Raman Spectroscopy and Multivariate Calibration Analysis Model in Predicting Rice Starch Digestibility

Predicting rice digestibility with high accuracy can be easily realized by using Raman scattering spectroscopy, according to a team of researchers from the RIKEN Center for Biosystems Dynamics Research and Hiroshima. This new study, led by Junya Ichinose of the RIKEN Center, published in Food Chemistry demonstrated the effectiveness of this model (1).

Researchers have used Raman scattering spectroscopy in many applications areas because of the technique’s benefits, which include its non-invasive nature (2). It is a technique that helps elucidate the chemical structure of the subject under study. As a light scattering technique, Raman scattering involves a molecule scattering light from a high-intensity laser light source (2). From this process, Raman spectroscopy helps provide information to analysts not just on a material’s chemical structure, but also on its polymorphism, intrinsic stress, and potential contamination or impurity (2).

In this study, the research team used Raman spectroscopy to determine the quality of rice for its digestibility, which can be influenced by environmental factors like soil quality and temperature. Rice is a staple food for over half the world’s population and a key carbohydrate source (1). Starch, a major component of rice, plays a crucial role in both nutrition and food processing (1). Because rice digestibility directly affects both nutritional value and the efficiency of rice as a food product, being able to predict it accurately is essential for optimizing rice production and processing.

Cooked rice | Image Credit: © lcrribeiro33@gmail
- stock.adobe.com

In the study, Ichinose and the team created a predictive model that could anticipate rice starch digestibility based on its molecular structure. The traditional methods for measuring digestibility involve time-consuming and complex biochemical tests (1). The hypothesis that Ichinose and the team attempted to prove was that this new method, by leveraging Raman scattering spectroscopy, can analyze how light interacts with molecular vibrations to provide a chemical fingerprint of the sample.

For this study, the research team collected rice samples from various cultivars grown under different environmental conditions to build the model. Using partial least squares (PLS) regression analysis, they examined the relationship between the Raman scattering spectra of purified rice starch and the digestibility index values obtained from biochemical analyses (1). The model they developed was highly accurate, achieving a coefficient of determination (R²) of 0.95 and a root mean square error of prediction (RMSEP) of 0.43 when applied to individual rice cultivars (1). This means the model could predict digestibility with remarkable precision when analyzing specific rice types.

However, the scientists also found that predicting rice digestibility is complicated by molecular structure and environmental factors. When the team combined data from all cultivars in a single model, there was a significant decrease in the accuracy of the model (1). The team suggests that each rice cultivar has a unique starch synthesis mechanism that interacts with environmental conditions in distinct ways, affecting digestibility (1). Thus, the conclusion was that predictive models need to be tailored to each specific rice variety to maintain accuracy.

The ability to accurately predict rice starch digestibility is particularly valuable for the food industry because it allows manufacturers to optimize processing conditions for different types of rice, ensuring that the final product meets nutritional and functional requirements. For instance, rice used in baby food or for individuals with specific dietary needs may require a particular digestibility profile, which could now be more easily achieved through this predictive modeling approach (1).

Moreover, this model has broad implications for agricultural practices because it offers a tool for selecting optimal growing conditions to produce rice with desired digestibility characteristics (1). Farmers and producers could use this information to adjust cultivation methods, including temperature and soil management, to improve the quality and consistency of their rice crops (1).

Rice is a staple food product for most of the global population. Heavy consumers include those in Asia, Sub-Saharan Africa, and South America (3). As a result, ensuring its quality and digestibility is important for the global economy. The research also opens the door for further exploration into the molecular mechanisms that govern starch synthesis and digestibility in rice (1). By gaining a deeper understanding of how these mechanisms interact with environmental factors, scientists could potentially develop new rice varieties with improved nutritional profiles or greater adaptability to changing climates.

References

1. Ichinose, J.; Oba, K.; Arase, Y.; et al. Quantitative prediction of rice starch digestibility using Raman spectroscopy and multivariate calibration analysis. Food Chem. 2024, 435, 137505. DOI: 10.1016/j.foodchem.2023.137505
2. Horiba Scientific, What is Raman Spectroscopy? Horiba Scientific. Available at: https://www.horiba.com/usa/scientific/technologies/raman-imaging-and-spectroscopy/raman-spectroscopy/ (accessed 2024-10-02).
3. USDA, Rice Sector at a Glance. USDA.gov. Available at: https://www.ers.usda.gov/topics/crops/rice/rice-sector-at-a-glance/ (accessed 2024-10-02).