A predictive model for the determination of cadmium concentration in cocoa beans using laser-induced plasma spectroscopy
Modelo predictivo para la determinación de la concentración de cadmio en granos de cacao mediante espectroscopia de plasma inducido por láser
DOI:
https://doi.org/10.15446/agron.colomb.v40n3.104911Palabras clave:
inorganic contaminants, heavy metals, partial least square regression, atomic spectroscopy (en)contaminantes inorgánicos, metales pesados, regresión por mínimos cuadrados parciales, espectroscopía atómica (es)
Descargas
This study proposes a predictive model to determine the concentration of cadmium (Cd) in cocoa beans based on laser-induced breakdown spectroscopy (LIBS) and partial least squares regression (PLSR-1 or PLS-1). The multivariate calibration model was developed using 46 cocoa bean samples, with Cd concentrations up to 1 mg kg-1. The increase of the LIBS signal in the Cd emission lines was evident when the cocoa bean sample was subjected to a solid-liquid-solid transformation (SLST). The range error ratio (RER) was 7.92, which allowed it to be classified as a screening model. Monte Carlo cross-validation was used, with 60% of samples for calibration and the remaining for testing. The standard error of cross-validation (SECV) and standard error of calibration (SEC) were 0.12 mg kg-1 and 0.05 mg kg-1, respectively. The proposed procedure is framed within the alternatives for the chemical analysis of cocoa.
Este estudio propone un modelo para predecir la concentración de cadmio (Cd) en granos de cacao basado en espectroscopía de plasma inducido por láser (LIBS) y regresión por mínimos cuadrados parciales (PLSR-1 o PLS-1). El modelo de calibración fue desarrollado a partir de 46 muestras de granos de cacao con concentración no mayor a 1 mg kg-1. El incremento en la señal LIBS fue evidente cuando la muestra de grano de cacao fue sometida a una transformación sólido líquido-sólido (SLST). La razón del rango de error (RER) es 7.92, lo que permite determinar que el modelo es de tamizaje. Se utilizó la estrategia de validación cruzada Montecarlo con el 60% de las muestras para calibración y las restantes para prueba. El error estándar de validación cruzada (SECV) y de calibración (SEC) fue 0.12 mg kg-1 y 0.05 mg kg-1, respectivamente. El procedimiento propuesto se ubica en el marco de las alternativas de inspección y análisis químico del cacao.
Referencias
Aguirre-Forero, S. E., Piraneque-Gambasica, N. V., & Vásquez-Polo, J. R. (2020). Heavy metals content in soils and cocoa tissues in Magdalena department Colombia: emphasis in cadmium. Entramado, 16(2), 298–310. https://doi.org/10.18041/1900-3803/entramado.2.6753 DOI: https://doi.org/10.18041/1900-3803/entramado.2.6753
Andrade-Garda, J. M. (2009). Basic chemometric techniques in atomic spectroscopy. RSC. DOI: https://doi.org/10.1039/9781847559661
Argüello, D., Chavez, E., Lauryssen, F., Vanderschueren, R., Smolders, E., & Montalvo, D. (2019). Soil properties and agronomic factors affecting cadmium concentrations in cacao beans: A nationwide survey in Ecuador. Science of the Total Environment, 649, 120–127. https://doi.org/10.1016/j.scitotenv.2018.08.292 DOI: https://doi.org/10.1016/j.scitotenv.2018.08.292
ASTM E1655-05. (2012). Standard practices for infrared multivariate quantitative analysis. ASTM International. https://doi.org/10.1520/E1655-17 DOI: https://doi.org/10.1520/E1655-17
Bertoldi, D., Barbero, A., Camin, F., Caligiani, A., & Larcher, R. (2016). Multielemental fingerprinting and geographic traceability of Theobroma cacao beans and cocoa products. Food Control, 65, 46–53. https://doi.org/10.1016/j.foodcont.2016.01.013 DOI: https://doi.org/10.1016/j.foodcont.2016.01.013
Bravo, D., Leon-Moreno, C., Martínez, C. A., Varón-Ramírez, V. M., Araujo-Carrillo, G. A., Vargas, R., Quiroga-Mateus, R., Zamora, A., & Gutierrez Rodríguez, E. A. (2021). The first national survey of cadmium in cacao farm soil in Colombia. Agronomy, 11(4), Article 761. https://doi.org/10.3390/agronomy11040761 DOI: https://doi.org/10.3390/agronomy11040761
Chavez, E., He, Z. L., Stoffella, P. J., Mylavarapu, R. S., Li, Y. C., Moyano, B., & Baligar, V. C. (2015). Concentration of cadmium in cacao beans and its relationship with soil cadmium in southern Ecuador. Science of the Total Environment, 533, 205–214. https://doi.org/10.1016/j.scitotenv.2015.06.106 DOI: https://doi.org/10.1016/j.scitotenv.2015.06.106
Checa, K., Gamarra, M., Soto, J., Ipanaqué, W., & La Rosa, G. (2019). Preliminary study of the relation between the content of cadmium and the hyperspectral signature of organic cocoa beans. Proceedings of the Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 1–7. https://doi.org/10.1109/CHILECON47746.2019.8987991 DOI: https://doi.org/10.1109/CHILECON47746.2019.8987991
De Jong, S. (1993). SIMPLS: An alternative approach to partial least-squares regression. Chemometric and Intelligent Laboratory Systems, 18(3), 251–263. https://doi.org/10.1016/0169-7439(93)85002-X DOI: https://doi.org/10.1016/0169-7439(93)85002-X
Escobar, S., Santander, M., Useche, P., Contreras, C., & Rodríguez, J. (2020). Aligning strategic objectives with research and development activities in a soft commodity sector: A technological plan for Colombian cocoa producers. Agriculture, 10(5), Article 141. https://doi.org/10.3390/agriculture10050141 DOI: https://doi.org/10.3390/agriculture10050141
Escobar, S., Santander, M., Zuluaga, M., Chacón, I., Rodríguez, J., & Vaillant, F. (2021). Fine cocoa beans production: Tracking aroma precursors through a comprehensive analysis of flavor attributes formation. Food Chemistry, 365,. Article 130627. https://doi.org/10.1016/j.foodchem.2021.130627 DOI: https://doi.org/10.1016/j.foodchem.2021.130627
Faber, N. M., & Ferré, F. J. (2008). On the numerical stability of two widely used PLS algorithms. Journal of Chemometrics, 22(2), 101–105. https://doi.org/10.1002/cem.1112 DOI: https://doi.org/10.1002/cem.1112
Fedecacao. (2021). Año cacaotero 2020-2021, el de mayor producción de cacao en la historia de Colombia. News. https://www.fedecacao.com.co/post/año-cacaotero-2020-2021-el-de-mayorproducción-de-cacao-en-la-historia-de-colombia
Filgueiras, P. R., Alves, J. C. L., Sad, C. M. S., Castro, E. V. R., Dias, J. C. M., & Poppi, R. J. (2014). Evaluation of trends in residuals of multivariate calibration models by permutation test. Chemometrics and Intelligent Laboratory Systems, 133, 33–41. https://doi.org/10.1016/j.chemolab.2014.02.002 DOI: https://doi.org/10.1016/j.chemolab.2014.02.002
Gramlich, A., Tandy, S., Andres, C., Chincheros Paniagua, J., Armengot, L., Schneider, M., & Schulin, R. (2017). Cadmium uptake by cocoa trees in agroforestry and monoculture systems under conventional and organic management. Science of the Total Environment, 580, 677–686. https://doi.org/10.1016/j.scitotenv.2016.12.014 DOI: https://doi.org/10.1016/j.scitotenv.2016.12.014
Järup, L., & Åkesson, A. (2009). Current status of cadmium as an environmental health problem. Toxicology and Applied Pharmacology, 238(3), 201–208. https://doi.org/10.1016/j.taap.2009.04.020 DOI: https://doi.org/10.1016/j.taap.2009.04.020
Jorhem, L., & Engman, J. (2000). Determination of lead, cadmium, zinc, copper, and iron in foods by atomic absorption spectrometry after microwave digestion: NMKL1 collaborative study. Journal of AOAC International, 83(5), 1189–1203. DOI: https://doi.org/10.1093/jaoac/83.5.1189
Menegatti, C. R., Nicolodelli, G., Senesi, G. S., da Silva, O. A., Filho,H. J. I., Villas-Boas, P. R., Marangoni, B. S., & Milori, D. M. B. P. (2019). Evaluation of LIBS under controlled atmosphere to quantify cadmium at low concentration in landfill leachates. Applied Physics B, 125, Article 74. https://doi.org/10.1007/s00340-019-7189-9 DOI: https://doi.org/10.1007/s00340-019-7189-9
Nicolodelli, G., Cabral, J., Menegatti, C. R., Marangoni, B., & Senesi, G. S. (2019). Recent advances and future trends in LIBS applications to agricultural materials and their food derivatives: An overview of developments in the last decade (2010–2019). Part I. Soils and fertilizers. TrAC - Trends in Analytical Chemistry, 115, 70–82. https://doi.org/10.1016/j.trac.2019.03.032 DOI: https://doi.org/10.1016/j.trac.2019.03.032
Niño, A. R., Ramírez, C. X., Hernández, R. C., Picón, H., Guerrero, J. E., & Mejía-Ospino, E. (2019). FTIR-ATR predictive model for determination of asphaltene solubility class index (ASCI) based on partial least-squares regression (PLS-R). Energy and Fuels, 33(12), 12213–12218. https://doi.org/10.1021/acs.energyfuels.9b02829 DOI: https://doi.org/10.1021/acs.energyfuels.9b02829
Oliveira, A. P. F., Milani, R. F., Efraim, P., Morgano, M. A., & Tfouni, S. A. V. (2021). Cd and Pb in cocoa beans: Occurrence and effects of chocolate processing. Food Control, 119, Article 107455. https://doi.org/10.1016/j.foodcont.2020.107455 DOI: https://doi.org/10.1016/j.foodcont.2020.107455
Otto, M. (2007). Chemometrics: Statistics and computer application in analytical chemistry (2nd ed.). Weinheim, Wiley-VCH.
Rodríguez Albarracín, H. S., Darghan Contreras, A. E., & Henao, M. C. (2019). Spatial regression modeling of soils with high cadmium content in a cocoa producing area of Central Colombia. Geoderma Regional, 16, Article e00214. https://doi.org/10.1016/j.geodrs.2019.e00214 DOI: https://doi.org/10.1016/j.geodrs.2019.e00214
Rodríguez Giraldo, Y., Rodríguez Sánchez, S., Torres, L. G., Montenegro, A. C., & Pichimata, M. A. (2022). Development of validation methods to determine cadmium in cocoa almond from the beans by ICP-MS and ICP-OES. Talanta Open, 5, Article 100078. https://doi.org/10.1016/j.talo.2021.100078 DOI: https://doi.org/10.1016/j.talo.2021.100078
Satarug, S. (2018). Dietary cadmium intake and its effects on kidneys. Toxics, 6(1), Article 15. https://doi.org/10.3390/toxics6010015 DOI: https://doi.org/10.3390/toxics6010015
Senesi, G. S., Cabral, J., Menegatti, C. R., Marangoni, B., & Nicolodelli, G. (2019). Recent advances and future trends in LIBS applications to agricultural materials and their food derivatives: An overview of developments in the last decade (2010–2019). Part II. Crop plants and their food derivatives. TrAC - Trends in Analytical Chemistry, 118, 453–469. https://doi.org/10.1016/j.trac.2019.05.052 DOI: https://doi.org/10.1016/j.trac.2019.05.052
Sezer, B., Bilge, G., & Boyaci, I. H. (2017). Capabilities and limitations of LIBS in food analysis. TrAC - Trends in Analytical Chemistry, 97, 345–353. https://doi.org/10.1016/j.trac.2017.10.003 DOI: https://doi.org/10.1016/j.trac.2017.10.003
Shen, T., Kong, W., Liu, F., Chen, Z., Yao, J., Wang, W., Peng, J., Chen, H., & He, Y. (2018). Rapid determination of cadmium contamination in lettuce using laser-induced breakdown spectroscopy. Molecules, 23(11), Article 2930. https://doi.org/10.3390/molecules23112930 DOI: https://doi.org/10.3390/molecules23112930
Vanderschueren, R., De Mesmaeker, V., Mounicou, S., Isaure, M. P., Doelsch, E., Montalvo, D., Delcour, J. A., Chavez, E., & Smolders, E. (2020). The impact of fermentation on the distribution of cadmium in cacao beans. Food Research International, 127, Article 108743. https://doi.org/10.1016/j.foodres.2019.108743 DOI: https://doi.org/10.1016/j.foodres.2019.108743
Wang, W., Kong, W., Shen, T., Man, Z., Zhu, W., He, Y., & Liu, F. (2021). Quantitative analysis of cadmium in rice roots based on LIBS and chemometrics methods. Environmental Sciences Europe, 33(1), Article 37. https://doi.org/10.1186/s12302-021-00480-4 DOI: https://doi.org/10.1186/s12302-021-00480-4
Xu, Q. S., & Liang, Y. Z. (2001). Monte Carlo cross validation. Chemometrics and Intelligent Laboratory Systems, 56(1), 1–11. https://doi.org/10.1016/S0169-7439(00)00122-2 DOI: https://doi.org/10.1016/S0169-7439(00)00122-2
Yang, P., Zhou, R., Zhang, W., Yi, R., Tang, S., Guo, L., Hao, Z., Li, X., Lu, Y., & Zeng, X. (2019). High-sensitivity determination of cadmium and lead in rice using laser-induced breakdown spectroscopy. Food Chemistry, 272, 323–328. https://doi.org/10.1016/j.foodchem.2018.07.214 DOI: https://doi.org/10.1016/j.foodchem.2018.07.214
Yang, P., Zhu, Y., Yang, X., Li, J., Tang, S., Hao, Z., Guo, L., Li, X., Zeng, X., & Lu, Y. (2018). Evaluation of sample preparation methods for rice geographic origin classification using laserinduced breakdown spectroscopy. Journal of Cereal Science, 80, 111–118. https://doi.org/10.1016/j.jcs.2018.01.007 DOI: https://doi.org/10.1016/j.jcs.2018.01.007
Yao, M., Yang, H., Huang, L., Chen, T., Rao, G., & Liu, M. (2017). Detection of heavy metal Cd in polluted fresh leafy vegetables by laser-induced breakdown spectroscopy. Applied Optics, 56(14), 4070–4075. https://doi.org/10.1364/ao.56.004070 DOI: https://doi.org/10.1364/AO.56.004070
Zhao, X., Zhao, C., Du, X., & Dong, D. (2019). Detecting and mapping harmful chemicals in fruit and vegetables using nanoparticle-enhanced laser-induced breakdown spectroscopy. Scientific Reports, 9(1), Article 906. https://doi.org/10.1038/s41598-018-37556-w DOI: https://doi.org/10.1038/s41598-018-37556-w
Cómo citar
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Descargar cita
Licencia
![Creative Commons License](http://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png)
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
© Centro Editorial de la Facultad de Ciencias Agrarias, Universidad Nacional de ColombiaSe autoriza la reproducción y citación del material que aparece en la revista, siempre y cuando se indique de manera explícita: nombre de la revista, nombre del autor(es), año, volumen, número y páginas del artículo fuente. Las ideas y afirmaciones consignadas por los autores están bajo su responsabilidad y no interpretan necesariamente las opiniones y políticas de la Universidad Nacional de Colombia. La mención de productos o firmas comerciales en la revista no implica una recomendación o apoyo por parte de la Universidad ni de la Facultad; el uso de tales productos debe ceñirse a las recomendaciones de las etiquetas.
La licencia Creative Commons utilizada por Agronomía Colombiana es la siguiente: Reconocimiento – NoComercial – CompartirIgual (by-nc-sa)
![Licencia de Creative Commons](https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png)
Agronomia Colombiana by Centro Editorial of Facultad de Ciencias Agrarias, Universidad Nacional de Colombia is licensed under a Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional License.
Creado a partir de la obra en http://revistas.unal.edu.co/index.php/agrocol/
Los autores que publican sus artículos en Agronomía Colombiana ceden de manera indefinida, todos los derechos patrimoniales, es decir, transformación, reproducción, comunicación pública, y distribución, y son otorgados sin ninguna limitación en cuanto a territorio se refiere al Centro Editorial de la Facultad de Ciencias Agrarias, Universidad Nacional de Colombia