Comparison of acoustic sensor systems for quality analysis of asparagus using scanning laser vibrometry for visualization

Acoustic vibration technology: Toward a promising fruit quality detection method

“…Moreover, Foerster et al. (2013) first transformed the time domain signal into a frequency domain signal by an fast Fourier transform (FFT) and divided the resulting frequency spectrum into several sections along the frequency axis to maintain only one resonance peak in each section. Then, the new time domain signals were acquired by the inverse FFT from each section and fitted by a damped oscillation function to extract a set of damped oscillation parameters to describe the dynamic behaviors (i.e., the magnitude, angular frequency, and number of oscillations) of asparagus (Figure 9e).…”

“…Therefore, some researchers evaluated fruit quality by calculating propagation velocity of surface waves. Muramatsu et al (1996) divided half of the circumference of the fruit by the time difference between the excitation signal emitted by a speaker and the response signal received by a microphone to F I G U R E 9 Acoustic vibration parameters in the time domain: (a) sound propagation velocity detected by a "Firm Tester" (Sugiyama et al, 2005); (b) impact parameters of the first half-wave (Mireei et al, 2015); (c) impact parameters of the self-collision response signal (Vursavusa et al, 2017); (d) waveform symmetry and decay index of the attenuation waveform; and (e) damped oscillation parameters (Foerster et al, 2013) calculate the propagation velocity. The mature fruits presented lower propagation velocity than immature ones.…”

“…The values of A max /A min decreased with the increase in watermelon ripeness, and the decay indices in the ripe and over-ripe watermelon groups were much higher than the unripe group. Moreover, Foerster et al (2013) first transformed the time domain signal into a frequency domain signal by an fast Fourier transform (FFT) and divided the resulting frequency spectrum into several sections along the frequency axis to maintain only one resonance peak in each section. Then, the new time domain signals were acquired by the inverse FFT from each section and fitted by a damped oscillation function to extract a set of damped oscillation parameters to describe the dynamic behaviors (i.e., the magnitude, angular frequency, and number of oscillations) of asparagus TA B L E 4 Acoustic vibration parameters extracted from the response signal of fruit in the time domain for quality assessment (Figure 9e).…”

“…I G U R E 9 Acoustic vibration parameters in the time domain: (a) sound propagation velocity detected by a "Firm Tester"(Sugiyama et al, 2005); (b) impact parameters of the first half-wave(Mireei et al, 2015); (c) impact parameters of the self-collision response signal(Vursavusa et al, 2017); (d) waveform symmetry and decay index of the attenuation waveform; and (e) damped oscillation parameters(Foerster et al, 2013) …”

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

“…Through computer analysis of these sound signals, the crispness and maturity of fruits and vegetables and other fresh foods are evaluated to achieve online control of fresh products. Foerster et al [ 83 ] used acoustic sensors to perform computerized resonance analysis, conduct internal quality inspection of asparagus, and check the cavitation phenomenon of asparagus to achieve excellent quality automatic classification.…”