Key Points
- A review article published in Agriculture highlights how Raman spectroscopy, when integrated with machine learning (ML), is transforming fruit quality detection.
- Despite challenges such as weak signal intensities, background fluorescence, and difficulty analyzing internal fruit components, advancements like SERS, SORS, and AI-driven algorithms are overcoming these limitations and improving detection accuracy.
- The authors predict that with continued improvements in data analysis and chemometrics, Raman spectroscopy will play an increasingly vital role in smart agriculture and automated food inspection systems.
A recent review article written by researchers from Guangdong Polytechnic Normal University highlights the role of Raman spectroscopy in the agri-food industry, particularly in fruit quality detection. This review article, published in the journal Agriculture, explored how Raman spectroscopy is being integrated with advanced machine learning (ML) algorithms to improve fruit disease detection (1). The review article also highlighted how this method can be used to analyze pesticide residues and verify geographic origins.
What is Raman spectroscopy?
Raman spectroscopy is a molecular spectroscopy technique that is based on molecular vibrations. It is nondestructive, meaning that it can analyze samples without destroying them. Raman spectroscopy is a technique that has been used in various industries apart from food analysis, including pharmaceuticals, environmental analysis, and the oil and gas industry (2). Raman spectroscopy is a popular technique of choice in the food industry because rapid, accurate, and non-invasive testing methods are in high demand (1).
What did the review article discuss?
In their review article, the researchers concentrated on discussing recent breakthroughs in technology that have helped improve the reliability and efficiency of this technique through computational enhancements, particularly artificial intelligence (AI) (1).
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).
