Determination of Geniposide inGardenia jasminoidesEllis Fruit by Near-Infrared Spectroscopy and Chemometrics

Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction

Aqueous extraction is the most common and cost-effective means of obtaining active ingredients from medicinal plants. However, botanical extracts generally contain high pigment content and complex chemical composition posing a challenge for the process analysis of aqueous extraction. Here, we employed Raman spectroscopy to monitor the physical and chemical properties during the extraction process using convolution neural network (CNN) with background subtraction. Real-time spectra were first preprocessed to eliminate fluorescence background interference. Next, two types of CNN models, the one-dimensional CNN (1D-CNN) based on one preprocessing method, and two-dimensional CNN (2D-CNN) based on a concatenation of differentially pretreated data blocks, were used to receive the preprocessed spectra data. Two case studies were conducted for 1D- and 2D-CNN: the extraction of Aurantii fructus, and the co-extraction of Radix Salvia miltiorrhiza and Rhizoma Ligusticum chuanxiong. Furthermore, partial least squares (PLS) models and sequential preprocessing through orthogonalization (SPORT) models were developed and compared with 1D-CNN and 2D-CNN, respectively. CNN-based methods were superior to other models in terms of prediction accuracy, with 2D-CNN yielding the best results. These results indicated that preprocessing and CNN methods were highly complementary, and could effectively remove the fluorescence effect and artefacts introduced by pretreatment in spectral profile. To the best of our knowledge, this is the first study to demonstrate that a combination of preprocessing and CNN leads to improved prediction performance of analytes when using Raman spectroscopy for online monitoring high-pigmented samples.

Monitoring of Natural Pigments in Henna and Jagua Tattoos for Fake Detection

Temporary tattoos are a popular alternative to permanent ones. Some of them use natural pigments such as lawsone in the famous henna tattoos. Recently, jagua tattoos, whose main ingredients are genipin and geniposide, have emerged as an interesting option. This study was conducted to identify the presence and concentration of henna and jagua active ingredients (lawsone; genipin and geniposide, respectively) in commercial tattoo samples. Since natural pigments are often mixed with additives such as p-phenylenediamine (PPD) in the case of henna, PPD has been included in the study. Green and simple extraction methods based on vortex or ultrasound-assisted techniques have been tested. To determine the compounds of interest liquid chromatography (LC) with diode-array detection (DAD) has been applied; and PPD absence was confirmed by LC-QTOF (quadrupole-time of flight tandem mass spectrometry). This work demonstrated that only one out of 14 henna samples analyzed contained lawsone. For jaguas, genipin was found in all samples, while geniposide only in two. Therefore, quality control analysis on these semi-permanent tattoos is considered necessary to detect these ingredients in commercial mixtures, as well as to uncover possible fraud in products sold as natural henna.

Recovery of Crocins From Floral Tissue of Gardenia jasminoides Ellis

In this research, a novel source of phytopigment crocins from fully open mature flowers of cape jasmine (Gardenia jasminoides) is introduced. Methanol and deionized water were appropriate solvents for pigment recovery with maximum yields of at least 17% from the floral tissue. Pigment separation by thin layer chromatography also confirmed the presence of the carotenoids, which dissolved well in these high-strength polar solvents, in fruit, flower, and leaf materials. The spectral patterns of the extracts from ultraviolet and nuclear magnetic resonance showed maximum absorption at ~420 nm and the chemical shift values were similar to those of crocetin aglycones (crocins) in the methanol extracts of a commercial source of yellow gardenia (fructus or fruit of Gardenia florida). Chemical compositions were then evaluated using aqueous-phase capillary electrophoresis of the methanol extracts. The methanolic extracts of the flowers and fruit had 11 principal ingredients in common. Among these, crocetin and crocin 2 belong to the crocin group and are known to be the major components of commercial yellow Gardenia. This research not only demonstrates a sustainable means of raw material utilization for natural product recovery, but also encourages a movement toward an edible landscape for the community.

Rapidly Simultaneous Determination of Six Effective Components in Cistanche tubulosa by Near Infrared Spectroscopy

Quantitative determination of multiple effective components in a given plant usually requires a very large amount of authentic natural products. In this study, we proposed a rapid and non-destructive method for the simultaneous determination of echinacoside, verbascoside, mannitol, sucrose, glucose and fructose in Cistanche tubulosa by near infrared spectroscopy (NIRS). Near infrared diffuse reflectance spectroscopy (DRS) and high performance liquid chromatography (HPLC) were conducted on 116 batches of C. tubulosa samples. The DRS data were processed using standard normal variety (SNV) and multiplicative scatter correction (MSC) methods. Partial least squares regression (PLSR) was utilized to build calibration models for components-of-interest in C. tubulosa. All models were then assessed by calculating the root mean square error of calibration (RMSEC), correlation coefficient of calibration (r). The r values of all six calibration models were determined to be greater than 0.94, suggesting each model is reliable. Therefore, the quantitative NIR models reported in this study can be qualified to accurately quantify the contents of six medicinal components in C. tubulosa.