One of the main topics of the review was how Raman spectroscopy is being applied to detect organic compounds in fruits, such as pigments, phenols, and sugars that indicate ripeness, nutritional content, and safety (1). Traditional methods for detecting organic compounds have often been criticized for their time-consuming procedures (1). Raman spectroscopy, on the other hand, is more efficient. Raman spectroscopy also allows analysts to scan fruits directly, extracting structural and compositional information within seconds (1).
What challenges have limited the use of Raman spectroscopy in agriculture and food analysis?
The review article also addressed why Raman spectroscopy has not been widely used in agricultural applications historically. There are three issues the researchers highlighted. One is weak signal intensities (1). The second issue is difficulty in analyzing internal fruit components, and the last issue is interference from background fluorescence (1).
Resolving these issues requires the use of advanced spectral techniques. The authors mention surface-enhanced Raman spectroscopy (SERS), coherent modulated Raman spectroscopy (CM-RS), and spatially offset Raman spectroscopy (SORS) as some of the spectral techniques being used (1). Each of these methodologies enhances signal quality and expands detection capabilities, especially when paired with machine learning tools that automate signal interpretation and reduce human error (1).
What benefit does machine learning offer?
The main benefit of machine learning (ML) is that it automates processes. For example, algorithms such as support vector machines (SVM), convolutional neural networks (CNN), and decision trees can now analyze complex spectral data with better accuracy (1). These computational models are trained to recognize patterns in spectral signatures that correlate with specific diseases, chemical residues, or varietal markers, making real-time diagnostics not only possible but increasingly practical for large-scale agricultural use (1).
Chemometrics has also played a huge role in improving the utility of Raman spectroscopy in this space. These methods can now process large data sets rapidly, enabling real-time decisions in the field or on the factory floor (1). The researchers highlighted an example regarding pesticide residue detection. Raman spectra are often convoluted by overlapping signals from multiple chemicals (1). Chemometrics, coupled with machine learning, can untangle these signals to provide accurate identifications and quantifications, even in complex mixtures (1).
What are the next steps in this work?
Looking ahead, the team express optimism about the broader application of Raman spectroscopy in food safety, environmental monitoring, and even biomedical diagnostics. Its non-invasive nature, combined with the increasing sophistication of data analysis techniques, makes Raman spectroscopy a prime candidate for integration into automated food inspection systems and smart agriculture technologies (1).
As consumer demand for transparency and safety in food supply chains continues to rise, tools like Raman spectroscopy could soon become more reliable in detecting the quality of fruits.
References
- Huang, Y.; Wang, H.; Huang, H.; et al. Raman Spectroscopy and Its Application in Fruit Quality Detection. Agriculture 2025, 15 (2), 195. DOI: 10.3390/agriculture15020195
- Horiba: What is Raman Spectroscopy? Horiba. Available at: https://www.horiba.com/int/scientific/technologies/raman-imaging-and-spectroscopy/raman-spectroscopy/#:~:text=Chemical%20structure%20and%20identity,the%20spectrum%20of%20the%20analyte. (accessed 2025-07-11).
