PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy
Ofélia Anjos,
Ilda Caldeira,
Tiago A. Fernandes,
Soraia Inês Pedro,
Cláudia Vitória,
Sheila Oliveira-Alves,
Sofia Catarino,
Sara Canas
Affiliations
Ofélia Anjos
Instituto Politécnico de Castelo Branco, Quinta da Senhora de Mércules, 6001-909 Castelo Branco, Portugal
Ilda Caldeira
Instituto Nacional de Investigação Agrária e Veterinária, Quinta de Almoínha, Pólo de Dois Portos, 2565-191 Dois Portos, Portugal
Tiago A. Fernandes
CQE—Centro de Química Estrutural, Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento (IST-ID), Universidade de Lisboa, 1049-001 Lisboa, Portugal
Soraia Inês Pedro
Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal
Cláudia Vitória
Faculdade de Ciências, Universidade da Beira Interior, 6201-556 Covilhã, Portugal
Sheila Oliveira-Alves
Instituto Nacional de Investigação Agrária e Veterinária, Quinta de Almoínha, Pólo de Dois Portos, 2565-191 Dois Portos, Portugal
Sofia Catarino
LEAF—Linking Landscape, Environment, Agriculture and Food Research Center, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal
Sara Canas
Instituto Nacional de Investigação Agrária e Veterinária, Quinta de Almoínha, Pólo de Dois Portos, 2565-191 Dois Portos, Portugal
Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.
