| 基于力声信号锯齿化多特征融合的香梨脆度评价 |
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| 引用本文： | 周婷,莫小明,查志华,张金阁,吴杰.基于力声信号锯齿化多特征融合的香梨脆度评价[J].农业工程学报,2022,38(13):305-312. |
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| 作者姓名： | 周婷 莫小明 查志华 张金阁 吴杰 |
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| 作者单位： | 1. 石河子大学机械电气工程学院，石河子 832000;;1. 石河子大学机械电气工程学院，石河子 832000; 2. 绿洲特色经济作物生产机械化教育部工程研究中心，石河子 832003; |
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| 基金项目： | 国家自然科学基金地区科学基金项目（31860466） |
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| 摘 要： | 为了实现香梨脆度更符合感官评价的检测，该研究采用质构仪与麦克风同步采集香梨果肉破裂时的力声响应信号，然后采用峰值法、傅里叶功率谱法和表观分形维数法对力声响应曲线的锯齿化特征进行度量，分别提取了29个力学锯齿度参数和14个声学锯齿度参数用于香梨果肉感官脆度预测。研究结果表明，在43个锯齿度特征参数中，35个特征参数与香梨果肉感官脆度存在显著相关性（P<0.05），可反映香梨脆度变化。通过主成分分析提取了前8个主成分解释原变量85.58%的信息，以这8个主成分构建预测感官脆度的多元线性回归模型，模型校正集相关系数为0.862，均方根误差为0.588，表明该模型具有较高的预测精度和稳定性，可为香梨脆度的仪器准确检测提供研究基础。
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| 关 键 词： | 脆度 果实 仪器检测 力声信号 主成分分析 |
| 收稿时间： | 2022/4/3 0:00:00 |
| 修稿时间： | 2022/6/13 0:00:00 |
Evaluation of crispness fusing features extracted from jagged curves of mechanical-acoustic signals of Korla pears |
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| Zhou Ting,Mo Xiaoming,Zha Zhihu,Zhang Jinge,Wu Jie.Evaluation of crispness fusing features extracted from jagged curves of mechanical-acoustic signals of Korla pears[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(13):305-312. |
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| Authors: | Zhou Ting Mo Xiaoming Zha Zhihu Zhang Jinge Wu Jie |
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| Institution: | 1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China;; 1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; 2. Research Center of Agricultural Mechanization for Economic Crop in Oasis, Ministry of Education, Shihezi 832003, China; |
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| Abstract: | Crispness is one of the most important internal indexes of Korla pear. However, there are serious differences in crispness in the Korla pears in recent years, due to the production areas, different varieties, and maturity changes. Although firmness has been widely used to evaluate the internal quality of fruit, there is quite different from the crispness index. The crispness can be attributed to the comprehensive perception of the contact pressure from teeth and sound from ears. The current evaluation of fruit crispness depends mainly on the sensory panel, where the sensory evaluators are easily tired and disturbed by the external environment during the evaluation process. Therefore, it is necessary for an objective evaluation of the crispness of the pear. In this study, a systematic evaluation was made for the crispness fusing features extracted from the mechanical-acoustic response of pears under rupture. Specifically, 50 pears were taken every 12 days and then refrigerated at -2~0°C. At the same time, the instrument measurement and sensory evaluation were performed on all samples after 60 days. The texture analyzer and microphone were combined to collect the mechanical-acoustic signals during the rupture of pear flesh. The jaggedness of the signal curves was extracted from the multiple features using the peak method, Fourier power spectrum, and apparent fractal dimensions. Among them, 29 jaggedness parameters were extracted from the force signals, and 14 jaggedness parameters from the acoustic signals. Firstly, Pearson''s correlation analysis was used to determine the relationship between 43 jaggedness parameters and sensory crispness of Korla pear flesh. The results showed that there was no significant difference in the sensory crispness in the five jaggedness parameters of the force curves and three jaggedness parameters of the acoustic curves. Specifically, there was a strong correlation (|R|>0.8) between the sensory crispness and low strain stiffness MLS, Young''s modulus MY, Richardson of the mechanical signature''s apparent fractal dimension MDR, Kolmogorov of the mechanical signature''s apparent fractal dimension MDKL, which were 0.876, 0.882, 0.859, and 0.886, respectively. The remaining 35 parameters were used as the evaluation indexes of pear flesh crispness. Then, the selection of jaggedness parameters was subjected to the autocorrelation analysis. Specifically, most of the parameters presented different degrees of correlation, where 34 parameters were strongly correlated. Subsequently, the principal component analysis was conducted to reduce the serious information redundancy. The first eight principal components were then determined to represent the jaggedness parameter information of force acoustic response signals, representing 85.58% of the total variability, according to the variance contribution rates of principal components analysis. Finally, the multiple linear regression analysis was carried out on the selected principal components and sensory crispness. A prediction model was constructed for the pear flesh crispness. All the coefficients of the model passed the T test (P<0.05). The correlation coefficient for the calibration set (Rc2) and validation set (Rp2) of the model were 0.862 and 0.889, respectively. The root mean square error of the calibration set (RMSEC) and validation set (RMSEP) were 0.588 and 0.551, respectively. Consequently, the improved model was stable and reliable to detect the crispness of the pear flesh. Therefore, the crispness model of pears can be expected to extract the multiple characteristic parameters in the process of picking, storing, and selling. This finding can provide the theoretical research basis for the instrument detection of the crispness of fruit. |
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| Keywords: | crispness fruit instrument test mechanical-acoustic signals PCA |
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