Chia (Salvia hispanica) seeds degradation studied by fuzzy-c mean (FCM) and hyperspectral imaging and chemometrics - fatty acids quantification
DOI:
https://doi.org/10.17268/sci.agropecu.2022.015Keywords:
polyunsaturated fatty acids, machine learning, fuzzy c-means, oleaginous seedsAbstract
Chia seeds are nutritious food because they have a high content of protein, polyunsaturated fatty acids (omega-3 and omega-6) and phenolic compounds. During storage, fatty acids are degraded, by oxidative and hydrolytic reactions, forming free fatty acids (FFA). In this work, we used Near Infrared Hyperspectral Imaging (NIR- HSI) and chemometrics to predict FFA acid value and fatty acids concentrations in chia seeds during storage. First, we explore the hyperspectral images by Fuzzy c-means (FCM), where it is possible to observe as chemical compounds are formed or degraded during storage. Second, PLSR models were developed to predict FFA value and fatty acids concentration. RPD values reached values higher then 2.0, indicating a good ability to estimate these chemical compounds, especially polyunsaturated fatty acids omega-3 and omega-6. Finally, NIR-hyperspectral imaging coupled with chemometrics allowed us to show the chemical degradation process of chia seeds during storage, mainly associated with polyunsaturated fatty acids degradation. Besides NIR-HSI showed to be a powerful technique to quantify the main fatty acids with high accuracy.
References
AOCS (2003). Cd 1-25, Iodine value of fats and oils. Off. methods Recomm. Pract. AOCS, 5th ed., Champaign, Illinois, USA.
Ballus, C. A., Meinhart, A. D., de Souza Campos Jr, F. A., da Silva, L. F. de O., de Oliveira, A. F., & Godoy, H. T. (2014). A quantitative study on the phenolic compound, tocopherol and fatty acid contents of monovarietal virgin olive oils produced in the southeast region of Brazil. Food Res. Int. 62, 74–83.
Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media.
Brasil, Y. L., Cruz-Tirado, J. P., & Barbin, D. F. (2022). Fast online estimation of quail eggs freshness using portable NIR spectrometer and machine learning. Food Control, 131, 108418.
Busilacchi, H., Bueno, M., Severin, C., Di Sapio, O., Quiroga, M., & Flores, V. (2013). Evaluación de Salvia hispanica L. cultivada en el sur de Santa Fe (República Argentina). Cultiv. Trop. 34, 55-59.
Caballero, D., Calvini, R., Amigo, & J. M. (2020). Chapter 3.3 - Hyperspectral imaging in crop fields: precision agriculture, in: Amigo, J.M.B.T.-D.H. in S. and T. (Ed.), Hyperspectral Imaging. Elsevier, pp. 453–473.
Choi, J.-Y., Kim, H.-C., & Moon, K.-D. (2021). Geographical origin discriminant analysis of Chia seeds (Salvia hispanica L.) using hyperspectral imaging. J. Food Compos Anal, 101, 103916.
Cruz-Tirado, J. P., Fernández Pierna, J. A., Rogez, H., Barbin, D., & Baeten, V. (2020). Authentication of cocoa (Theobroma cacao) bean hybrids by NIR-hyperspectral imaging and chemometrics. Food Control, 107445.
Cruz-Tirado, J. P., Lucimar da Silva Medeiros, M., & Barbin, D. F. (2021). On-line monitoring of egg freshness using a portable NIR spectrometer in tandem with machine learning. J. Food Eng., 306, 110643.
Cruz-Tirado, J. P., Oliveira, M., de Jesus Filho, M., Godoy, H. T., Amigo, J. M., & Barbin, D. F. (2021). Shelf life estimation and kinetic degradation modeling of chia seeds (Salvia hispanica) using principal component analysis based on NIR- hyperspectral imaging. Food Control, 123.
da Silva Medeiros, M. L., Cruz-Tirado, J. P., Lima, A. F., de Souza Netto, J. M., et al. (2022). Assessment oil composition and species discrimination of Brassicas seeds based on hyperspectral imaging and portable near infrared (NIR) spectroscopy tools and chemometrics. J. Food Compos. Anal., 107, 104403.
De Falco, B., Amato, M., & Lanzotti, V. (2017). Chia seeds products: an overview. Phytochem. Rev., 16, 745-760.
de Falco, B., Fiore, A., Rossi, R., Amato, M., & Lanzotti, V., (2018). Metabolomics driven analysis by UAEGC-MS and antioxidant activity of chia (Salvia hispanica L.) commercial and mutant seeds. Food Chem., 254, 137-143.
Escalona-García, L. A., Pedroza-Islas, R., Natividad, R., Rodríguez-Huezo, M. E., Carrillo-Navas, H., & Pérez-Alonso, C. (2016). Oxidation kinetics and thermodynamic analysis of chia oil microencapsulated in a whey protein concentrate-polysaccharide matrix. J. Food Eng., 175, 93-103.
Giaretta, D., Lima, V. A., & Carpes, S. T. (2018). Improvement of fatty acid profile in breads supplemented with Kinako flour and chia seed. Innov. Food Sci. Emerg. Technol., 49, 211–214.
Grancieri, M., Martino, H. S. D., & Gonzalez de Mejia, E. (2019). Chia seed (Salvia hispanica L.) as a source of proteins and bioactive peptides with health benefits: A review. Compr. Rev. Food Sci. Food Saf. 18, 480-499.
Hartigan, J. A. (1975). Clustering algorithms. John Wiley & Sons, Inc.
Hartman, L. (1973). Rapid preparation of fatty acid methyl esters from lipids. Lab. Pract., 22, 475-476.
Joseph, J. D., & Ackman, R. G. (1992). Capillary column gas chromatographic method for analysis of encapsulated fish oils and fish oil ethyl esters: collaborative study. J. AOAC Int. 75, 488–506.
Knez Hrnčič, M., Ivanovski, M., Cör, D., & Knez, Ž. (2019). Chia Seeds (Salvia hispanica L.): an overview—phytochemical profile, isolation methods, and application. Molecules, 25, 11.
Mannucci, A., Castagna, A., Santin, M., Serra, A., Mele, M., & Ranieri, A. (2019). Quality of flaxseed oil cake under different storage conditions. LWT, 104, 84-90.
Mata, T. M., Correia, D., Pinto, A., Andrade, S., Trovisco, I., et al. (2017). Fish oil acidity reduction by enzymatic esterification. Energy Procedia, 136, 474-480.
Mendez, J., Mendoza, L., Cruz-Tirado, J. P., Quevedo, R., & Siche, R. (2019). Trends in application of NIR and hyperspectral imaging for food authentication. Sci. Agropecu., 10, 143-161.
Mobaraki, N., & Amigo, J. M., (2018). HYPER-Tools. A graphical user-friendly interface for hyperspectral image analysis. Chemom. Intell. Lab. Syst., 172, 174-187.
Mohd Ali, N., Yeap, S. K., Ho, W. Y., Beh, B. K., Tan, S. W., & Tan, S. G. (2012). The promising future of chia, Salvia hispanica L. J. Biomed. Biotechnol.
Muñoz, L. A., Cobos, A., Diaz, O., & Aguilera, J. M. (2012). Chia seeds: Microstructure, mucilage extraction and hydration. J. Food Eng., 108, 216–224.
Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L., & Engelsen, S. B. (2000). Interval partial least-squares regression (i PLS): A comparative chemometric study with an example from near-infrared spectroscopy. Appl. Spectrosc., 54, 413–419.
Oliveira-Alves, S. C., Vendramini-Costa, D. B., Betim Cazarin, C. B., Maróstica Júnior, M. R., Borges Ferreira, J. P., et al. (2017). Characterization of phenolic compounds in chia (Salvia hispanica L.) seeds, fiber flour and oil. Food Chem., 232, 295-305.
Osborne, B. G. (2006). Near infrared spectroscopy in food analysis. Encycl. Anal. Chem. Appl. theory Instrum.
Saeys, W., Mouazen, A. M., & Ramon, H. (2005). Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy. Biosyst. Eng., 91, 393-402.
Salvatierra-Pajuelo, Y. M., Azorza-Richarte, M. E., & Paucar-Menacho, L. M. (2019). Optimización de las características nutricionales, texturales y sensoriales de cookies enriquecidas con chía (Salvia hispánica) y aceite extraído de tarwi (Lupinus mutabilis). Sci. Agropecu., 10(1), 7-17.
Ullah, R., Nadeem, M., Khalique, A., Imran, M., Mehmood, S., Javid, A., & Hussain, J. (2016). Nutritional and therapeutic perspectives of Chia (Salvia hispanica L.): a review. J. Food Sci. Technol., 53, 1750-1758.
Verma, H., Verma, D., & Tiwari, P. K. (2021). A population based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image. Expert Syst. Appl., 167, 114121.
Wang, F., Wang, R., Jing, W., & Zhang, W. (2012). Quantitative dissection of lipid degradation in rice seeds during accelerated aging. Plant Growth Regul., 66, 49-58.
Wang, Z., Fan, S., Wu, J., Zhang, C., Xu, F., Yang, X., & Li, J. (2021). Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 254, 119666.
Yu, Z., Fang, H., Zhangjin, Q., Mi, C., Feng, X., & He, Y., (2021). Hyperspectral imaging technology combined with deep learning for hybrid okra seed identification. Biosyst. Eng., 212, 46-61.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Scientia Agropecuaria
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors who publish in this journal accept the following conditions:
a. The authors retain the copyright and assign to the magazine the right of the first publication, with the work registered with the Creative Commons attribution license, which allows third parties to use the published information whenever they mention the authorship of the work and the First publication in this journal.
b. Authors may make other independent and additional contractual arrangements for non-exclusive distribution of the version of the article published in this journal (eg, include it in an institutional repository or publish it in a book) as long as it clearly indicates that the work Was first published in this journal.
c. Authors are encouraged to publish their work on the Internet (for example, on institutional or personal pages) before and during the review and publication process, as it can lead to productive exchanges and a greater and faster dissemination of work Published (see The Effect of Open Access).
