A green method for the authentication of sugarcane spirit and prediction of density and alcohol content based on near infrared spectroscopy and chemometric tools

• Published in: Food research international 2023-08, Vol.170, p.112830-112830, Article 112830
• Main Authors: Oliveira, Sheila, Duarte, Ellen, Gomes, Mirelly, Nagata, Noemi, Fernandes, David Douglas de Sousa, Veras, Germano
• Format: Article
• Language: English
• Subjects: Algorithms; Cachaça; Chemometrics; Edible Grain; Geographic origin; Multivariate calibration; One-class classification; Quality control; Saccharum; Spectroscopy, Near-Infrared; Vibrational spectroscopy
• ISSN: 0963-9969; 1873-7145
• DOI: 10.1016/j.foodres.2023.112830

[Display omitted] •Geographical origin of cachaça samples was determined by using NIR spectroscopy.•One-class classification techniques were used as chemometric tools.•DD-SIMCA and OC-PLS models were evaluated.•Density and alcohol content were determined by using PLS regression. Cachaça is a Brazilian beverage obtained from the fermentation of sugarcane juice (sugarcane spirit) and is considered one of the most consumed alcoholic beverages in the world with a strong economic impact on the northeastern Brazil, more specifically in the Brejo. This microregion produces sugarcane spirits with high quality associated to edaphoclimatic conditions. In this sense, analysis for sample authentication and quality control that uses solvent-free, environmentally friendly, rapid and non-destructive methods is advantageous for cachaça producers and production chain. Thus, in this work commercial cachaça samples using near-infrared spectroscopy (NIRS) were classified based on geographical origin using one-class classification Data-Driven in Soft Independent Modelling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OCPLS) and predicted quality parameters of alcohol content and density based on different chemometric algorithms. A total of 150 sugarcane spirits samples were purchased from the Brazilian retail market being 100 from Brejo and 50 from other regions of Brazil. The one-class chemometric classification model was obtained with DD-SIMCA using the Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as preprocessing algorithm and sensibility was 96.70 % and specificity 100 % in the spectral range 7,290–11,726 cm−1. Satisfactory results were obtained in the model constructs for density and the chemometric model, iSPA-PLS algorithm with baseline offset as preprocessing, obtained root mean square errors of prediction (RMSEP) of 0.0011 mg/L and Relative Error of Prediction (REP) of 0.12 %. The chemometric model for alcohol content prediction used the iSPA-PLS algorithm with Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as algorithm as preprocessing obtaining RMSEP and REP of 0.69 and 1.81 % (v/v), respectively. Both models used the spectral range from 7,290–11,726 cm−1. The results reflected the potential of vibrational spectroscopy coupled with chemometrics to build reliable models for identifying the geographical origin of cachaça samples for predicting quality parameters