Application of UPLC-Orbitrap-HRMS targeted metabolomics in screening of allelochemicals and model plants of ginseng.

Continuous cropping of ginseng leads to serious declines in yield and quality because of self-toxicity of allelochemicals and other factors in soil. However, because of the long growth cycle and low survival rate of ginseng, rapid screening of autotoxic activity is difficult. Therefore, it is important to analyze the allelochemicals and identify a model plant with autotoxic responses similar to those of ginseng. In this study, UPLC-Orbitrap-HRMS targeted metabolomics and verification of autotoxic activity were used to analyze a problem soil from continuously cropped ginseng. Allelochemical markers were screened by OPLS-DA. Seeds and seedlings of maize, Chinese cabbage, cucumber, green beans, wheat, sunflower, and oats were selected to identify potential model plants. Model plants with autotoxic responses similar to those of ginseng were evaluated by comparing morphological, physiological, and biochemical characteristics. The n-butanol extract of the continuously cropped problem soil had the most significant autotoxic activity. Twenty-three ginsenosides and the contributions to autotoxic effects were screened and evaluated. Of potential model plants, seeds and seedlings of cucumber showed similar growth inhibition to that of ginseng under the action of allelochemicals. Thus, metabolomics can be used to screen allelochemicals in soil and predict the autotoxic effects, and the cucumber plant model can be used to rapidly screen allelopathic activity of ginseng. The study will provide reference for methodology in allelopathy research on ginseng.

Rapid detection and quality evaluation of Shuang-Huang-Lian injection by ATR-IR and NIR spectroscopy combined with chemometrics.

Quality evaluation and consistency evaluation of drugs are the keys to ensure the therapeutic effect and safety of drugs. In this study, attenuated total refraction infrared (ATR-IR) spectroscopy and near-infrared (NIR) spectroscopy combined with chemometrics were used for rapid detection and quality evaluation of active components of Shuang-Huang-Lian injection (SHLI), a traditional Chinese medicine preparation commonly used in China. Taking the chromatographic detection results as a reference, the partial least squares (PLS) model based on ATR-IR and NIR data was constructed by removing the bands with serious noise interference and low signal frequency band. The results showed that the PLS model achieved satisfactory results for the prediction of the three active components (chlorogenic acid, baicalin and phillyrin) in SHLI, indicating that the two spectral techniques combined with the PLS regression method could be successfully used for rapid quantitative analysis of the three active ingredients in SHLI. Relatively, the PLS model based on the ATR-IR spectrum has higher R2 and smaller RMSE than it based on the NIR spectrum. Furthermore, based on the rapid quantitative analysis of the three active ingredients in SHLI, the quality of 140 SHLI samples from seven manufacturers was evaluated by TOPSIS (technique for order preference by similarity to the ideal solution) analysis, and the consistency of different batches of SHLI products from the same manufacturer was evaluated. The results showed that there were differences in the quality of SHLI produced by different manufacturers, and the quality of different batches of SHLI produced by the same manufacturer was not completely consistent. In conclusion, ATR-IR and NIR spectroscopy combined with chemometrics can be used for accurate and rapid quantitative analysis and quality evaluation of SHLI. This study provides a good idea for the rapid quantitative analysis and quality evaluation of drugs or food based on spectroscopic technology and chemometrics.

A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics.

Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.

Rapid detection of adulteration of olive oil with soybean oil combined with chemometrics by Fourier transform infrared, visible-near-infrared and excitation-emission matrix fluorescence spectroscopy: A comparative study.

Several spectroscopic techniques have been used to detect olive oil adulteration. To evaluate the performance of these spectral techniques on this issue, this work performed a comparative study on identifying adulterated olive oil with different concentrations of soybean oil based on Fourier-transform infrared (FTIR), visible-near-infrared (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics. Principal component analysis (PCA)/ multi-way-PCA analysis showed the feasibility of the three spectral methods for the identification of olive oil adulteration. The accuracy of FTIR and Vis-NIR based on partial least squares discriminant analysis (PLS-DA) for adulterated olive oil was 100%, while the accuracy of EEMs based on unfold-PLS-DA was only 73%. The accuracy of EEMs combined with back-propagation artificial neural network based on self-weighted alternating trilinear decomposition is 100%. In comparison, FTIR and Vis-NIR are superior for the detection of olive oil adulteration due to the convenience of instrument operation and modeling.

Regulatory network of ginsenoside biosynthesis under Ro stress in the hairy roots of Panax ginseng revealed by RNA sequencing.

P. ginseng C.A. Meyer is a valuable Chinese herbal medicine that belongs to the Araliaceae family. Major obstacles to the continuous cropping of ginseng have severely restricted the sustainable development of the ginseng industry. The allelopathic effects of triterpenoid saponins play an important role in disorders related to continuous cropping; however, the mechanisms underlying the allelopathic autotoxicity of triterpenoid ginsenosides remain unknown. In this study, we performed mRNA and miRNA sequencing analyses to identify candidate genes and miRNAs that respond differentially to ginsenoside Ro stress in ginseng and their targets. The growth of the ginseng hairy roots was significantly inhibited under Ro stress (0.5 mg/L, Ro-0.5). The inhibition of root growth and injury to root-tip cells promoted the accumulation of the endogenous hormones indole-3-acetic acid and salicylic acid and inhibited the accumulation of abscisic acid and jasmonate acid. The accumulation of ginsenosides, except Rg3, was significantly inhibited under Ro-0.5 stress. An mRNA analysis of the Ro-0.5 and control groups showed that differentially expressed genes were mostly concentrated in the hormone signal transduction pathway. ARF7 and EFM were upregulated, whereas XTH23 and ZOX1 were downregulated. These genes represent important potential candidates for hormone-responsive continuous cropping diseases. In total, 74 differentially expressed miRNAs were identified based on the miRNA sequencing analysis, of which 22 were upregulated and 52 were downregulated. The target genes of ptc-miR156k_L + 1, mtr-miR156b-5p, gma-miR156a_R + 1, and mtr-miR156e all belonged to TRINITY_DN14567_c0_g4, which is a gene in the plant hormone signal transduction pathway. These four miRNAs were all negatively correlated with mRNA, indicating their likely involvement in the response of ginseng to continuous cropping disorders and the regulation of ginsenoside synthesis. Our findings provide useful insights for removing the barriers to continuous ginseng cropping and have important implications in the genetic engineering of plant stress responses.

Rapid discrimination of adulteration in Radix Astragali combining diffuse reflectance mid-infrared Fourier transform spectroscopy with chemometrics.

Fraud in the global food and related products supply chain is becoming increasingly common due to the huge profits associated with this type of criminal activity and yet strategies to detect fraudulent adulteration are still far from robust. Herbal medicines such as Radix Astragali suffer adulteration by the addition of less expensive materials with the objective to increase yield and consequently the profit margin. In this paper, diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) was used to detect the presence of Jin Quegen in Radix Astragali. 900 fake samples of Radix Astragali produced by 6 different regions were constructed at the levels of 2%, 5%, 10%, 30% and 50% (w/w). DRIFTS data were analyzed using unsupervised classification method such as principal component analysis (PCA), and supervised classification method such as linear discrimination analysis (LDA), K-nearest neighbor (K-NN), linear discrimination analysis combining K-nearest neighbor (LDA-KNN) and partial least squares discriminant analysis (PLS-DA). The results of PCA showed that it was feasible to detect the adulteration of Radix Astragali by the combination of drift technique and chemometrics. PLS-DA obtained the best classification results in all four supervised methods with mean-centralization as the data preprocessing method, the prediction accuracy of PLS-DA model for the six groups of sample ranged from 95.00% to 98.33%. At the same time, LDA-KNN also achieved good classification results, and its correct prediction rate were also between 86.67% and 100.0%. The prediction results confirmed that the combination of DRIFTS technology and chemometrics can distinguish the amount of adulteration present in Radix Astragali. Additionally, the innovative strategy designed can be used to test the fraud of various forms of herbal medicine in other products.