Nondestructive detection of tomato quality based on multiregion combination model

Li, Li; Limin, Lu; Zhao, Xian‐Hao; Hu, De‐Yuan; Tang, Tianyu; Li, Li

doi:10.1111/jfpe.14100

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…The combination of spectroscopy and chemometrics analysis techniques has become one of the core technologies for fruit quality testing. The detectable objects include apples, pears, oranges, etc., and the detectable indicators include water content, soluble solids content, sugar and acidity degree [8][9][10][11][12][13][14] , etc. However, during the process of data collection, spectroscopy instruments are susceptible to external factors such as electromagnetic noise and sample background, leading to a large amount of irrelevant information in the collected spectral data [15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…Detecting SSC and firmness indicators in Cerasus Humilis fruits is important for identifying ripeness, determining optimal harvest times, enhancing product value, and increasing fruit farmers' income. Traditionally, the SSC of Cerasus Humilis fruit is tested by the refractometer method, which requires the fruit to be extruded into juice and then titrated, and the firmness is tested with the fruit texture analyzer (TA-xtplus) [2]. Obviously, the methods for determining SSC and firmness of Cerasus Humilis fruit are different.…”

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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“…Traditionally, the SSC and TA of cherry tomatoes are tested by the refractometer method, which requires the fruit to be extruded into juice and then titrated, and the firmness is tested with the fruit firmness tester. However, the above methods have a single index, are time-consuming and destructive and are difficult to be applied to the batch testing of fruits [4].…”

Section: Introductionmentioning

confidence: 99%

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes

Tan,

Mo,

Ruan

et al. 2023

Foods

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Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R2 of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R2 of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits.

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Nondestructive detection of tomato quality based on multiregion combination model

Cited by 4 publications

References 45 publications

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

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

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

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes

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