Integration of vibration and optical techniques for watermelon firmness assessment

Ding, Chengqiao; Wang, Dachen; Feng, Zhe; Li, Weihao; Cui, Di

doi:10.1016/j.compag.2021.106307

Cited by 15 publications

(10 citation statements)

References 36 publications

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“…When PCs = 7, the model achieved the best prediction accuracy, and the Rp, RMSEP, and RPD values of the prediction set were 0.989, 0.175, and 6.5, respectively. The distribution scatter plot is shown in Figure 5 b [ 26 , 27 , 28 ]. Figure 5 c shows the optimization effect of the EGC model.…”

Section: Results and Analysesmentioning

confidence: 99%

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Dong

Yang

Liu

et al. 2021

Sensors

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Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

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Section: Results and Analysesmentioning

confidence: 99%

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Dong

Yang

Liu

et al. 2021

Sensors

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“…An effective extraction method can save time, reduce the workload, and increase the robustness of the model. This paper uses the CARS algorithm and SPA to extract the characteristic wavelengths (Ding et al, 2021; Hu et al, 2018). The former selects the wavelength points with the largest regression coefficient based on the Monte Carlo sampling algorithm, and the latter retains the wavelength points with the largest projection vector after repeated cycles.…”

Section: Methodsmentioning

confidence: 99%

Nondestructive detection of tomato quality based on multiregion combination model

Limin

Zhao

et al. 2022

J Food Process Engineering

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To quickly and accurately identify the quality of tomatoes, a method was proposed to predict the total soluble solid content (SSC), total titratable acidity (TA), and vitamin C (VC) content of tomatoes based on a multiregion combined model of the visible–near‐infrared spectrum. The results show that the competitive adaptive re‐weighted sampling algorithm combined with the partial least squares regression (CARS‐PLSR) model has the best prediction effect on SSC, TA, and VC content in “stem + equator”, “stem + bottom” and “stem + bottom” combinations. The prediction accuracy is 97.2%, 96.7%, and 97.7%, respectively, and the relative percent deviation (RPD) value is 5.870, 5.401, and 5.942, respectively. Practical Application This indicates that the CARS‐PLSR model based on the multiregion combination of visible–near‐infrared spectroscopy is reliable for predicting tomatoes' SSC, TA, and VC content. The results provide a theoretical basis for developing a portable fruit quality detector.

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“…Another important class of contact type noninvasive sensors is impact based sensors, which work on the principle of analyzing the viable interactions between food and applied impact that are duration and strength of impact (Chen & Opara, 2013a, 2013b; Garcı́a, Ruiz‐Altisent, & Barreiro, 1995). Customarily these sensors are predominantly associated with the assessment of textural features of fruits in particular “fruit firmness” (Abbaszadeh et al, 2014; Cui, Gao, Zhang, & Ying, 2015; Ding, Wang, Feng, Li, & Cui, 2021). The theory of elastic impact as well as Hertz's theory establishes the foundation for the assessment of firmness by impact‐based sensors.…”

Section: Contact Type Nondestructive Texture Measurementsmentioning

confidence: 99%

Emerging nondestructive techniques to quantify the textural properties of food: A state‐of‐art review

Mishra

Sahni

Pandiselvam

et al. 2023

Journal of Texture Studies

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Texture is an important sensory attribute that drives consumer acceptance of any food material. In recent times consumers' demand for high‐quality food urges food industries to provide food with consistent textural properties. However, texture measurement not just requires a trained sensory panel but also a considerable amount of time and effort. On the flip side, human observation could be subjective hence repeatability of the result may not be ensured and/or relied on. Contrary to that, objective methods for texture measurement are reliable and consistent, but are not suitable for in‐line application and also destructive in nature. The mentioned crisis has made industries opt for nondestructive texture analysis techniques. In the past decade, considerable research has been carried out on nondestructive texture analysis methods such as micro‐deformation, and acoustic and optical techniques, showing feasibility for in‐line applications. The current review focuses on the working principles and most recent applications of nondestructive techniques for texture analysis of food products. Moreover, a detailed review of contact and noncontact‐type texture measurement has been presented in this article. The literature survey is concluded with future research aspects and challenges involved in the commercialization of the nondestructive texture analysis techniques.

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Integration of vibration and optical techniques for watermelon firmness assessment

Cited by 15 publications

References 36 publications

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Nondestructive detection of tomato quality based on multiregion combination model

Emerging nondestructive techniques to quantify the textural properties of food: A state‐of‐art review

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