Predicting Quality Properties of Pears during Storage Using Hyper Spectral Imaging System

Taghinezhad, Ebrahim; Rasooli Sharabiani, Vali; Shahiri, Mohammadali; Moinfar, Abdolmajid; Szumny, Antoni

doi:10.3390/agriculture13101913

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…Additionally, recent studies have also shown that HSI is feasible for visualizing the distribution of internal quality in fruits, such as SSC and PH distribution of cherries, mandarins and kiwifruit [12][13][14], and firmness of peach [15]. Beyond these examples, research using hyperspectral imaging and chemometrics has shown promising results in maturity analysis and quality evaluation of blueberries, persimmons, bananas, and other fruits [16][17][18][19]. The above researches mainly focus on the prediction of single indicators of SSC and hardness during the sample maturity stages.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Wang,

Yang,

et al. 2024

Horticulturae

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Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

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Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Wang,

Yang,

et al. 2024

Horticulturae

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Development of a predictive model for assessing quality of winter jujube during storage utilizing hyperspectral imaging technology

Wei,

Liu,

Fan

et al. 2024

J Food Process Engineering

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The necessity for precise determination of fruit storage durations is paramount in both industrial and domestic spheres, with hyperspectral imaging (HSI) technology emerging as a pivotal tool for prognosticating the physicochemical attributes indicative of mature fruit quality. This investigation employed hyperspectral imaging to conduct a noninvasive analysis of variations in soluble solids content (SSC), hardness, and moisture content (MC) in jujube fruit over the course of storage at divergent temperatures. Throughout the storage intervals (0, 7, 14, and 21 days) at varying temperatures (4 and 20°C), both physicochemical and spectral data were amassed. The raw spectral information underwent preprocessing through multiple scattering correction (MSC), standard normal variation (SNV), and Savitzky–Golay (SG) algorithms to refine the methodologies for SSC, hardness, and moisture content. Subsequently, the competitive adaptive reweighted sampling (CARS) algorithm facilitated the discernment and elimination of extraneous variables, thereby enhancing feature wavelength extraction. This process underpinned the development of partial least squares regression (PLSR), back propagation (BP), and genetic algorithm‐back propagation (GA‐BP) models predicated on CARS‐derived features, culminating in the selection of an optimal model. The findings underscore the capability of hyperspectral imaging technology to swiftly and nondestructively ascertain the SSC, hardness, and MC of jujube throughout the storage phase, thereby enabling the assessment of quality attributes over varying storage durations and facilitating the surveillance of jujube quality maintenance during storage.Practical ApplicationsThe winter jujube garners appreciation from consumers owing to its exquisite flavor, yet its quality diminishes over time in storage due to various factors, thereby impacting its market value. Consequently, it is imperative to surveil the quality alterations of jujube throughout its storage to mitigate the degradation of its quality. Hyperspectral imaging technology offers a sophisticated means to forecast the physicochemical index changes indicative of the fruit's quality at maturity. This research delineates the development of predictive models for the soluble solids, hardness, and moisture content of jujube at divergent temperatures throughout the storage interval, selecting the paramount model through a holistic assessment, thereby fully harnessing the capabilities of hyperspectral imaging technology in monitoring jujube quality during storage. Moreover, the methodology employed herein is adaptable to other fruits in storage, harboring the potential for future application in the real‐time quality monitoring of fruits as they exit the storage facilities.

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Research on the wavelength intersection union fusion selection method for near-infrared spectroscopy analysis of apple sugar degree

Zeng,

Huang,

Fang

et al. 2024

2024 International Conference on Optoelectronic Information and Optical Engineering (OIOE 2024)

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By using wavelength selection methods to eliminate irrelevant and redundant information from near-infrared spectroscopy data before modeling, the performance of the apple sugar degree prediction model can be improved. However, there are many shortcomings in commonly used single-wavelength selection methods. This paper combines the competitive reweighted sampling (CARS) algorithm, the successive projection algorithm (SPA), and the genetic algorithm (GA), which are three primary single-wavelength selection methods, to merge the wavelength subsets obtained from them. These subsets are then combined through intersection and union fusion, resulting in 8 Venetian wavelength subsets. A fructose prediction model for apples is established by combining them with the Ridge Regression model. The results show that the number of wavelength subsets obtained by the preferred CARS∪SPA method is 74, accounting for 14.4% of the total wavelengths. Its R2p is maximum at 0.952, and the minimum RMSEp is 0.036. This indicates that the wavelength selection method of CARS∪SPA reduces the multicollinearity of spectral data, effectively utilizes complementary information in the wavelength variables obtained by CARS and SPA methods, and enhances the robustness and accuracy of the model. The research work can provide theoretical basis for the rapid grading of apple quality and the development of related detection devices.

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Predicting Quality Properties of Pears during Storage Using Hyper Spectral Imaging System

Cited by 5 publications

References 36 publications

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Development of a predictive model for assessing quality of winter jujube during storage utilizing hyperspectral imaging technology

Research on the wavelength intersection union fusion selection method for near-infrared spectroscopy analysis of apple sugar degree

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