| 研究課題/領域番号 |
22K20606
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| 研究種目 |
研究活動スタート支援
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| 配分区分 | 基金 |
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| 審査区分 |
0604:社会経済農学、農業工学およびその関連分野
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| 研究機関 | 国立研究開発法人農業・食品産業技術総合研究機構 |
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研究代表者 |
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)
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| 研究期間 (年度) |
2022-08-31 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
2,860千円 (直接経費: 2,200千円、間接経費: 660千円)
2023年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2022年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
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| キーワード | komatsuna / freshness assessment / fluorescence fingerprint / informative fluorophores / selectivity ratio / PLSR analysis / broccoli / freshness estimation / Vis-NIR spectroscopy / freshness marker / NMR spectroscopy / vegetable freshness |
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| 研究開始時の研究の概要 |
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.
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| 研究実績の概要 |
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.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
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.
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| 今後の研究の推進方策 |
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.
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