Monofloral honey samples (Coffea robusta) from Vietnam were determined for their chemical compositions. This is the first report on the chemical composition and antioxidant activity of coffee honey from Vietnam. These samples were characterized by their high contents of total and reducing sugars, total phenolic contents, and total flavonoid contents. The contents of seven phenolic acids (PAs) were quantified by high performance liquid chromatography (HPLC) and analyzed with the assistance of principle component analysis (PCA) to differentiate the honey samples into groups. The hydroxymethylfurfural (HMF) (0.048–2.933 mg/kg) and free acid contents (20.326–31.163 meq/kg) of coffee honey were lower in Nepal, which reflected the freshness of the honey when conducting this survey. The coffee honey had total sugar and reducing sugar contents 831.711 g/kg and 697.903 g/kg, respectively. The high level of total phenolic (0.642 mg GAE/g) and flavonoid (0.0341 mg GE/g) contents of coffee honey contributed to their antioxidant activity of this honey sample. Among the coffee honey tested, the IC₅₀ of DPPH radical-scavenging activities value was 1.134–17.031 mg/mL, while the IC₅₀ of ABTS radical-scavenging activities value was 115.381–213.769 mg/mL. The phenolic acids composition analysis displayed that gallic acid appeared in high concentrations in all studied honey samples, ranging from 0.037–1.015 mg/kg, and ferulic acid content ranged from 0.193 to 0.276 mg/kg. The content of trigonelline and caffeine in coffee honey samples ranged from 0.314–2.399 mg/kg and 8.946–37.977 mg/kg. The data in this article highlight the relevance of coffee honey as a healthy substance.

Growing concerns about food safety and waste have increased consumer demand for high-quality agricultural products, particularly at the postharvest stage. This demand has prompted the development of non-destructive methods to assess or inspect the internal quality of fruits and vegetables. The backscattering imaging technique, also known as diffuse reflectance imaging, is considered a highly promising approach. Numerous studies have focused on practical applications, using laser light at selected wavelengths to develop quick multispectral methods. Due to the rapid interaction of photons with biological tissue, together with the highly computational performance of machine vision, backscattering imaging can offer a valuable alternative to traditional methods. Its primary benefits include quick measurements without chemical sample preparation, easy integration with high-throughput automatic quality control, and reduced waste, since this non-destructive technique does not damage samples. This review presents a comprehensive overview of backscattering imaging, including the measurement geometry, data analysis, and design considerations for vision systems. Additionally, it explores this technique’s advantages, challenges, and accuracy, as demonstrated using various case studies.

The color of the plant leaves is a major concern in many areas of agriculture. Pigmentation and its pattern provide the possibility to distinguish genotypes and a basis for annual crop management practices. For example, the nutrient and water status of plants is reflected in the chlorophyll content of leaves that are strongly linked to the lamina coloration. Pests and diseases (virus or bacterial infections) also cause symptoms on the foliage. These symptoms induced by biotic and abiotic stressors often have a specific pattern, which allows for their prediction based on remote sensing. In this report, an RGB (red, green and blue) image processing system is presented to determine leaf lamina color variability based on RGB-based color indices. LeafLaminaMap was developed in Scilab with the Image Processing and Computer Vision toolbox, and the code is available freely at GitHub. The software uses RGB images to visualize 29 color indices and the R, G and B values on the lamina, as well as to calculate the statistical parameters. In this case study, symptomatic (senescence, fungal infection, etc.) and healthy grapevine (Vitis vinifera L.) leaves were collected, digitalized and analyzed with the LeafLaminaMap software according to the mean, standard deviation, contrast, energy and entropy of each channel (R, G and B) and color index. As an output for each original image in the sample set, the program generates 32 images, where each pixel is constructed using index values calculated from the RGB values of the corresponding pixel in the original image. These generated images can subsequently be used to help the end-user identify locally occurring symptoms that may not be visible in the original RGB image. The statistical evaluation of the samples showed significant differences in the color pattern between the healthy and symptomatic samples. According to the F value of the ANOVA analysis, energy and entropy had the largest difference between the healthy and symptomatic samples. Linear discriminant analysis (LDA) and support vector machine (SVM) analysis provided a perfect recognition in calibration and confirmed that energy and entropy have the strongest discriminative power between the healthy and symptomatic samples. The case study showed that the LeafLaminaMap software is an effective environment for the leaf lamina color pattern analysis; moreover, the results underline that energy and entropy are valuable features and could be more effective than the mean and standard deviation of the color properties.

This study aimed to develop and characterize a nano-liposome system encapsulating adenosine (ADE) and cordycepin (COR) from the aqueous extract of Cordyceps militaris to enhance their bioavailability and stability.