Chemometric Assessment of the Interactions Between the Metal Contents,Antioxidant Activity,Total Phenolics,and Flavonoids in Mushrooms

The elemental composition and antioxidant activity have been obtained for regional mushrooms species, wild and cultivated, from Dambovita County, Romania. A multivariate approach and data mining techniques (principal component analysis and cluster analysis, respectively) were applied in order to evaluate the correlation matrix for these parameters for both caps and stipes of mushrooms. The associations between metallic elements (i.e., Fe, Cu, Cd, Pb, Co, Ni, Zn, Mn, Cr, Na, K, Ca and Mg) correlated with antioxidant activity, total phenolics, and total flavonoids were carried out by using a full factorial linear design. The multivariate functional analysis revealed on associated accumulation of several elements in mushrooms. In the cluster analysis, the dataset was treated to appreciate the correlation between metals group (e.g., heavy metals, such as Fe, Cu, Cd, Pb, Co, Ni, Zn, Mn, Cr, and important nutritional elements, such as Na, K, Ca, and Mg). Fungal species with similar characteristics in terms of metal accumulation formed two distinctive clusters. From a point of view of the consumer, the content of Cd and especially Pb in the fruiting bodies of the analyzed mushrooms species may be considered elevated, so that mapping the risk is compulsory. Applying the chemometric tools in the sense mentioned above, as well as in helping the scientific research by optimizing the number of data points, was shown to be extremely useful.     

Quantitative iterative target transformation factor analysis

Traditionally, improvement of the constrained alternating least squares (ALS) solution has been executed by the addition of a priori information in the initial estimates and or constraints. However, there are cases where this information simply does not exist or is impossible to acquire under the process conditions. Therefore, new strategies are required to produce starting estimates close to the actual solution without the need of a priori information. Quantitative iterative target transformation factor analysis (QITTFA) is developed as a solution to this problem. The QITTFA approach combines the strengths of both iterative target transformation factor analysis (ITTFA) and simple‐to‐use interactive self‐modelling mixture analysis (SIMPLISMA) to (1) produce a solution space spanned by the independent factors such that the variance contribution due to noise is reduced, (2) to iteratively refine the solutions space prior to ALS and (3) to select the most pure variables from the refined solution space using the purity criterion. It has been observed that the QITTFA approach markedly improves the conventional SIMPLISMA and second derivative SIMPLISMA performance in the presence and absence of selectivity. In addition, components of differing spectral characteristics (narrow or broad spectral features) can be resolved, without a priori knowledge of the shapes of the pure components. This has been demonstrated with a simulated high performance liquid chromatography‐diode array detection (HPLC‐DAD) dataset, a laboratory‐based UV–Vis calibration dataset and a gaseous near infrared (NIR) dataset from an industrial process. Copyright © 2007 John Wiley & Sons, Ltd.     

A simple and effective method for identification of Fraxini Cortex from different sources by multi-mode fingerprint combined with chemometrics

Fraxini Cortex has a long history of being used as a medicinal plant in traditional Chinese medicine. However, it is challenging to differentiate and make quality evaluations for Fraxini Cortex from different origins due to their similarities in morphological features, as well as general chemical composition using traditional chemical analytical methods. In this study, a simple and effective method was developed to identify Fraxini Cortex from different origins by multi-mode fingerprint combined with chemometrics. Digital images of the high-performance thin-layer chromatography profiles were converted to grayscale intensity, and the common patterns of high-performance thin-layer chromatography fingerprints were generated with ChemPattern software. Authentication and quality assessment were analyzed by similarity analysis, hierarchical cluster analysis, principal component analysis, and multivariate analysis of variance. The ultra-high-performance liquid chromatography fingerprints were analyzed by similarity analysis, principal component analysis, and orthogonal partial least square-discriminant analysis. When combined with chemometrics, high-performance thin-layer chromatography and ultra-high-performance liquid chromatography fingerprint provided a simple and effective method to evaluate the comprehensive quality of Fraxini Cortex, and to distinguish its two original medicinal materials (Fraxinus chinensis Roxb. and Fraxinus rhynchophylla Hance.) recorded in the Chinese Pharmacopeia and its three adulterants (Fraxinus mandschurica Rupr., Fraxinus pennsylvanica Marsh., and Juglans mandshurica Maxim.). A similar workflow may be applied to establish a differentiation method for other medicinal and economic plants.     

Evaluation and quantitative analysis of 11 compounds in Morinda officinalis using ultra‐high performance liquid chromatography and photodiode array detection coupled with chemometrics

Morinda officinalis (Rubiaceae) is a traditional Chinese medicine widely used for the treatment of impotence and osteoporosis in clinical therapy. In the present study, a rapid and simple ultra‐high performance liquid chromatography with photodiode array detection method was developed and validated for the simultaneous determination of 11 bioactive compounds in M. officinalis . This assay method was validated with respect to linearity (R ²  > 0.9991), precision, repeatability, limit of detection, limit of quantification, and accuracy (with observed recovery rates between 94.21 and 100.38%). The quantitative results revealed significant differences in the concentrations of the selected compounds. Additionally, chemometric methods, including hierarchical clustering analysis, principal component analysis, and partial least‐squares discriminate analysis, were applied to compare and sort the 25 batches of M. officinalis samples based on the quantitative data of the analytes. All of the samples were clearly divided into two groups: the Hainan samples were successfully discriminated from the samples from other origins. Simultaneous determination of multiple compounds using the proposed method combined with chemometrics could be a viable strategy to compare and evaluate the quality of M. officinalis .     

基于超高效液相色谱-四极杆飞行时间质谱的非靶向代谢组学用于不同来源单花蜜的差异分析

不同的蜜源植物具有结构多样的次生代谢产物。该研究以8种不同蜜源单花蜜(洋槐蜜、枣花蜜、荆条蜜、椴树蜜、荞麦蜜、麦卢卡蜜、枸杞蜜、益母草蜜)为研究对象,建立了基于超高效液相色谱-四极杆飞行时间质谱技术(UPLC-Q-TOF-MS^E)的非靶向代谢组学方法,考察了不同蜜源中次生代谢产物的差异。该研究采用固相萃取前处理方法和UPLC-Q-TOF-MS^E方法,获得不同蜜源单花蜜的植物代谢组信息,并构建了多变量统计分析模型,对不同来源的单花蜜进行模式识别和差异分析,发现洋槐蜜、枣花蜜、荆条蜜、椴树蜜、荞麦蜜、麦卢卡蜜相互间存在不同程度的显著差异。结合模型的变量重要性投影、方差分析与最大差异倍数值,根据精确前体离子和碎片离子质量信息检索Chemspider、HMDB数据库,该研究筛选并鉴定出32个代谢差异化合物,其中黄酮类化合物18个、酚酸类化合物7个、苯苷与萜苷类化合物6个、甾体类化合物1个;研究发现麦卢卡蜜和荞麦蜜以黄酮类化合物为主要差异代谢物,荆条蜜中酚酸类化合物为特征性表达,苯苷与萜苷类化合物主要为椴树蜜的特征代谢物。该研究从植物代谢组学角度初步揭示了不同单花蜜的代谢产物差异性以及特征化合物,为基于化学分析技术的蜂蜜溯源识别与质量评价提供了有效的研究策略。     

Metabolomic fingerprint classification of Brachychiton acerifolius organs via UPLC-qTOF-PDA-MS analysis and chemometrics

Brachychiton acerifolius, or Sterculia acerifolia as formerly known, is a member of a genus reported for a myriad of bioactive compounds. Metabolome analysis of B. acerifolius – leaves, flowers and seeds – and quantification of its major compounds are demonstrated in this study. Metabolites were analysed via UPLC-PDA-qTOF-( ± ) ESI-MS and UPLC/ITMS, with a total of 56 metabolites characterised including 30 flavonoids, 2 anthocyanins, 6 phenolic acids (i.e. citric and hydroxycitric acid conjugates) and 8 fatty acids (FAs). Multivariate data analyses (i.e. principle component analysis and orthogonal partial least square-discriminate analysis) were applied to identify metabolite markers for each organ. Pelargonidin-O-glucoside and naringenin-O-glucuronide were found exclusively in flowers versus flavone enrichment in leaves (i.e. luteolin-O-glucuronide and apigenin-O-rhamnosyl glucuronide). Gas chromatography/mass spectrometry analysis revealed the presence of toxic cyclopropene FAs in seeds which may restrict its use. Antioxidant activity assessment for the three organs was performed in comparison with vitamin C as positive control. Leaves showed the highest activity (IC₅₀ 0.015 mg/mL).     

近红外与中红外光谱技术在土壤分析中的应用

传统的土壤分析方法周期长、成本高,不能在短时间内获取所需的土壤信息,如何快速、准确地获取土壤信息成为农业现代化的必然要求。漫反射光谱技术以其快速、廉价、非破坏与无污染等特点,已成为环境研究中获取相关土壤信息的重要技术手段,其中的近红外(NIR,780~2 500 nm)和中红外(MIR,2 500~25 000 nm)光谱技术预测土壤理化特性逐渐成为国内外学者研究的重点领域。文章介绍了近红外和中红外光谱技术的基本原理和分析方法,综述了该技术在土壤分析方面的具体应用,并对此提出了一些观点和展望。     

Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.     

Metabolite Fingerprinting Using 1H-NMR Spectroscopy and Chemometrics for Classification of Three Curcuma Species from Different Origins

Curcuma longa, Curcuma xanthorrhiza, and Curcuma manga have been widely used for herbal or traditional medicine purposes. It was reported that turmeric plants provided several biological activities such as antioxidant, anti-inflammatory, hepatoprotector, cardioprotector, and anticancer activities. Authentication of the Curcuma species is important to ensure its authenticity and to avoid adulteration practices. Plants from different origins will have different metabolite compositions because metabolites are affected by soil nutrition, climate, temperature, and humidity. ¹H-NMR spectroscopy, principal component analysis (PCA), and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for authentication of C. longa, C. xanthorrhiza, and C. manga from seven different origins in Indonesia. From the ¹H-NMR analysis it was obtained that 14 metabolites were responsible for generating classification model such as curcumin, demethoxycurcumin, alanine, methionine, threonine, lysine, alpha-glucose, beta-glucose, sucrose, alpha-fructose, beta-fructose, fumaric acid, tyrosine, and formate. Both PCA and OPLS-DA model demonstrated goodness of fit (R² value more than 0.8) and good predictivity (Q² value more than 0.45). All OPLS-DA models were validated by assessing the permutation test results with high value of original R² and Q². It can be concluded that metabolite fingerprinting using ¹H-NMR spectroscopy and chemometrics provide a powerful tool for authentication of herbal and medicinal plants.     

Daubechies小波主成分回归法机理及算法研究

将小波变换与主成分回归相结合,提出一种新型多元校正算法---小波基主成分回归法。理论分析和仿真实验表明,该法可更有效地去除噪声,提取有用信息。将其用于氯霉素及甲硝唑实际药物体系分析,与主成分回归法(PCR)相比,得到的回收率总平均相对误差由1.70%下降到0.90%。此外,通过将统计判据和小波多尺度分析相结合,发展了一种新的因子数判定方法。理论和实验研究表明,该法比传统因子数判定法具有更高的可靠性。