Nondestructive determination of watermelon flesh firmness by frequency response

Abbaszadeh, Rouzbeh; Rajabipour, Ali; Ying, Yibin; Delshad, Mojtaba; Mahjoob, M. J.; Ahmadi, Hojjat

doi:10.1016/j.lwt.2014.08.029

Cited by 24 publications

(12 citation statements)

References 13 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%

“…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: Introductionmentioning

confidence: 99%

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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“…From Table S3, the average relative error of the neural network model of crispness was 1.42 AE 1.9%, which was relatively stable; this result was even more accurate than that of LDV method (Taniwaki et al, 2009a(Taniwaki et al, , 2009bAbbaszadeh et al, 2013aAbbaszadeh et al, , 2015b. By contrast, the average relative error of the MLR of crispness was 6.79 AE 5.64%, which is not accurate enough.…”

Section: Verification Of Artificial Neural Network and Multiple Linear Regression Modelsmentioning

confidence: 89%

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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“…Cuiguan çeşidi armutların sertliğini incelemek için LDV ile çevrimiçi analiz yöntemi kullanılan bir çalışmada, titreşim parametresi olarak tepe değeri (A), f 2 , yarım yükseklikte tepe genişliği (w), tepe alanı (S), EI değeri ve sertlik katsayısı (SC) seçilmiş ve armut sertliği için duyusal değerlendirme sonuçlarına göre farklı sınıflandırma modelleri oluşturulmuştur [54]. [59] sonraki yılda yaptıkları çalışmada, karpuzun meyve eti sertliğini lazer Doppler yöntemiyle incelerken, genlik spektrumu ve faz spektrumunu kısmi en küçük kareler ve aşamalı çoklu doğrusal regresyon analizleriyle değerlendirmişlerdir. Her iki istatistiksel analiz için faz spektrumunun değerlendirilmesiyle yüksek uyumluluk gözlenirken, aşamalı çoklu doğrusal regresyon analizinden elde edilen sonuçlarla, penetrometreyle ölçülen sertlik değeri arasında çok iyi bir korelasyon (R 2 =0.99) olduğu görülmüştür.…”

Section: Lazer Doppler Titreşim (Ldv) Yöntemi (Laser Doppler Vibration Method)unclassified

Gıda Analizlerinde Hasarsız Akustik Yöntemlerin Kullanımı

Odabaş

Çakmak

2021

Harran Üniversitesi Mühendislik Dergisi

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Gıda ürünlerin kalitesinin değerlendirilmesinde hasar veren gıda analiz yöntemlerine alternatif olarak geliştirilen ve hasar vermeyen spektroskopik ve kromatografik tekniklerin yanı sıra, mekanik yöntemlerin kullanımının son yıllarda arttığı görülmektedir. Bu teknikler arasında yer alan akustik ve ultrasonik tekniklerin özellikle büyük hacimli gıdaların kalitesinin değerlendirilmesinde kullanımı ile analiz sırasında örneklerin kaybının engellenebileceği ve bu şekilde ekonomik bir kazanç sağlanacağı öngörülmektedir. Akustik teknikler, büyüklük, hacim, şekil ve doku fark etmeksizin birçok gıdanın sertlik, iç kusur, olgunluğunun belirlenmesinde ve gıdaların seçilen kalite özelliklerine göre sınıflandırılmasında başarıyla kullanılabilmektedir. Bu derleme çalışmasında ağırlıklı olarak meyve ve sebzelerin kalitesinin incelenmesinde kullanılan akustik teknikler, lazer Doppler titreşim ve ultrasonik yöntemler incelenmiş olup, farklı gıda gruplarında bu tekniklerin kullanımıyla elde edilen sonuçlar detaylı olarak kıyaslanmıştır.

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Nondestructive determination of watermelon flesh firmness by frequency response

Cited by 24 publications

References 13 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

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

Gıda Analizlerinde Hasarsız Akustik Yöntemlerin Kullanımı

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