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Article

PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy

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Instituto Politécnico de Castelo Branco, Quinta da Senhora de Mércules, 6001-909 Castelo Branco, Portugal
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Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal
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Centro de Biotecnologia de Plantas da Beira Interior, 6001-909 Castelo Branco, Portugal
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Instituto Nacional de Investigação Agrária e Veterinária, Quinta de Almoínha, Pólo de Dois Portos, 2565-191 Dois Portos, Portugal
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MED—Mediterranean Institute for Agriculture, Environment and Development, Instituto de formação avançada, Universidade de Évora, Pólo da Mitra, Ap. 94, 7006-554 Évora, Portugal
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CQE—Centro de Química Estrutural, Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento (IST-ID), Universidade de Lisboa, 1049-001 Lisboa, Portugal
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DCeT—Departamento de Ciências e Tecnologia, Universidade Aberta, Rua da Escola Politécnica, 141-147, 1269-001 Lisboa, Portugal
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Faculdade de Ciências, Universidade da Beira Interior, 6201-556 Covilhã, Portugal
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LEAF—Linking Landscape, Environment, Agriculture and Food Research Center, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal
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CEFEMA—Center of Physics and Engineering of Advanced Materials, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal
*
Authors to whom correspondence should be addressed.
Academic Editors: Mercedes Del Río Celestino and Rafael Font Villa
Sensors 2022, 22(1), 286; https://doi.org/10.3390/s22010286
Received: 3 December 2021 / Revised: 24 December 2021 / Accepted: 29 December 2021 / Published: 31 December 2021
(This article belongs to the Special Issue Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods)
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. View Full-Text
Keywords: NIR; calibration models; PLS-R; volatile phenols; aged wine spirit NIR; calibration models; PLS-R; volatile phenols; aged wine spirit
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MDPI and ACS Style

Anjos, O.; Caldeira, I.; Fernandes, T.A.; Pedro, S.I.; Vitória, C.; Oliveira-Alves, S.; Catarino, S.; Canas, S. PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy. Sensors 2022, 22, 286. https://doi.org/10.3390/s22010286

AMA Style

Anjos O, Caldeira I, Fernandes TA, Pedro SI, Vitória C, Oliveira-Alves S, Catarino S, Canas S. PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy. Sensors. 2022; 22(1):286. https://doi.org/10.3390/s22010286

Chicago/Turabian Style

Anjos, Ofélia, Ilda Caldeira, Tiago A. Fernandes, Soraia Inês Pedro, Cláudia Vitória, Sheila Oliveira-Alves, Sofia Catarino, and Sara Canas. 2022. "PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy" Sensors 22, no. 1: 286. https://doi.org/10.3390/s22010286

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