Abstract
Hyperspectral imaging has emerged as a powerful tool for the non-destructive assessment of plant properties, including the quantification of phytochemical contents. Traditional methods for antioxidant analysis in holy basil (Ocimum tenuiflorum L.) are time-consuming, while hyperspectral imaging has the potential to rapidly observe holy basil. In this study, we employed hyperspectral imaging combined with machine learning techniques to determine the levels of total phenolic contents in Thai holy basil. Spectral data were acquired from 26 holy basil cultivars at different growth stages, and the total phenolic contents of the samples were measured. To extract the characteristics of the spectral data, we used 22 statistical features in both time and frequency domains. Relevant features were selected and combined with the corresponding total phenolic content values to develop a neural network model for classifying the phenolic content levels into 'low' and 'normal-to-high' categories. The neural network model demonstrated high performance, achieving an area under the receiver operating characteristic curve of 0.8113, highlighting its effectiveness in predicting phenolic content levels based on the spectral data. Comparative analysis with other machine learning techniques confirmed the superior performance of the neural network approach. Further investigation revealed that the model exhibited increased confidence in predicting the phenolic content levels of older holy basil samples. This study exhibits the potential of integrating hyperspectral imaging, feature extraction, and machine learning techniques for the rapid and non-destructive assessment of phenolic content levels in holy basil. The demonstrated effectiveness of this approach opens new possibilities for screening antioxidant properties in plants, facilitating efficient decision-making processes for researchers based on comprehensive spectral data.