• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

Monitoring the absolute vegetable freshness index: light-NMR spectroscopy fusion approach

Research Project

Project/Area Number 22K20606
Research Category

Grant-in-Aid for Research Activity Start-up

Allocation TypeMulti-year Fund
Review Section 0604:Agricultural economics and rural sociology, agricultural engineering, and related fields
Research InstitutionNational Agriculture and Food Research Organization

Principal Investigator

李 心悦  国立研究開発法人農業・食品産業技術総合研究機構, 食品研究部門, 研究員 (40963947)

Project Period (FY) 2022-08-31 – 2025-03-31
Project Status Granted (Fiscal Year 2023)
Budget Amount *help
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2023: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Keywordskomatsuna / freshness assessment / fluorescence fingerprint / informative fluorophores / selectivity ratio / PLSR analysis / broccoli / freshness estimation / Vis-NIR spectroscopy / freshness marker / NMR spectroscopy / vegetable freshness
Outline of Research at the Start

Vegetable freshness is an important factor for people to evaluate vegetable quality. This research aims to develop a nondestructive on-site monitoring method for vegetable freshness based on novel freshness marker metabolites validated by light-NMR spectroscopy fusion approach, with the following procedures:
(1) search for freshness marker metabolites by NMR metabolomics,
(2) find characteristic spectral absorption wavebands that are highly correlated with freshness marker compounds,
(3) build the robust prediction model based on (1) and (2) for freshness estimation.

Outline of Annual Research Achievements

This year, the feasibility of fluorescence spectroscopy for assessing the freshness of komatsuna (Japanese mustard spinach) was investigated. Different batches of komatsuna were acquired from the farm and treated under different storage conditions (including storage temperatures and durations) to achieve different freshness degrees, and then the corresponding fluorescence fingerprints of each fraction of komatsuna (including blade, stem, and petiole) were collected. Taking the freshness indicated by cumulative temperature as the objective and the collected fluorescence spectrum of komatsuna as the predictor, partial least squares regression (PLSR) analysis was respectively performed on each part of the komatsuna to establish a freshness prediction model with relatively high accuracy (the coefficients of determination of prediction are around 0.7-0.8). The models were built based on the informative fluorophores chosen by the stepwise selectivity ratio method. Based on the wavelengths of excitation and emission with higher selectivity ratios, it was known that the informative fluorophores (fluorescence components) important for the freshness prediction model construction could be attributed to chlorophylls, vitamins, or a specific component involved in stress responses, by which allows for the understanding of the model construction.

Current Status of Research Progress
Current Status of Research Progress

2: Research has progressed on the whole more than it was originally planned.

Reason

We have previously developed methods using near-infrared (NIR) spectroscopy to assess the freshness of green vegetables, such as komatsuna and broccoli. This time, we estimate the freshness of komatsuna using a different non-destructive spectroscopy named fluorescence fingerprint (FF), which is a three-dimensional structure consisting of an excitation-emission matrix containing fluorophore intensities. We found that FF exhibited similar freshness prediction capabilities to NIR spectroscopy and provided complementary information (important fluorescence components) on komatsuna freshness compared to NIR spectroscopy. Furthermore, it was also found that the fluorescence components important for model construction are different in each fraction of komatsuna (including blade, stem, and petiole), which is different from the conclusions reported in studies of other vegetables such as spinach. This discovery is to be published and has not been reported so far. Therefore, the research is progressing smoothly as per the proposal.

Strategy for Future Research Activity

To verify the applicability of fluorescence spectroscopy and investigate whether there are common informative fluorophores in different green vegetables for freshness assessment, it is planned to apply the same research approach to broccoli. It is expected that a reliable prediction model for vegetable freshness evaluation can be constructed based on the screened useful fluorescence signals, and the prediction model can be constructed with fewer wavelengths to improve the evaluation speed, which is beneficial to practical applications.

Report

(2 results)
  • 2023 Research-status Report
  • 2022 Research-status Report
  • Research Products

    (8 results)

All 2024 2023 2022

All Journal Article (1 results) (of which Peer Reviewed: 1 results) Presentation (7 results) (of which Int'l Joint Research: 3 results)

  • [Journal Article] NMR-based metabolomic identification of freshness markers reveals the working mechanism of visible and near-infrared spectroscopy to predict post-harvest broccoli freshness2024

    • Author(s)
      Li Xinyue、Sekiyama Yasuyo、Ohishi Manato、Takahashi Megumu、Matsumoto Saki、Watanabe Takashi、Nakamura Nobutaka、Nagata Masayasu、Tsuta Mizuki
    • Journal Title

      Postharvest Biology and Technology

      Volume: 211 Pages: 112810-112810

    • DOI

      10.1016/j.postharvbio.2024.112810

    • Related Report
      2023 Research-status Report
    • Peer Reviewed
  • [Presentation] Nondestructive Estimation of Green Vegetable Freshness with Science-Based NIR Spectroscopy2023

    • Author(s)
      Xinyue Li
    • Organizer
      21st International Conference on Near Infrared Spectroscopy (NIR 2023)
    • Related Report
      2023 Research-status Report
    • Int'l Joint Research
  • [Presentation] Nondestructive Spectroscopic Estimation of Freshness of Post-harvest Vegetables2023

    • Author(s)
      Xinyue LI, Mizuki TSUTA, Yasuyo SEKIYAMA, Saki MATSUMOTO, Takashi WATANABE, Nobutaka NAKAMURA
    • Organizer
      農業環境工学関連学会2023年合同大会
    • Related Report
      2023 Research-status Report
  • [Presentation] Nondestructive assessment of broccoli freshness using Vis-NIR spectroscopy2023

    • Author(s)
      李心悦, 蔦瑞樹, 関山恭代
    • Organizer
      第39回近赤外フォーラム
    • Related Report
      2023 Research-status Report
  • [Presentation] Nondestructive estimation of broccoli freshness by NIR spectroscopy2023

    • Author(s)
      李 心悦
    • Organizer
      農研機構食品研究成果展示会2023
    • Related Report
      2023 Research-status Report
  • [Presentation] Estimation of broccoli freshness using Vis-NIR spectroscopy2022

    • Author(s)
      Xinyue Li , Manato Ohishi , Megumu Takahashi, Masayasu Nagata, Nobutaka Nakamura, Takashi Watanabe, Saki Matsumoto, Yasuyo Sekiyama and Mizuki Tsuta
    • Organizer
      第80回農業食料工学会年次大会
    • Related Report
      2022 Research-status Report
  • [Presentation] Estimation of vegetable freshness by visible and near-infrared spectroscopy with the assistance of NMR metabolomics analysis2022

    • Author(s)
      Mizuki Tsuta, Xinyue Li, Manato Ohishi, Megumu Takahashi, Takashi Watanabe, Saki Matsumoto, Nobutaka Nakamura, Masayasu Nagata and Yasuyo Sekiyama
    • Organizer
      The 6th conference on quality management and food safety (QMFS2021+1)
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [Presentation] Visible near-infrared spectroscopy for the prediction of the cumulative temperature of broccoli as a freshness indicator assisted by NMR metabolomics2022

    • Author(s)
      Mizuki Tsuta, Xinyue Li, Manato Oishi, Megumu Takahashi, Takashi Watanabe, Saki Matsumoto, Masayasu Nagata, Yasuyo Sekiyama
    • Organizer
      The XX CIGR World Congress 2022
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research

URL: 

Published: 2022-09-01   Modified: 2024-12-25  

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi