Application of path modelling to identify causal relationships of in vitro production of sesquiterpenes in Gyrinops walla Gaetner
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Sep 30, 2022
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Abstract
The recent recovery of Gyrinops walla as a potential producer of market-quality agarwood in mature damaged woods and branches, the intense illicit felling and exportation walla leading to the verge of extinction from Sri Lankan flora. The sustainable utilization of G. walla undoubtedly enhances the foreign exchange of the country by tissue culture-based techniques for the sustainable exploitation and conservation of the vulnerable G. walla species. Partial Least Square Structural Equation Model (PLS-SEM) was developed to elucidate the causal relationships between the latent variables; the cultured materials (CULTM), type of elicitation (ELI), concentration/intensities of the elicitors (CON), time of incubation (INCB) with the occurrence of sesquiterpenes (SES). The path coefficients of the PLS-SEM model showed direct effects of CULTM -> SES (β = -0.509, t = 6.468, p < 0.05) (hypothesis - H3), INCB -> SES (β = 0.421, t = 5.037, p < 0.05) (hypothesis - H1) and ELI + CON -> SES (β = -0.282, t = 5.792, p < 0.05) (hypothesis-H6) on induction of the production of sesquiterpenes. The PLS-SEM model developed the study proved the applicability in exploring the causal relationships between the deterministic factors that effect on artificial elicitation in the production of sesquiterpenes.
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Munasinghe, S., Somaratne, S., Weerakoon, S., & Ranasinghe, C. (2022). Application of path modelling to identify causal relationships of in vitro production of sesquiterpenes in Gyrinops walla Gaetner. Journal of Research and Multidisciplinary, 5(2), 584-601. https://doi.org/10.5281/jrm.v5i2.67
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References
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Lowry, P. B., & Gaskin, J., 2014. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE transactions on professional communication, 57(2), 123-146.
MOE, 2012. The Notational Red List 2012 of Sri Lanka. Conservation status of the Fauna and Flora. Ministry of Environment, Colombo, Sri Lanka. Viii :476.
Monecke, A. & Leisch, F., 2012. SEM-PLS: structural equation modeling using partial least squares. Journal of Statistical Software, 48 (3), 1-32.
Munasinghe, S. P., Somaratne, S., Weerakoon, S. R., & Ranasinghe, C., 2020. Prediction of chemical composition for callus production in Gyrinops walla Gaetner through machine learning. Information Processing in Agriculture, 7(4), 511-522.
Peter, J. P., & Churchill, G. A., 1986. Relationships among research design choices and psychometric properties of rating scales: a meta-analysis. Journal of Marketing Research, 23(1), 1–10.
Ringle, C. M., Sarstedt, M., & Straub, D. W., 2012. Editor's Comments: A Critical Look at the Use of PLS-SEM in" MIS Quarterly". MIS quarterly, iii-xiv.
Ringle, C. M., Wende, S., & Will, A., 2005. SmartPLS 2.0 (M3) beta software. Hamburg, Germanay.
Rossiter, J. R., 2002. The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19(4): 305-335.
Schwarz, Gideon E., 1978. "Estimating the dimension of a model", Annals of Statistics, 6 (2): 461–464.
Townsend, C. C., 1981. Thymeliaceae. In A revised Handbook to the Flora of Ceylon, Vol. II, M. D. Dassanyake and F. R. Fosberg (edts.), Amerind Publishing Co. Pvt. Ltd., New Delhi.
Tumminello, M., Lillo, F., & Mantegna, R. N., 2007. Kullback-Leibler distance as a measure of the information filtered from multivariate data. Physical Review E, 76(3), 031123.
Yi, M. Y., & Davis, F. D., 2003. Developing and validating an observational learning model of computer software training and skill acquisition. Information systems research, 14(2), 146-169.
Vijayasree, N., Udayasri, P., Aswani, K.Y., Ravi, B. B., Phani, K .Y, and Vijay, V. M., 2010. Advancements in the Production of Secondary Metabolites. Journal of Natural Products,3, 112-123.
Wright, S., 1960. Path coefficients and path regressions: alternative or complementary concepts?. Biometrics, 16(2), 189-202.
Wold, H., (1982). Soft modeling: the basic design and some extensions. In: J Loreskog, K.G., Wold, H. (Eds.), Systems under Indirect Observation, Part 2. North-Holland, Amsterdam, 1–54.
Wold, H., (1985). Partial least squares. In: Kotz, S., Johnson, N.L. (Eds.), Encyclopedia of Statistical Sciences, 6. Wiley, New York, 581–591.
Akaike, H., 1985. Prediction and entropy. In Selected Papers of Hirotugu Akaike (pp. 387-410). Springer, New York, NY.
Barclay, B., Higgins, C., &Thompson, R., 1995. The Partial Least Squares (PLS) Approach to Causal Modelling: Personal Computer Adoption and Use as an Illustration. Technology Studies, 2 (2),285 – 309.
Bentler, P.M. & Bonett, D.G., 1980. Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88 (3), 588-606.
Chen, W., Li, J., Zhu, H., Xu, P., Chen, J., & Yao, Q., 2017. Arbuscular mycorrhizal fungus enhances lateral root formation in Poncirus trifoliata (L.) as revealed by RNA-Seq analysis. Frontiers in Plant Science, 8, 2039.
Cheng, E. W., 2001. SEM being more effective than multiple regression in parsimonious model testing for management development research. Journal of Management Development, 20(7), 650-667.
Chin, W. W., Marcolin, B. L., & Newsted, P. R., 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information systems research, 14(2), 189-217.
Chin, W.W., 1998. The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-336). Mahwah, NJ: Lawrence Erlbaum Associates.
Chin, W., & Newsted, P., 1999. Structural equation modeling analysis with small samples using partial least squares. In R. H. Hoyle (Ed.), Statistical Strategies for Small Sample Research (pp. 307–342). Thousand Oaks: SAGE.
Dijkstra, T. K., & Henseler, J., 2015a. Consistent partial least squares path modeling. MIS quarterly, 39(2).
Dijkstra, T. K., & Henseler, J., 2015b. Consistent and asymptotically normal PLS estimators for linear structural equations. Computational statistics & data analysis, 81, 10-23.
Falk, R. F. & Miller, N. B., 1992. A Primer for Soft Modeling. Akron, OH: University of Akron Press.
Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C., 2016. Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecological Processes, 5(1), 19.
Fornell, C., & Larker, D., 1981. Structural equation modeling and regression: guidelines for research practice. Journal of Marketing Research, 18(1), 39-50.
Gerbing, D. W., & Anderson, J. C., 1988. An updated paradigm for scale development incorporating uni-dimensionality and its assessment. Journal of marketing research, 25(2), 186-192.
Ghosh, A., Pandey, B., Agrawal, M., & Agrawal, S. B., 2020. Interactive effects and competitive shift between Triticum aestivum L.(wheat) and Chenopodium album L.(fat-hen) under ambient and elevated ozone. Environmental Pollution, 114764.
Goodhue, D., Lewis, W., & Thompson, R., 2012. Does PLS Have Advantages for Small Sample Size or Non-Normal Data? MIS Quarterly, 36(3), 981-1001.
Gunatilleke, I.A.U.N., Gunatilleke, C.V.S., Dilhan, M.A.A.B., 2005. Plant biogeography and conservation of the south-western hill forests of Sri Lanka. Raf. Bul. Zoo. ; 12(1): 9-22.
Hair. J. F., Black., W. C., Babin., B. J., Anderson., R. E., &Tatham., R., 2006. Multivariant Data Analysis. New Jersey: Pearson International Edition. 9.
Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M., 2017. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)., 2nd Ed., Thousand Oakes, CA: Sage.
Hair, J. F., Anderson, R. E., Tatham, R. L. and Black, W. C., 1998. Multivariate data analysis. (5th ed.). Prentice-Hall, New Jersey.
Henseler, Jr., Ringle, C. M., and Sarstedt, M., 2015. A New Criterion for assessing discriminant Validity in Variance-based Structural Equation Modeling., Journal of the Academy of Marketing Science, 43(1): 115-135.
Hu, Li-tze, Bentler, Peter M., 1998. Fit indices in covariance structure modeling: sensitivity to under parameterized model misspecification. Psychological Methods 3 (4), 424–453.
Igbaria, M., Guimaraes, T., & Davis, G. B., 1995. Testing the determinants of microcomputer usage via a structural equation model. Journal of management information systems, 11(4), 87-114.
Karuppusamy, S., 2009. A review on trends in production of secondary metabolites from higher plants by in vitro tissue, organ and cell cultures. Journal of Medicinal Plant Research, 3(13), 1222-1239.
Lila, K. M., 2005. Valuable secondary products from in vitro culture, Secondary Products In Vitro. CRC Press LLC.
Loehlin, J.C. (1998). Latent variable models: An introduction to factor, path, and structural analysis. Hillsdale, NJ: Erlbaum.
Lohmöller, J.B., 1989. Latent Variables Path Modeling with Partial Least Squares. Physica-Verlag, Heildelberg.
Lowry, P. B., & Gaskin, J., 2014. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE transactions on professional communication, 57(2), 123-146.
MOE, 2012. The Notational Red List 2012 of Sri Lanka. Conservation status of the Fauna and Flora. Ministry of Environment, Colombo, Sri Lanka. Viii :476.
Monecke, A. & Leisch, F., 2012. SEM-PLS: structural equation modeling using partial least squares. Journal of Statistical Software, 48 (3), 1-32.
Munasinghe, S. P., Somaratne, S., Weerakoon, S. R., & Ranasinghe, C., 2020. Prediction of chemical composition for callus production in Gyrinops walla Gaetner through machine learning. Information Processing in Agriculture, 7(4), 511-522.
Peter, J. P., & Churchill, G. A., 1986. Relationships among research design choices and psychometric properties of rating scales: a meta-analysis. Journal of Marketing Research, 23(1), 1–10.
Ringle, C. M., Sarstedt, M., & Straub, D. W., 2012. Editor's Comments: A Critical Look at the Use of PLS-SEM in" MIS Quarterly". MIS quarterly, iii-xiv.
Ringle, C. M., Wende, S., & Will, A., 2005. SmartPLS 2.0 (M3) beta software. Hamburg, Germanay.
Rossiter, J. R., 2002. The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19(4): 305-335.
Schwarz, Gideon E., 1978. "Estimating the dimension of a model", Annals of Statistics, 6 (2): 461–464.
Townsend, C. C., 1981. Thymeliaceae. In A revised Handbook to the Flora of Ceylon, Vol. II, M. D. Dassanyake and F. R. Fosberg (edts.), Amerind Publishing Co. Pvt. Ltd., New Delhi.
Tumminello, M., Lillo, F., & Mantegna, R. N., 2007. Kullback-Leibler distance as a measure of the information filtered from multivariate data. Physical Review E, 76(3), 031123.
Yi, M. Y., & Davis, F. D., 2003. Developing and validating an observational learning model of computer software training and skill acquisition. Information systems research, 14(2), 146-169.
Vijayasree, N., Udayasri, P., Aswani, K.Y., Ravi, B. B., Phani, K .Y, and Vijay, V. M., 2010. Advancements in the Production of Secondary Metabolites. Journal of Natural Products,3, 112-123.
Wright, S., 1960. Path coefficients and path regressions: alternative or complementary concepts?. Biometrics, 16(2), 189-202.
Wold, H., (1982). Soft modeling: the basic design and some extensions. In: J Loreskog, K.G., Wold, H. (Eds.), Systems under Indirect Observation, Part 2. North-Holland, Amsterdam, 1–54.
Wold, H., (1985). Partial least squares. In: Kotz, S., Johnson, N.L. (Eds.), Encyclopedia of Statistical Sciences, 6. Wiley, New York, 581–591.