Abstract
This work evaluates near-infrared (NIR) spectroscopy coupled with chemometric tools for determining the superficial content of citral ( SCCtSCCt<math><mi>S</mi> <msub><mrow><mi>C</mi></mrow> <mrow><mi>C</mi> <mi>t</mi></mrow> </msub> </math> ) on microparticles. To perform this evaluation, using spray drying, citral was encapsulated in a matrix of dextrin using twelve combinations of citral:dextrin ratios (CDR) and inlet air temperatures (IAT). From each treatment, six samples were extracted, and their SCCtSCCt<math><mi>S</mi> <msub><mrow><mi>C</mi></mrow> <mrow><mi>C</mi> <mi>t</mi></mrow> </msub> </math> and NIR absorption spectral profiles were measured. Then, the spectral profiles, pretreated and randomly divided into modeling and validation datasets, were used to build the following prediction models: principal component analysis-multilinear regression (PCA-MLR), principal component analysis-artificial neural network (PCA-ANN), partial least squares regression (PLSR) and an artificial neural network (ANN). During the validation stage, the models showed R2R2<math> <msup><mrow><mi>R</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </math> values from 0.73 to 0.96 and a root mean squared error (RMSE) range of [0.061-0.140]. Moreover, when the models were compared, the full and optimized ANN models showed the best fits. According to this study, NIR coupled with chemometric tools has the potential for application in determining SCCtSCCt<math><mi>S</mi> <msub><mrow><mi>C</mi></mrow> <mrow><mi>C</mi> <mi>t</mi></mrow> </msub> </math> on microparticles, particularly when using ANN models.