A recent article in Cereal Chemistry looked at how fluorescence spectroscopy could improve the way wheat quality is predicted. The study focused on analyzing spectral differences in flour fractions, gluten, starch, and dough. The goal was to make quality assessments faster and more accurate.
Traditional testing methods can be time-consuming and require large amounts of material. This research offers an alternative that could help the food industry work more efficiently.
Image Credit: kreebz/Shutterstock.com
Advances in Spectroscopic Techniques
Fluorescence spectroscopy is a non-destructive method that is already used in food science and agriculture. It works by measuring the fluorescence emitted by a sample when exposed to specific light wavelengths. This allows for quick analysis of chemical composition.
In wheat quality testing, fluorescence spectroscopy can offer an alternative to standard methods like rheology tests and baking trials. These traditional approaches depend heavily on measuring flour protein content, but they are not always accurate when predicting quality.
To address this, researchers have been exploring more advanced techniques, such as near-infrared (NIR) and Raman spectroscopy. NIR is well-established for estimating protein content. Fluorescence and Raman spectroscopy add further detail by providing insights into the chemical makeup of wheat. This combination helps improve predictions of quality beyond protein levels alone.
Using Spectroscopy to Predict Wheat Quality
The researchers tested whether fluorescence spectroscopy could capture more useful information by analyzing flour fractions and dough, rather than just whole flour. They worked with 50 wheat flour samples from different cultivars, provided by Mühlenchemie GmbH & Co. KG.
The flour was separated into fine and coarse particles using air classification. These were then sieved into smaller size groups. Dough was prepared from the flour, and fluorescence spectra were recorded using a BioView Sensor. Measurements covered excitation wavelengths from 270 to 550 nanometers and emission wavelengths from 310 to 590 nanometers.
To explore the connection between the spectral data and wheat quality, the team used statistical models. These included principal component analysis (PCA), principal component regression (PCR), and partial least-squares regression (PLSR). These models aimed to predict key dough properties, such as water absorption, development time, stability, and softening. All measurements were taken in duplicate to ensure consistency.
By comparing fluorescence data with results from traditional farinograph tests, the study aimed to improve prediction accuracy while reducing the need for more demanding testing methods.
Key Findings
The results showed clear spectral differences between whole flour, flour fractions, and dough. These changes were linked to how the wheat components behave during processing. Several types of fluorescent compounds were identified, including tryptophan, phenolic acids, and vitamins. Their signal strengths varied by sample type.
Flour and sieve fractions were effective in predicting water absorption. The whole flour samples achieved a coefficient of determination (RCV2) f 0.79, while the 32–50 µm fraction performed slightly better, reaching an RCV2 of 0.81. In contrast, dough samples showed stronger predictive power for rheological properties, especially dough development time, with an RCV2 of 0.90.
Preprocessing steps such as air classification and sieving increased the concentration of proteins and phenolic compounds in the flour. These changes resulted in noticeable spectral differences. PCA showed clear distinctions among flour, gluten, starch, and dough samples. However, some overlap was observed among sieve and air-classified fractions.
The fluorescence spectra from dough and gluten offered additional insight into rheological behavior. These spectra may help explain how dough performs during mixing and handling, suggesting their value in predicting processing characteristics.
Although flour and its fractions were more accurate in predicting water absorption, the spectra from dough and gluten were better suited for predicting development time and softening. The fine flour fraction consistently showed weaker prediction accuracy in farinograph measurements. This is likely because its protein content includes more "wedge" proteins, which behave differently than the "adherent" proteins found in other fractions.
Practical Applications in Wheat Quality Assessment
This research has significant implications for the cereal industry. Fluorescence spectroscopy offers a rapid, cost-effective alternative to traditional wheat quality assessments, enabling mills and breeders to streamline quality control, reduce costs, and improve product consistency. The ability to predict farinograph parameters from spectra could enhance formulations and processing conditions in bread-making and other wheat-based products.
Additionally, integrating fluorescence spectroscopy with techniques like NIR and chemometric modeling could further improve predictive accuracy through data fusion. The study also suggests potential applications in real-time monitoring systems for milling and baking, allowing producers to make immediate quality adjustments. This advancement could lead to more efficient flour production and higher-quality end products.
Conclusion and Next Steps
This study showed that fluorescence spectroscopy can improve wheat quality predictions by capturing detailed spectral differences in flour fractions and dough. Future research should focus on testing a wider range of samples and combining this approach with other spectroscopic methods to increase reliability.
Being able to track composition changes during milling and kneading opens up new options for quality control. If used regularly, fluorescence spectroscopy could help the wheat industry produce higher-quality products more efficiently. As demand grows for consistent, high-quality wheat-based foods, these advanced methods may become an important part of modern food production.
Journal Reference
Ziegler, D., et al. (2025) Fluorescence Spectroscopy of Flour Fractions and Dough: Analysis of Spectral Differences and Potential to Improve Wheat Quality Prediction. Cereal Chemistry. DOI: 10.1002/cche.10881, https://onlinelibrary.wiley.com/doi/10.1002/cche.10881
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.