基于介电特性及ANN的油桃糖度无损检测方法  

Non-destructively detecting sugar content of nectarines based on dielectric properties and ANN

作　　者：商亮[1] 谷静思[2] 郭文川[1] 

机构地区：[1]西北农林科技大学机械与电子工程学院,杨凌712100 [2]西北农林科技大学食品科学与工程学院,杨凌712100

出　　处：《农业工程学报》2013年第17期257-264,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(31171720);中央高校基本科研业务费专项资金项目(ZD2012017)

摘　　要：为了探索利用果品的介电特性无损预测内部品质的可能性,该文采用矢量网络分析仪测量了10d贮藏期间,300个99-1油桃在20～4500MHz频率下的相对介电常数和介电损耗因子,以糖度作为内部品质指标,基于x-y共生距离的样本划分法确定了含243个样本的校正集和57个样本的预测集;建立了预测油桃糖度的偏最小二乘、支持向量机及极限学习机模型,并综合比较了采用全频谱以及利用无信息变量消除法和连续投影算法分别提取的特征变量作为各模型输入变量时,对各模型拟合效果的影响。结果表明:连续投影算法结合极限学习机预测效果最好(预测相关系数为0.887,预测均方根误差为0.782);与全频谱和无信息变量消除法相比,连续投影算法在简化模型及提高模型稳定性方面性能良好。该研究结果表明,基于油桃介电特性无损检测糖度是可行的,可为应用介电特性无损检测果品的内部品质指标提供了一种新方法。Sugar content is the main attribute of a fruit’s internal qualities. It is usually determined by soluble solids content （SSC）. Since the traditional method used in detecting SSC with an Abbe-refractor is destructive, it is not suitable for on-line detection. To find a method to measure SSC nondestructively and quickly, the dielectric properties （relative dielectric constant and dielectric loss factor） of intact postharvest 99-1 nectarines at 1 day intervals during 10 days’ storage were measured with a vector network analyzer （E5071C） and an open-ended coaxial-line probe （85070E） at 101 discrete frequencies over the frequency range of 20~4500 MHz. The sugar content of the fruit juice of each sample was measured with a digital refractometer. Altogether, 300 nectarines were used in the study. One-hundred and one relative dielectric constant values and 101 dielectric loss factor values at 101 discrete frequencies for each sample were used as variables to build models. A significance analysis was done to investigate whether frequencies and storage time had a significant influence on the values of permittivities. Sample set partitioning based on joint x-y distances （SPXY） was used to subset partitioning. Uninformative variables elimination （UVE） and successive projection algorithm （SPA） were applied to extract the characteristic variables from the original dielectric spectra of dielectric constant and dielectric loss factor. The modeling methods, such as partial least squares （PLS） and artificial neural network technology, such as support vector regression （SVR） and extreme learning machine （ELM） were applied to establish models for predicting SSC from permittivities. The experimental results showed that as the frequency increased, the relative dielectric constant of nectarines decreased, but the dielectric loss factor changed from decreasing to increasing. The analysis of variance indicated that storage time and frequency had a significant influence on dielectric properties.

关 键 词：无损检测 介电特性 模型 油桃 可溶性固形物含量 人工神经网络 支持向量机 极限学习机 

分 类 号：S183[农业科学—农业基础科学]