Pitahaya Aging Diagnostic by Impedance/Capacitance Spectroscopy

Cortés, Henry A.; Edgar, Villegas; Suárez, Rócio; Castillo, José; Gonçalves, Luís Moreira; Bueno, Paulo Roberto

doi:10.1007/s12161-014-9878-7

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Cortes et al. (2015) analyzed complex impedance during the normal deterioration process of pitayas by using a self‐made double‐needle probe and found that electrical parameters can be applied to evaluate the aging process. These studies demonstrate that electrical parameters can reflect internal quality changes of fruits and vegetables without causing damage to the food.…”

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

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“…Dielectric characteristic non-destructive detection technology is based on this property [32,66]. The detection based on dielectric properties is a technology that utilizes the dielectric properties of fruits to detect fruit sugar content, SSC, moisture content, disease, hardness, pH value, and other aspects for non-destructive testing, which has the advantages of fast (short measurement and preparation time), convenience (low requirements for the tested substance), and non-destructive (non-destructive online monitoring) [67][68][69][70][71][72][73].…”

Section: Dielectric Properties Technologymentioning

confidence: 99%

Research Progress of Rapid Non-Destructive Detection Technology in the Field of Apple Mold Heart Disease

Li,

Yang,

Wang

et al. 2023

Molecules

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Apples are rich in vitamins and dietary fiber and are one of the essential fruits in people’s daily diet. China has always been a big apple consumer, and with the improvement of people’s life quality, nutrition, and health requirements, the demand for high-quality apples has increased year by year. Apple mold heart disease is one of the main diseases affecting apple quality. However, this disease cannot be easily detected from the surface, so it is difficult to detect mold heart disease. Therefore, this paper focuses on the analysis of seven non-destructive detection technologies, including near infrared spectroscopy technology, hyperspectral technology, Raman spectroscopy technology, electronic nose technology, acoustic technology, electrical technology, and magnetic technology, summarizes their application status in the detection of apple mold heart disease, and then analyzes their advantages and disadvantages. Combined with the current rapid development of artificial intelligence (AI) technology, this paper proposes the future development trends of using non-destructive technologies to detect apple mold heart disease. It is expected to provide basic theory and application references for the intelligent detection of apple mold heart disease.

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