多光谱图像
阿达布思
人工智能
Boosting(机器学习)
模式识别(心理学)
偏最小二乘回归
校准
计算机科学
人工神经网络
近红外光谱
均方误差
数学
支持向量机
机器学习
统计
光学
物理
作者
Qi-Ping Huang,Quansheng Chen,Huanhuan Li,Gengping Huang,Qin Ouyang,Jing Zhao
标识
DOI:10.1016/j.jfoodeng.2015.01.006
摘要
Total volatile basic nitrogen (TVB-N) content is one of core indicators for evaluating pork’s freshness. This paper attempted to non-destructively sensing TVB-N content in pork meat using near infrared (NIR) multispectral imaging technique (MSI) with multivariate calibration. First, a MSI system with 3 characteristic wavebands (i.e. 1280 nm, 1440 nm and 1660 nm) was developed for data acquisition. Then, gray level co-occurrence matrix (GLCM) was used for characteristic extraction from multispectral image data. Next, we proposed a novel algorithm for modeling-back propagation artificial neural network (BP-ANN) and adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, and we compared it with two commonly used algorithms. Experimental results showed that the BP-AdaBoost algorithm is superior to others with the root mean square error of prediction (RMSEP) = 6.9439 mg/100 g and the correlation coefficient (R) = 0.8325 in the prediction set. This work sufficiently demonstrated that the MSI technique has a high potential in non-destructively sensing pork freshness, and the nonlinear BP-AdaBoost algorithm has a strong performance in solution to a complex data processing.
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