Abstract:One of the most pressing challenges in people's life is food safety. While many people prefer to purchase meals online since the dawn of the Internet era, regulating food safety online confronts numerous obstacles. A set of food safety evaluation data on violations and dangers was generated by analyzing feedback data from third-party operating systems. A distributed long-term and short-term memory network model was proposed to estimate trader risk values, and a quick warning system for or network attractors wa… Show more
“…There is an increasing demand for alternative analytical techniques in the search for fast, accurate and reliable quality control systems for mass production to prevent fraud and thus ensure food safety (Cagri-Mehmetoglu, 2018). Most food and agricultural products research based on HSI adopts hyperspectral reflectance imaging technology (Hossen et al, 2021), which measures the reflectance from visible light area to short wave infrared area (Pingzhen et al, 2022). Many studies have been applied in honey adulteration.…”
As a natural agricultural product, honey is favored by consumers, and its variety and adulteration have a huge impact on the quality. Acacia honey, red jujube honey and rape honey were used as experimental objects, and their spectral reflectance curves were obtained through a near-infrared spectral image acquisition system. Spectral features were extracted from the preprocessed spectral reflectance curves, and a honey variety classification model based on near-infrared spectral features was established by machine learning. After statistical analysis, Principal Component Analysis Support Vector Machine after processing data through Successive Projections Algorithm (SPA-SVM) is the optimal classification model for three varieties of acacia honey, red jujube honey and rape honey, and the correct rate of honey variety classification reaches 95.83%. The spectral reflectance curve was used to establish a honey adulteration identification model based on the partial least squares-discriiminate analysis (PLS-DA), and the classification accuracy was 97.92% in the test set.
“…There is an increasing demand for alternative analytical techniques in the search for fast, accurate and reliable quality control systems for mass production to prevent fraud and thus ensure food safety (Cagri-Mehmetoglu, 2018). Most food and agricultural products research based on HSI adopts hyperspectral reflectance imaging technology (Hossen et al, 2021), which measures the reflectance from visible light area to short wave infrared area (Pingzhen et al, 2022). Many studies have been applied in honey adulteration.…”
As a natural agricultural product, honey is favored by consumers, and its variety and adulteration have a huge impact on the quality. Acacia honey, red jujube honey and rape honey were used as experimental objects, and their spectral reflectance curves were obtained through a near-infrared spectral image acquisition system. Spectral features were extracted from the preprocessed spectral reflectance curves, and a honey variety classification model based on near-infrared spectral features was established by machine learning. After statistical analysis, Principal Component Analysis Support Vector Machine after processing data through Successive Projections Algorithm (SPA-SVM) is the optimal classification model for three varieties of acacia honey, red jujube honey and rape honey, and the correct rate of honey variety classification reaches 95.83%. The spectral reflectance curve was used to establish a honey adulteration identification model based on the partial least squares-discriiminate analysis (PLS-DA), and the classification accuracy was 97.92% in the test set.
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