Detection of adulteration in Chinese honey using NIR and ATR-FTIR spectral data fusion
| Autor: | Han Song, Xinhao Yang, Hongxia Zhao, Liu Guo, Furong Huang, Liqun Li, Peiwen Guang, Maoxun Yang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
China
Support Vector Machine Food Contamination 02 engineering and technology 010402 general chemistry 01 natural sciences Sensitivity and Specificity Analytical Chemistry Spectroscopy Fourier Transform Infrared Fourier transform infrared spectroscopy Least-Squares Analysis Instrumentation Spectroscopy Second derivative Fusion Principal Component Analysis Spectroscopy Near-Infrared business.industry Chemistry Particle swarm optimization Reproducibility of Results Pattern recognition Honey 021001 nanoscience & nanotechnology Sensor fusion Atomic and Molecular Physics and Optics 0104 chemical sciences Support vector machine Glucose Spectrophotometry Kernel (statistics) Hyperparameter optimization Artificial intelligence 0210 nano-technology business Algorithms Food Analysis |
| Zdroj: | Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy. 235 |
| ISSN: | 1873-3557 |
| Popis: | The aim of this study is to find a fast, accurate, and effective method for the detection of adulteration in honey circulating in the market. Near-infrared spectroscopy and mid-infrared spectroscopy data on natural honey and syrup-adulterated honey were integrated in the experiment. A method for identifying natural honey and syrup-adulterated honey was established by incorporating these data into a Support Vector Machine (SVM). In this experiment, 112 natural pure honey samples of 20 common honey types from 10 provinces in China were collected, and 112 adulterated honey samples with different percentages of syrup (10, 20, 30, 40, 50, and 60%) were prepared using six types of syrup commonly used in industry. The total number of samples was 224. The near- and mid-infrared spectral data were obtained for all samples. The raw spectra were pre-processed by First Derivative (FD) transform, Second Derivative (SD) transform, Multiple Scattering Correction (MSC), and Standard Normal Variate Transformation (SNVT). The above-corrected data underwent low-level and intermediate-level data fusion. In the last step, Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed as the optimization algorithms to find the optimal penalty parameter c and the optimal kernel parameter g for the SVM, and to establish the best SVM-based detection model for natural honey and syrup-adulterated honey. The results reveal that, compared to low-level data fusion, intermediate-level data fusion significantly improves the detection model. With the latter, the accuracy, sensitivity and specificity of the optimal SVM model all reach 100%, which makes it ideal for the identification of natural honey and syrup-adulterated honey. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect