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

Spent substrate from mushroom cultivation: exploitation potential toward various applications and value-added products

Spent mushroom substrate (SMS) is the residual biomass generated after harvesting the fruitbodies of edible/medicinal fungi. Disposal of SMS, the main by-product of the mushroom cultivation process, often leads to serious environmental problems and is financially demanding. Efficient recycling and valorization of SMS are crucial for the sustainable development of the mushroom industry in the frame of the circular economy principles. The physical properties and chemical composition of SMS are a solid fundament for developing several applications, and recent literature shows an increasing research interest in exploiting that inherent potential. This review provides a thorough outlook on SMS exploitation possibilities and discusses critically recent findings related to specific applications in plant and mushroom cultivation, animal husbandry, and recovery of enzymes and bioactive compounds.

Rapid detection method of Pleurotus eryngii mycelium based on near infrared spectral characteristics

Edible fungus is a large fungus with edible and medicinal value. Rapid detection of mycelium phenotypic characteristics is of great significance for edible fungus breeding and intelligent cultivation. Traditional method based on experienced observation easily led to make mistakes on distinguishing the growth stages, which impacted on the yield and quality of edible fungus. Therefore, in view of the lack of accurate and efficient detection technology during the growth stages of Pleurotus eryngii mycelium, a rapid detection method of Pleurotus eryngii mycelium at different growth stages is proposed based on the characteristics of near-infrared spectroscopy. First, the spectral data of mycelium of Pleurotus eryngii at six different growth stages were scanned. Second, the multivariate scattering correction method (MSC) was used to pre-process the raw spectral data, and then the competitive adaptive reweighted sampling algorithm (CARS) was adopted to detect the characteristic wave number of the effective variables for Pleurotus eryngii mycelium. In addition, the mathematical model between the mycelium of Pleurotus eryngii and the characteristic wave number of near-infrared spectrum was established by using feed forward neural network (BP). Finally, and the coding vector output by the network was used to detect to the growth stages. The results showed that the BP neural network structure of MSC-CARS-BP detection model was 86-85-85-95-6, and the accuracy of identifying different growth stages of Pleurotus eryngii mycelium was 99.67%. The research results could provide a new idea and technical support for the rapid detection of Pleurotus eryngii mycelium at different growth stages.

Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat

The study assessed the feasibility of merging data acquired from hyperspectral imaging (HSI) and electronic nose (e-nose) to develop a robust method for the rapid prediction of intramuscular fat (IMF) and peroxide value (PV) of pork meat affected by temperature and NaCl treatments. Multivariate calibration models for prediction of IMF and PV using median spectra features (MSF) and image texture features (ITF) from HSI data and mean signal values (MSV) from e-nose signals were established based on support vector machine regression (SVMR). Optimum wavelengths highly related to IMF and PV were selected from the MSF and ITF. Next, recurring optimum wavelengths from the two feature groups were manually obtained and merged to constitute "combined attribute features" (CAF) which yielded acceptable results with (R_c² = 0.877, 0.891; RMSEC = 2.410, 1.109; R_p² = 0.790, 0.858; RMSEP = 3.611, 2.013) respectively for IMF and PV. MSV yielded relatively low results with (R_c² = 0.783, 0.877; RMSEC = 4.591, 0.653; R_p² = 0.704, 0.797; RMSEP = 3.991, 0.760) respectively for IMF and PV. Finally, data fusion of CAF and MSV was performed which yielded relatively improved prediction results with (R_c² = 0.936, 0.955; RMSEC = 1.209, 0.997; R_p² = 0.895, 0.901; RMSEP = 2.099, 1.008) respectively for IMF and PV. The results obtained demonstrate that it is feasible to mutually integrate spectral and image features with volatile information to quantitatively monitor IMF and PV in processed pork meat. Graphical abstract.

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

The use of spectroscopic methods, such as near-infrared or Raman, for quality control applications combined with the constant search for finer details leads to the acquisition of increasingly complex data sets. This should not prevent the user from characterizing a sample by identifying and mapping its chemical compounds. Multivariate data analysis methods make it possible to obtain qualitative and quantitative information from such data sets. However, samples containing a large (and/or unknown) number of species, segregated trace compounds (present in few pixels), low signal-to-noise ratios (SNR), and often insufficient spatial resolutions still represent significant hurdles for the analyst.

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

Hyperspectral imaging within the near infrared (NIR) region offers a fast and reliable way for determining the properties of renewable carbon materials. The chemical information provided by a spectrum combined with the spatial information of an image allows mathematical operations that can be performed in both the spectral and spatial domains. Here, we show that hyperspectral NIR imaging can be successfully used to determine the properties of hydrothermally prepared carbon on the material and pixel levels. Materials produced from different feedstocks or prepared under different temperatures can also be distinguished, and their homogeneity can be evaluated. As hyperspectral imaging within the NIR region is non-destructive and requires very little sample preparation, it can be used for controlling the quality of renewable carbon materials destined for a wide range of different applications.