黄瓜水分和硬度高光谱特征波长选择与预测模型构建

为实现高光谱对黄瓜新鲜度的快速、准确检测,以硬度和失水率作为品质指标,采用高光谱成像技术对同一批次不同贮藏日期的黄瓜进行检测。采用Savitzky-Golar法、多元散射校正、标准正态变量变换3种方法对黄瓜高光谱数据进行预处理,并对预处理结果进行对比,确定Savitzky-Golar预处理方法;运用竞争性自适应重加权算法、偏最小二乘、连续投影算法对高光谱特征波长进行选择,针对硬度指标分别选取了25,13,20个特征波长,针对失水率指标,分别选取了20,16,20个特征波长;运用BP神经网络构建黄瓜硬度和失水率预测模型。结果表明,基于连续投影算法所筛选出的特征波长光谱信息所建立的BP模型判别效果最佳,其对硬度判别的训练集准确率和测试集准确率分别为95.24%,91.67%;对失水率判别的训练集准确率和测试集准确率分别为97.78%,95.00%。

Selection of hyperspectral characteristic wavelength and construction of prediction model for cucumber hardness and moisture

In order to achieve fast and accurate detection of cucumber freshness by hyperspectral technology,taking the hardness and rate of water loss as the quality index,the hyperspectral imaging technology was used to test the cucumber with different storage dates in the same batch.Firstly,Savitzky-Golar method,multivariate scattering correction(MSC)and standard normal variable transformation(SNV)were used to preprocess the collected hyperspectral data of cucumber,and the pretreatment results were compared to determine that the Savitzky-Golar method was more effective.Then,competitive adaptive reweighted sampling(CARS),partial least squares(PLS)and successive projections algorithm(SPA)were used to select the hyperspectral characteristic wavelengths,and 25,13 and 20 characteristic wavelengths were selected for the hardness index,respectively.20,16,and 20 characteristic wavelengths were selected for the index of water loss rate,respectively.Finally,the BP neural network was used to distinguish the cucumber hardness and water loss rate based of the characteristic wavelengths.The results showed that the BP neural network combined with SPA method had the best discrimination effects,and the accuracy of the training set and the test set for hardness discrimination were 95.24%and 91.67%,respectively.The accuracy of training set and test set for rate of water loss were 97.78%and 95.00%,respectively.