دانلود مقاله انگلیسی رایگان:ارزیابی کیفی چای سیاه کیمون با ترکیب داده های بدست آمده از طیف سنجی بازتابنده مادون قرمز نزدیک و حسگرهای بینایی ماشین - 2021
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  • Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors
    Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors

    سال انتشار:

    2021


    عنوان انگلیسی مقاله:

    Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors


    ترجمه فارسی عنوان مقاله:

    ارزیابی کیفی چای سیاه کیمون با ترکیب داده های بدست آمده از طیف سنجی بازتابنده مادون قرمز نزدیک و حسگرهای بینایی ماشین


    منبع:

    Sciencedirect - Elsevier - Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 252 (2021) 119522: doi:10:1016/j:saa:2021:119522


    نویسنده:

    Yan Song


    چکیده انگلیسی:

    Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a singlesensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades.
    Keywords: Keemun black tea | Near-infrared reflectance spectroscopy | Computer vision system | Feature fusion | Convolutional neural network | Quality identification


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 2040 کیلوبایت

    قیمت: رایگان


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