Non‐destructive sugar content assessment of multiple cultivars of melons by dielectric properties

Liu, Dayang; Wang, Enfeng; Wang, Guanglai; Wang, Pan; Wang, Congcong; Wang, Zhuanwei

doi:10.1002/jsfa.11070

Cited by 14 publications

(5 citation statements)

References 33 publications

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“…ELM was a sort of machine learning system or approach based on the Feedforward Neuron Network, which could be used to solve both supervised and unsupervised learning issues (Huang, Huang, Song, & You, 2015; Liu, Wang, et al, 2021; Qin, Chen, Zhang, & Fu, 2021). The connection weight between the input layer and the hidden layer, as well as the threshold of the neurons in the hidden layer, are generated at random by the algorithm and do not need to be modified throughout the training process.…”

Section: Methodsmentioning

confidence: 99%

Assessment of kiwifruit firmness by using airflow and laser technique

Sun

Luo

et al. 2023

Journal of Texture Studies

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Firmness is a valid and widely acknowledged indication of fruit quality that is directly connected to physical structure and mechanical qualities. The deformation signals of kiwifruit for firmness assessment were acquired using an assessment system based on airflow and laser technology in this investigation. Using partial least squares regression (PLSR), genetic algorithm optimization of bp neural network (GA-BP), and an extreme learning machine (ELM), deformation data from kiwifruit was used to create models of Magness-Taylor penetration firmness prediction. The ELM model outperformed the PLSR model, and GA-BP model in the prediction set, with a correlation coefficient of 0.876 and a root mean squared error of 3.576 N in the prediction set. These findings showed that an assessment system based on airflow and laser techniques can be utilized to assess the firmness of kiwifruit quickly and nondestructively.

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Section: Methodsmentioning

confidence: 99%

Assessment of kiwifruit firmness by using airflow and laser technique

Sun

Luo

et al. 2023

Journal of Texture Studies

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“…Therefore, it is necessary to extract characteristic wavelengths related to moisture information from a large number of spectral variables (Guo et al, 2020). In this study, successive projections algorithm (SPA) (Liu et al, 2021), competitive adaptive reweighted sampling (CARS) (Liu et al, 2020), interval random frog (IRF) (Long, Lian, Ma, Song, & He, 2019), iterative retained information variables (IRIV), variable combination population analysis (VCPA), and variable combination population analysis combined with genetic algorithm (VCPA‐GA) (Lan et al, 2021) are used to extract characteristic wavelengths, to simplify and improve the accuracy of the model.…”

Section: Methodsmentioning

confidence: 99%

Research on the online rapid sensing method of moisture content in famous green tea spreading

Liu

Yang

Luo

et al. 2021

J Food Process Engineering

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Moisture content is an important indicator in the process of green tea spreading. In order to realize the rapid detection of moisture content in the green tea spreading process, this paper collects 200 near‐infrared spectroscopy data during the spreading process and establishes a quantitative model of moisture content. To improve the performance of the model, standard normal variables (SNV), mean centering (MC), max–min normalization (Max–Min), detrending (Detrend), and Savitzky–Golay filtering (SG FIR) are used to improve the signal‐to‐noise ratio of the original spectrum. By comparing the partial least squares regression (PLSR) models established by the different pretreatment methods, SG FIR is determined as the best preprocessing method. To simplify the prediction model, the successive projections algorithm, competitive adaptive reweighted sampling, interval random frog, iterative retained information variables, variable combination population analysis (VCPA), and VCPA‐GA are used to extract the characteristic wavelengths from the preprocessed spectrum by SG FIR method. Then, based on the full spectrum and characteristic variables, the PLSR and support vector regression (SVR) models are established. The results show that established SVR prediction model based on VCPA‐GA extracting the characteristic wavelengths has the best effect. Among them, the correlation coefficients of the calibration set (Rc) is 0.9785, and the correlation coefficients of the prediction set (Rp) is 0.9658. Relative percent deviations are 3.6787. The results show that the VCPA‐GA + SVR model, based on near‐infrared spectroscopy technology, is the most effective and can accurately predict the moisture content in the green tea spreading process. Practical Applications At present, near‐infrared spectroscopy technology has been widely used in food detection. This study solved the problem of the inability to quantitatively predict the moisture content during the green tea spreading process by discussing different spectral preprocessing methods, characteristic wavelength selection methods and machine learning algorithms, and realized the rapid online detection of moisture during the green tea spreading process. This research has important guiding significance for the intelligentization of green tea processing equipment.

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“…The main reason for this is that as passion fruit ripens, the membrane permeability and free water content increase. As a result, the ability of the cell membrane in mature fruit to bind charges weakens, leading to a gradual decrease in ε ′ and ε ′′ [42,43]. Overall, in the frequency range of 0.05-5 kHz, there are significant differences in the dielectric properties of passion fruit under different GDD.…”

Section: Analysis Of Dielectric Properties Under Different Gddmentioning

confidence: 99%

Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics

Lin,

Chen,

Liu

et al. 2024

Applied Sciences

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This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit’s electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (SSC), Acidity and pulp percentage (PP) in passion fruit. The investigation entailed measuring the relative dielectric constant (ε′) and dielectric loss factor (ε″) for 192 samples across a spectrum of 34 frequencies from 0.05 to 100 kHz. The analysis revealed that with increasing frequency and fruit maturity, both ε′ and ε″ showed a declining trend. Moreover, there was a discernible correlation between the fruit’s physicochemical indicators and dielectric properties. In refining the dataset, 12 outliers were removed using the Local Outlier Factor (LOF) algorithm. The study employed various advanced feature extraction techniques, including Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear Regression (PI-LR) and Genetic Algorithm (GA). All the variables and the selected variables after screening were used as inputs to build Extreme Gradient Boosting (XGBoost) and Categorical Boosting (Cat-Boost) models to predict the SSC, Acidity and PP in passion fruit. The results indicate that the PI-RF-XGBoost model demonstrated superior performance in predicting both the SSC (R2 = 0.9240, RMSE = 0.2595) and the PP (R2 = 0.9092, RMSE = 0.0014) of passion fruit. Meanwhile, the GA-CatBoost model exhibited the best performance in predicting Acidity (R2 = 0.9471, RMSE = 0.1237). In addition, for the well-performing algorithms, the selected features are mainly concentrated within the frequency range of 0.05–6 kHz, which is consistent with the frequency range highly correlated with the dielectric properties and quality indicators. It is feasible to predict the quality indicators of fruit by detecting their low-frequency dielectric properties. This research offers significant insights and a valuable reference for non-destructive testing methods in assessing the quality of golden passion fruit.

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Non‐destructive sugar content assessment of multiple cultivars of melons by dielectric properties

Cited by 14 publications

References 33 publications

Assessment of kiwifruit firmness by using airflow and laser technique

Assessment of kiwifruit firmness by using airflow and laser technique

Research on the online rapid sensing method of moisture content in famous green tea spreading

Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics

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