lower than 5%.