Feasibility of non‐destructive evaluation for apple crispness based on portable acoustic signal. (8th November 2020)

• Record Type: Journal Article
• Authors: Liu, Yang; Cai, Mingjin; Zhang, Wangyou; Feng, Wanling; Sun, Xingyuan; Zhang, Yang; Zhou, Hongli
• Is Part Of: International journal of food science & technology. Volume 56:Number 5(2021)
• Page Start: 2375
• Page End: 2383
• Publication Date: 2020-11-08
• Subjects: Acoustic signal -- apple -- crispness -- non‐destructive measurement; Food industry and trade -- Periodicals; 664
• DOI: 10.1111/ijfs.14861
• Languages: English
• ISSNs: 0950-5423

Abstract

Summary: In this paper, apple crispness was evaluated by sensory evaluation and compared with non‐destructive measurements of portable acoustic signal to discuss the feasibility of non‐destructive evaluation for apple crispness based on portable acoustic signal. Acoustic eigenvalues from the acoustic signal were processed by time domain and Hilbert–Huang transform (HHT), followed by analysing the correlations with apple crispness that had been evaluated via sensory evaluation. Multiple linear regression (MLR) and artificial neural network (ANN) were applied to predict apple crispness. The results proved that crispness correlates significantly ( P < 0.01) with four acoustic eigenvalues, including waveform index, sound intensity, energy of low frequency and energy of high frequency. The average relative error of apple crispness predicted by ANN was 1.42 ± 1.9%, remarkably lower ( P < 0.01) that of MLR (6.79 ± 5.64%), implying that the model predicted by ANN is more accurate than that of MLR. Abstract : The framework about non‐destructive evaluation of apple crispness based on portable acoustic signal. With the aim of exploring non‐destructive and rapid evaluation of apple crispness based on portable acoustic signal, the portable acoustic signal was collected; meanwhile, apple crispness was evaluated via sensory evaluation. The combination application of acoustic signal processing method, including time domain and Hilbert–Huang transform (HHT), was used to analyse the correlations with apple crispness. The correlations between apple crispness and the acoustic eigenvalues were analysed by Pearson theory. Finally, the apple crispness prediction models were established through multiple linear regression (MLR) and artificial neural network (ANN). This method has good applicability and portability in non‐destructive evaluation of crispness of agricultural products; in addition, it would be further explored as an portable APP of cell phone for consumers or retailers.