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      An examination of the principle of non-destructive flesh firmness measurement of peach fruit by using VIS-NIR spectroscopy

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      , ,
      Heliyon
      Elsevier
      Food science, Analytical chemistry

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          Abstract

          Evaluating the maturity of peach fruit is desirable during both the preharvest and postharvest periods, and flesh firmness (FF) is a representative maturity index. Although a non-destructive FF measurement technique using visible (VIS) and near-infrared (NIR) spectroscopy has been developed, the principle has been unclear. This study was conducted to examine the structure of the FF prediction model by comparing with that of the model for measuring water-soluble pectin (WSP) content. Those two prediction models have the same information regions related to the colors of pericarp and mesocarp (chlorophyll) and to a water band in the NIR region. Moreover, a statistical heterospectroscopy analysis between NIR and 1H nuclear magnetic resonance (NMR) spectra suggests the possibility that absorptions of methanol and succinate as well as galacturonic acid embedded in a water band play important roles in predicting FF. This approach would enhance the reliability of nondestructive VIS-NIR prediction models in many practical situations.

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          Most cited references28

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          Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies.

          Statistical heterospectroscopy (SHY) is a new statistical paradigm for the coanalysis of multispectroscopic data sets acquired on multiple samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecules measured by different techniques across cohorts of samples. The potential of SHY is illustrated using both 600-MHz 1H NMR and UPLC-TOFMS data obtained from control rat urine samples (n = 54) and from a corresponding hydrazine-treated group (n = 58). We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/z data from MS, is readily achievable for a variety of metabolites, which leads to improved efficiency of molecular biomarker identification. In addition to structure, higher level biological information can be obtained on metabolic pathway activity and connectivities by examination of different levels of the NMR to MS correlation and anticorrelation matrixes. The SHY approach is of general applicability to complex mixture analysis, if two or more independent spectroscopic data sets are available for any sample cohort. Biological applications of SHY as demonstrated here show promise as a new systems biology tool for biomarker recovery.
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            PRIMe: a Web site that assembles tools for metabolomics and transcriptomics.

            PRIMe (http://prime.psc.riken.jp/), the Platform for RIKEN Metabolomics, is a Web site that has been designed and implemented to support research and analysis workflows ranging from metabolome to transcriptome analysis. The site provides access to a growing collection of standardized measurements of metabolites obtained by using NMR, GC-MS, LC-MS, and CE-MS, and metabolomics tools that support related analyses (SpinAssign for the identification of metabolites by means of NMR, KNApSAcK for searches within metabolite databases). In addition, the transcriptomics tools provide Correlated Gene Search, and Cluster Cutting for the analysis of mRNA expression. Use of the tools and database can contribute to the analysis of biological events at the levels of metabolites and gene expression, and we describe one example of such an analysis for Arabidopsis thaliana using the batch-learning self-organizing map (BL-SOM), which is provided via the Web site.
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              Statistical indices for simultaneous large-scale metabolite detections for a single NMR spectrum.

              NMR-based metabolomics has become a practical and analytical methodology for discovering novel genes, biomarkers, metabolic phenotypes, and dynamic cell behaviors in organisms. Recent developments in NMR-based metabolomics, however, have not concentrated on improvements of comprehensiveness in terms of simultaneous large-scale metabolite detections. To resolve this, we have devised and implemented a statistical index, the SpinAssign p-value, in NMR-based metabolomics for large-scale metabolite annotation and publicized this information. It enables simultaneous annotation of more than 200 candidate metabolites from the single (13)C-HSQC (heteronuclear single quantum coherence) NMR spectrum of a single sample of cell extract.
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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                01 March 2018
                February 2018
                01 March 2018
                : 4
                : 2
                : e00531
                Affiliations
                [1]Food Research Institute, NARO, 2-1-12 Kannondai, Tsukuba, Ibaraki 305-8642, Japan
                Author notes
                []Corresponding author. ikehata@ 123456affrc.go.jp
                Article
                S2405-8440(17)32894-3 e00531
                10.1016/j.heliyon.2018.e00531
                5857633
                e5d332ab-b32b-463e-82a7-2ac86266e098
                © 2018 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 5 October 2017
                : 27 November 2017
                : 29 January 2018
                Categories
                Article

                food science,analytical chemistry
                food science, analytical chemistry

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