Treffer: Classificação de carvão comercial para uso doméstico por espectroscopia no infravermelho próximo ; Classification of commercial charcoal for domestic use by near infrared spectroscopy

Charcoal for domestic use presents great variation in quality because in most furnaces the carbonization process is difficult to control and often inadequate raw material is used, generating a product with heterogeneous and dubious quality. The state of São Paulo created the Premium Seal in 2003 to certify the charcoal marketed in the state and assigned minimum reference values for such certification. Given the need for fast and reliable methods to classify charcoal for domestic use an alternative is near infrared (NIR) spectroscopy. Thus, the aim of this study was to apply NIR spectroscopy coupled with multivariate statistics in order to classify into categories and estimate the quality of commercial vegetable charcoal for domestic use. 76 charcoal samples from nine suppliers were selected for NIR spectra acquisition on the transverse face (TV) and rolling surface (SR) using the integration sphere and optical fiber from raw (untreated) and sanded charcoal and for chemical characterization by method of the Immediate Chemical Analysis (AQI). Analysis of variance was performed in the results of the AQI to assign quality classes to the coal as a function of the fixed carbon content (TCF). Principal Component Analysis (PCA) was done from the spectra of the charcoals to verify if it is possible to group samples with similar chemical characteristics. Subsequently, Discriminant Analysis based on Partial Least Squares (PLS-DA) was carried out to classify the charcoals according to their suppliers and to predict their quality through the TCF. The charcoal specimens had an average moisture content of 5.53%, volatile material content (TMV) of 23.84% and TCF of 76.14%. The PCA made from the spectra of the raw and sanded charcoals was not able to distinguish groups in this data set indicating the heterogeneity of the samples. PLS-DA resulted in a high percentage of correct classifications for both suppliers and quality classes. From the spectra obtained by the integrating sphere, the model generated to classify by supplier ...