Qualitative Identification Research Articles

Studi Tentang Mutu Biji Pala Dengan Pengeringan Matahari dan Pengasapan

The quality of nutmeg seeds is determined, among others, by their moisture content and the presence of aflatoxin contamination. To maintain quality, the moisture content of nutmeg seeds must be reduced to meet export standards. This can be achieved through sun drying or smoking. Aflatoxin is a mycotoxin produced by the Aspergillus fungi. The study aims to obtain nutmeg seeds with the appropriate moisture content according to the Indonesian National Standard (SNI) through sun drying and smoking methods. Also, it is to qualitatively identify the presence of aflatoxin in the nutmeg seeds. A total of 27 kg of nutmeg was divided into three treatment groups for drying and smoking. The drying and smoking treatments were arranged following a Randomized Block Design with three replications. Nutmeg essential oil was extracted using steam distillation. Subsequently, a qualitative identification of aflatoxin contamination was carried out using PDA media. The results showed that sun drying the nutmeg seeds for 72 hours resulted a moisture content of 7 - 8%. Meanwhile, the qualitative identification revealed the presence of Aspergillus niger, Penicillium sp, and Rhizopus stolonifer in the tested samples. No A. flavus, however, which is the main fungus responsible for aflatoxin production, was found in the results. Therefore, by drying the nutmeg seeds to a moisture content below 10%, the emergence of aflatoxin-causing fungi can be prevented.

Qualitative Identification of Secondary Metabolites and Determination of the Toxicity of Extracts Obtained from the Flower of Kalanchoe Pinnata

Kalanchoe pinnata, also known as air leaf or life leaf, is a plant used in traditional medicine in different world regions. In Mexico, it is included in the Atlas de la Medicina Tradicional Mexicana with a wide variety of applications, such as antimicrobial, anti-inflammatory, anticancer, and antihistamine, among others. However, neither a secondary metabolite profile of the flower has been reported nor information on its possible toxicity. The latter is the purpose of this work. A phytochemical profile of extracts with solvents of different polarity (aqueous, methanol, ethanol, and ether) was carried out. In this profile, the structural compounds could be qualitatively determined by chemical reactions, and some changes in coloring or precipitation were observed. The acute toxicity test of the extracts was performed with an aquatic organism, Artemia sp, and a terrestrial organism Eisenia foetida, as well as the evaluation of the antioxidant capacity of the extracts in the organism of Eisenia foetida. The ABTS radical method and TROLOX were applied as synthetic antioxidants for the evaluation of the inhibition percentage. Most important secondary metabolites were qualitatively identified in the extracts of K. pinnata flowers. Mainly in the alcoholic extracts (methanol and ethanol) tannins, alkaloids, and flavonols were found. As mentioned above, they are reported to have toxicological effects. The toxicity and antioxidant activity tests confirm the preliminary results obtained in the identification of secondary metabolites. It is therefore concluded that the flower of Kalanchoe pinnata contains secondary metabolites that may be of great therapeutic interest.

Rapid capture and quantification of food-borne spores based on the double-enhanced Fe3O4@PEI@Ag@PEI core-shell structure SERS sensor

To realize rapid capture and quantification of food-borne spores and prevent their potential harm, Fe3O4@PEI@Ag@PEI core-shell structure nanoparticles were combined with flower-like AgNPs for double enhancement and efficient capture of spores. The developed sensor showed excellent reproducibility and SERS enhancement factor (AEF) is 4.6 × 104. Orthogonal partial least-squares discrimination analysis and linear discriminant analysis accurately identified the three spores (Bacillus subtilis, Bacillus cereus, and Clostridium perfringens), and the qualitative identification accuracy of linear discriminant analysis was 100 %. Efficient enrichment of B. subtilis spores was realized within 5 min, with a detection limit of 3 cfu/mL. Spiked tests revealed that this sensor was effective in detecting spores in milk, orange juice, and water samples, with recovery ratio of 95.2−103.9 % and relative standard deviation of 3.1−7.7 %. Thus, the developed sensor was accurate and reliable, and could achieve rapid identification and quantitative detection of food-borne spores.

A novel intelligent identification approach based on modified hierarchical diversity entropy and extension theory for diagnosis of rotating machinery faults

Aiming at the troubles of difficult extraction of fault features and low fault recognition rate in rotating equipment fault detection approach, a new technique for intelligent diagnosis based on modified hierarchical diversity entropy (MHDE) and extension theory (ET) is proposed in the thesis. Firstly, MHDE employs to comprehensively describe the fault information of the given signals. Secondly, the MHDE feature sets are regarded as the characteristic parameters of the extension matter element model, and the matter element model in various states is established. Finally, the testing datasets are fed into the matter element model for each operating conditions, and the correlation function is used to compute the comprehensive correlation between the testing datasets and the various conditions of the rotating machinery, so as to realize the qualitative and quantitative identification of the testing datasets. The reliability and superiority of the proposed new approach is validated by real experimental datasets on various rotating machinery types. The analysis results show that the proposed novel technology can effectively excavate the feature information and accurately identify various fault conditions of rotating machinery. In addition, compared with other combined model technology in the paper, the proposed intelligent fault diagnosis technology has better classification performance.

Suspect and nontarget screening of per- and polyfluoroalkyl substances based on ion mobility mass spectrometry and machine learning techniques

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening techniques are powerful tools for the comprehensive identification of per- and polyfluoroalkyl substances (PFASs), but the interference of complex matrices (especially for wastewater) pose an analytical challenge. This study explored the potential of combining ion mobility spectrometry (IMS) with HRMS and machine learning techniques to achieve the rapid and accurate suspect and nontarget screening of PFAS in wastewater. There were fewer interfering peaks and a clearer spectrum in the data acquired by IMS-HRMS than conventional HRMS. The introduction of collision cross section (CCS) in PFAS homologous series search could filter out 63% of false positive results. Retention time and CCS prediction models were helpful in improving the confidence for PFAS qualitative identification and the random forest algorithm combined with RDKit descriptor performed best for CCS prediction. With the inclusion of extra dimensional information, this study also proposed a comprehensive and concise confidence assignment criterion to better convey the certainty of the qualitative identification of PFAS. Finally, a total of 56 potential PFASs were identified in the wastewater sample using the newly developed method and 45 of them were identified outside reference standards, emphasizing the importance of suspect and nontarget screening for PFAS.

<i>Lonicera caerulea</i> L. Comparative metabolomics study of polyphenolic composition by tandem mass spectrometry of four varieties of Far Eastern honeysuckle

Relevance. The article presents a comparative metabolomic study of blue honeysuckle (L. caerulea) berries, varieties Volhova, Tomichka, Goluboe vereteno, Amfora, presented in the collection of the Far Eastern Experimental Station of N.I. Vavilov All-Russian Institute of Plant Genetic Resources.Methods. To determine the complex of secondary metabolites in extracts of blue honeysuckle berries. used the method of tandem mass spectrometry. Fractional maceration was used to obtain highly concentrated extracts. In extracts of L. caerulea berries, 75 compounds of the polyphenol group were reliably identified by tandem mass spectrometry.Results. Based on the results of the qualitative identification of the component composition of blue honeysuckle berry extracts, a Venn diagram was constructed showing the similarity and difference in polyphenol groups of various L. caerulea varieties. Berries of L. caerulea contain a large amount of biologically active substances, in particular, they have a large number of compounds with antioxidant activity. The isolated polyphenol complex included compounds of the following groups: flavones, flavonols, flavan-3-ols, flavanones, anthocyanins, stilbenes, coumarins, phenolic acids. Also, for the first time in the extracts of L. caerulea berries, 30 chemical compounds of the polyphenol group were identified (flavones Pentahydroxydimethoxyflavone, Cirsiliol, jaceosidin, Sophoraisoflavone A, Dihydroxy tetramethoxyflavone, Chrysoeriol-O-hexoside, Formononetin-7-O-glucoside, flavon-3-ols (epi)-Afzelechin, (epi)-Catechin gallate, stilbenes Pinosylvin, Resveratrol, Dihydroresveratrol, coumarins Fraxetin, Umbelliferone, etc.), not previously mentioned in studies on L. caerulea.

B-256 Genotype Distribution of the Hepatitis C Virus: 10 Years Data of a Clinical Laboratory in Brazil

Abstract Background Hepatitis C virus (HCV) infection is a significant health problem worldwide and is a major cause of severe liver disease including chronic hepatitis, cirrhosis, and hepatocellular carcinoma. HCV is divided into six genotypes with several sub-genotypes depending on the geographic region, some genotypes are endemic. The purpose of this study was to describe the frequency of the HCV genotype (G) profile by gender and age using a database of a large Brazilian clinical laboratory. Methods This was a retrospective analysis from January 2012 to December 2022 based on molecular data of 3.566 isolated from clinical samples. The automated extraction and purification of nucleic acids were conducted from EDTA plasma samples of individuals suspected of HCV infection. Then, nucleic acid amplification was performed using oligonucleotide probes labeled with fluorescent dyes specific for the qualitative identification of HCV genotypes 1 to 6 and subtypes A and B of genotype 1. Results A total of 3.566 HCV genotype cases were analyzed during the period. The genotype distribution was (2.653/74.4%) G1 (48.1% G1A and 46.4% G1B), (733/ 20.5%) G3, (133/3.7%) G2, (35/0.9%) G4, (11/0.3%) G5 and (1/0.02%) G6. Twelve patients had multiple genotypes over the last ten years. At least one individual changed the genotype over a 3-year evaluation period, and 11 (0.30%) changed it twice. The genotypes G1 and G3 were predominant in all years analyzed. The prevalence of G1 and G3 was 75% and 19.6% among females and 74% and 21.2% among male patients, respectively. The analysis was conducted on individuals aged 6 to 99 years, with an overall average age of 52.6±13 years. The average age among men was 51.1 ± 12 years and among women, it was 54.7 ± 13 years. According to the age groups evaluated, genotype 1 was the most frequent, ranging from 69.3% (21 to 30 years old) to 75.8% (over 60 years old). The main subtype in the over 60 years old group was 1B. The highest frequency of genotype 2 was observed in the group over 60 years old (6.1%). The patient's symptoms, clinical history, or further diagnostic procedures were not accessible. Conclusion The study found that genotypes 1 and 3 were the most prevalent in Brazil over the ten-year period, with genotype 1 being the most frequent among all age groups. Genotype 1B was most prevalent in individuals above 60 years old. The change of genotype per individual was less than 1%, but further attention is required for potential virus tropism.

B-246 The Evaluation of Actionable Mutations By Multiplex Analysis Using IntelliPlex® Lung Cancer Panel for Non-Small Cell Cancer Management

Abstract Background Understanding the genetic alterations which occur in genes associated with various cancers can improve therapy outcomes by promoting tailored treatments for each patient. Molecular profiling is therefore a crucial tool for precision medicine and enables for a personalized approach to cancer management. Current guidelines for molecular testing of Non-Small Cell Lung Cancer (NSCLC) patients involves treatment selection of a targeted Tyrosine Kinase Inhibitor based on the minimal testing of EGFR, ALK and ROS1 genes and strongly recommending the additional testing of KRAS, BRAF, MET, RET, NTRK and ERBB2 (Her2) genes to fully evaluate the patient’s mutational status. Method The IntelliPlex Lung Cancer Panel, based on PlexBio’s multiplexing πCode® technology, is an in vitro molecular amplification assay intended for the qualitative identification of 74 DNA mutations in the KRAS, NRAS, PIK3CA, BRAF, EGFR, HER2, MEK1 and AKT1 genes and 28 gene variants of the ALK, ROS1, RET, NTRK1 and MET genes. The assay is suitable for the use with formalin-fixed paraffin-embedded (FFPE) tumor tissues from NSCLC patients. The performance of IntelliPlex Lung Cancer Panel was evaluated on FFPE specimens by comparing the results with two commercially available molecular amplification assays. Discrepant samples were resolved by a corresponding digital PCR method kit. Results The IntelliPlex Lung Cancer Panel can detect DNA mutations at very low allele frequencies (as low as 0.1%) while only requiring DNA quantities of 10ng. Most RNA rearrangements are detected with 10 or more copies present in the sample while only low RNA input quantities (50 ng) are required. The IntelliPlex Lung Cancer Panel can analyze the mutational status of 102 targets in less than 5.5 h from sample to results and allows screening of up to 48 samples in parallel. Among 50 FFPE DNA specimens tested, the overall agreement for EGFR and KRAS mutations between IntelliPlex system and the comparison method were both found to be 98%. And the overall agreement after resolved the discrepancies by digital PCR was 100%. Conclusion The multiplex amplification and detection capabilities of the IntelliPlex Lung Cancer Panel provides a comprehensive and efficient way to evaluate the individual mutational status from lung cancer patients with high sensitivity and specificity. Accurate genetic analysis provides valuable information which can support precision medicine decisions for better cancer management. This can also be used further with non-invasive whole blood sampling (i.e., “liquid biopsy”) for subsequent treatment monitoring.

Qualitative identification and quantitative comparison of Physochlainae Radix from different regions based on chemometric methods.

Physochlainae Radix (PR) is an essential herbal medicine that has been generally applied for treating cough and asthma. In this study, a comprehensive strategy for quality evaluation of PR from different origins was established by integrating qualitative identification, quantitative analysis, and chemometric methods. A total of 58 chemical components were identified by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS), and a sensitive and rapid UHPLC-QqQ-MS/MS method was established for the simultaneous determination of 12 compounds. In addition, multivariate statistical analysis was applied for discriminant analysis to compare the differences among 30 batches of PR samples. The results showed that the 30 batches of PR collected from four provinces could be clustered into three categories, in which scoparone, protocatechuic acid, tropic acid, and scopolin were important components to distinguish the primary and non-primary producing areas, as well as superior and inferior products of PR. Chemometric results were consistent and validated each other, and systematically explained the intrinsic quality characteristics of PR. This study first demonstrated that LC-MS combined with multivariate statistical analysis, provided a comprehensive and effective means for quality evaluation of PR.

Detection of the explosive nitroaromatic compound simulants with chemosensory systems based on quartz crystal microbalance and chemiresistive sensor arrays

The work is devoted to investigations of possibility of rapid detection and subsequent identification of explosive substances by using the arrays of two types of sensor elements: quartz crystal microbalances and chemiresistive electrodes. Thin layers of calixarene compounds and composites of electrically conductive polymers, respectively, were used as the sensitive coatings. Several types of nitroaromatic compounds from nitrotoluene series were chosen as simulants of explosive substances: O-Nitrotoluene (2-MNT) and Nitrobenzene (MNB), the concentration of these volatile compounds varied from 10 to 100 ppm. The observed detection threshold, depending on the type of analyzed explosive simulants, was within the range of 1 to 10 ppm for quartz crystal sensors with calixarene sensitive films, and the response time was within 10…20 s for quartz crystal sensors and up to 1 min for chemiresistive sensors. It has been shown that among the tested calixarenes there are samples with high selective sensitivity. The possibility of qualitative identification of explosives at relatively low concentrations by using the statistical methods of chemical patterns recognition (the so-called “electronic nose”) has been demonstrated.

Intelligent recognition and identification of fracture types and parameters for borehole images based on developed convolutional neural networks and post-processing

Borehole imaging is one of the main methods for fracture observation in the rock formations, but there is still a lack of identification systems for deriving fracture parameters from borehole images. Additionally, previous research were limited to sinusoidal fractures. In this study, an intelligent system is developed for multiple types of qualitative fracture identification in a quick and robust manner using convolution neural networks and post-processing. Initially, borehole imaging was carried out in six coal mines to develop a borehole image database that includes a wide variety of rock types and fractures. Afterward, the acquired borehole images of fractures were categorized into three types, such as sinusoidal fractures, vertical fractures and broken areas. In this connection, dual convolutional neural networks were developed to recognize fracture type pixels. Meanwhile, the calculation models associated with corresponding fracture parameters were established to quantitatively describe the fracture characteristics. Eventually, the post-processing programs were developed to identify the geometric parameters for each fracture type. The results indicate that the developed dual convolutional neural networks has the capability to detect fracture pixels with a recognition accuracy of 77.1% for each sinusoidal and vertical fracture pixels, and recognication accuracy of 72.9% for broken area pixels. The post-processing program was developed based on Hough transform and a thinning algorithm, that enables it to the the average recognition rate of 88.71% with an average false alarm rate of 16.67% for all three types of fractures.

Qualitative identification of lonicerae japonicae flos in traditional chinese medicine using metabarcoding combined with specific mini-barcodes.

Lonicerae japonicae flos, also known as Jinyinhua (JYH), is an important component of traditional Chinese patent medicine (TCPM) products. However, the potential for adulteration and substitution with low-quality materials highlights the need for a reliable and sensitive approach to identify the species composition of TCPM products for consumer safety. We used universal ITS2 primers to amplify TCPMs containing JYH. However, the results were inconclusive, as only one operational taxonomic unit (OTU) was identified as Lonicera sp., which could not be identified at the species level. To confirm the species identification of Lonicera sp. in TCPM, we developed a short mini-barcode primer based on the psbA-trnH region, which, in combination with DNA metabarcoding technology, allowed for qualitative and quantitative analysis of artificially mixed samples. We applied the mini-barcode to distinguish TCPMs containing JYH and demonstrated its relatively accurate quantitative ability in identifying two Lonicera species. Our study presents a method for qualitative and quantitative identification of JYH, providing a promising application of DNA metabarcoding technology in the quality control of TCPM products.

Qualitative Identification of Single/Mixture Gases Based on Fe-ZnO Sensor Array and PSO-BP Neural Network

Qualitative Identification of Single/Mixture Gases Based on Fe-ZnO Sensor Array and PSO-BP Neural Network

Investigation on the removal of contaminants from washing machine discharge using Strychnos potatorum (clearing nut) – A potential purifying agent

The objective of the present study is to develop eco-friendly purifying agents from natural materials, Strychnos potatorum (SP) – a coagulant. The seeds of SP were used for wastewater treatment (washing machine effluent). Physico-chemical parameters like turbidity, total dissolved solids (TDS), electrical conductivity (EC), pH, calcium (Ca2+), magnesium (Mg2+) and phosphate (PO43−) were tested and the data obtained were compared with BIS standards. In the current work, physical and chemical parameters have been examined to determine the quality of water for washing machine discharge, before and after treatment (coagulation) using SP seeds. The testing procedure was carried out using a Jar test of coagulant mass 1000 mg/L with rapid mixing at 150 rpm for 5 min, slow mixing at 30 rpm for 25 min and a settling period of 1 h. Coagulation results were reported for the use of SP seeds, where the effectiveness of removing turbidity and PO43− was determined to be 81.54 and 41.45%. The presence of various functional groups in SP seed was assessed by fourier transform infrared (FT-IR) spectroscopy. Qualitative identification of phytochemical constituents of seed extract for this species was also carried out. The experimental findings strongly highlighted the ability of SP seeds to remove turbidity and PO43− from wastewater.

Identification of Anthocyanidins in Anthocyanin Extract of Purple Sweet Potato (Ipomoea batatas l), black rice (Oryza sativa l), and Black Glutinous rice (Oryza sativa v. glutinosa)

Anthocyanins originating from different commodities have different constituent components and anthocyanidin levels. This will undoubtedly affect the anthocyanin content of total in foodstuffs. Local items such as; Purple sweet potato, black rice, and black glutinous rice have different structural components and levels of anthocyanidins. The research objective was to measure the total anthocyanin content and identify anthocyanidins qualitatively and quantitatively from anthocyanin extracts of purple sweet potato, black rice, and black glutinous rice. The research method used was an experimental method , qualitative and quantitative identification analyses were carried out using the UHPLC-PDA (Ultra high Performance Liquid Chromatographic) instrument system. Based on the study with pH-difference measurements, the total anthocyanin extract in purple sweet potato is 0.6 mg / g of material, much lower than black rice (5.50 mg / g of material) and black glutinous rice (6.00 mg / g of material). g material). Based on the identification of the purple sweet potato anthocyanin extract, the main components of anthocyanidins were Delfinidin, Cyanidin, and Malvidin with concentrations of 0.12 mg, 0.12 mg, and 1.20mg / 100g of ingredients, respectively. Meanwhile, black rice and black glutinous rice were identified with cyanidine and malvidin with concentrations of 0.73 and 13.17 mg / 100g and 1.07 mg and 17.6 mg / 100g of ingredients.

Preparation of a multifunctional and ultrasensitive Au@AgNPs-Van/PDMS film SERS substrate for rapid detection of foodborne pathogens in beef

We present a novel and super-sensitive vancomycin (Van) modified Au@AgNP self-assembly with polydimethylsiloxane (PDMS) (Au@AgNPs/Van-PDMS) film surface-enhanced Raman scattering (SERS) substrate for identification of foodborne pathogens in beef, without the need for in vitro bacterial culture. The results illustrated that the Au@AgNPs/Van-PDMS film exhibited high reproducibility and Raman enhancement effect (1.09 × 105). Clostridium perfringens, Bacillus subtilis and Staphylococcus aureus isolated from beef captured by Au@AgNPs/Van-PDMS film exhibited high reproducibility and significant differences. Principal component analysis score plots distinguished classes of foodborne pathogens, and the qualitative identification of linear discriminant analysis was correct at 100%. The detection limit of S. aureus in beef was as low as 3 CFU/mL. This method could provide an effective means and technical support for realizing the application of SERS technology for quick and highly sensitive detection of foodborne pathogens in complex environments.

OPTIMIZED ECOLOGICAL NETWORK APPROACH OF HIGHLY URBANIZED CITIES: THE CASE OF ADANA CITY

Abstract. One of the most significant challenges in urban areas, where the process of rapid urban expansion takes place, is the loss of agricultural lands and natural habitats. The conversion of these areas into residential and commercial zones leads to a decline in urban biodiversity and the progressive loss of vital habitat areas. Analyzing habitat connectivity and conducting landscape measurements provide valuable insights for the development of land use and management strategies, enhancing our understanding of the spatial structure of the landscape, and directing conservation efforts. Incorporating measures such as green corridors and landscape connection networks into urban planning management becomes crucial in order to mitigate the adverse effects of habitat fragmentation and enhance ecosystem resilience within cities. Remote sensing techniques offer opportunities to create habitat connectivity models that enable the quantitative and qualitative identification of fragmented habitat patches. These models serve as tools to evaluate the effectiveness of conservation measures and monitor the potential impacts of future land use changes on habitat networks. Within this context, an optimized approach to habitat connectivity is presented, aiming to contribute to landscape planning and ecological-based studies in a city with undergoing rapid urbanization like Adana. By identifying degraded areas and introducing new habitat patches, a significant improvement in the connectivity of the habitat network has been observed. The findings indicated that the addition of new habitat patches to degraded areas can substantially enhance the city's overall habitat connectivity.

Detection of the Early Fungal Infection of Citrus by Fourier Transform Near-Infrared Spectra

Early fungal infection of citrus is one of the common diseases found during the storage period of citrus, and fungus that infects citrus will spread to the entire batch of citrus as the degree of infection deepens, causing enormous economic losses. Therefore, early detection of fungal infection of citrus is fundamental. The purpose of this study is to explore the qualitative identification of early fungal infections in citrus by using Fourier transform near-infrared (FT-NIR) combined with a variety of chemometric methods. First, discrete wavelet transform (DWT) is used to filter the noise of the spectral signal, then combined with a PLS-DA model, that helps discriminate healthy from infected Citrus. Subsequently, four different feature variable selection methods were introduced, Then, the linear discriminant analysis (LDA) and support vector machine (SVM) two classifiers were combined to establish a qualitative model for the degree of fungal infection. The modeling results show that the SVM modeling effect is better than LDA, and the DWT-CARS-SVM based on the RBF kernel function has the best result, the accuracy rates of the training set and test set are 100% and 97%. The results indicate that FT-NIR spectroscopy, combined with chemometric methods, is able to distinguish early fungal infections in citrus.

Phytochemical profile of petals from black Dahlia pinnata by flow injection analysis-electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry.

Dahlia pinnata Cav. is a flower native to Mexico that has many applications; in particular, its petals have been used for ornamental, food, and medicinal purposes, for example to treat skin rashes and skin cracks. It has been reported that the medicinal properties of plants are generally related to the phytochemical constituents they possess. However, there are few studies on black D. pinnata. The present study was aimed at qualitatively and quantitatively determining the phytochemical profile of petals from black D. pinnata. Phytochemicals from Dahlia petals were extracted by consecutive maceration (hexane, dichloromethane, and methanol); then, the extracts were analyzed through colorimetric assays and UV-Vis spectroscopy for qualitative identification and quantification of phytochemical compounds, respectively. The methanolic extract was analyzed by flow injection analysis-electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry (FIA-ESI-FTICR-MS) in negative and positive mode. Quantitative phytochemical profiling of the methanolic extract by UV-Vis spectroscopy indicated high contents of phenolic compounds (34.35 ±3.59 mg EQ/g plant) and sugars (23.91 ±1.99 mg EQ/g plant), while the qualitative profiling by FIA-ESI-FTICR-MS allowed the tentative identification of several flavonoids and phenolic acids. Kaempferol-3-rutinoside, pelargonidin-3-(6″-malonylglucoside)-5-glucoside, rutin, kaempferol-3-(2″,3″-diacetyl-4″-p-coumaroylrhamnoside), and myricetin-3-(2‴-galloylrhamnoside) were the main compounds detected. The results expand our knowledge of the phytochemical constituents of petals from black D. pinnata.

Rapid Characterization of Male and Female Taxus chinensis by Near-Infrared (NIR) Spectroscopy and High-Performance Liquid Chromatography (HPLC) with Orthogonal Partial Least Squares—Discriminant Analysis (OPLS-DA)

Taxus chinensis var. mairei (T. chinensis) is a dioecious plant that is important in traditional Chinese medicine. However, information about male and female T. chinensis is absent. To establish a simple, rapid, and reliable method for the qualitative identification of male and female T. chinensis, near-infrared spectroscopy (NIR) and high-performance liquid chromatography (HPLC) with the orthogonal partial least-squares discriminate analysis (OPLS-DA) mode was performed. NIR and discriminant analysis (DA) were employed for the first time to distinguish male and female T. chinensis. Meanwhile, HPLC also was optimized to determine the contents of the bioactive ingredients 10-deacetyltaxol (10-DAT) and paclitaxel of T. chinensis. Herein, the results of the validation showed that the DA model using NIR had a correct differentiation of nearly 100% for male and female T. chinensis. Moreover, the HPLC results indicated that the content of paclitaxel and 10-DAT was higher in female plants than in males. Additionally, based on the HPLC content results of male and female T. chinensis, the OPLS-DA successfully distinguished male and female T. chinensis. Thus, combining HPLC and OPLS-DA to initially differentiate between males and females allowed further quantitative and qualitative analysis of male and female T. chinensis. This method provides an approach to the identification and quality control of traditional Chinese medicine.