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Multivariate modelling on biomass properties of cassava stems based on an experimental design

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Abstract

Based on a factorial experimental design (three locations × three cultivars × five harvest times × four replicates) conducted with the objective of investigating variations in fuel characteristics of cassava stem, a multivariate data matrix was formed which was composed of 180 samples and 10 biomass properties for each sample. The properties included as responses were two different calorific values and ash, N, S, Cl, P, K, Ca, and Mg content. Overall principal component analysis (PCA) revealed a strong clustering for the growing locations, but overlapping clusters for the cultivar types and almost no useful information about harvest times. PCA using a partitioned data set (60 × 10) for each location revealed a clustering of cultivars. This was confirmed by soft independent modelling of class analogy (SIMCA) and partial-least-squares discriminant analysis (PLS-DA), and indicated that the locations gave meaningful information about the differences in cultivar, whereas harvest time was not found to be a differentiating factor. Using the PLS technique, it was revealed that ash, K, and Cl content were the most important responses for PLS-DA models. Furthermore, using PLS regression of fuel and soil variables it was also revealed that fuel K and ash content were correlated with the soil P, Si, Ca, and K content, whereas fuel Cl content was correlated with soil pH and content of organic carbon, N, S, and Mg in the soil. Thus, the multivariate modelling used in this study reveals the possibility of performing rigorous analysis of a complex data set when an analysis of variance may not be successful.

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Acknowledgements

The study was financed by the EU−China Energy and Environment Program (EEP-PMU/CN/126077/RE006), the Swedish Energy Agency (32805-1), the Chinese Ministry of Agriculture 948 Project (“948”-2011-S7), the Royal Swedish Academy of Engineering Science, the China Academy of Engineering, and the Swedish Governmental Strategic Project Bio4Energy. The authors are grateful to Dr Guangcan Tao, China Agricultural University, for his help during the early planning of the field experiments.

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Authors and Affiliations

  1. Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences, 901 83, Umeå, Sweden

    Maogui Wei, Paul Geladi, Torbjörn A. Lestander & Shaojun Xiong

  2. College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China

    Guanghui Xie

  3. National Energy R&D Centre for Non-food Biomass, China Agricultural University, Beijing, 100193, China

    Guanghui Xie

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  1. Maogui Wei
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  2. Paul Geladi
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  3. Torbjörn A. Lestander
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  4. Guanghui Xie
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  5. Shaojun Xiong
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Correspondence to Maogui Wei.

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Wei, M., Geladi, P., Lestander, T.A. et al. Multivariate modelling on biomass properties of cassava stems based on an experimental design. Anal Bioanal Chem 407, 5443–5452 (2015). https://doi.org/10.1007/s00216-015-8706-2

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  • DOI: https://doi.org/10.1007/s00216-015-8706-2

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