Biochemical methane potential prediction for mixed feedstocks of straw and manure in anaerobic co-digestion

Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics

As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour.

Co-densification of rice straw and cow dung in different food-to-microorganism ratios for biogas production

Agricultural residues such as rice straw (RS) are desirable raw materials for biogas generation. However, the recalcitrant nature of RS hinders biogas production, and its low bulk density increases storage space requirements, transportation needs, and overall costs. These challenges could be resolved by pretreatment and pelletization. In this study, various thermal pretreatments were performed, and the best conditions (temperature and time) were identified. Also, rice straw and cow dung pellets (RCP) at different food-to-microorganism (F/M) ratios (0.5-2.5) were prepared. Parameters such as bulk density, moisture absorption, and drop shatter tests were conducted to evaluate the physical properties. Finally, the biochemical methane potential (BMP) study of the best RCP with varying total solids (TS: 4-12%) content was investigated. The results indicate that hot air oven pretreatment (for 60 min at 120 °C) resulted in maximum solubilization. In physical characteristics, RCP with an F/M ratio of 2.5 pellets was ideal. The bulk density of RCP 2.5 was found to be around 25 times that of the raw. Also, the TS 8% yielded maximum biomethane (279 mL/g-VSconsumed) as compared to other TS contents. Overall, this study will propel the growth of bioenergy while simultaneously tackling the pressing issues related to RS management.

Construction of rapid prediction models for TN and TP in dairy farms slurry under different seasons by near infrared spectroscopy

Rapidly and stably measuring the total nitrogen (TN) and total phosphorus (TP) contents of dairy slurry was a critical step before the field application, which was a key component of sustainable agriculture and slurry resource utilization. In present study, near-infrared spectroscopy (NIRS) models were established for swiftly determining TN and TP contents in the slurry from diverse dairy farms under various conditions. A total of 828 samples were gathered from 33 intensive dairy farms in Tianjin City throughout spring, summer, autumn and winter. The analysis initially explored the seasonal influences on the distribution characteristics of TN and TP in slurry and the effects on NIRS. Subsequently, the study employed partial least squares (PLS) regression to establish the single-season models for predicting slurry TN and TP contents. The results demonstrated that the TN models were excellent in all the seasons, with Rp2 values ranging from 0.94 to 0.95 and RPD values between 3.88 and 4.29. The TP models were superior during spring, summer, and winter compared to autumn. Lastly, the global models of four seasons (GMFS) were developed to predict TN and TP contents. For TN, the Rp2 and RPD values were 0.85 and 2.38, which were deemed very good. For TP, the Rp2 and RPD values were 0.76 and 1.87, indicating good performance. Consequently, the GMFS incorporating data from diverse seasons exhibited a broader applicability scope, albeit with reduced precision and accuracy compared to the single-season models. These models accommodated various situational needs during slurry reapplication to farmland, providing a theoretical basis for the precise and scientifically-informed reuse of dairy farm slurry in agricultural fields.

Two-stage biohydrogen and methane production from sugarcane-based sugar and ethanol industrial wastes: A comprehensive review

The transition to renewable energy sources is crucial to ensure a sustainable future. Although the sugar and ethanol industries benefit from this transition, there are untapped opportunities to utilize the waste generated from the sugar and ethanol process chains through two-stage anaerobic digestion (TSAD). This review comprehensively discusses the utilization of various sugarcane-based industrial wastes by TSAD for sequential biohydrogen and methane production. Factors influencing TSAD process performance, including pH, temperature, hydraulic retention time, volatile fatty acids and alkalinity, nutrient imbalance, microbial population, and inhibitors, were discussed in detail. The potential of TSAD to reduce emissions of greenhouse gases is demonstrated. Recent findings, implications, and promising future research related to TSAD, including the integration of meta-omics approaches, gene manipulation and bioaugmentation, and application of artificial intelligence, are highlighted. The review can serve as important literature for the implementation, improvement, and advancements in TSAD research.

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model

To evaluate corn quality quickly, the feasibility of near-infrared spectroscopy (NIRS) coupled with chemometrics was analyzed to detect the moisture, oil, protein, and starch content in corn. A backward interval partial least squares (BiPLS)-principal component analysis (PCA)-extreme learning machine (ELM) quantitative analysis model was constructed based on BiPLS in conjunction with PCA and the ELM. The selection of characteristic spectral intervals was accomplished by BiPLS. The best principal components were determined by the prediction residual error sum of squares of Monte Carlo cross validation. In addition, a genetic simulated annealing algorithm was utilized to optimize the parameters of the ELM regression model. The established regression models for moisture, oil, protein, and starch can meet the demand for corn component detection with the prediction determination coefficients of 0.996, 0.990, 0.974, and 0.976; the prediction root means square errors of 0.018, 0.016, 0.067, and 0.109; and the residual prediction deviations of 15.704, 9.741, 6.330, and 6.236, respectively. The results show that the NIRS rapid detection model has higher robustness and accuracy based on the selection of characteristic spectral intervals in conjunction with spectral data dimensionality reduction and nonlinear modeling and can be used as an alternative strategy to detect multiple components in corn rapidly.

Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects

Anaerobic co-digestion (AcoD) offers several merits such as better digestibility and process stability while enhancing methane yield due to synergistic effects. Operation of an efficient AcoD system, however, requires full comprehension of important operational parameters, such as co-substrates ratio, their composition, volatile fatty acids/alkalinity ratio, organic loading rate, and solids/hydraulic retention time. AcoD process optimization, prediction and control, and early detection of system instability are often difficult to achieve through tedious manual monitoring processes. Recently, artificial intelligence (AI) has emerged as an innovative approach to computational modeling and optimization of the AcoD process. This review discusses AI applications in AcoD process optimization, control, prediction of unknown input/output parameters, and real-time monitoring. Furthermore, the review also compares standalone and hybrid AI algorithms as applied to AcoD. The review highlights future research directions for data preprocessing, model interpretation and validation, and grey-box modeling in AcoD process.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.

Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection

To analyze the state of anaerobic digestion (AD), fast detection models of volatile fatty acids (VFAs) were constructed using near-infrared transmission spectroscopy combined with partial least squares regression to measure concentrations of the acetic acid (AA), propionic acid (PA) and total acid (TA) in biogas slurry. CARS-SA-BPSO algorithm was proposed based on competitive adaptive reweighted sampling (CARS) and simulated annealing binary particle swarm optimization algorithm (SA-BPSO) for selecting feature wavelengths of the AA, PA and TA. Regression models were established with the determination coefficient of prediction (R_p²) of 0.989, root mean squared error of prediction (RMSEP) of 0.111 and residual predictive deviation (RPD) of 9.706 for AA; R_p² of 0.932, RMSEP of 0.116 and RPD of 3.799 for PA; R_p² of 0.895, RMSEP of 0.689 and RPD of 3.676 for TA. It is sufficient to meet the fast detection needs of the AA and PA concentrations in biogas slurry, and basically meet the measuring demand of the TA concentration. CARS-SA-BPSO effectively improves the performance of the calibration model using sensitive wavelength selections, which provides theoretical support for establishing the spectral quantitative regression model to meet the requirements of practical application.

Fast and robust NIRS-based characterization of raw organic waste: Using non-linear methods to handle water effects

Fast characterization of organic waste using near infrared spectroscopy (NIRS) has been successfully developed in the last decade. However, up to now, an on-site use of this technology has been hindered by necessary sample preparation steps (freeze-drying and grinding) to avoid important water effects on NIRS. Recent research studies have shown that these effects are highly non-linear and relate both to the biochemical and physical properties of samples. To account for these complex effects, the current study compares the use of many different types of non-linear methods such as partial least squares regression (PLSR) based methods (global, clustered and local versions of PLSR), machine learning methods (support vector machines, regression trees and ensemble methods) and deep learning methods (artificial and convolutional neural networks). On an independent test data set, non-linear methods showed errors 28% lower than linear methods. The standard errors of prediction obtained for the prediction of total solids content (TS%), chemical oxygen demand (COD) and biochemical methane potential (BMP) were respectively 8%, 160 mg(O₂).gTS^-1 and 92 mL(CH₄).gTS^-1_. These latter errors are similar to successful NIRS applications developed on freeze-dried samples. These findings hold great promises regarding the development of at-site and online NIRS solutions in anaerobic digestion plants.

Rapid detection of talc content in flour based on near-infrared spectroscopy combined with feature wavelength selection

Excessive illegal addition of talc in flour has always been a serious food safety issue. To achieve rapid detection of the talc content in flour (TCF) by near-infrared spectroscopy (NIRS), this study used a Fourier transform near-infrared spectrometer technique. The identification of efficient spectral feature wavelength selection (FWS), such as backward interval partial-least-square (BiPLS), competitive adaptive reweighted sampling (CARS), hybrid genetic algorithm (HGA), and BiPLS combined with CARS; BiPLS combined with HGA; and CARS combined with HGA, was also discussed in this paper, and the corresponding partial-least-square regression models were established. Comparing with whole spectrum modeling, the accuracy and efficiency of regressive models were effectively improved using feature wavelengths of TCF selected by the above algorithms. The BiPLS, combined with HGA, had the best modeling performance; the determination coefficient, root-mean-squared error (RMSE), and residual predictive deviation of the validation set were 0.929, 1.097, and 3.795, respectively. BiPLS combined with CARS had the best dimensionality reduction effect. Through the FWS by BiPLS combined with CARS, the number of modeling wavelengths decreased to 72 from 1845, and the RMSE of the validation set was reduced by 11.6% compared with the whole spectra model. The results showed that the FWS method proposed in this paper could effectively improve detection accuracy and reduce modeling wavelength variables of quantitative analysis of TCF by NIRS. This provides theoretical support for TCF rapid detection research and development in real-time.

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

The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment.

Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system

The soluble solids content (SSC) is an important factor in the internal quality detection of apples. It is essential to have reliable and high-speed measurement of the SSC. However, almost all traditional equipment is inconvenient and expensive. We designed a handheld nondestructive SSC detector based on near-infrared (NIR) spectroscopy, which is composed of a portable NIR spectrometer, cloud server, smartphone app, and prediction model of SSC. We preprocessed the spectrum with multiplicative scatter correction (MSC), standard normal variable transformation (SNV), and Savitzky-Golay (S-G) smoothing algorithms. Besides, the linear weight reduction of the particle swarm optimization algorithm is carried out, and we establish the model of an extreme learning machine optimized with the improved particle swarm optimization (IPSO-ELM) algorithm. The R², root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) of the model are 0.993, 0.0155, and 11.6, respectively, which are better than the traditional model obviously. In addition, the number of wavelengths reduced from 228 to 70 as the model is simplified with the uninformative variable elimination (UVE) method. The time of training is reduced by 29.30% compared with the original spectrum. It can be verified that the IPSO-ELM model has good prediction performance, and the NIR diffuse reflectance spectroscopy is a reliable nondestructive measurement of SSC in apples.

Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA

Selecting the decisive characteristic variables is particularly important to analyze the soluble solids content (SSC) of an apple with visible/near-infrared spectroscopy (VIS-NIRS) technology. The multi-population genetic algorithm (MPGA) was applied to variable selection for the first time, to the best of our knowledge. A hybrid variable selection method combined competitive adaptive reweighted sampling (CARS) with MPGA (CARS-MPGA) was proposed. In this method, CARS was firstly used to shrink the variable space, and then the MPGA was used to further fine select the characteristic variables. Based on CARS-MPGA, a nondestructive quantitative detection SSC model of an apple was established and compared with the models established by different variable selection methods, such as successive projections algorithm, synergy interval partial least squares, and genetic algorithm. The experiments showed that the CARS-MPGA model was the best. The number of modeling variables was only 64, and the determination coefficients, root mean squared error, and residual predictive deviation for the prediction set were 0.853, 0.443, and 2.612, respectively. The results demonstrated that the CARS-MPGA is a reliable variable selection method and can be used for fast nondestructive detection SSC of an apple.

Rapid detection of cellulose and hemicellulose contents of corn stover based on near-infrared spectroscopy combined with chemometrics

The feasibility of near-infrared spectroscopy (NIRS) combined with chemometrics for the rapid detection of the cellulose and hemicellulose contents in corn stover is discussed. Competitive adaptive reweighted sampling (CARS) and genetic simulated annealing algorithm (GSA) were combined (CARS-GSA) to select the characteristic wavelengths of cellulose and hemicellulose and to reduce the dimensionality and multicollinearity of the NIRS data. The whole spectra contained 1845 wavelength variables. After CARS-GSA optimization, the number of characteristic wavelengths of cellulose (hemicellulose) was reduced to 152 (260), accounting for 8.24% (14.09%) of all wavelengths. The coefficients of determination of the regression models for predicting the cellulose and hemicellulose contents were 0.968 and 0.996, the root mean square errors of prediction (RMSEPs) were 0.683 and 0.648, and the residual predictive deviations (RPDs) were 5.213 and 16.499, respectively. The RMSEP of the cellulose and hemicellulose regression models was 0.152 and 0.190 lower for CARS-GSA than for the full-spectrum, and the RPD was increased by 0.949 and 3.47, respectively. The results showed that the CARS-GSA model substantially reduced the number of characteristic wavelengths and significantly improved the predictive ability of the regression model.

Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations

Fast characterization of solid organic waste using near infrared spectroscopy has been successfully developed in the last decade. However, its adoption in biogas plants for monitoring the feeding substrates remains limited due to the lack of applicability and high costs. Recent evolutions in the technology have given rise to both more compact and more modular low-cost near infrared systems which could allow a larger scale deployment. The current study investigates the relevance of these new systems by evaluating four different Fourier transform near-infrared spectroscopic systems with different compactness (laboratory, portable, micro spectrometer) but also different measurement configurations (polarized light, at distance, in contact). Though the conventional laboratory spectrometer showed the best performance on the various biochemical parameters tested (carbohydrates, lipids, nitrogen, chemical oxygen demand, biochemical methane potential), the compact systems provided very close results. Prediction of the biochemical methane potential was possible using a low-cost micro spectrometer with an independent validation set error of only 91 NmL(CH₄).gTS^-1 compared to 60 NmL(CH₄).gTS^-1 for a laboratory spectrometer. The differences in performance were shown to result mainly from poorer spectral sampling; and not from instrument characteristics such as spectral resolution. Regarding the measurement configurations, none of the evaluated systems allowed a significant gain in robustness. In particular, the polarized light system provided better results when using its multi-scattered signal which brings further evidence of the importance of physical light-scattering properties in the success of models built on solid organic waste.

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

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.