Maturity assessment of watermelon by acoustic method

Choe, Ungho; Kang, Hyonchol; Ham, Jin Kwan; Ri, Kyonghui; Choe, Ungyong

doi:10.1016/j.scienta.2021.110735

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…This method had a better accuracy in detecting the hardness of large-sized fruits. Choe et al [41] studied the transmission velocity of sound in the watermelon sample based on the piezoelectric transducers (used as a transmitter and a receiver) to evaluate the maturity of watermelon, in which sugar content was used as an indicator of watermelon maturity. The results indicated that the determination coefficients of sound velocity and sugar content of samples used in the experiment could reach 0.987 and 0.975, respectively.…”

Section: Acoustic Techniquementioning

confidence: 99%

Research Progress on Non-Destructive Detection of Internal Quality of Fruits with Large Size and Thick Peel: A Review

Wang,

Mei,

2023

Agriculture

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Postharvest quality detection and grading are important factors to improve the commercial value of fruit. The natural biological characteristics bring challenges to the rapid and non-destructive evaluation of the internal quality of fruits with large size and thick peel. A lot of studies have tried to establish a feasible technique to achieve rapid, non-destructive, and accurate detection for this kind of fruit in commercial real-time grading. This article focuses on large-sized and thick-skinned fruits and comprehensively reviews the latest technical progress in the non-destructive detection of internal quality. It can provide a valuable reference for the development of postharvest processing technology for this kind of fruit.

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Section: Acoustic Techniquementioning

confidence: 99%

Research Progress on Non-Destructive Detection of Internal Quality of Fruits with Large Size and Thick Peel: A Review

Wang,

Mei,

2023

Agriculture

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“…In terms of acoustical analysis of maturity of agricultural products, Khoshnam et al (2016) used acoustic impulse response method to analyze five kinds of melons with different maturity, and found that there was a good correlation between the resonance frequency and maturity of melons, and the resonance frequency decreased with the increase of maturity. Ungho et al (2022) developed a device using piezoelectric ceramics to measure sound velocity to detect watermelon ripeness. The device was used to measure sound velocity in different parts of watermelon and sugar content in the middle of watermelon.…”

Section: Introductionmentioning

confidence: 99%

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

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Cracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time‐domain features, 13 frequency‐domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray‐scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and of the classification model. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.Practical applicationsCracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identification combine with feature fusion. The features of time domain, frequency domain and Mel spectrogram were extracted from the effective sound signal, and the features were fused. The influence of three feature selection methods, three models and three optimization algorithms on walnut sound signal recognition was analyzed and compared. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, and the method proposed in this study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.

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“…Over the past few years, many nondestructive techniques have been developed for firmness assessment, such as acoustic vibration [6] , spectroscopy [7] , and ultrasonic [8] . Among them, the acoustic vibration method is one of the most frequently used methods to nondestructively evaluate the firmness of the entire fruit based on vibration parameters [9,10] . Based on existing studies, a series of vibration parameters were extracted to evaluate the fruit firmness, such as f 2 m, f 2 m 2/3 , and f 2 m 2/3 ρ 1/3 [11][12][13] , where f is the resonance frequency, m and ρ are the mass and the density of fruit.…”

Section: Introduction mentioning

confidence: 99%

Detection of apple firmness with a novel loudspeaker-based excitation device

Ding¹,

Wang²,

Feng³

et al. 2022

International Journal of Agricultural and Biological Engineering

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Firmness is one of the important indices to evaluate the internal quality of fruit. In this study, a noncontact loudspeaker-based detection system was developed to evaluate apple firmness. The structural parameters of the excitation device were modified in the single-factor experiments, and the best combination of structural parameters was that the inner diameter of the gasket was 40 mm; the distance between fruit surface and loudspeaker was 95 mm. Besides, the proper posture style was that the apple was placed with its stem upward. After the modification of the Laser Doppler Vibrometer (LDV) method, the vibration response signals of 48 apples were measured to establish the firmness prediction model. The results showed that the better prediction performance of stiffness was obtained in multiple models. The Back Propagation Neural Network (BPNN) model had the best prediction performance by using parameters of elasticity index (EI), the peak value at the second resonance frequency f 2 (A 2 ), and peak area S, with a correlation coefficient of prediction (r p ) of 0.914; root mean square error of prediction (RMSEP) of 0.491 N/mm. Therefore, the proposed detection system is feasible to nondestructively detect apple firmness, which has the potential to be applied in online detection.

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Maturity assessment of watermelon by acoustic method

Cited by 8 publications

References 12 publications

Research Progress on Non-Destructive Detection of Internal Quality of Fruits with Large Size and Thick Peel: A Review

Research Progress on Non-Destructive Detection of Internal Quality of Fruits with Large Size and Thick Peel: A Review

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Detection of apple firmness with a novel loudspeaker-based excitation device

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