Hyperspectral Imaging Technique to Characterize Digestate and Visualize Physical Impurities in Anaerobically Digested Biowaste.

Methods used to monitor anaerobic digestion (AD) indicators are commonly based on wet chemical analyses, which consume time and materials. In addition, physical disturbances, such as floating granules (FGs), must be monitored manually. In this study, we present an eco-friendly, high-throughput methodology that uses near-infrared hyperspectral imaging (NIR-HSI) to build a machine-learning model for characterizing the chemical composition of the digestate and a target detection algorithm for identifying FGs. A total of 732 digestate samples were used to develop and validate a model for calculating total nitrogen (TN), total organic carbon (TOC), total ammonia nitrogen (TAN), and chemical oxygen demand (COD), which are the chemical indicators of responses to disturbances in the AD process. Among these parameters, good model performance was obtained using the dried digestates data set, where the coefficient of determination (R2test) and the root-mean-square error (RMSEtest) were 0.82 and 1090 mg/L for TOC, and 0.86 and 690 mg/L for TN, respectively. Furthermore, the unique spectral features of the FGs in reactors with a lipid-rich substrate meant that they could also be identified by the HSI system. Based on these findings, developing NIR-HSI solutions to monitor the digestate properties in AD plants has great potential for industrial application.

Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste.

Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R2 = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R2 value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.

Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods.

Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R2 values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.

Research progress in mechanism of berberine's antitumor action / 中国中药杂志

Malignant tumor, an important factor threatening human life and health, brings huge economic burden to patients. At present, chemoradiotherapy is still the main treatment method for tumor diseases, but there are also great side effects when it plays a therapeutic role. Traditional Chinese medicine in the prevention and treatment of tumor diseases has many advantages such as few side effects, improving the physiological state of patients, and slowing down the side effects of radiotherapy and chemotherapy. Berberine is an effective component of rhizoma coptidis, with a very good antitumor effect. It can inhibit tumor cell proliferation, promote tumor cell apoptosis, inhibit tumor metastasis and angiogenesis, regulate tumor autophagy, reverse multi-drug resistance of tumor, regulate the body immunity, and affect tumor metabolic reprogramming to play its role. Compared with chemical preparations, berberine has a wide range of sources, with high safety and easy access, and has great potential in the prevention and treatment of malignant tumors. In this article, we would mainly review the research progress on the antitumor mechanism of berberine in recent years.

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.