Orange essential oil was microencapsulated by complex coacervation with whey protein isolate (WPI): carboxymethylcellulose (CMC), WPI:sodium alginate (SA) and WPI:chitosan (CH). Effect of pH, protein:polysaccharide ratio and solid concentration on coacervation efficiency were selected for the best coacervation conditions. Tannic acid (TA), sodium tripolyphosphate, oxidised tannic acid and transglutaminase enzyme (TG) were used as cross-linking agents. Highest encapsulation efficiency (EE) for wet coacervated microcapsules ranged from 88% to 94%. Microcapsules were freeze and spray dried to evaluate their effect on its integrity. EE was higher than 80% in freeze dried coacervated microcapsules with and without cross-linking agent, but they formed a solid cake. Spray-dried samples formed a free fluid solid (10–20 µm), where the systems WPI:CMC and WPI:CH cross-linked with TA and TG, respectively showed the highest EE (47% and 50% respectively), representing 400% improvement compared to the samples without cross-linking.

Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1–30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R²c: ≥ 0.99, standard error of calibration (SEC) of 0.39–0.82, and standard error of prediction (SEP) of 0.45–0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry.