WANG Qiao-hua1, 2, ZHOU Kai1, WU Lan-lan1, WANG Cai-yun1
1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China 2. National Research and Development Center for Egg Processing, Huazhong Agricultural University, Wuhan 430070, China
摘要： 借助高光谱成像仪采集贮期白壳鸡蛋的透射高光谱数据，对比测量常规表征新鲜度的哈夫单位值，用Matrix Laboratory (MATLAB)和Statistical Analysis System (SAS)等软件，同时结合化学计量法对样品鸡蛋的高光谱数据进行分析处理，建立了基于高光谱技术的鸡蛋新鲜度预测模型。选用高光谱500～1 000 nm的波段作为敏感波段进行研究，用马氏距离剔除鸡蛋异常样本数据，并对鸡蛋高光谱数据进行了微分校正，通过比较发现高光谱二阶微分与鸡蛋哈夫单位值之间的线性度高，因此选用高光谱二阶微分数据来进一步研究，并对其进行了小波去噪、光滑处理及标准化处理。选用近年新提出来的competitive adaptive reweighted sampling (CARS)变量选取法对高光谱进行降维，提取出32个特征参数，建立了白壳蛋基于全波段的偏最小二乘法(partial least square, PLS)预测模型和基于特征参数的多元回归模型，验证集的相关系数分别为0.88，0.93，均方误差分别为7.565，6.44。用验证集的蛋对基于高光谱二阶微分全波段的偏最小二乘法预测模型、基于特征参数的多元回归模型分别进行验证，两个模型判别白壳蛋新鲜和不新鲜的最高准确率达100%，88%。
关键词：鸡蛋;新鲜度;高光谱;偏最小二乘法;竞争性自适应重加权算法
Abstract：This research collected the transmission hyper-spectral data of eggs with hyper-spectral imager. Haugh unit value was used as freshness norm. With the help of MATLAB and SAS software combined with stechiometry method, the hyper-spectral data of sample eggs was analyzed and processed. The prediction model of egg freshness was established based on hyper-spectral technology. The research chose the band range from 500 to 1 000 nm as sensitive band. The hyper-spectral data of abnormal samples were removed by using mahalanobis distance. Differential correction was done on hyper-spectral data. After the comparison, there was a high linearity between the second-order differential data of hyper-spectra and haugh unit value. Therefore, this paper conducted a further research on the second-order differential data of hyper-spectra. And it was treated with wavelet denoising, smoothing and standardizing. This paper chose the newly proposed CARS variable selection method to do dimensionality reduction on hyper-spectral data. And thirty-two characteristic parameters were extracted. They were used to establish partial least square prediction model based on all band and multiple regression model based on characteristic parameters on white shell eggs. The correlation coefficients of white shell eggs were 0.88 and 0.93 respectively, and the corresponding mean square errors being 7.565 and 6.44. Inspections were conducted on PLS prediction model based on all band hyper-spectral second-order differential and multiple regression model based on characteristic parameters by using eggs of validation set. The accuracy rates of these two models to discriminate white shell eggs’ freshness and non-freshness were 100% and 88% respectively.
Key words：Egg;Freshness;Hyper-spectra;Partial least square;CARS
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