Searching for higher quality cultures with a new spectroscopic method

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Based on near infrared spectroscopy (NIRS) and data fusion technology, the researchers proposed a new analytical method, through which the accuracy of the spectrometric method to detect the quality of agricultural products.

This study, published on Analytica Chemicala Acta, was conducted by a research team led by Professor WU Yuejin of the Hefei Institutes of Physical Sciences (HFIPS) of the Chinese Academy of Sciences.

NIRS is a fast, non-destructive spectroscopic analysis technique that can play an important role in the fields of food production and crop breeding. Data fusion technology is a framework containing methods and tools for combining data from different sources, which has received a lot of attention in recent years.

According to Professor WU, this new method was developed based on the fusion of near-infrared spectral signals measured in two different modes, namely diffuse reflectance spectra (NIRr) and diffuse transmission spectra (NIRt).

The researchers assumed that the NIRr and NIRt spectra of the same set of samples are complementary, so merging the two types of spectral signals can provide more complete sample information.

By analyzing the NIRr and NIRt spectra of three groups of rice flour samples and selecting appropriate chemometric algorithms to extract and integrate the complementary information, the researchers established several calibration models to obtain more accurate predictions of three components main ones (including amyloidosis, proteins and fats). content) of rice flour.

This method could help seed breeders breed high-quality rice varieties and help grain farmers produce higher-quality rice more efficiently. This could also be applied to detecting the quality of other products in the future.

Reference: Xu Z, Cheng W, Fan S, et al. Data fusion of near-infrared diffuse reflectance spectra and transmission spectra for the precise determination of rice flour constituents. Anal. Chem. Act. 2022;1193(339384). doi: 10.1016/j.aca.2021.339384

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