The physicochemical properties and fatty acid composition of two new woody oil resources: Camellia hainanica seed oil and Camellia sinensis seed oil

The Antioxidant, Anti-Inflammatory and Moisturizing Effects of Camellia oleifera Oil and Its Potential Applications

Camellia oleifera oil (CO oil) extracted from C. oleifera seeds has a 2300-year consumption history in China. However, there is relatively little research regarding its non-edible uses. This study determined the physicochemical properties of CO oil extracted via direct pressing, identified its main components using GC-MS, and evaluated its antioxidant, moisturizing, and anti-inflammatory activities. The results revealed that CO oil's acid, peroxide, iodine, and saponification values were 1.06 ± 0.031 mg/g, 0.24 ± 0.01 g/100 g, 65.14 ± 8.22 g/100 g, and 180.41 ± 5.60 mg/g, respectively. CO oil's tocopherol, polyphenol, and squalene contents were 82.21 ± 9.07 mg/kg, 181.37 ± 3.76 mg/kg, and 53.39 ± 6.58 mg/kg, respectively; its unsaturated fatty acid (UFA) content was 87.44%, and its saturated fatty acid (SFA) content was 12.56%. CO oil also demonstrated excellent moisture retention properties, anti-inflammatory effects, and certain free radical scavenging. A highly stable CO oil emulsion with competent microbiological detection was developed using formulation optimization. Using CO oil in the emulsion significantly improved the formulation's antioxidant and moisturizing properties compared with those of the emulsion formulation that did not include CO oil. The prepared emulsion was not cytotoxic to cells and could reduce cells' NO content; therefore, it may have potential nutritional value in medicine and cosmetics.

Can omic tools help generate alternative newer sources of edible seed oil?

There are three pathways for triacylglycerol (TAG) biosynthesis: De novo TAG biosynthesis, phosphatidylcholine-derived biosynthesis, and cytosolic TAG biosynthesis. Variability in fatty acid composition is mainly associated with phosphatidylcholine-derived TAG pathway. Mobilization of TAG-formed through cytosolic pathway into lipid droplets is yet unknown. There are multiple regulatory checkpoints starting from acetyl-CoA carboxylase to the lipid droplet biogenesis in TAG biosynthesis. Although a primary metabolism, only a few species synthesize oil in seeds for storage, and less than 10 species are commercially exploited. To meet out the growing demand for oil, diversifying into newer sources is the only choice left. The present review highlights the potential strategies targeting species like Azadirachta, Callophyllum, Madhuca, Moringa, Pongamia, Ricinus, and Simarouba, which are not being used for eating but are otherwise high yielding (ranging from 1.5 to 20 tons per hectare) with seeds having a high oil content (40-60%). Additionally, understanding the toxin biosynthesis in Ricinus and Simarouba would be useful in developing toxin-free oil plants. Realization of the importance of cell cultures as "oil factories" is not too far into the future and would soon be a commercially viable option for producing oils in vitro, round the clock.

Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism

Cultivar identification is a basic task in oil tea (Camellia oleifera C.Abel) breeding, quality analysis, and an adjustment in the industrial structure. However, because the differences in texture, shape, and color under different cultivars of oil tea are usually inconspicuous and subtle, the identification of oil tea cultivars can be a significant challenge. The main goal of this study is to propose an automatic and accurate method for identifying oil tea cultivars. In this study, a new deep learning model is built, called EfficientNet-B4-CBAM, to identify oil tea cultivars. First, 4725 images containing four cultivars were collected to build an oil tea cultivar identification dataset. EfficientNet-B4 was selected as the basic model of oil tea cultivar identification, and the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B4 to build EfficientNet-B4-CBAM, thereby improving the focusing ability of the fruit areas and the information expression capability of the fruit areas. Finally, the cultivar identification capability of EfficientNet-B4-CBAM was tested on the testing dataset and compared with InceptionV3, VGG16, ResNet50, EfficientNet-B4, and EfficientNet-B4-SE. The experiment results showed that the EfficientNet-B4-CBAM model achieves an overall accuracy of 97.02% and a kappa coefficient of 0.96, which is higher than that of other methods used in comparative experiments. In addition, gradient-weighted class activation mapping network visualization also showed that EfficientNet-B4-CBAM can pay more attention to the fruit areas that play a key role in cultivar identification. This study provides new effective strategies and a theoretical basis for the application of deep learning technology in the identification of oil tea cultivars and provides technical support for the automatic identification and non-destructive testing of oil tea cultivars.