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Light measurement enables estimation of the chemical attributes of spice extracts

Date:
January 9, 2024
Source:
University of Tsukuba
Summary:
Researchers have developed a nondestructive method utilizing light to estimate the total amount of polyphenols and flavonoids in spice extracts, along with their antioxidant and reducing capacities. By comprehensively capturing the autofluorescence emitted by components, such as polyphenols and flavonoids, they have demonstrated the precise evaluation of chemical constituents using the application of machine learning methods.
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Researchers at the University of Tsukuba have developed a nondestructive method utilizing light to estimate the total amount of polyphenols and flavonoids in spice extracts, along with their antioxidant and reducing capacities. By comprehensively capturing the autofluorescence emitted by components, such as polyphenols and flavonoids, they have demonstrated the precise evaluation of chemical constituents using the application of machine learning methods.

Spices and other plant-derived products contain many active components, such as polyphenols and flavonoids. However, even the slightest variations in conditions can considerably affect the extraction efficiency of these active components, posing challenges in determining the exact quantity of active components in the extract solution.

In this study, researchers comprehensively measured the fluorescence emitted by polyphenols and flavonoids and analyzed the acquired data using machine learning methods. This approach yielded a highly accurate, simple, and rapid method of estimating the total polyphenol and flavonoid contents and antioxidant capacity.

The crucial factor in achieving accuracy was to integrate measurements acquired at multiple concentrations. While the conventional practice during measuring fluorescence involves diluting the sample to a single concentration, the wide variation in component amounts in plant extracts renders determining a universally suitable dilution concentration. Consequently, the researchers conducted exhaustive fluorescence measurements at four different dilution levels and integrated this data into the machine learning process.

Therefore, machine learning was able to accurately estimate important indices for evaluating spice extracts, including the total polyphenol content, total flavonoid content, antioxidant capacity, and reducing capacity. Notably, the optical measurement's estimation of total flavonoid content, in particular, represents a groundbreaking achievement, marking the effectiveness of this method where such estimations have not been conducted optically in the past.

This work was partly supported by JSPS KAKENHI Grant Number 17H03891 and 22J11143 and the Sapporo Bioscience Foundation.


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Materials provided by University of Tsukuba. Note: Content may be edited for style and length.


Journal Reference:

  1. Thi Bao Chau Bui, Daiki Iida, Yutaka Kitamura, Mito Kokawa. Utilization of multiple-dilution fluorescence fingerprint facilitates prediction of chemical attributes in spice extracts. Food Chemistry, 2024; 438: 138028 DOI: 10.1016/j.foodchem.2023.138028

Cite This Page:

University of Tsukuba. "Light measurement enables estimation of the chemical attributes of spice extracts." ScienceDaily. ScienceDaily, 9 January 2024. <www.sciencedaily.com/releases/2024/01/240109120455.htm>.
University of Tsukuba. (2024, January 9). Light measurement enables estimation of the chemical attributes of spice extracts. ScienceDaily. Retrieved February 23, 2024 from www.sciencedaily.com/releases/2024/01/240109120455.htm
University of Tsukuba. "Light measurement enables estimation of the chemical attributes of spice extracts." ScienceDaily. www.sciencedaily.com/releases/2024/01/240109120455.htm (accessed February 23, 2024).

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