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Chinese Materia Medica Resources (CMMRs) are crucial for developing the tradition and industry of traditional Chinese medicine. Given the increasing demand for CMMRs, an accurate and effective understanding of CMMRs is urgently needed. Chinese medicinal plants (CMPs) are the most important sources of CMMRs. Traditional methods of investigating medicinal plant resources have limitations, including severe subjectivity and poor timeliness, which make it difficult to meet the demand for real-time monitoring of large-scale medicinal plant resources. In recent years, remote sensing technology has become an important means of obtaining information on medicinal plants, and the application of this technology has made up for the shortcomings of traditional methods. This paper first discusses the development of investigation methods of CMMRs; points out the importance of remote sensing technology in the application of spatial distribution and information identification and extraction of Chinese medicinal plant resources (CMPRs); analyzes the characteristics of CMPs in different planting patterns, different habitats, and different regions from the perspective of remote sensing information extraction; and explores the selection of suitable data sources, providing a reference for medicinal plant identification and information extraction. Secondly, according to the existing classification and identification methods, previous studies are summarized from the perspectives of classification scales, classification features, and classification accuracy, and the advantages and disadvantages of the commonly used remote sensing classification methods in the investigation of CMPRs are summarized and compared. Finally, the development trend of remote sensing technology in the identification and information extraction of CMPs is examined, and the key technical problems to be solved in the identification and classification of CMPs and the extraction of area information are summarized so as to provide technical support and experience references for the application of remote sensing in the investigation of CMPRs. [ABSTRACT FROM AUTHOR]

Production of fermentable sugars from renewable lignocellulosic biomass could provide a sustainable substrate for biofuel production by microbial fermentation. However, the inherent structure of lignocellulose largely hinders enzymatic saccharification efficiency to obtain sugars, thus the development of efficient pretreatment methods to reduce biomass recalcitrance is an important issue. In this study, the effects of two typical processes including hydrothermal (HTP) and citric acid pretreatments (CAP) on the enzymatic hydrolysis yield of reed biomass were investigated. The results indicate that, when performing HTP at 180 °C for 120 min to deconstruct reed, the pretreated biomass led to a higher glucose yield of 87.1% after 72 h enzymatic hydrolysis with cellulase (10 FPU/g substrate). Furthermore, under the pretreatment conditions with 6% (w/v) citric acid, 150 °C, and 60 min, the glucose yield of pretreated reed was 63.4% after hydrolysis with cellulase (10 FPU/g substrate). In this case, adding Tween 80 (100 mg/g substrate) significantly facilitated the enzymatic saccharification activity resulting in the glucose yield of CA-pretreated reed to 84.2%. Compared with the untreated reed, the glucose yield of pretreated residues after HTP and CAP was largely improved by 4.97–5.18 folds which was mainly due to the enhanced crystallinity (50.0–53.2% vs. 45.9%). The total fermentable sugars of 34.7–34.8 g can be produced from 100 g of raw reed biomass based on the proposed HTP and CAP processes. The mechanism of enhanced enzymatic hydrolysis after pretreatments was elucidated through physicochemical characterization techniques. In summary, the proposed pretreatments show high potential application for biorefinery from renewable, abundant reed biomass. [ABSTRACT FROM AUTHOR]

Crop-distribution information constitutes the premise of precise management for crop cultivation. Euclidean distance and spectral angle mapper algorithms (ED and SAM) mostly use the spectral similarity and difference metric (SSDM) to determine the spectral variance associated with the spatial location for crop distribution acquisition. These methods are relatively insensitive to spectral shape or amplitude variation and must reconstruct a reference curve representing the entire class, possibly resulting in notable indeterminacy in the ultimate results. Few studies utilize these methods to compute the spectral variance associated with time and to define a new index for crop identification—namely, the spectral variance at key stages (SVKS)—even though this temporal spectral characteristic could be helpful for crop identification. To integrate the advantages of sensibility and avoid reconstructing the reference curve, an object self-reference combined algorithm comprising ED and SAM (CES) was proposed to compute SVKS. To objectively validate the crop-identification capability of SVKS-CES (SVKS computed via CES), SVKS-ED (SVKS computed via ED), SVKS-SAM (SVKS computed via SAM), and five spectral index (SI) types were selected for comparison in an example of maize identification. The results indicated that SVKS-CES ranges can characterize greater interclass spectral separability and attained better identification accuracy compared to other identification indexes. In particular, SVKS-CES2 provided the greatest interclass spectral separability and the best PA (92.73%), UA (100.00%), and OA (98.30%) in maize identification. Compared to the performance of the SI, SVKS attained greater interclass spectral separability, but more non-maize fields were incorrectly identified as maize fields via SVKS usage. Owning to the accuracy-improvement capability of SVKS-CES, the omission and commission errors were obviously reduced via the combined utilization of SVKS-CES and SI. The findings suggest that SVKS-CES application is expected to further spread in crop identification. [ABSTRACT FROM AUTHOR]

Efficient production of second-generation biofuels (bioethanol and biobutanol) from renewable lignocellulosic biomass holds strategic importance and aligns with sustainability objectives. However, it poses a persistent long-term challenge because of the biomass recalcitrance. Therefore, development of mild, environmentally friendly biomass pretreatment methods is a critical concern. To this end, in this study, a ternary deep eutectic solvent (DES) was synthesized with choline chloride, succinic acid, and glycerol at a molar ratio of 1:0.5:0.5. The ternary DES pretreatment largely enhanced glucan digestibility of corn stover (CS) to 94.7 % which was 4.60-fold to untreated CS. Mechanism of improved enzymatic efficiency of DES-pretreated CS was revealed by characterization methods such as SEM, XRD, and FTIR technologies. In addition, adding 1.0 % H 2 SO 4 to the ternary DES further increased enzymatic hydrolysis yield to nearly 100 %. Subsequently, the H 2 SO 4 -assisted DES-pretreated CS was used as substrate to produce C2-C4 bioalcohols (ethanol and butanol) by microbial fermentations. The results indicate that 46.6–49.2 g/L ethanol could be obtained by S. cerevisiae under separate hydrolysis and fermentation (SHF), and simultaneous saccharification and fermentation (SSF) strategies. Meanwhile, up to 11.9 g/L butanol was achieved from CS hydrolysate by Clostridium acetobutylicum with fed-batch mode. Finally, an artificial microbial consortium by co-culturing C. acetobutylicum and S. cerevisiae was developed which efficiently co-produced 12.4 g/L biobutanol and 5.7 g/L bioethanol from CS hydrolysate. Overall, this study provides a sustainable and green pretreatment technology that deconstruct lignocellulose and possess universality to produce various low-carbon bioalcohols by microbial fermentations. [Display omitted] ● A ternary deep eutectic solvent (DES) was synthesized for biomass pretreatment. ● Glucose yield of DES-pretreated corn stover (CS) reached 94.7 % after enzymolysis. ● Combined H 2 SO 4 with DES pretreatment facilitated sugars yield to nearly 100 %. ● Up to 46.6–49.2 g/L ethanol and 11.9 g/L butanol were produced by monocultures. ● A co-culture system co-produced 18.1 g/L bioalcohols and 26.2 g/L ABE from CS. [ABSTRACT FROM AUTHOR]