Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning

Zou, Zhiyong; WU, Qingsong; Wang, Jian; Xu, Lijia; Zhou, Man; Lü, Zhiwei; He, Yong; Wang, Yuchao; Bi, Liu; Zhao, Yongpeng

doi:10.1016/j.saa.2022.121785

Cited by 10 publications

(7 citation statements)

References 56 publications

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“…The raw spectral data collected by the hyperspectral system is affected by a large amount of redundant information, resulting in a decrease in classification accuracy ( Zou et al., 2023 ). The raw spectral data collected by hyperspectral systems are composed of a large number of bands and have multicollinearity.…”

Section: Methodsmentioning

confidence: 99%

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

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Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.

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Section: Methodsmentioning

confidence: 99%

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

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“…This fluorescence hyperspectral camera has the advantages of high sensitivity and strong signal [20]. The fluorescence hyperspectral resolution was 2.8 nm, and the pixel size was 2048 × 946 [43]. In this system, a xenon lamp is used as the excitation light source for the fluorescence imaging system, and the fluorescence hyperspectral range of the system can be detected from 250 nm to 1100 nm.…”

Section: Fluorescence Hyperspectral Image Acquisitionmentioning

confidence: 99%

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

Kang,

Fan,

Zhan

et al. 2024

Molecules

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A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475–1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.

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“…Hyperspectral imaging technology has been applied in many fields, such as peanut (Zou et al, 2022b) germination prediction (Zou et al, 2022c), mildew detection (Zou et al, 2022a), hot pot (Zou et al, 2023) oil detection (Zou et al, 2022d), fruit grading (Zou et al, 2021), etc. Using physical and chemical methods to judge the quality of honey (Kek et al, 2017;Wan et al, 2018), the results are often very accurate (Shamsudin et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Honey quality detection based on near-infrared spectroscopy

et al. 2023

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As a natural agricultural product, honey is favored by consumers, and its variety and adulteration have a huge impact on the quality. Acacia honey, red jujube honey and rape honey were used as experimental objects, and their spectral reflectance curves were obtained through a near-infrared spectral image acquisition system. Spectral features were extracted from the preprocessed spectral reflectance curves, and a honey variety classification model based on near-infrared spectral features was established by machine learning. After statistical analysis, Principal Component Analysis Support Vector Machine after processing data through Successive Projections Algorithm (SPA-SVM) is the optimal classification model for three varieties of acacia honey, red jujube honey and rape honey, and the correct rate of honey variety classification reaches 95.83%. The spectral reflectance curve was used to establish a honey adulteration identification model based on the partial least squares-discriiminate analysis (PLS-DA), and the classification accuracy was 97.92% in the test set.

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Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning

Cited by 10 publications

References 56 publications

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

Honey quality detection based on near-infrared spectroscopy

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