NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study

Abstract

Commercial mushroom growth on substrate material produces a heterogeneous waste that can be used for bioenergy purposes. Hyperspectral imaging in the near-infrared (NHI) was used to experimentally study a number of spent mushroom substrate (SMS) packed samples under different conditions (wet vs. dry, open vs. plastic covering, and round or cuboid) and to explore the possibilities of direct characterization of the fresh substrate within a plastic bag. Principal components analysis (PCA) was used to remove the background of images, explore the important studied factors, and identify SMS and mycelia (Myc) based on the pixel clusters within the score plot. Overview PCA modeling indicated high moisture content caused the most significant effects on spectra followed by the uneven distribution of Myc and the plastic cover. There were well-separated pixel clusters for SMS and Myc under different conditions: dry, wet, or wet and plastic covering. The loading peaks of the related component and the second derivative of the mean spectra of pixel clusters of SMS and Myc indicated that there are chemical differences between SMS and Myc. Partial least squares discriminant analysis (PLS-DA) models were calculated and classification of SMS and Myc was successful, whether the materials were dry or wet. Peak shifts because of high moisture content and unexpected peaks from the plastic covering were found. Although the best results were obtained for dried cylinders, it was shown that almost equally good results could be obtained for the wet material and for the wet material covered by plastic. Furthermore, PLS-DA prediction showed that a side face hyperspectral image could represent the information for the entire SMS cylinder when Myc was removed. Thus, the combination of NHI and multivariate image analysis has great potential to develop calibration models to directly predict the contents of water, carbohydrates, lignin, and protein in wet and plastic-covered SMS cylinders.

Hyperspectral Imaging to Determine the Properties and Homogeneity of Renewable Carbon Materials

“…NIR‐based models for char and liquid properties from hydrothermal treatment have been reported, indicating that char and liquid components can be predicted even based on a smaller amount of calibration samples. In addition, hyperspectral imaging has recently been used for studying pellets made from energy crops, visualizing the extractive contents of wood, and characterizing spent mushroom substrate . Herein, we determine the performance of NIR‐based hyperspectral imaging in predicting the properties of hydrothermally prepared carbon on the material and pixel levels.…”

“…In addition, hyperspectral imaging has recently been used for studying pellets made from energy crops, [11] visualizing the extractivec ontents of wood, [12] and characterizing spent mushroom substrate. [13] Herein, we determine the performance of NIR-based hyperspectral imaging in predictingt he properties of hydrothermally prepared carbon on the material and pixel levels. The obtained results will illustrate the potential of hyperspectral imaginga safuture tool for assessing the quality of renewable carbon materials for aw ide range of different applications.…”

A Hierarchical Multivariate Curve Resolution Methodology To Identify and Map Compounds in Spectral Images

“…This can occur at the surface of a sample, at the edge of two large domains, or inside the sample if the depth of penetration of the analysis is sufficient to acquire underlying layers of materials. 1,7,8 The analysis of complex samples can present data analysis challenges. When the identity and the number of compounds are unknown, or when the data set is characterized by a low SNR, identifying the present species and calculating their chemical map become nontrivial tasks.…”

“…The ability to draw chemical maps of segregated samples using spectral images is of growing interest for many research and industrial applications. Applications include near-infrared (NIR) and Raman analysis in the food and pharmaceutical industries, − imaging mass spectrometry (MS) for biological samples, and magnetic resonance imaging (MRI) for medical purposes . The use of multispectral, instead of single-wavelength, acquisitions typically leads to the creation of more robust models.…”

“…The most common is the presence of boundaries between homogeneous domains of pure compounds. This can occur at the surface of a sample, at the edge of two large domains, or inside the sample if the depth of penetration of the analysis is sufficient to acquire underlying layers of materials. ,, …”