Nondestructive assessment of watermelon flesh color by laser vibrometry

Abbaszadeh, Rouzbeh; Rajabipour, Ali; Mahjoob, M. J.; Mikani, Mohaddeseh; Ahmadi, Hojjat; Delshad, Mojtaba

doi:10.1016/j.eaef.2014.09.001

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…In addition, the average relative error of ANN based on the acoustic eigenvalues extracted by Time-HHT is significantly different from that of Time-FT-HHT, which prompts that the ANN model established by acoustic eigenvalues from Time-FT-HHT is superior over that of acoustic eigenvalues from Time-HHT. All the average relative errors of ANN model are remarkably lower than those of MLR models, and this result is close to LDV (Abbaszadeh et al, 2015a(Abbaszadeh et al, , 2015b. Some acoustic eigenvalues extracted by Timedomain, FT and HHT from the portable and nondestructive acoustic signals are correlated significantly with apple firmness based on portable acoustic signals.…”

Section: Ann Of Firmnesssupporting

confidence: 65%

“…In addition, the average relative error of ANN based on the acoustic eigenvalues extracted by Time‐HHT is significantly different from that of Time‐FT‐HHT, which prompts that the ANN model established by acoustic eigenvalues from Time‐FT‐HHT is superior over that of acoustic eigenvalues from Time‐HHT. All the average relative errors of ANN model are remarkably lower than those of MLR models, and this result is close to LDV (Abbaszadeh et al ., 2015a, 2015b).…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, firmness and quality texture of kiwifruit, peach and pear were explored through LDV, and the results validated that the signals of LDV will reveal internal fruit disorders and the quality level (Muramatsu et al ., 1997, 1999; Murayama et al ., 2006; Taniwaki et al ., 2009). The vibrational response spectrum of LDV can measure watermelon firmness (Abbaszadeh et al ., 2015a, 2015b). Moreover, LDV has the potential of real‐time detection of agricultural products in‐line in agricultural industries, and is extensively proved to be fast and non‐destructive (Zhang et al ., 2014, 2015b, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of apple firmness prediction models based on non‐destructive acoustic signal

Liu

Qian-wen

Huang

et al. 2021

Int J of Food Sci Tech

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This paper aimed to explore the non‐destructive detection of apple firmness based on portable acoustic signals for consumers. The acoustic eigenvalues processed by Time‐domain, Fourier Transform (FT), and Hilbert Huang Transform (HHT) are used to analyse the correlations with apple firmness. The apple firmness prediction models are established through Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). The results proved that the acoustic eigenvalues of wave index form and total energy of 8–9 kHz correlated with apple firmness (P < 0.05), the significant acoustic eigenvalues of sound intensity, amplitude difference, max frame energy, maximum power spectral density, power spectral density characteristics and total energy of 0–1 kHz significantly correlated with apple firmness (P < 0.01). The average relative error of ANN model was significantly lower than that of MLR model (P < 0.05). Compared with Time‐FT, the acoustic eigenvalues processed by a combination of Time‐FT‐HHT possessed a superior effect on the prediction model established by ANN of apple firmness.

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Section: Ann Of Firmnesssupporting

confidence: 65%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of apple firmness prediction models based on non‐destructive acoustic signal

Liu

Qian-wen

Huang

et al. 2021

Int J of Food Sci Tech

View full text Add to dashboard Cite

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“…It has high accuracy in detecting fruit hardness and can be used to detect the maturity of large-size and thick-skinned fruits. In the study of laser Doppler vibrometer, Abbaszadeh et al [49] used the correlation between phase shift frequency and the color of the watermelon to detect the content of lycopene in the watermelon, and a R 2 of 0.86 was obtained. Abbaszadeh et al [50,51] constructed stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR) models based on amplitude and phase information to predict watermelon hardness.…”

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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“…Taniwaki et al (2009a) and(2009b) dedicated to the study of non-destructive food texture and established the LDV non-destructive detection method of crispness and texture of persimmons and pears. Abbaszadeh et al (2013aAbbaszadeh et al ( ), (2013bAbbaszadeh et al ( ), (2015a, and (2015b) also concentrated on non-destructive detection of by LDV; the quality indices, including total soluble solids (TSS), titratable acidity (TA), TSS/TA ratio and texture of watermelon, were all measured based on vibrational response spectrum. However, the cost of LDV system is expensive and the portability is not desirable.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of non‐destructive evaluation for apple crispness based on portable acoustic signal

Liu

Cai

Zhang

et al. 2020

Int J of Food Sci Tech

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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 AE 1.9%, remarkably lower (P < 0.01) that of MLR (6.79 AE 5.64%), implying that the model predicted by ANN is more accurate than that of MLR.

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Nondestructive assessment of watermelon flesh color by laser vibrometry

Cited by 6 publications

References 20 publications

Comparison of apple firmness prediction models based on non‐destructive acoustic signal

Comparison of apple firmness prediction models based on non‐destructive acoustic signal

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

Feasibility of non‐destructive evaluation for apple crispness based on portable acoustic signal

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