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

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 51 200 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 R_P², 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.