Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

Integrated VIS/NIR Spectrum and Genome-Wide Association Study for Genetic Dissection of Cellulose Crystallinity in Wheat Stems

Cellulose crystallinity is a crucial factor influencing stem strength and, consequently, wheat lodging. However, the genetic dissection of cellulose crystallinity is less reported due to the difficulty of its measurement. In this study, VIS/NIR spectra and cellulose crystallinity were measured for a wheat accession panel with diverse genetic backgrounds. We developed a reliable VIS/NIR model for cellulose crystallinity with a high determination coefficient (R2) (0.95) and residual prediction deviation (RPD) (4.04), enabling the rapid screening of wheat samples. A GWAS of the cellulose crystallinity in 326 wheat accessions revealed 14 significant SNPs and 13 QTLs. Two candidate genes, TraesCS4B03G0029800 and TraesCS5B03G1085500, were identified. In summary, this study establishes an efficient method for the measurement of cellulose crystallinity in wheat stems and provides a genetic basis for enhancing lodging resistance in wheat.

Application of Rapid Identification and Determination of Moisture Content of Coptidis Rhizoma From Different Species Based on Data Fusion

BACKGROUND

For thousands of years, traditional Chinese medicine (TCM) has been clinically proven, and doctors have highly valued the differences in utility between different species.

OBJECTIVE

This study aims to replace the complex methods traditionally used for empirical identification by compensating for the information loss of a single sensor through data fusion. The research object of the study is Coptidis rhizoma (CR).

METHOD

Using spectral optimization and data fusion technology, near infrared (NIR) and mid-infrared (MIR) spectra were collected for CR. PLS-DA (n = 134) and PLSR (n = 63) models were established to identify the medicinal materials and to determine the moisture content in the medicinal materials.

RESULTS

For the identification of the three species of CR, the mid-level fusion model performed better than the single-spectrum model. The sensitivity and specificity of the prediction set coefficients for NIR, MIR, and data fusion qualitative models were all higher than 0.95, with an AUC value of 1. The NIR data model was superior to the MIR data model. The results of low-level fusion were similar to those of the NIR optimization model. The RPD of the test set of NIR and low-level fusion model was 3.6420 and 3.4216, respectively, indicating good prediction ability of the model.

CONCLUSIONS

Data fusion technology using NIR and MIR can be applied to identify CR species and to determine the moisture content of CR. It provides technical support for the rapid determination of moisture content, with a fast analysis speed and without the need for complex pretreatment methods.

HIGHLIGHTS

This study is the first to introduce spectral data fusion technology to identify CR species. Data fusion technology is feasible for multivariable calibration model performance and reduces the cost of manual identification. The moisture content of CR can be quickly evaluated, reducing the difficulty of traditional methods.

Optimization of Mixed Fermentation Conditions of Dietary Fiber from Soybean Residue and the Effect on Structure, Properties and Potential Biological Activity of Dietary Fiber from Soybean Residue

Soybean residue is a by-product of soybean product production that is wasted unreasonably at present. Accomplishing the efficient utilization of soybean residue can save resources. A composite microbial system was constructed using lactic acid bacteria (LAB) and Saccharomyces cerevisiae (SC), and modified soybean residue was prepared by solid fermentation. In order to explore the value of modified soybean residue as a food raw material, its physical and chemical properties, adsorption properties, and antioxidant properties were studied. The results showed that the soluble dietary fiber (SDF) yield of mixed fermentation (MF) increased significantly. Both groups of soybean residues had representative polysaccharide infrared absorption peaks, and MF showed a looser structure and lower crystallinity. In terms of the adsorption capacity index, MF also has a higher adsorption capacity for water molecules, oil molecules, and cholesterol molecules. In addition, the in vitro antioxidant capacity of MF was also significantly higher than that of unfermented soybean residue (UF). In conclusion, our study shows that mixed fermentation could increase SDF content and improve the functional properties of soybean residue. Modified soybean residue prepared by mixed fermentation is the ideal food raw material.

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.

Monitoring Time-Non-Stable Surfaces Using Mobile NIR DLP Spectroscopy

In recent years, Near Infrared (NIR) spectroscopy has increased in popularity and usage for different purposes, including the detection of particular substances, evaluation of food quality, etc. Usually, mobile handheld NIR spectroscopy devices are used on the surfaces of different materials, very often organic ones. The features of these materials change as they age, leading to changes in their spectra. The ageing process often occurs only slowly, i.e., corresponding reflection spectra can be analyzed each hour or at an even longer interval. This paper undertakes the problem of analyzing surfaces of non-stable, rapidly changing materials such as waxes or adhesive materials. To obtain their characteristic spectra, NIR spectroscopy using a Digital Light Projection (DLP) spectrometer was used. Based on earlier experiences and the current state of the art, Artificial Neural Networks (ANNs) were used to process spectral sequences to proceed with an enormous value of spectra gathered during measurements.

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.

Non-Invasive Detection of Anti-Inflammatory Bioactivity and Key Chemical Indicators of the Commercial Lanqin Oral Solution by Near Infrared Spectroscopy

Quality control methods of current traditional Chinese medicine (TCM) preparation is time-consuming and difficult to assess in terms of overall efficiency of the drug. A non-destructive rapid near-infrared spectroscopy detection system for key chemical components and biological activity of Lanqin oral solution (LOS), one of the best-selling TCM formulations, was established for comprehensive quality evaluation. Near infrared spectral scanning was carried out on 101 batches of commercial LOS under the penetrated vial state and traditional state. RAW 264.7 cells were cultured to detect the anti-inflammatory ability of LOS, and the reference concentrations of epigoitrin, geniposide, and baicalin were obtained by HPLC. The quantitative models were optimized by three kinds of variable selection methods. The correlation coefficients of prediction value of the models were greater than 0.94. The system also passed the external validation. The performance of the non-invasive models was similar to the traditional models. The established non-destructive system can be applied to the rapid quality inspection of LOS to avoid unqualified drugs from entering the market and ensure drug effectiveness. The biological activity index of LOS was introduced and predicted by NIRs for the first time, which provides a new idea about the quality control of TCM formulations.

Fluorescence Microscopy Methods for the Analysis and Characterization of Lignin

Lignin is one of the most studied and analyzed materials due to its importance in cell structure and in lignocellulosic biomass. Because lignin exhibits autofluorescence, methods have been developed that allow it to be analyzed and characterized directly in plant tissue and in samples of lignocellulose fibers. Compared to destructive and costly analytical techniques, fluorescence microscopy presents suitable alternatives for the analysis of lignin autofluorescence. Therefore, this review article analyzes the different methods that exist and that have focused specifically on the study of lignin because with the revised methods, lignin is characterized efficiently and in a short time. The existing qualitative methods are Epifluorescence and Confocal Laser Scanning Microscopy; however, other semi-qualitative methods have been developed that allow fluorescence measurements and to quantify the differences in the structural composition of lignin. The methods are fluorescence lifetime spectroscopy, two-photon microscopy, Föster resonance energy transfer, fluorescence recovery after photobleaching, total internal reflection fluorescence, and stimulated emission depletion. With these methods, it is possible to analyze the transport and polymerization of lignin monomers, distribution of lignin of the syringyl or guaiacyl type in the tissues of various plant species, and changes in the degradation of wood by pulping and biopulping treatments as well as identify the purity of cellulose nanofibers though lignocellulosic biomass.