Freshwater Fish Freshness On-Line Detection Method Based on Near-Infrared Spectroscopy
HUANG Tao1, LI Xiao-yu1*, PENG Yi2, TAO Hai-long1, LI Peng1, XIONG Shan-bai3
1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China 2. Wuhan Agricultural Identification Promotion Station, Wuhan 430012, China 3. The Sub Centre (Wuhan) of National Technology and Research and Development of Staple Freshwater Fish Processing, Wuhan 430070, China
摘要： 新鲜度是反映鱼类品质以及可否食用的重要指标，在线检测直接关系到食品质量与安全的实施应用，因此对淡水鱼新鲜度进行在线无损检测具有重要意义。应用近红外光谱对淡水鱼新鲜度进行在线检测，试验装置采用自行搭建的淡水鱼近红外光谱在线采集装置，试验时样品在输送链上以0.5 m·s-1的速度运动，采集其近红外漫反射光谱(900～2 500 nm)，并用支持向量机(support vector machine, SVM)建立淡水鱼新鲜度在线检测模型。采用光谱理化值共生距离(sample set partitioning based on joint X-Y distance algorithm, SPXY)算法对样本集进行划分，其中校正集111条(新鲜57条，变质54条)、测试集37条(新鲜19条，变质18条)，通过对比不同的光谱预处理方法对预测结果的影响，明确了一阶导结合标准化预处理为最优光谱预处理方法，经过该方法预处理后所建模型对校正集的正确识别率为97.96%，对测试集的识别率为95.92%。为了提高模型运行速度对建模所用光谱变量进行优化，分别采用遗传算法(genetic algorithm, GA)、连续投影算法(successive projection algorithm, SPA)和竞争性自适应重加权算法(competitive adaptive reweighed sampling algorithm, CARS) 三种不同的特征变量选择方法对特征波长进行筛选，通过建模比较分析确定CARS为最优波长选择方法，以所选的10个特征波长建立淡水鱼新鲜度支持向量机检测模型，模型对校正集的正确识别率为100%，对测试集的识别率为93.88%。该研究可为近红外光谱用于淡水鱼新鲜度在线检测提供技术支持。
关键词：近红外光谱;变量选择;在线;新鲜度;淡水鱼
Abstract：In the present study, the near infrared spectrum of freshwater fish was used to detect the freshness on line, and the near infrared spectra on-line acquisition device was built to get the fish spectrum. In the process of spectrum acquisition, experiment samples move at a speed of 0.5 m·s-1, the near-infrared diffuse reflection spectrum (900~2 500 nm) could be got for the next analyzing, and SVM was used to build on-line detection model. Sample set partitioning based on joint X-Y distances algorithm (SPXY) was used to divide sample set, there were 111 samples in calibration set (57 fresh samples and 54 bad samples), and 37 samples in test set (19 fresh samples and 18 bad samples). Seven spectral preprocessing methods were utilized to preprocess the spectrum, and the influences of different methods were compared. Model results indicated that first derivative (FD) with autoscale was the best preprocessing method, the model recognition rate of calibration set was 97.96%, and the recognition rate of test set was 95.92%. In order to improve the modeling speed, it is necessary to optimize the spectra variables. Therefore genetic algorithm (GA), successive projection algorithm (SPA) and competitive adaptive reweighed sampling (CARS) were adopted to select characteristic variables respectively. Finally CARS was proved to be the optimal variable selection method, 10 characteristic wavelengths were selected to develop SVM model, recognition rate of calibration set reached 100%, and recognition rate of test set was 93.88%. The research provided technical reference for freshwater fish freshness online detection.
Key words：Near infrared spectra;Variable selection;On-line;Freshness;Freshwater fish
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