Hyperspectral Reflectance and Fluorescence Imaging System for Food Quality and Safety

Kim, M. S.; Chen, Y. R.; Mehl, Patrick M.

doi:10.13031/2013.6099

Cited by 122 publications

(28 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before the fluorescence hyperspectral image acquisition, highpass-filter was placed in front of the camera lens to prevent transmittance of radiations smaller than 455 nm, so as to remove spectral contamination caused by pseudo-fluorescence (Kim, Chen, & Mehl, 2001). Therefore, the effective fluorescence spectrum collected by the spectrometer ranged from 455 to 1000 nm.…”

Section: Image Acquisition and Data Extractionmentioning

confidence: 99%

Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Sun

et al. 2019

Journal of Food Science

View full text Add to dashboard Cite

In order to rapidly and nondestructively identify tea grades, fluorescence hyperspectral imaging (FHSI) technology was proposed in this paper. A total of 309 Tieguanyin tea samples with three different grades were collected and the fluorescence hyperspectral data was acquired by hyperspectrometer (400 to 1000 nm). The characteristic wavelengths were respectively selected by Bootstrapping Soft Shrinkage (BOSS), Variable Iterative Space Shrinkage Approach (VISSA) and Model Adaptive Space Shrinkage (MASS) algorithms. Then, Support Vector Machine (SVM) was applied to establishing the relationship between the characteristic peaks, the full spectra, three characteristic spectra and the labels of tea grades. The results showed that VISSA‐SVM model had the best classification performance, but the model precision can still be improved. Thus, Artificial Bee Colony (ABC) algorithm was introduced to optimize the parameters of SVM model. The accuracy and Kappa coefficient of test set of VISSA‐ABC‐SVM model were improved to 97.436% and 0.962, respectively. Therefore, the combination of fluorescence hyperspectra with VISSA‐ABC‐SVM model can accurately identify the grade of Tieguanyin tea. Practical Application The rapid and accurate nondestructive tea grade identification method contributes to the construction of the tea online grade detection system. FHSI technology can solve the shortcomings of the reported methods and improved the identification accuracy of tea grades. It can be applied to the rapid detection of tea quality by tea companies, tea market, tea farmers and other demanders.

show abstract

Section: Image Acquisition and Data Extractionmentioning

confidence: 99%

Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Sun

et al. 2019

Journal of Food Science

View full text Add to dashboard Cite

show abstract

“…The entire process of image acquisition was operated in the dark cover to avoid the influence of external light. First, a high pass filter was placed in front of the camera lens to prevent transmittance of radiations smaller than 475 nm, so as to remove spectral F I G U R E 1 The structure of the fluorescence hyperspectral imaging system contamination caused by pseudo-fluorescence (Kim, Chen, & Mehl, 2001). Therefore, the hyperspectral image was captured only in the wavelength range of 475 to 1,000 nm.…”

Section: Fluorescence Hyperspectral Image Acquisitionmentioning

confidence: 99%

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Sun

et al. 2019

J Food Process Preserv

View full text Add to dashboard Cite

In this study, the feasibility of the fluorescence hyperspectral imaging (FHSI) technology to detect the viability of soybean seeds was investigated. Viable and nonviable seed samples were obtained by artificial aging method. Hyperspectral images of samples were collected by the FHSI device and then the spectral data were collected. Characteristic wavelengths were respectively selected by three variable selection methods, eliminating a large number of redundant information irrelevant to the viability of soybean seeds. Support vector machine (SVM) models based on the full spectra and the optimal spectral data were developed to identify the viability of soybean seeds. To further improve the accuracy of the model, the adaptive boosting (AdaBoost) algorithm was used. The results showed that the accuracy of the calibration and validation sets in the CARS‐SVM‐AdaBoost model (22 characteristic wavelengths) reached 100%, indicating that the combination of FHSI technology and the optimization model can greatly improve the recognition accuracy. Practical applications A rapid and accurate nondestructive identification method of viability of soybean seeds can contribute to the construction of the online seed viability detection system. FHSI technology has the advantages of high sensitivity and comprehensive analysis of sample information. Combined with the optimization model proposed in this paper, the recognition accuracy can be greatly improved. It can be applied to the online seed viability detection by seed companies, seed quality inspection departments, and soybean breeding units.

show abstract

“…Light was transmitted via two optical fibers to the sample surfaces to provide near-uniform illumination. The detailed information of system was described by Kim et al [28].…”

Section: Hyperspectral Image Acquisitionmentioning

confidence: 99%

“…In order to extract the actual spectral response of the samples, the influence of both the white and dark current image was removed and thus the calibrated image, I R , was achieved by the following equation [28].…”

Section: Data Extraction and Pretreatment Of Spectramentioning

confidence: 99%

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

et al. 2019

View full text Add to dashboard Cite

The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed.

show abstract

Hyperspectral Reflectance and Fluorescence Imaging System for Food Quality and Safety

Cited by 122 publications

References 20 publications

Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

Contact Info

Product

Resources

About