基于力声信息融合感知的香梨果肉脆度评价

Instrument detection on Korla pear flesh crispness using mechanical-acoustic information fusion

为实现香梨脆度更符合感官评价的仪器检测,该研究采用万能材料试验机与麦克风以51 200 Hz高频同步采集香梨果肉破裂时的力声信号,在力声曲线锯齿度分析区间进行数据层信息融合,并分别转换为格拉姆角和场图与角差场图、对称极坐标图、马尔可夫变迁场图及递归图,通过ResNet50网络挖掘与感官脆度评分相关的图像深度特征,经特征降维的主成分输入到超参数优化后的K近邻、极限学习机、随机森林及支持向量回归模型,实现香梨果肉脆度的定量预测。研究结果表明,马尔可夫变迁场图像全连接层特征降维后的主成分与感官脆度评分的相关系数绝对均值最大,为0.64,最适宜定量表征香梨果肉感官脆度评分差异,通过主成分分析提取了前10个主成分解释图像高维全连接层特征95.75%的信息,以这10个主成分构建的ResNet50-SVR(support vector regression)模型预测集决定系数为0.96,预测集均方根误差为0.24,预测偏差比为4.88,具有较高的预测精度和稳定性,可实现对香梨果肉脆度较为准确的仪器检测,该研究结果为香梨果肉的脆度评价提供了一定的理论依据。

Korla pear is a characteristic fruit in Xinjiang.Among them,the crisp texture is one of its excellent quality parameters.However,the crispness differences of Korla pears can vary gradually in recent years,due to the origin,variety,and maturity.The current sensory evaluation by experts or trained panelists can be the most accurate to detect the crispness.However,the evaluation process has been limited to the time-consuming and labor-intensity.The accuracy of evaluation can gradually decrease over time.Alternatively,instrument detection can share fast and stable advantages over sensory evaluation.In this study,the instrument detection was performed on the Korla pear flesh crispness using mechanical-acoustic information fusion.50 pears were selected to test the crispness every 7 days during the 35-day storage periods,resulting in a total of six crispness gradient samples:crisp,relatively crisp,slightly crisp,slightly mealy,relatively mealy,and mealy.Then,the mechanical-acoustic signals during the rupture of pear flesh were synchronously collected at 51200 Hz sampling rate using a universal material testing machine combined with a microphone.Subsequently,the information on jaggedness analysis interval in mechanical-acoustic signals was fused at the data level.The correlation between mechanical-acoustic signals was utilized to align with the processing mode of the human brain's comprehensive perception of crispness.Later,mechanical-acoustic fusion signals were converted into the different images of the Gramian angular summation field(GASF),Gramian angular difference field(GADF),symmetric dot pattern(SDP),Markov transition field(MTF),and recurrence plot(RP).The deep features of different images were extracted by the ResNet50 network.8,8,9,10,and 10 principal components were obtained after PCA dimensionality reduction.Furthermore,Pearson’s correlation analysis was made to obtain the absolute mean correlation coefficients between principal components of different image features and sensory crispness scores.The results showed that the MTF image was the most suitable to quantitatively characterize the crispness scores of pear flesh with the highest absolute mean correlation coefficient.Finally,the principal components of MTF images were input into the KNN,ELM,RF,and SVR optimized by PSO.The ResNet50-SVR model achieved the best prediction accuracy and stability.The RP2,RMSEP,and RPD values were 0.96,0.24,and 4.88,respectively.Consequently,this finding can provide a strong foundation for instrument detection of the crispness of fruits and vegetables.