Dados do Trabalho

Título

Near and mid-infrared spectra characterization of key coffee compounds for chemical interpretation of multivariate models

Introdução

Infrared spectroscopy is an advanced analytical technique that enhances the efficiency, safety, and quality of food analysis. Both the mid-infrared (MIR) and near-infrared (NIR) regions play significant roles in this domain. However, the chemical interpretation of multivariate models often lags behind in these applications. Such interpretation is crucial for understanding the molecular structure’s cause and effect relationship concerning specific absorption types in NIR and MIR, thus facilitating an analytical understanding of these measurements. Coffee, one of the most commonly analyzed products using NIR and MIR spectroscopy, contains several compounds that interact with energy in these spectral regions.

Material e Métodos

To support further studies utilizing infrared analysis in coffee, this research directly analyzed pure compounds (i.e., analytical standards) of chlorogenic acids, caffeine, theobromine, caffeic acid, p-coumaric acid, theobromine, ferulic acid, gallic acid, cellulose, quercetin, catechin, sucrose, glucose, casein, and lipids using (benchtop and portable) NIR and (benchtop) MIR instruments.

Resultados e Discussão

These compounds, commonly found in coffee, exhibited distinct spectral behaviors associated with their absorption bands. This indicates that interpretating chemometric multivariate models using pure spectra in parallel can be beneficial for identifying significant signals and correlating them with their contents. However, variations in laboratory conditions during the spectral acquisition must be considered to ensure reliable interpretation, necessitating alignment with the conditions used for pure compound analysis. Additionally, these pure compound spectra can be incorporated into chemometric multivariate models as input signal, aiding in the resolution of signal mixtures.

Conclusão

Beyond coffee analysis, these results and interpreted pure spectra provide a foundation for applications involving other food matrices with similar characteristics.