Published on 01 January 2025

Using fluorescence spectra to quantitatively evaluate the constituent content and functionality of hydroponically grown <i>Fragaria × ananassa</i>

View Dataset
SANO, HIDEMICHI;Kawaguchi, Satoru;Iimori, Toshifumi;Kuragano, Masahiro;Tokuraku, Kiyotaka;Uwai, Koji

Description

Using fluorescence spectra to quantitatively evaluate the constituent content and functionality of hydroponically grown Fragaria × ananassaHidemichi Sano, Satoru Kawaguchi, Toshifumi Iimori, Masahiro Kuragano, Kiyotaka Tokuraku, and Koji UwaiGraduate School of Engineering, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran, Hokkaido 050-8585, Japan
e-mail addressHidemichi Sano: [email protected] Kawaguchi: skawaguchi@ muroran-it.ac.jpToshifumi Iimori: [email protected] Kuragano: [email protected] Tokuraku: [email protected] Uwai: [email protected]
Correspondence: [email protected];Tel.: +81-0143-46-5775
Abstract: Strawberry leaves contain polyphenolic compounds such as elagitannins and flavonoids and are thought to possess antioxidant and amyloid-β (Aβ) aggregation inhibitory properties. However, strawberry leaves are usually discarded because their functional components vary with growth stages and environmental factors, and a method that immediately evaluates such functional strawberry leaf components in the field has not yet been developed. In this study, the fluorescence spectra obtained by irradiating strawberry leaves with excitation light were used to analyze strawberry leaves during hydroponic cultivation. Standard methods that assess the functional components (chlorophyll, total polyphenol and flavonoid content, ellagic acid, ferulic acid, naringenin) and functionality (free radical–scavenging activity, ferric reducing antioxidant power, oxygen radical absorptivity, and Aβ aggregation inhibition activity) of plants were performed. The correlations between fluorescence spectra and these parameters were analyzed using spectral indices analyses. The correlation between each spectral index and component amount and functionality of the obtained predictive model was 0.84 ≥ R ≥ 0.61. This new method facilitates the simultaneous and nondestructive prediction of both component content and functionality of strawberry leaves and allows the determination of optimal strawberry leaves for utilization as a source of functional foods and medicinal material.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.3

FAIR Score

13%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

Assigned Domain

Subfield

Neurology

Field

Medicine

Domain

Health Sciences

Confidence Score

40%

Source

Scholar Data Model

Keywords

Other agricultural, veterinary and food sciences not elsewhere classified

Normalization Factors

FT

15.38

CTw

1.00

MTw

1.00