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A non-destructive and comprehensive 13 C isotopic evaluation approach based on 1 H NMR spectroscopy was developed. The carbon isotope distribution (CID) of most of the components (S/N ≥ 1000) in food matrices were evaluated using frequency distribution of peak area ratios (PAR) of decoupling to non- 13 C-decoupling spectra at natural abundance. The approach was applied successfully to vinegar and it was found that the PAR of fermented vinegars is obviously narrower than that of the blended one and the one produced via chemically defined culture medium. Besides, the extra additives can also be evaluated by their characteristic PAR values. It was found that the sugars are the most commonly added components into the blended vinegars. The results obtained from the developed approach show good validity (baseline effect, RSD < 0.1%) and internal reproducibility (RSD < 0.1%). Practicability of the method is confirmed by gas chromatography-isotope ratio mass spectrometry (GC-IRMS) detection method. The results imply that the proposed approach could be used not only for the discrimination but also for the primary authentication of the blended components in the fermented food matrices., Competing Interests: Declaration of competing interest It is stated that all of the authors have contributed in conducting experiments and preparation of this manuscript and are well aware of this submission. All the authors have read this manuscript and claim that they have no conflict of interest for publishing this manuscript in Talanta., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Metabolomics generate a profile of small molecules from cellular/tissue metabolism, which could directly reflect the mechanisms of complex networks of biochemical reactions. Traditional metabolomics methods, such as OPLS-DA, PLS-DA are mainly used for binary class discrimination. Multiple groups are always involved in the biological system, especially for brain research. Multiple brain regions are involved in the neuronal study of brain metabolic dysfunctions such as alcoholism, Alzheimer's disease, etc. In the current study, 10 different brain regions were utilized for comparative studies between alcohol preferring and non-preferring rats, male and female rats respectively. As many classes are involved (ten different regions and four types of animals), traditional metabolomics methods are no longer efficient for showing differentiation. Here, a novel strategy based on the decision tree algorithm was employed for successfully constructing different classification models to screen out the major characteristics of ten brain regions at the same time. Subsequently, this method was also utilized to select the major effective brain regions related to alcohol preference and gender difference. Compared with the traditional multivariate statistical methods, the decision tree could construct acceptable and understandable classification models for multi-class data analysis. Therefore, the current technology could also be applied to other general metabolomics studies involving multi class data., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Soy sauce a well known seasoning all over the world, especially in Asia, is available in global market in a wide range of types based on its purpose and the processing methods. Its composition varies with respect to the fermentation processes and addition of additives, preservatives and flavor enhancers. A comprehensive (1)H NMR based study regarding the metabonomic variations of soy sauce to differentiate among different types of soy sauce available on the global market has been limited due to the complexity of the mixture. In present study, (13)C NMR spectroscopy coupled with multivariate statistical data analysis like principle component analysis (PCA), and orthogonal partial least square-discriminant analysis (OPLS-DA) was applied to investigate metabonomic variations among different types of soy sauce, namely super light, super dark, red cooking and mushroom soy sauce. The main additives in soy sauce like glutamate, sucrose and glucose were easily distinguished and quantified using (13)C NMR spectroscopy which were otherwise difficult to be assigned and quantified due to serious signal overlaps in (1)H NMR spectra. The significantly higher concentration of sucrose in dark, red cooking and mushroom flavored soy sauce can directly be linked to the addition of caramel in soy sauce. Similarly, significantly higher level of glutamate in super light as compared to super dark and mushroom flavored soy sauce may come from the addition of monosodium glutamate. The study highlights the potentiality of (13)C NMR based metabonomics coupled with multivariate statistical data analysis in differentiating between the types of soy sauce on the basis of level of additives, raw materials and fermentation procedures., (Copyright © 2016 Elsevier B.V. All rights reserved.)