Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy

Fang, Lijie; Liu, Dayang; Wang, Zhuanwei

doi:10.1016/j.compag.2015.08.012

Cited by 48 publications

(29 citation statements)

References 38 publications

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“…For this reason, Guo, Shang, Wang, and Zhu (2013) deduced that the traditional linear fitting method might be inapplicable to the prediction of SSC in fruits. Moreover, the relationship between the electrical properties and SSC of Dangshansu pears (Guo, Fang, Liu, & Wang, 2015) during the maturation period was discussed through nonlinear models, such as extreme learning machines (ELM) and GRNN. The prediction results were compared with results of linear models like the multiple linear regression models and the partial least squares regression method, and it was found that the artificial neural network has the best prediction performances.…”

Section: Introductionmentioning

confidence: 99%

A nondestructive testing method for soluble solid content in Korla fragrant pears based on electrical properties and artificial neural network

Lan

Wang

Hao

et al. 2020

Food Science & Nutrition

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The detection of soluble solid content in Korla fragrant pear is a destructive and time‐consuming endeavor. In effort to remedy this, a nondestructive testing method based on electrical properties and artificial neural network was established in this study. Specifically, variations of electrical properties (e.g., equivalent parallel capacitance, quality factor, loss factor, equivalent parallel resistance, complex impedance, and equivalent parallel inductance) of Korla fragrant pears with accumulated temperature were tested using a workbench developed by ourselves. After that the characteristic variables of electrical properties were constructed by principal component analysis (PCA). In addition, three models were constructed to predict SSC in Korla fragrant pears based on the characteristic variables: general regression neural network (GRNN), back‐propagation neural network (BPNN), and adaptive network fuzzy inference system (ANFIS). The results indicated that the GRNN model has the best prediction effects of SSC (R2 = 0.9743, RMSE = 0.2584), superior to that of the BPNN and ANFIS models. Results facilitate a successful, alternative application for rapid assessment of SSC of the maturation stage Korla fragrant pear.

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Section: Introductionmentioning

confidence: 99%

A nondestructive testing method for soluble solid content in Korla fragrant pears based on electrical properties and artificial neural network

Lan

Wang

Hao

et al. 2020

Food Science & Nutrition

View full text Add to dashboard Cite

show abstract

“…In recent years, with the rapid development of sensors, non-destructive detection technology has been used in fruit quality evaluation. For example, multispectral [ 4 , 5 ], fluorophore [ 6 , 7 , 8 ], near-infrared spectroscopy [ 9 , 10 , 11 , 12 ], electronic nose [ 13 , 14 , 15 ], and dielectric technology [ 16 , 17 ] have been applied for the evaluation of the soluble solid content in fresh fruits. Among these technologies, near-infrared spectroscopy is currently the most widely used method for evaluating fresh fruit SSC due to its fast, simple, and non-destructive characteristics.…”

Section: Introductionmentioning

confidence: 99%

Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Yang

Gao

Yan

et al. 2020

Sensors

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Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.

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“…Sugar content and firmness determine whether an apple is sweet and crisp, and also determine whether the apple is appealing to customers. Since 80–85% compositions of soluble solids in fruits are sugar, therefore, soluble solids content (SSC) usually is used as a measure of sugar content and measured by digital refractometer or Abbe refractometer on fruit juice (Guo, Fang, Liu, & Wang, ). Firmness is determined by penetrating into flesh until a given depth, usually 10 mm.…”

Section: Introductionmentioning

confidence: 99%

Determining sugar content and firmness of ‘Fuji’ apples by using portable near‐infrared spectrometer and diffuse transmittance spectroscopy

Zhu

Tian

2018

J Food Process Engineering

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‘Fuji’ apples produced in four counties of Shaanxi Province, China were used as samples to explore potential applications of portable spectrometers in determining sugar content and firmness of apples produced in different areas. Eighteen and twenty wavelengths were selected as characteristic wavelengths (CWs) using successive projections algorithm (SPA) from pretreated full spectra for sugar content and firmness, respectively. Two linear models (multiple linear regression (MLR) and partial least squares regression (PLSR)) and other two nonlinear models (general regression neural network (GRNN) and extreme learning machine (ELM) were adopted to build sugar content and firmness determination models. The results indicate that not only for sugar content, but also for firmness, PLSR had better performance than MLR, and ELM performed better than GRNN. PLSR‐SPA had the best determination performance for sugar content and firmness. The research offers useful spectrometer technologies on developing portable detectors for internal qualities of apples. Practical applications ‘Fuji’ apples are very popular among consumers around the world since they are sweeter and crisper than many other apple cultivars. Although several studies have been conducted on predicting internal qualities of ‘Fuji’ apples by using NIR spectroscopy, few studies have been reported on sugar content and firmness determination of ‘Fuji’ apples produced in different areas. In this study, a portable near‐infrared spectrometer and diffuse transmittance spectroscopy was used to determine sugar content and firmness of ‘Fuji’ apples produced in different areas of Shaanxi Province, whose ‘Fuji’ apple production and plant area ranks the first in China. This study offers useful technologies for developing portable detector and online detection instrument on quality attributes of ‘Fuji’ apples.

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Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy

Cited by 48 publications

References 38 publications

A nondestructive testing method for soluble solid content in Korla fragrant pears based on electrical properties and artificial neural network

A nondestructive testing method for soluble solid content in Korla fragrant pears based on electrical properties and artificial neural network

Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Determining sugar content and firmness of ‘Fuji’ apples by using portable near‐infrared spectrometer and diffuse transmittance spectroscopy

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