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The role of chemosensory relationships to improve raw materials’ selection for Premium cigar manufacture

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Abstract

The selection of lots of raw materials to be used in tobacco products’ blends, especially for Premium cigars, holds a high degree of subjectivity. To overcome this, it is proposed a methodology to choose, among the available lots, only those which comply with precise quality specifications. As an integral part of the approach, this work revealed the constituents and parameters that should be analyzed in the raw materials because they are important to predict cigar strength. It was also defined NIR spectroscopic models that are good enough to analyze the important properties on products and raw materials. Two batches of 27 products (accumulating 3780  cigars) and dust samples (322 in total) were evaluated. The scores of strength were regressed on 33 variables of the cigars and the smoke, as well as on 1050 NIR reflectance measurements. The reference concentrations of the significant properties were regressed on the reflectance. Calibration and validation performances were estimated for the chemosensory and NIR spectroscopic models through partial least squares (PLS 1) and support vector regression (SVR) algorithms. The ratio nicotine/tar and the relative nicotine transfer in the smoke, together with the concentration within the products of total alkaloids as nicotine, total nitrogen, and total ash, were the significant characteristics. The prediction performance of the new chemometrics models through SVR demonstrated their usefulness for this industrial context. This work contributed to define, for the first time, a methodology for choosing the lots of raw materials and managing the optimal aging time given to processed leaves.

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Abbreviations

a:

Intercept of the regression model

b:

Slope of the regression model

C:

Hyperparameter of the support vector regression Model, known as regularization constant

n :

Number of samples or objects

NIR spectroscopy:

Near-infrared spectroscopy

PCA:

Principal component analysis

PLS 1:

Partial least square for predicting one variable

p-value:

Coefficient used to decide in a hypothesis testing.

r :

Pearson correlation coefficient of a simple linear regression

r 2 :

Coefficient of determination of a simple linear regression

R 2 :

Coefficient of determination of the multivariate calibration model.

SECV:

Standard error of cross-validation

SEL:

Standard error of laboratory

SEP:

Standard error of prediction

SVR:

Support vector regression

u (x i):

Uncertainty associated with the ith value from the reference method

u (y i):

Uncertainty associated with the ith value from the NIR spectroscopy

W i :

Weights of the ith pair (x;y)

x i :

Observed value of the independent variable in the ith sample of validation set

y i :

Observed value of the dependent variable in the ith sample of the validation set

ε :

Hyperparameter of the support vector regression, known as loss function

σ :

Hyperparameter of the support vector regression, known as Kernel scale

i :

Ith value

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Acknowledgements

This work received support from Tabacuba and Tabacalera SLU, a former Imperial Brands PLC company. Authors appreciate the help of all colleagues from the “Instituto de Investigaciones del Tabaco” and “Reemtsma Cigarettenfabriken GmbH,” for their help during all phases of experimentation. We are grateful to María Esther Hernandez Reyes, BSc for her valuable assistance during establishing sensory profiles, and to Oscar Rivera, BEng and Nelson Rodríguez, PhD for their contribution with tobacco sampling and processing. Special thanks to Mr. Christian Schulz, Michael Intorp, PhD, Mr. Justo M. Mendaza Martínez and Ms. Gema B. Hidalgo Castaño.

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Authors and Affiliations

  1. Instituto de Investigaciones del Tabaco, Carretera al Tumbadero Km 8 ½, San Antonio de Los Baños, 38100, Artemisa, Cuba

    Amaury Borges-Miranda

  2. Centro de Aplicaciones de Tecnologías de Avanzada, Calle 7ma A entre 214 y 216, Playa, 11300, La Habana, Cuba

    Francisco José Silva-Mata

  3. Academia de Ciencias de Cuba, Calle Cuba 460 Entre Amargura y Teniente Rey, Habana Vieja, 10100, La Habana, Cuba

    Isneri Talavera-Bustamante

  4. Laboratorio Universitario de la Composición y Estructura de las Sustancias, Instituto de Ciencia y Tecnología de los Materiales, Zapata y G. Vedado, 10400, La Habana, Cuba

    Juan Jiménez-Chacón & Manuel Álvarez-Prieto

  5. Facultad de Química, Universidad de La Habana, Zapata y G. Vedado, 10400, La Habana, Cuba

    Manuel Álvarez-Prieto & Carlos S. Pérez-Martínez

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  1. Amaury Borges-Miranda
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Correspondence to Amaury Borges-Miranda.

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Borges-Miranda, A., Silva-Mata, F.J., Talavera-Bustamante, I. et al. The role of chemosensory relationships to improve raw materials’ selection for Premium cigar manufacture. Chem. Pap. 75, 4075–4091 (2021). https://doi.org/10.1007/s11696-021-01577-z

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