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NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary
Maogui Wei1, Paul Geladi2, Shaojun Xiong2
1Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences, 90183, Umeå, Sweden. maogui.wei@slu.se.
Analytical and Bioanalytical Chemistry
|January 25, 2017
Summary
Near-infrared hyperspectral imaging (NHI) effectively characterizes spent mushroom substrate (SMS) and mycelia (Myc). This technology can predict water, carbohydrate, lignin, and protein content in wet, plastic-covered SMS for bioenergy applications.
Area of Science:
- Agricultural Science
- Biotechnology
- Analytical Chemistry
Background:
- Commercial mushroom cultivation generates spent mushroom substrate (SMS), a heterogeneous waste stream with potential for bioenergy production.
- Accurate characterization of SMS is crucial for optimizing its utilization in bioenergy applications.
- Existing methods for SMS analysis may be time-consuming or destructive, necessitating rapid, non-invasive techniques.
Purpose of the Study:
- To investigate the feasibility of using near-infrared hyperspectral imaging (NHI) for direct characterization of spent mushroom substrate (SMS) and mycelia (Myc).
- To explore the influence of various sample conditions (moisture, covering, shape) on NHI spectral data.
- To develop multivariate image analysis models for predicting SMS composition and identifying its components.
Main Methods:
- Experimental study of packed SMS samples under diverse conditions (wet/dry, open/plastic-covered, cuboid/cylindrical) using NHI.
- Application of Principal Component Analysis (PCA) for background removal, factor exploration, and pixel clustering of SMS and Myc.
- Development of Partial Least Squares Discriminant Analysis (PLS-DA) models for classification and prediction of SMS and Myc characteristics.
Main Results:
- PCA effectively separated SMS and Myc clusters under various conditions, with moisture content identified as the most significant factor affecting spectra.
- PLS-DA models successfully classified SMS and Myc, demonstrating chemical differences between the two components.
- NHI and PLS-DA showed potential for predicting water, carbohydrate, lignin, and protein content in wet, plastic-covered SMS, even from a single side-face image.
Conclusions:
- NHI combined with multivariate image analysis is a promising non-invasive technique for direct characterization of SMS and Myc.
- The developed models can accurately predict key chemical components in SMS, facilitating its use in bioenergy.
- This approach offers efficient quality control and process monitoring for spent mushroom substrate utilization.
