High-throughput analysis of chemical components and theoretical ethanol yield of dedicated bioenergy sorghum using dual-optimized partial least squares calibration models

High-throughput phenotyping of nutritional quality components in sweet potato roots by near-infrared spectroscopy and chemometrics methods

The lack of an efficient approach for quality evaluation of sweet potatoes significantly hinders progress in quality breeding. Therefore, this study aimed to establish a near-infrared spectroscopy (NIRS) assay for high-throughput analysis of sweet potato root quality, including total starch, amylose, amylopectin, the ratio of amylopectin to amylose, soluble sugar, crude protein, total flavonoid content, and total phenolic content. A total of 125 representative samples were utilized and a dual-optimized strategy (optimization of sample subset partitioning and variable selection) was applied to NIRS modeling. Eight optimal equations were developed with an excellent coefficient of determination for the calibration (R2C) at 0.95-0.99, cross-validation (R2CV) at 0.93-0.98, external validation (R2V) at 0.89-0.96, and the ratio of prediction to deviation (RPD) at 6.33-11.35. Overall, these NIRS models provide a feasible approach for high-throughput analysis of root quality and permit large-scale screening of elite germplasm in future sweet potato breeding.

Weight interpretation of artificial neural network model for analysis of rice (Oryza sativa L.) with near-infrared spectroscopy

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ANN model was build based on NIR spectra and nutrient values of 110 rice samples.

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Good correlation between ANN predicted and experimental nutrient values observed.

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Scientific interpretation of weights agreed well with previously reported results.

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Interpretation of weights was also in good agreement with conventional PLS analysis.

Prediction models for major nutrients of rice were built using near-infrared (NIR) spectral data based on the artificial neural network (ANN). Scientific interpretation of the weight values was proposed and performed to understand the wavenumbers contributing to the prediction of nutrients. NIR spectra were acquired from 110 rice samples. Carbohydrate and moisture contents were predicted with values for the determination coefficient, relative root mean square error, range error ratio, and residual prediction deviation of 0.98, 0.11 %, 44, and 7.3, and 0.97, 0.80 %, 27, and 5.8, respectively. The results agreed well with ones reported in the previous studies and acquired by the conventional partial least squares (PLS)-variable importance in projection method. This study demonstrates that the combination of NIR and ANN is a powerful and accurate tool to monitor nutrients of rice and scientific interpretation of weights can be performed to overcome black box nature of the ANN.

Heat Stress-Mediated Constraints in Maize (Zea mays) Production: Challenges and Solutions

An increase in temperature and extreme heat stress is responsible for the global reduction in maize yield. Heat stress affects the integrity of the plasma membrane functioning of mitochondria and chloroplast, which further results in the over-accumulation of reactive oxygen species. The activation of a signal cascade subsequently induces the transcription of heat shock proteins. The denaturation and accumulation of misfolded or unfolded proteins generate cell toxicity, leading to death. Therefore, developing maize cultivars with significant heat tolerance is urgently required. Despite the explored molecular mechanism underlying heat stress response in some plant species, the precise genetic engineering of maize is required to develop high heat-tolerant varieties. Several agronomic management practices, such as soil and nutrient management, plantation rate, timing, crop rotation, and irrigation, are beneficial along with the advanced molecular strategies to counter the elevated heat stress experienced by maize. This review summarizes heat stress sensing, induction of signaling cascade, symptoms, heat stress-related genes, the molecular feature of maize response, and approaches used in developing heat-tolerant maize varieties.

Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy

Near-infrared spectroscopy (NIR) is a non-destructive, fast, and low-cost method to measure the grain quality of different cereals. However, the feasibility for determining the critical biochemicals, related to the classifications for food, feed, and fuel products are not adequately investigated. Fourier-transform (FT) NIR was applied in this study to determine the eight biochemicals in four types of sorghum samples: hulled grain flours, hull-less grain flours, whole grains, and grain flours. A total of 20 hybrids of sorghum grains were selected from the two locations in China. Followed by FT-NIR spectral and wet-chemically measured biochemical data, partial least squares regression (PLSR) was used to construct the prediction models. The results showed that sorghum grain morphology and sample format affected the prediction of biochemicals. Using NIR data of grain flours generally improved the prediction compared with the use of NIR data of whole grains. In addition, using the spectra of whole grains enabled comparable predictions, which are recommended when a non-destructive and rapid analysis is required. Compared with the hulled grain flours, hull-less grain flours allowed for improved predictions for tannin, cellulose, and hemicellulose using NIR data. This study aimed to provide a reference for the evaluation of sorghum grain biochemicals for food, feed, and fuel without destruction and complex chemical analysis.

A systematic high-throughput phenotyping assay for sugarcane stalk quality characterization by near-infrared spectroscopy

BACKGROUND

Sugarcane (Saccharum officinarum L.) is an economically important crop with stalks as the harvest organs. Improvement in stalk quality is deemed a promising strategy for enhancing sugarcane production. However, the lack of efficient approaches for systematic evaluation of sugarcane germplasm largely limits improvements in stalk quality. This study is designed to develop a systematic near-infrared spectroscopy (NIRS) assay for high-throughput phenotyping of sugarcane stalk quality, thereby providing a feasible solution for precise evaluation of sugarcane germplasm.

RESULTS

A total of 628 sugarcane accessions harvested at different growth stages before and after maturity were employed to take a high-throughput assay to determine sugarcane stalk quality. Based on high-performance anion chromatography (HPAEC-PAD), large variations in sugarcane stalk quality were detected in terms of biomass composition and the corresponding fundamental ratios. Online and offline NIRS modeling strategies were applied for multiple purpose calibration with partial least square (PLS) regression analysis. Consequently, 25 equations were generated with excellent determination coefficients (R²) and ratio performance deviation (RPD) values. Notably, for some observations, RPD values as high as 6.3 were observed, which indicated their exceptional performance and predictive capability.

CONCLUSIONS

This study provides a feasible method for consistent and high-throughput assessment of stalk quality in terms of moisture, soluble sugar, insoluble residue and the corresponding fundamental ratios. The proposed method permits large-scale screening of optimal sugarcane germplasm for sugarcane stalk quality breeding and beyond.

Precise high-throughput online near-infrared spectroscopy assay to determine key cell wall features associated with sugarcane bagasse digestibility

BACKGROUND

Sugarcane is one of the most crucial energy crops that produces high yields of sugar and lignocellulose. The cellulose crystallinity index (CrI) and lignin are the two kinds of key cell wall features that account for lignocellulose saccharification. Therefore, high-throughput screening of sugarcane germplasm with excellent cell wall features is considered a promising strategy to enhance bagasse digestibility. Recently, there has been research to explore near-infrared spectroscopy (NIRS) assays for the characterization of the corresponding wall features. However, due to the technical barriers of the offline strategy, it is difficult to apply for high-throughput real-time analyses. This study was therefore initiated to develop a high-throughput online NIRS assay to rapidly detect cellulose crystallinity, lignin content, and their related proportions in sugarcane, aiming to provide an efficient and feasible method for sugarcane cell wall feature evaluation.

RESULTS

A total of 838 different sugarcane genotypes were collected at different growth stages during 2018 and 2019. A continuous variation distribution of the near-infrared spectrum was observed among these collections. Due to the very large diversity of CrI and lignin contents detected in the collected sugarcane samples, seven high-quality calibration models were developed through online NIRS calibration. All of the generated equations displayed coefficient of determination (R2) values greater than 0.8 and high ratio performance deviation (RPD) values of over 2.0 in calibration, internal cross-validation, and external validation. Remarkably, the equations for CrI and total lignin content exhibited RPD values as high as 2.56 and 2.55, respectively, indicating their excellent prediction capacity. An offline NIRS assay was also performed. Comparable calibration was observed between the offline and online NIRS analyses, suggesting that both strategies would be applicable to estimate cell wall characteristics. Nevertheless, as online NIRS assays offer tremendous advantages for large-scale real-time screening applications, it could be implied that they are a better option for high-throughput cell wall feature prediction.

CONCLUSIONS

This study, as an initial attempt, explored an online NIRS assay for the high-throughput assessment of key cell wall features in terms of CrI, lignin content, and their proportion in sugarcane. Consistent and precise calibration results were obtained with NIRS modeling, insinuating this strategy as a reliable approach for the large-scale screening of promising sugarcane germplasm for cell wall structure improvement and beyond.

Stalk architecture, cell wall composition, and QTL underlying high stalk flexibility for improved lodging resistance in maize

BACKGROUND

Stalk fracture caused by strong wind can severely reduce yields in maize. Stalks with higher stiffness and flexibility will exhibit stronger lodging resistance. However, stalk flexibility is rarely studied in maize. Stalk fracture of the internode above the ear before tasseling will result in the lack of tassel and pollen, which is devastating for pollination in seed production. In this study, we focused on stalk lodging before tasseling in two maize inbred lines, JING724 and its improved line JING724A1 and their F_2:3 population.

RESULTS

JING724A1 showed a larger stalk fracture angle than JING724, indicating higher flexibility. In addition, compared to JING724, JING724A1 also had longer and thicker stalks, with a conical, frustum-shaped internode above the ear. Microscopy and X-ray microcomputed tomography of the internal stalk architecture revealed that JING724A1 had more vascular bundles and thicker sclerenchyma tissue. Furthermore, total soluble sugar content of JING724A1, especially the glucose component, was substantially higher than in JING724. Using an F_2:3 population derived from a JING724 and JING724A1 cross, we performed bulk segregant analysis for stalk fracture angle and detected one QTL located on Chr3: 14.00-19.28 Mb. Through transcriptome data analysis and ∆ (SNP-index), we identified two candidate genes significantly associated with high stalk fracture angle, which encode a RING/U-box superfamily protein (Zm00001d039769) and a MADS-box transcription factor 54 (Zm00001d039913), respectively. Two KASP markers designed from these two candidate genes also showed significant correlations with stalk fracture angle.

CONCLUSIONS

The internode shape and glucose content are possibly correlated with stalk flexibility in maize. Two genes in the detected QTL are potentially associated with stalk fracture angle. These novel phenotypes and associated loci will provide a theoretical foundation for understanding the genetic mechanisms of lodging, and facilitate the selection of maize varieties with improved flexibility and robust lodging resistance.

Prediction of holocellulose and lignin content of pulp wood feedstock using near infrared spectroscopy and variable selection

Wood is the main feedstock source for pulp and paper industry. However, chemical composition variations from multispecies and multisource feedstock heavily affect the production continuity and stability. As a rapid and non-destructive analysis technique, near infrared (NIR) spectroscopy provides an alternative for wood properties on-line analysis and feedstock quality control. Herein, near infrared spectroscopy coupled with partial least squares (PLS) regression was used to predict holocellulose and lignin contents of various wood species including poplars, eucalyptus and acacias. In order to obtain more accurate and robust prediction models, a comparison was conducted among several variable selection methods for NIR spectral variables optimization, including competitive adaptive reweighted sampling (CARS), Monte Carlo-uninformative variable elimination (MC-UVE), successive projections algorithm (SPA), and genetic algorithm (GA). The results indicated that CARS method displayed relatively higher efficiency over other methods in elimination of uninformative variables as well as enhancement of the predictive performance of models. CARS-PLS models showed significantly higher robustness and accuracy for each property using lowest variable numbers in cross validation and external validation, demonstrating its applicability and reliability for prediction of multispecies feedstock properties.

Gold Nanoparticles with Different Particle Sizes for the Quantitative Determination of Chlorpyrifos Residues in Soil by SERS

Chlorpyrifos (CPF) is widely used in the prevention and control of crop pests and diseases in agriculture. However, the irrational utilization of pesticides not only causes environmental pollution but also threatens human health. Compared with the conventional techniques for the determination of pesticides in soil, surface-enhanced Raman spectroscopy (SERS) has shown great potential in ultrasensitive and chemical analysis. Therefore, this paper reported a simple method for synthesizing gold nanoparticles (AuNPs) with different sizes used as a SERS substrate for the determination of CPF residues in soil for the first time. The results showed that there was a good linear correlation between the SERS characteristic peak intensity of CPF and particle size of the AuNPs with an R2 of 0.9973. Moreover, the prepared AuNPs performed great ultrasensitivity, reproducibility and chemical stability, and the limit of detection (LOD) of the CPF was found to be as low as 10 μg/L. Furthermore, the concentrations ranging from 0.01 to 10 mg/L were easily observed by SERS with the prepared AuNPs and the SERS intensity showed a good linear relationship with an R2 of 0.985. The determination coefficient (Rp2) reached 0.977 for CPF prediction using the partial least squares regression (PLSR) model and the LOD of CPF residues in soil was found to be as low as 0.025 mg/kg. The relative standard deviation (RSD) was less than 3.69% and the recovery ranged from 97.5 to 103.3%. In summary, this simple method for AuNPs fabrication with ultrasensitivity and reproducibility confirms that the SERS is highly promising for the determination of soil pesticide residues.

A Rapid and Nondestructive Method for the Prediction of Ibuprofen Tablet Hardness Using NIR Diffuse Reflectance Spectroscopy

Introduction:

This study aimed at developing a technology to measure the hardness of Ibuprofen (IBU) tablets and optimize the IBU formulation using Near-infrared (NIR) spectroscopy.

Materials and Methods:

Tablets (400 mg±5%, 10mm in diameter) consisting of IBU, microcrystalline cellulose SH-103, carboxymethyl starch sodium, magnesium stearate, silicon dioxide were formed of various hardness (2kg, 4kg, 6kg, 8kg, 10kg, 12kg). The reflectance NIR spectra of various tablets were employed to establish 9 calibrations models, which were further used to predict tablet hardness by Partial least squares (PLS) and principal component regression (PCR) analysis.

Results and Conclusion:

Cross-validation with independent samples shows that PLS is the optimal predictive model. Which R2=0.9832, RSECV=0.334 and RSE=0.0669. This study established a new, simple, rapid, nondestructive and reliable methodology to optimize the IBU tablet hardness.

Rapid and Quantitative Determination of Sildenafil in Cocktail Based on Surface Enhanced Raman Spectroscopy

Sildenafil (SD) and its related compounds are the most common adulterants found in herbal preparations used as sexual enhancer or man’s virility products. However, the abuse of SD threatens human health such as through headache, back pain, rhinitis, etc. Therefore, it is important to accurately detect the presence of SD in alcoholic beverages. In this study, the Opto Trace Raman 202 (OTR 202) was used as a surface-enhanced Raman spectroscopy (SERS) active colloids to detect SD. The results demonstrated that the limit of detection (LOD) of SD was found to be as low as 0.1 mg/L. Moreover, 1235, 1401, 1530, and 1584 cm⁻¹ could be qualitatively determined as SD characteristic peaks. In a practical application, SD in cocktail could be easily detected using SERS based on OTR 202. Also, there was a good linear correlation between the intensity of Raman peaks at 1235, 1401, 1530, and 1584 cm⁻¹ and the logarithm of SD concentration in cocktail was in the range of 0.1–10 mg/L (0.9822 < R² < 0.9860). The relative standard deviation (RSD) was less than 12.7% and the recovery ranged from 93.0%–105.8%. Moreover, the original 500–1700 cm⁻¹ SERS spectra were pretreated and the partial least squares (PLS) was applied to establish the prediction model between SERS spectra and SD content in cocktail and the highest determination coefficient (Rp²) reached 0.9856. In summary, the SD in cocktail could be rapidly and quantitatively determined by SERS, which was beneficial to provide a rapid and accurate scheme for the detection of SD in alcoholic beverages.

Lignocellulose derived functional oligosaccharides: production, properties, and health benefits

Lignocellulosic biomass (LB) is the renewable feedstock for the production of fuel/energy, feed/food, chemicals, and materials. LB could also be the versatile source of the functional oligosaccharides, which are non-digestible food ingredients having numerous applications in food, cosmetics, pharmaceutical industries, and others. The burgeoning functional food demand is expected to be more than US$440 billion in 2022. Because of higher stability at low pH and high temperature, oligosaccharides stimulate the growth of prebiotic bifidobacteria and lactic acid bacteria. Xylooligosaccharides (XOS) are major constituents of oligosaccharides consisting of 2-7 xylose monomeric units linked via β-(1,4)-linkages. XOS can be obtained from various agro-residues by thermochemical pretreatment, enzymatic or chemoenzymatic methods. While thermochemical methods are fast, reproducible, enzymatic methods are substrate specific, costly, and produce minimum side products. Enzymatic methods are preferred for the production of food grade and pharmaceutically important oligosaccharides. XOS are potent prebiotics having antioxidant properties and enhance the bio-adsorption of calcium and improving bowel functions, etc. LB can cater to the increasing demand of oligosaccharides because of their foreseeable amount and the advancements in technology to recover oligosaccharides. This paper summarizes the methods for oligosaccharides production from LB, classification, and benefits of oligosaccharides on human health.

Gold Nanoparticles for Qualitative Detection of Deltamethrin and Carbofuran Residues in Soil by Surface Enhanced Raman Scattering (SERS)

The residues of deltamethrin (DM) and carbofuran (CBF) in soil is becoming an intractable problem causing soil hardening and environmental pollution. This paper reports a very simple method via improved reduction of chloroauric acid by the trisodium citrate method for fabricating gold nanoparticles (AuNPs), which were used as a surface enhanced Raman scattering (SERS) active colloids with the advantages of ultrasensitivity, reproducibility and chemical stability. The results demonstrated that the limits of detection (LODs) of the DM and CBF were found to be as low as 0.01 mg/L. The SERS intensity showed a good linear relationship with DM (R² = 0.9908) and CBF (R² = 0.9801) concentration from 0.01 to 10 mg/L. In a practical application, DM and CBF residues in soil were easily detected by SERS with the flexible AuNPs colloids, and the LODs of DM and CBF were found to be as low as 0.056 mg/kg and 0.053 mg/kg, respectively. Moreover, DM in soil could be qualitatively detected by the characteristic peaks at 560 and 1000 cm^-1, and CBF in soil could be qualitatively detected by the characteristic peaks at 1000 and 1299 cm^-1. The determination coefficient (R²_p) for DM and CBF reached 0.9176 and 0.8517 in partial least squares (PLS) model. Overall, it is believed that the prepared AuNPs can provide technical support for the accurate detection of pesticide residues in soil by SERS technique.

Brittle Culm 1 Encodes a COBRA-Like Protein Involved in Secondary Cell Wall Cellulose Biosynthesis in Sorghum

Plant mechanical strength contributes to lodging resistance and grain yield, making it an agronomically important trait in sorghum (Sorghum bicolor). In this study, we isolated the brittle culm 1 (bc1) mutant and identified SbBC1 through map-based cloning. SbBC1, a homolog of rice OsBC1 and Arabidopsis thaliana AtCOBL4, encodes a COBRA-like protein that exhibits typical structural features of a glycosylphosphatidylinositol-anchored protein. A single-nucleotide mutation in SbBC1 led to reduced mechanical strength, decreased cellulose content, and increased lignin content without obviously altering plant morphology. Transmission electron microscopy revealed reduced cell wall thickness in sclerenchyma cells of the bc1 mutant. SbBC1 is primarily expressed in developing sclerenchyma cells and vascular bundles in sorghum. RNA-seq analysis further suggested a possible mechanism by which SbBC1 mediates cellulose biosynthesis and cell wall remodeling. Our results demonstrate that SbBC1 participates in the biosynthesis of cellulose in the secondary cell wall and affects the mechanical strength of sorghum plants, providing additional genetic evidence for the roles of COBRA-like genes in cellulose biosynthesis in grasses.

An NIRS-based assay of chemical composition and biomass digestibility for rapid selection of Jerusalem artichoke clones

BACKGROUND

High-throughput evaluation of lignocellulosic biomass feedstock quality is the key to the successful commercialization of bioethanol production. Currently, wet chemical methods for the determination of chemical composition and biomass digestibility are expensive and time-consuming, thus hindering comprehensive feedstock quality assessments based on these biomass specifications. To find the ideal bioethanol feedstock, we perform a near-infrared spectroscopic (NIRS) assay to rapidly and comprehensively analyze the chemical composition and biomass digestibility of 59 Jerusalem artichoke (Helianthus tuberosus L., abbreviated JA) clones collected from 24 provinces in six regions of China.

RESULTS

The distinct geographical distribution of JA accessions generated varied chemical composition as well as related biomass digestibility (after soluble sugars extraction and mild alkali pretreatment). Notably, the soluble sugars, cellulose, hemicellulose, lignin, ash, and released hexoses, pentoses, and total carbohydrates were rapidly and perfectly predicted by partial least squares regression coupled with model population analyses (MPA), which exhibited significantly higher predictive performance than controls. Subsequently, grey relational grade analysis was employed to correlate chemical composition and biomass digestibility with feedstock quality score (FQS), resulting in the assignment of tested JA clones to five feedstock quality grades (FQGs). Ultimately, the FQGs of JA clones were successfully classified using partial least squares-discriminant analysis model coupled with MPA, attaining a significantly higher correct rate of 97.8% in the calibration subset and 91.1% in the validation subset.

CONCLUSIONS

Based on the diversity of JA clones, the present study has not only rapidly and precisely examined the biomass composition and digestibility with MPA-optimized NIRS models but has also selected the ideal JA clones according to FQS. This method provides a new insight into the selection of ideal bioethanol feedstock for high-efficiency bioethanol production.

Proteomic researches for lignocellulose-degrading enzymes: A mini-review

Protective action of lignin/hemicellulose networks and crystalline structures of embedded cellulose render lignocellulose material resistant to external enzymatic attack. To eliminate this bottleneck, research has been conducted in which advanced proteomic techniques are applied to identify effective commercial hydrolytic enzymes. This mini-review summarizes researches on lignocellulose-degrading enzymes, the mechanisms of the responses of various lignocellulose-degrading strains and microbial communities to various carbon sources and various biomass substrates, post-translational modifications of lignocellulose-degrading enzymes, new lignocellulose-degrading strains, new lignocellulose-degrading enzymes and a new method of secretome analysis. The challenges in the practical use of enzymatic hydrolysis process to realize lignocellulose biorefineries are discussed, along with the prospects for the same.