WeChat Mini Program
Old Version Features

    • THE USE OF GC-MS AND FTIR SPECTROSCOPY COUPLED WITH MULTIVARIATE ANALYSIS FOR THE DETECTION OF RED GINGER OIL ADULTERATION

    Suryati Syafri,Irwandi Jaswir,Faridah Yusof,Abdul Rohman,Dachriyanus Hamidi

    RASAYAN Journal of Chemistry（2022）

    Cited 6|Views11
    Abstract
    The ginger oil traded worldwide could come from various sources. Standard quality is the most critical aspect of ensuring customer safety. This study aims to develop an analytical method for red ginger oil (RGO) authentication. Chemical compositions of red ginger oil were determined by Gas Chromatography-Mass Spectrometry (GC-MS). The Fourier Transform Infrared Spectroscopy (FTIR) coupled with multivariate analysis (discriminant analysis (DA), partial least square (PLS), and principal component regression (PCR) were used to identify and quantify the adulterant. The total terpenoid compounds were 55.72%, with the percentage of monoterpenes at 34.29% and sesquiterpenes at 21.43%. E-Citral (19.01%), Z-Citral (14.82%), Geranyl Acetate (11.90%), Geraniol (9.56%), 1,8-Cineole (5.84%), and camphene (4.92%) were identified as the main constituents. The best PLS model for quantifying the level of palm oil in RGO was at the wavenumber 3100–2700 cm–1, while the region of 3100 – 2700 and 1850 – 650 cm–1 was suitable for detection of soybean adulterants. FTIR spectroscopy coupled with chemometrics produced accurate and fast authentication of red ginger oil without the used solvent. Then, the GC-MS technique could identify the chemical constituents present in the red ginger oil.
    More
    Translated text
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined