Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method

Zhang, Xu; Sun, Jialiang; Li, Pengpeng; Zeng, Fanyi

doi:10.1016/j.lwt.2021.112295

Cited by 60 publications

(23 citation statements)

References 48 publications

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“…Multiplicative scatter correction (MSC) is a scattering correction effect, which helps to eliminate the scattering effect caused by the above reasons and enhance the spectral variability. The moving window smooth spectral matrix (Nirmaf) belongs to the smooth effect, which improve the signal-to-noise ratio of the spectrum and reduce the influence of random noise 16 , 17 . Preprocessing methods are different and related to each other.…”

Section: Methodsmentioning

confidence: 99%

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

Zhao

Pan

Wei

et al. 2022

Sci Rep

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Grass community classification is the basis for the development of animal husbandry and dynamic monitoring of environment, which has become a critical problem to further strengthen the intelligent management of grassland. Compared with grass survey based on satellite remote sensing, the visible near infrared (NIR) hyperspectral not only monitor dynamically in a short distance, but also have high dimensions and detailed spectral information in each pixel. However, the hyperspectral labeled sample for classification is expensive and manual selection is more subjective. In order to solve above limitations, we proposed a visible-NIR hyperspectral classification model for grass based on multivariate smooth mapping and extreme active learning (MSM–EAL). Firstly, MSM is used to preprocess and reconstruct the spectrum. Secondly, by jointing XGBoost and active learning (AL), the advanced samples with the largest amount of information are actively selected to improve the performance of target classification. Innovation lies in: (1) MSM global enhanced preprocessing spectral reconstruction algorithm is proposed, in which isometric feature mapping is effectively applied to the grass hyperspectral for the first time. (2) EAL framework is constructed to solve the issue of high cost and small number for hyperspectral labeled samples, at the same time, enhance the physical essence behind spectral classification more intuitively. A field hyperspectral collection platform is assembled to establish nm resolution visible-NIR hyperspectral dataset of grass, Grass1, containing 750 samples, which to verify the effectiveness of the model. Experiments on the Grass1 dataset confirmed that compared with the full spectrum, the time consumption of MSM was reduced by 9.471 s with guaranteed overall accuracy (OA). Comparing EAL with AL, and other classification algorithms, EAL improves OA 22.2% over AL, and XAL has the best performance value on Kappa, Macro, Recall and F1-score, respectively. Altogether, the lightweight MSM–EAL model realizes intelligent and real-time classification, providing a new method for obtaining high-precision inter group classification of grass.

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Section: Methodsmentioning

confidence: 99%

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

Zhao

Pan

Wei

et al. 2022

Sci Rep

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“…This is an efficient method of discrimination, better than other traditional algorithms [2]. The algorithm is intended to find a maximum margin hyperplane that can separate the data into a predefined class [18]. The SVM uses the kernel function to map data in a high dimensional space that can separate multiple categories.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…HSI [17] is a new conventional spectroscopy and computer-based vision technology [18]. HSI can provide spatial and spectral information on an object by integrating conventional spectroscopic and imaging technology [15].…”

Section: Introductionmentioning

confidence: 99%

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Huang

et al. 2021

Agriculture

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A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

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“…As a nondestructive testing method, spectroscopy has been widely used in tea species identification and quality testing [ 19 , 20 ]. Firmani et al [ 21 ] coupled NIR spectroscopy with PLS–DA and soft independent modelling of class analogies (SIMCA).…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics

Sun

et al. 2022

Foods

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Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS–DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS–DA. MSC_VIP_PLS–DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.

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Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method

Cited by 60 publications

References 48 publications

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics

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