This study examines the effects of convective air and vacuum drying at 40, 50, and 60 ℃ on the drying behavior, color, pigments, phenolic content, and antioxidant capacity of peppermint leaves. The drying data were modeled using eight drying models, with the Midilli model being the best fit for both drying methods with the highest R² (>0.99) and lowest values of χ² (<0.003) and root mean square error (RMSE) (<0.035). Results showed that convective drying at 60 ℃ had the highest drying rate (0.62 d.b./h) compared to vacuum drying (0.25 d.b./h) at the same drying temperature. Effective moisture diffusivity increased with the increase in drying temperature and ranged from 1.00 × 10⁻¹³ to 5.16 × 10⁻¹³ (m² s⁻¹). Activation energy ranged from 39.72 to 41.46 (kJ mol⁻¹). Furthermore, vacuum drying resulted in higher lightness and lower redness (a*) values than convective drying at higher temperatures. Both methods increased chlorophyll a and b contents, while β-carotene and phenolic contents significantly decreased, particularly at higher temperatures. This study highlights that both convective and vacuum drying methods affect the drying behavior and quality of peppermint leaves, with lower temperatures being more effective in preserving color and antioxidant properties. Future studies should focus on optimizing drying conditions to further enhance the retention of key bioactive compounds and explore the potential of other drying techniques for improved peppermint preservation.

This study aimed to set a computer-integrated multichannel spectral imaging system as a high-throughput phenotyping tool for the analysis of individual cowpea seeds harvested at different developmental stages. The changes in germination capacity and variations in moisture, protein and different sugars during twelve stages of seed development from 10 to 32 days after anthesis were non-destructively monitored. Multispectral data at 20 discrete wavelengths in the ultraviolet, visible and near infrared regions were extracted from individual seeds and then modelled using partial least squares regression and linear discriminant analysis (LDA) models. The developed multivariate models were accurate enough for monitoring all possible changes occurred in moisture, protein and sugar contents with coefficients of determination in prediction of 0.93, 0.80 and 0.78 and root mean square errors in prediction (RMSEP) of 6.045%, 2.236% and 0.890%, respectively. The accuracy of PLS models in predicting individual sugars such as verbascose and stachyose was reasonable with of 0.87 and 0.87 and RMSEP of 0.071% and 0.485%, respectively; but for the prediction of sucrose and raffinose the accuracy was relatively limited with of 0.24 and 0.66 and RMSEP of 0.567% and 0.045%, respectively. The developed LDA model was robust in classifying the seeds based on their germination capacity with overall correct classification of 96.33% and 95.67% in the training and validation datasets, respectively. With these levels of accuracy, the proposed multichannel spectral imaging system designed for single seeds could be an effective choice as a rapid screening and non-destructive technique for identifying the ideal harvesting time of cowpea seeds based on their chemical composition and germination capacity. Moreover, the development of chemical images of the major constituents along with classification images confirmed the usefulness of the proposed technique as a non-destructive tool for estimating the concentrations and spatial distributions of moisture, protein and sugars during different developmental stages of cowpea seeds.