Comparison of multispectral modeling of physiochemical attributes of greengage: Brix and pH values

Wang, Xiwei; Xing, Xiaoyang; Zhao, Minghua; Yang, Junrong

doi:10.1590/fst.21320

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…It has been reported that a variety of components in foods and plants were detected by hyperspectral imaging technology. A method using modified Adaboost RT based on an AOTF hyperspectral imaging system was employed to test the Brix and pH values and a method using PLSR based on a short-wave near-infrared hyperspectral imaging system was used to test the TVB-N value of pork (Wang et al, 2020). Thus, it seems to be feasible that hyperspectral imaging technology could be used to test the quality of leaves of leaf-use ginkgo.…”

Section: Hyperspectral Image-based Measurement Of Total Flavonoid Con...mentioning

confidence: 99%

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

et al. 2023

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Ginkgo biloba leaf is a kind of food raw material with high concentration of total flavonoid content (TFC). Since conventional measurements of TFC are time-consuming and destructive, the approach of hyperspectral imaging-based prediction was investigated in this study. Hyperspectral images were collected in visual near-infrared (VIS-NIR) and short-wave near-infrared (SW-NIR). Initial comparative analysis on the full spans of the two wavelengths using partial least squares regression (PLSR) and support vector regression (SVR) showed that the TFC of the leaves could be barely captured only when PLSR working on short-wave infrared, with 2 p R and p RMSE on testing set being respectively 0.5496 and 0.6384 mg/g. To further improve prediction performance meeting industrial requirements, another comparative study on feature selection was conducted to fine-tune the PLSR on SW-NIR using a genetic algorithm (GA) and successive projections algorithm (SPA). Results showed that GA-PLSR with 50 characteristic wavelengths mostly from the ranges of 1100-1200 nm and 1400-1500 nm had a significant improvement in performance, giving 0.8482 and 0.2967 mg/g for 2 p R and p RMSE , respectively. Therefore, an approach, GA-PLSR modeling on SW-NIR hyperspectral images, was established for the ginkgo leaves industry to rapidly and non-invasively predict the leaf concentration from the total flavonoid of ginkgo biloba seedling leaf.

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Section: Hyperspectral Image-based Measurement Of Total Flavonoid Con...mentioning

confidence: 99%

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

et al. 2023

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“…The regions of interest in the hyperspectral images corrected by black and white is extracted by using ENVI5.3 (Exelis Visual Information Solutions Inc., USA) software. (Wang et al, 2021;Zou et al, 2022b), which can basically eliminate shadow interference compared with using NVDI to extract the sample pixel, and then the data was exported to Excel. The data of the first 20 bands were removed to avoid the damage of noisy data in the spectral boundary region.…”

Section: Hyperspectral Data Acquisition and Preprocessingmentioning

confidence: 99%

Hyperspectral imaging with a band matrix reduction method to detect early drought stress in tomato

et al. 2023

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Drought stress is one of the key abiotic stresses affecting plant growth, crop yield and food quality. The main objective of this study is to investigate the potential effectiveness of hyperspectral imaging with band selection method for the rapid detection of the early drought stress of tomatoes. First, the unsupervised algorithm -K-means and statistical histogram are used to extract samples representing each experimental treatment group. Then, to solve problems related to the high redundancy and correlation of hyperspectral data, band matrix reduction method (BMRM) based on recursive feature elimination theory is proposed to determine the optimal band subset. The band matrix is constructed according to the band ranking obtained by the discrimination coefficient - ( )Coef i , which is calculated from the average spectral curve and the first-derivative spectrum. Finally, the effectiveness of waveband selection algorithms was validated by comparison with successive projections algorithm, competitive adaptive reweighted sampling, recursive feature elimination with cross-validation and full spectrum. The results demonstrated that BMRM achieved higher classification accuracy with fewer bands selected, and the amount of calculation is not greatly improved. The proposed method provides a more accurate, and effective way of detecting early drought stress.

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“…Chen et al (2022) used hyperspectral technology combined with the inversion model of LS-SVM to achieve rapid online monitoring of soybean breakage rate by combine harvesters (Chen et al, 2022). Wang et al (2021) used multi-spectral technology to model the physical and chemical properties of green vegetables, and used a variety of machine learning algorithm models to analyze and predict Brix and pH values (Wang et al, 2021).…”

Section: Identification Of Peanut Storage Period Based On Hyperspectr...mentioning

confidence: 99%

Identification of peanut storage period based on hyperspectral imaging technology

et al. 2022

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Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387~1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage.

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Comparison of multispectral modeling of physiochemical attributes of greengage: Brix and pH values

Cited by 8 publications

References 11 publications

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

Hyperspectral imaging with a band matrix reduction method to detect early drought stress in tomato

Identification of peanut storage period based on hyperspectral imaging technology

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