Innovative Method Predicts Honey’s Properties Using Spectroscopy and Data Analysis

Researchers have developed a cutting-edge method to predict honey’s key properties—such as sugar profile, moisture content, pH, and antioxidant activity—using advanced spectroscopy combined with statistical modeling or machine learning. This non-invasive technique analyzes honey’s unique spectral signature, enabling fast and accurate assessment of its quality, origin, and purity without the need for chemical reagents or time-consuming lab tests. By integrating technologies like near-infrared (NIR) or Fourier-transform infrared (FTIR) spectroscopy with predictive algorithms, this approach offers a powerful tool for honey authentication, food safety, and fraud detection. The method is gaining traction in quality control labs and the food industry, where rapid, reliable verification of natural products is essential.