基于声振信号的香梨内部早期褐变判别

Detection of early browning in pears using vibro-acoustic signals

香梨内部发生的褐变病害对香梨品质有严重影响,迫切需要对香梨内部早期褐变实现快速准确判别以减少贮藏期损失并提高商品率。该研究基于压电梁式传感器搭建声振无损检测装置系统,从香梨声振响应信号中提取了11个时域特征参数和7个频域特征参数,分别组成时域特征向量、频域特征向量和组合域特征向量(时域和频域参数组合),然后利用补偿距离评估技术评估各特征参数对香梨内部褐变的敏感性,输入敏感性较大的特征参数训练香梨内部褐变K-近邻域(K-nearest neighbor,KNN)判别模型。通过对模型判别结果的混淆矩阵分析,采用3个时域参数(波形因子、峭度、方根幅值)和1个频域参数(频率方差)构建香梨内部早期褐变KNN模型(近邻数K=5)用于判别早期褐变香梨,准确率和F1值分别为91.84%和92.59%;对已识别的褐变香梨,采用2个时域参数(波形因子、裕度因子)和1个频域参数(均方频率)构建香梨内部轻度褐变KNN模型(K=7)进一步判别其中的轻度褐变香梨,准确率和F1值分别为81.82%和83.33%。研究结果可为今后声振法香梨内部褐变实时在线检测和自动化分级技术研发提供参考。

Core browning in Korla pear(Pyrus bretschneideri Rehd.)occurs generally during storage at room temperature.The browning disorder can significantly reduce the shelf stability,and thereby to cause considerable economic losses.Moreover,the browning part of pears can be taken in the juicing process,leading to the juice toxins over the safety limit for drinking.Therefore,a reliable and rapid method has been urgently demanding to nondestructively detect internal disorder for high-quality fruits.In this study,an acoustic system using the piezoelectric beam transducers was developed for nondestructively detecting disorder of pears with different internal browning.The obtained response signals were analyzed to extract 11 statistical features in time domain,and seven statistical features in frequency domain.Accordingly,three modes of feature vectors were formed in the time domain,frequency domain,and time-frequency domain.A Compensation Distance Evaluation Technology(CDET)was also used to evaluate the sensitivities of each parameter in feature vectors.Normally,the larger values of sensitivity evaluation factor can imply the higher sensitivities to the browning classes of pears.Based on sensitivity evaluation factors values in the healthy and browning of pears,the descending order of 11 time-domain features were the mean(T1),shape factor(T11),kurtosis(T6),square root amplitude value(T5),clearance indicator(T8),peak(T3),impulse factor(T9),root mean square(T2),short-time energy(T4),kurtosis factor(T7),and crest factor(T10).The sensitivities of seven frequency-domain features were also ranked in order,the variance(F2),mean square(F6),root mean square(F7),standard deviation(F3),mean(F1),kurtosis(F4),and gravity(F5).Combining two types of features,the descending order of all the features was as follows:T11,F2,T6,T5,F7,F6,T1,F3,F4,F1,F5,T8,T3,T9,T2,T4,T10,and T7.In the slight browning and moderate browning of pears,the sensitivities of time-domain features can be ranked in the descending order of T7,T11,T8,T3,T9,T10,T6,T2,T5,T4,and T1.The obtained order for the frequency-domain features was F4,F3,F7,F6,F1,F5,and F2.In the combined time-frequency features,the order was as follows:T11,T8,F6,T3,F7,F3,T7,F4,F1,F2,T9,F5,T10,T6,T2,T5,T4,and T1.Subsequently,a K-nearest neighbor(KNN)algorithm was utilized to train the classifier using the first n sensitive features as the inputted data.Therefore,a browning discrimination model was constructed for the moderate disorder,whereas,a slight browning discrimination model for the mild disorder.Both models performed the best,when combining the features from the time-domain and frequency-domain.In the browning discrimination model,a high overall accuracy of 91.84%was obtained with the specific feature vectors,including three time-domain features(T11,T6 and T5),and one frequency-domain feature(F2).In slight browning discrimination model,the slight browning of pears can be further identified with an accuracy of 81.82%.In the case of slight browning,the specific feature vectors were adopted,including two time-domain features(T11 and T8),and one frequency-domain feature(F6).In the confusion matrix analysis,the high values of F1 indicated that two discrimination models can be used to achieve the high robustness and performance,and further to be generalized for the identification of fruits.These findings can provide a sound theoretical basis and strategy for the industrial real-time in-line detection,and automatic grading in the internal browning disorder of pears.