This work aims the development of an analytical alternative to determine phenolic compounds in fruits exploiting a greener microwave-assisted extraction and photometric detection by smartphone-based colorimetry. Acerola cherry (Malpighia emarginata) was selected as a model and the extraction was optimized by the Doehlert design, taking into account temperature, solvent composition, and extraction time. The Folin–Ciocalteau method was used to determine total phenolic content in the extracts, with a 5-fold reduction in reagent amount and waste generation. The optimal extraction was achieved with 17% v/v ethanol, at 38 °C, for 50 min. The total phenolic content determined by the proposed procedure (146 ± 4 mg GAE g⁻¹ dry weight) agreed with the reference procedure (145.0 ± 0.7 mg GAE g⁻¹ dry weight) at the 95% of confidence level. Due to these characteristics, the proposed approach is an alternative for screening fruits in research with bioactive compounds.

Lactose, the main carbohydrate in cow's milk, may cause health problems for consumers with intolerance. Lactose determination in milk is hindered by the matrix complexity and lack of chromophore groups. Chromatography, volumetric, and spectrophotometric approaches involving chemical derivatization are time-consuming and require laborious sample preparation, which is incompatible with the high analytical demand. In this context, a novel approach is presented for lactose determination in milk exploiting smartphone-based digital-image photometry. It was based on a modification of the Benedict's method, involving formation of the violet Cu(I)/2,2′-biquinoline-4,4′-dicarboxylate (BCA) complex instead of the copper(I) oxide precipitate, aiming at improvement of sensitivity and precision. Sample pretreatment and analyte derivatization were performed in Eppendorf tubes with minimal reagent amounts and a smartphone camera was used for image acquisition under controlled conditions. Measurements were based on the RGB color system, taking channel G as the analytical response because of the complementarity with the color of the complex. Under the optimized conditions, the proposed procedure yielded a linear response up to 20 mg L⁻¹ (r = 0.999), with a limit of detection of 1.5 mg L⁻¹, which is compatible with determination of lactose in milk and dairy products categorized with low content of the sugar. The procedure takes less than 10 min, with a coefficient of variation of 3.0% (n = 12) and consumes as low as 160 μg Cu and 430 μg BCA per determination, thus being a more practical, fast, cost-effective, and environmental friendly analytical method.

Cinnamon (Cinnamomum zeylanicum) is a spice largely consumed worldwide, but there is still restricted information about its fingerprint. This work aimed to investigate the mineral composition as a possible marker for the classification of cinnamon samples. To this aim, the mineral composition (P, S, Mg, Ca, K, Cu, Zn, B, Fe, Al, Mn, and Si contents) of 56 ground cinnamon samples from different regions of the State of São Paulo, Brazil was determined by inductively coupled plasma optical emission spectroscopy (ICP OES). Principal component analysis was exploited for sample classification, and the microelements presented the best correlation: PC1, PC2, and PC3 explained 93% of the observed variance at 95% confidence level. Si, Al, Fe, and Cu presented the most significant contributions to cluster analysis. Samples were classified into six groups, in which those presenting C. zeylanicum were well clustered, and the samples acquired in bulk as well as those whose labels declared traces of grains and/or spices presented the highest variability. Thus, it was pioneeringly demonstrated the possibility of identifying C. zeylanicum in commercial cinnamon powders, using microelements as authenticity markers.