Pre- and post-harvest spectral estimation of carnosic acid and rosmarinic acid in rosemary

Rosemary extracts, including carnosic and rosmarinic acids (CA and RA, respectively), are known for their antimicrobial and antioxidant capabilities. Traditional quantification methods of CA and RA (later on called selected phytochemicals) are often destructive and time-consuming. This study presents a spectral, non-destructive, and time-efficient approach for estimating selected phytochemicals in pre- and post-harvest stages. We acquired spectral data from field-grown rosemary plants, dry leaves, and powder as well as UAV-borne hyperspectral imagery. The analysis included a transformation sequence (second derivative, Yeo–Johnson, and standardization), followed by partial least squares regression (PLSR). To mimic real-life scenarios, we investigated a training–testing strategy denoted by “leave-one-day-out”, systematically excluding each day's data from training. For CA estimation, the PLSR model achieved a coefficient of determination (R²) of 0.75 with a relative root mean square error (RRMSE) of 10.42% at the canopy level, 0.80 (RRMSE: 8.91%) for dry leaves, and 0.76 (RRMSE: 9.09%) for powder. RA estimation was challenging at the canopy level with an R² of 0.52 (RRMSE: 13.42%), but improved in post-harvest samples, reaching R² of 0.79 (RRMSE: 10.0%) for dry leaves and 0.75 (RRMSE: 9.78%) for powder. These results demonstrated the efficiency of the proposed approach. It offers a reliable alternative to traditional methods, with potential applications in agriculture and post-harvest industry.