Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Abstract

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection

“…The sample component content of Cset covered the sample component content of Vset, which shows that the distribution of the sample set divided by SPXY is reasonable, and the model established in this way can better predict unknown samples [ 31 ]. The minimum content of cellulose in the ITset was less than the minimum value for Cset, and the maximum content of hemicellulose was greater than the maximum value for Cset, resulting in a good test of the robustness of the regression model [ 9 ].…”

“…The minimum RPDs for C and H were 3.448 and 10.529, and these RPDs are greater than 3. It is generally believed that when the relative RMSE is less than 5%, the model can meet the needs of actual chemical analysis [ 9 ]. The largest relative RMSEs for cellulose and hemicellulose in the model were 1.914% and 3.999, respectively, and these values are less than 5%.…”

“…Near-infrared spectroscopy (NIRS) has the advantages of enabling rapid, simple, and nondestructive analyses, and it has been widely used in quantitative analysis (QA) and qualitative analysis of agricultural products and wastes [ 8 ]. NIRS mainly records the frequency overtone and absorption combination of a hydrogen-containing group’s vibrations [ 9 ]. The absorption wavelengths and intensities for different groups or for the same group are different in different chemical environments.…”

Detection of Soluble Solids Content (SSC) in Pears Using Near-Infrared Spectroscopy Combined with LASSO–GWF–PLS Model

“…(1) Competitive adaptive reweighted sampling: The competitive adaptive reweighted sampling (CARS) algorithm is a method used for feature wavelength screening, and its basic idea is to select the optimal combination of wavelengths in the full spectrum by iterative competition [10][11][12][13]. Specifically, the CARS algorithm sequentially selects a subset of N wavelengths and uses an exponential decreasing function and adaptive reweighted sampling to determine the number of wavelengths selected for each sampling.…”

Development and validation of a near-infrared spectroscopy model for the prediction of muscle protein in Chinese native chickens

“…Previous research has proven that spectral pretreatment, wavelength selection, and outlier sample elimination could improve the prediction accuracy of models ( Ran et al, 2020 ; Jiang et al, 2021 ). The feature wavelength was mostly selected using statistic method such as genetic simulated annealing algorithm, variable combination population analysis, variable iterative space shrinkage approach and competitive adaptive reweighted sampling ( Jiang et al, 2021 ; Liu et al, 2021 ). Few research analyzed the NIRS of the protein extracted from muscles when selecting the optimal wavelength.…”