Application of hyperspectral imaging for spatial prediction of soluble solid content in sweet potato

Shao, Yuanyuan; Liu, Yi; Xuan, Guantao; Wang, Yongxian; Gao, Zongmei; Hu, Zhichao; Han, Xiang; Gao, Chong; Wang, Kaili

doi:10.1039/c9ra10630h

Cited by 30 publications

(13 citation statements)

References 41 publications

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“…Based on the iPLSR for feature variable selection, machine learning methods including k-nearest neighbor (KNN) and PLSR models achieved better results in glucose prediction ( R 2 P = 0.88) compared with that in sucrose prediction ( R 2 P = 0.36) [ 129 ]. This imaging technique combined with SPA–SVR and CARS–MLR was capable of visualizing the spatial distribution of soluble solid content (SSC) in sliced sweet potatoes [ 80 ]. Moreover, total anthocyanin (TA) and moisture content in processed potato and sweet potatoes were detected during convective hot-air drying and microwave drying [ 51 , 94 ].…”

Section: Applications For Tuber Quality Assessmentmentioning

confidence: 99%

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Xue

2021

Foods

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Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.

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Section: Applications For Tuber Quality Assessmentmentioning

confidence: 99%

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Xue

2021

Foods

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“…Mountrakis et al [ 56 ] also reported that SVR often produces higher accuracy than traditional methods because of its ability to handle small training datasets successfully. The SVR models performed better than the PLSR models in the quantitative determination of biochemicals, such as phosphorus in seafood [ 57 ], soluble solid content in sweet potato [ 78 ], and soluble solid content in Agaricus bisporus [ 79 ]. Ge et al [ 28 ] used Vis–NIR–SWIR to determine leaf physiological traits in maize, and the SVR model performed slightly better than the PLSR model for 5 traits.…”

Section: Discussionmentioning

confidence: 99%

Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery

Che

Zhong

et al. 2023

Plant Phenomics

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Phycobilisomes and chlorophyll-a ( Chla ) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE ( R Test 2 = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC ( R Test 2 = 0.94, MAPE = 7.18%, RPD = 4.16) and APC ( R Test 2 = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R Test 2 = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R Test 2 = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.

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“…The competitive adaptive reweighted sampling (CARS) algorithm and the successive projections algorithm (SPA) were used to filter the characteristic wavelength points related to the Brix to eliminate the irrelevant spectral data, reduce the computational effort and reduce the modeling time [31][32][33]. Fig 11 shows the results of the characteristic wavelengths selection using CARS, (a) the variation in the number of variables when different sampling times were selected, (b) the variation in the root mean square of cross-validation with the number of sampling times, and (c) a graph of the selection results of characteristic wavelengths using CARS.…”

Section: Characteristic Wavelengths Extractionmentioning

confidence: 99%

Grading detection of “Red Fuji” apple in Luochuan based on machine vision and near-infrared spectroscopy

et al. 2022

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A quality detection system for the “Red Fuji” apple in Luochuan was designed for automatic grading. According to the Chinese national standard, the grading principles of apple appearance quality and Brix detection were determined. Based on machine vision and image processing, the classifier models of apple defect, contour, and size were constructed. And then, the grading thresholds were set to detect the defective pixel ratio t, aspect ratio λ, and the cross-sectional diameter Wp in the image of the apple. Spectral information of apples in the wavelength range of 400 nm~1000 nm was collected and the multiple scattering correction (MSC) and standard normal variable (SNV) transformation methods were used to preprocess spectral reflectance data. The competitive adaptive reweighted sampling (CARS) algorithm and the successive projections algorithm (SPA) were used to extract characteristic wavelength points containing Brix information, and the CARS-PLS (partial least squares) algorithm was used to establish a Brix prediction model. Apple defect, contour, size, and Brix were combined as grading indicators. The apple quality online grading detection platform was built, and apple’s comprehensive grading detection algorithm and upper computer software were designed. The experiments showed that the average accuracy of apple defect, contour, and size grading detection was 96.67%, 95.00%, and 94.67% respectively, and the correlation coefficient Rp of the Brix prediction set was 0.9469. The total accuracy of apple defect, contour, size, and Brix grading was 96.67%, indicating that the detection system designed in this paper is feasible to classify “Red Fuji” apple in Luochuan.

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Application of hyperspectral imaging for spatial prediction of soluble solid content in sweet potato

Abstract: Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in ‘Beijing 553’ and ‘Red Banana’ sweet potatoes.

Cited by 30 publications

References 41 publications

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery

Grading detection of “Red Fuji” apple in Luochuan based on machine vision and near-infrared spectroscopy

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