Basils of the genus Ocimum are aromatic plants grown widely throughout the tropical and temperate regions. The essential oils obtained from their aerial parts are enriched with volatile organic compounds with high market demand for food and pharmaceutical industries. The volatile organic compounds have been shown to exhibit biological activities. Therefore, their novel applications have been extensively explored in the last few decades. The most widely available basils in the tropical areas include white holy basil (O. sanctum var. Shyama), red holy basil (O. sanctum var. Rama), Thai basil (O. basilicum var. thyrsiflorum), lemon basil (O. citriodorum), and tree basil (O. gratissimum). Over 60 volatiles of different classes have been exclusively described, and some of them could be useful as biomarkers for genotype specification. The major volatile ingredient is the phenylpropanoids, such as methyl eugenol, which has the potential as a natural product for mitigating Oriental fruit fly (Bactrocera dorsalis) during tropical fruit production. Moreover, basil essential oils are also used to control diseases of the fruits during post-harvest storage. As a result, the application of basil essential oils as a sustainable defect control strategy for tropical fruit value chains seems intriguing. This review provides comprehensive information on plant taxonomy and volatile compositions of the essential oil fractions from different basil species. Their biological activities and applications are also discussed, mainly during the pre- and post-production of tropical fruits. Additionally, the available techniques to enhance the efficacy of the volatile active compounds are also described.

Proximate analysis is essential for assessing the nutritional composition of legumes. Near-Infrared Spectroscopy (NIRS) is widely used as a rapid, non-destructive method. However, commercial NIRS calibration models are primarily developed using major commercial legumes and may not represent the spectral variability of underutilised legumes, limiting their transferability across species. This study aimed to evaluate the proximate composition of local market legumes and adjust existing NIR equations for predicting proximate constituents. Twenty-six dry seed legume samples, representing four genera and multiple species from northern Thailand and southern China, were analysed using standard wet-chemistry methods. Reference analyses showed that soybeans contained the highest protein and fat levels among the legumes studied. Partial Least Squares regression was used to adjust prediction models, with 5–7 newly acquired samples per model. Model performance was evaluated using leave-two-out cross-validation. The adjusted models yielded root mean square error values of 0.987, 0.586, 0.671, 0.363, and 0.976 for protein, fat, moisture, ash, and fibre, respectively. Spectral adjustment improved agreement between predicted and reference values, particularly for protein, fat, and ash. This study demonstrated the initial potential of adapting existing NIRS calibration models to diverse legumes. Future work must prioritise independent external validation with expanded sample cohorts to establish operational robustness.