Determination of 10-Hydroxy-2-Decenoic Acid of Royal Jelly Using Near-Infrared Spectroscopy Combined with Chemometrics

Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat

In this study, a method of mid-level data fusion with the fingerprint region was proposed, which was combined with the characteristic wavelengths that contain fingerprint information in NIR and FT-MIR spectra to detect the DON level in FHB wheat during wheat processing. NIR and FT-MIR raw spectroscopy data on normal wheat and FHB wheat were obtained in the experiment. MSC was used for pretreatment, and characteristic wavelengths were extracted by CARS, MGS and XLW. The variables that can effectively reflect fingerprint information were retained to build the mid-level data fusion matrix. LS-SVM and PLS-DA were applied to investigate the performance of the single spectroscopic model, mid-level data fusion model and mid-level data fusion with fingerprint information model, respectively. The experimental results show that mid-level data fusion with a fingerprint information strategy based on fused NIR and FT-MIR spectra represents an effective method for the classification of DON levels in FHB wheat samples.

Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging

This research explored the feasibility of early warning and diagnostic visualization of Sclerotinia infected tomato by using hyperspectral imaging technology. Healthy tomato plants and tomato plants with Sclerotinia sclerotiorum were cultivated, and hyperspectral images at 400-1000 nm were collected from healthy and infected tomato leaves at 1, 3, 5, and 7 days of incubation. After preprocessing the spectra with first derivative (FD), second derivative (SD), standard normal variant (SNV), and multiplicative scatter correction (MSC) partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to construct tomato sclerotinia identification model and select the best preprocessing method. On this basis, two band screening methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were introduced to reduce data redundancy and improve the model's prediction accuracy. The results showed that the accuracy of the validation sets and operation speed of the CARS-PLS and CARS-SVM models were 87.88% and 1.8 s, and 87.95% and 1.78 s, respectively. The experiment was based on the SNV-CARS-SVM prediction model combined with image processing, spectral extraction, and visualization analysis methods to create diagnostic visualization software, which opens a new avenue to the implementation of online monitoring and early warning system for sclerotinia infected tomato.

Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy

To monitor environmental water pollution effectively and meet human water needs, it is crucial to develop a fast, simple, and accurate method for monitoring chemical oxygen demand (COD) in various water systems. In this study, COD prediction models for different water systems were developed by combining near-infrared (NIR) spectroscopy with partial least squares regression (PLSR). Samples of wastewater, surface water, and seawater were collected from Guangzhou, Guangdong Province, China. Three pretreatment methods were used to preprocess the spectra in order to improve the accuracy and minimalism of the model. We investigate the performance of two variable selection algorithms, namely, binary gray wolf optimization (BGWO) and competitive adaptive reweighting sampling (CARS). The results show that both BGWO and CARS improved the performance of the model in terms of higher accuracy and less wavelength input; both of the combined model performances were better than that of PLSR alone, and CARS-PLSR achieved the best results. Using CARS-PLSR, surface water, wastewater, and seawater model inputs were reduced by 96 %, 96 %, and 82 % as compared to the PLSR results, respectively, and the testing sets R² reached 0.860, 0.815, and 0.692, respectively. The spectral variable selection algorithm could identify the important spectral variables between COD content and NIR spectra in three water systems, thereby improving the accuracy and simplicity of the PLSR model for COD prediction. Our results have important practical value for predicting COD content in different water systems by NIR spectroscopy.

Efficient Biosynthesis of 10-Hydroxy-2-decenoic Acid Using a NAD(P)H Regeneration P450 System and Whole-Cell Catalytic Biosynthesis

10-Hydroxy-2-decenoic acid (10-HDA) is an α,β-unsaturated medium-chain carboxylic acid containing a terminal hydroxyl group. It has various unique properties and great economic value. We improved the two-step biosynthesis method of 10-HDA. The conversion rate of the intermediate product trans-2-decenoic acid in the first step of 10-HDA synthesis could reach 93.1 ± 1.3% by combining transporter overexpression and permeation technology strategies. Moreover, the extracellular trans-2-decenoic acid content was five times greater than the intracellular content when 2.0% (v/v) triton X-100 and 1.2% (v/v) tween-80 were each used. In the second step of 10-HDA synthesis, we regenerated NAD(P)H by overexpressing a glucose dehydrogenase with the P450 enzyme (CYP153A33/M228L-CPRBM3) in Escherichia coli, improving the catalytic performance of the trans-2-decenoic acid terminal hydroxylation. Finally, the yield of 10-HDA was 486.5 mg/L using decanoic acid as the substrate with two-step continuous biosynthesis. Our research provides a simplified production strategy to promote the two-step continuous whole-cell catalytic biosynthesis of 10-HDA and other α,β-unsaturated carboxylic acid derivatives.

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.

10-HDA Induces ROS-Mediated Apoptosis in A549 Human Lung Cancer Cells by Regulating the MAPK, STAT3, NF-κB, and TGF-β1 Signaling Pathways

10-Hydroxy-2-decenoic acid (10-HDA), also known as royal jelly acid, has a variety of physiological functions, and recent studies have shown that it also has anticancer effects. However, its anticancer mechanisms have not been clearly defined. In this study, we investigated the underlying mechanisms of 10-HDA in A549 human lung cancer cells. We used Cell Counting Kit-8 assay, scratch wound healing assay, flow cytometry, and western blot analysis to investigate its apoptotic effects and underlying mechanism. Our results showed that 10-HDA inhibited the proliferation of three types of human lung cancer cells and had no significant toxic effects on normal cells. Accompanying reactive oxygen species (ROS), 10-HDA induced A549 cell apoptosis by regulating mitochondrial-associated apoptosis, and caused cell cycle arrest at the G0/G1 phase in a time-dependent manner. Meanwhile, 10-HDA also regulated mitogen-activated protein kinase (MAPK), signal transducer and activator of transcription 3 (STAT3), and nuclear factor kappa B (NF-κB) signaling pathways by increasing the expression levels of phosphorylated c-Jun N-terminal kinase, p-p38, and I-κB, and additionally, by decreasing the expression levels of phosphorylated extracellular signal-regulated kinase, p-STAT3, and NF-κB. These effects were blocked by MAPK inhibitors and N-acetyl-L-cysteine. Furthermore, 10-HDA inhibited cell migration by regulating transforming growth factor beta 1 (TGF-β1), SNAI1, GSK-3β, E-cadherin, N-cadherin, and vimentin. Taken together, the results of this study showed that 10-HDA induced cell cycle arrest and apoptosis in A549 human lung cancer cells through ROS-mediated MAPK, STAT3, NF-κB, and TGF-β1 signaling pathways. Therefore, 10-HDA may be a potential therapy for human lung cancer.