色度计
人工智能
色差
模式识别(心理学)
多层感知器
颜色分析
感知器
数学
主成分分析
计算机科学
人工神经网络
计算机视觉
光学
物理
GSM演进的增强数据速率
作者
Yao Zou,Wanjun Ma,Qian Tang,Wei Xu,Liqiang Tan,Deyang Han,Yun Tian,Yue Yuan
摘要
Abstract Instrumental examination of Sichuan Dark Tea (SDT) quality instead of human panel sensory evaluation is important for quality control. This study attempted to create a high‐precision method to rapidly and accurately evaluate SDT color quality. Colorimeter combined with multi‐layer perceptron (MLP) was utilized to extract CIELAB color parameters of dried tea, liquor, and infused tea, respectively, and established the prediction models of color attributes scores with the optimal color parameters selected by a principal component analysis (PCA). MLP models established could accurately predict color total scores of SDT, the glossiness of dried tea, and chroma of infused tea ( R p = 0.889–0.989; RMSEP = 0.393–0.631). Besides, models based on tea pigments could accurately predict infused tea color total scores ( R p = 0.920, RMSEP = 0.531). Parameter a * was significantly correlated with almost all of the color evaluation factors of SDT and seemed to be the characteristic color parameter. The color quality of Sichuan Dark Tea can be excellently estimated by the method utilizing colorimeter coupled with MLP. Practical applications To meet the requirement of dark tea production and the costumer's expectation, this study attempted to create a high‐precision method quickly and accurately evaluating color quality of SDT. Usually, in the massive production, tea color evaluated by eyesight is time‐consuming, subjective, and has poor accuracy due to visual fatigue of human. In this work, we optimized color parameters with PCA to improve the performance of MLP models established. The results can provide a theoretical basis for the evaluation of tea color quality by instrument, and make SDT quality control more convenient, accurate, and time‐saving.
科研通智能强力驱动
Strongly Powered by AbleSci AI