Evaluation of <scp>Dianhong</scp> black tea quality using near‐infrared hyperspectral imaging technology

Ren, Guangxin; Wang, Yujie; Ning, Jingming; Zhang, Zhengzhu

doi:10.1002/jsfa.10836

Cited by 18 publications

(4 citation statements)

References 35 publications

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“…The gray-level co-occurrence matrix is a classical statistical texture analysis tool in which image texture features can be extracted by means of statistical approaches from the co-occurrence matrix ( Khodabakhshian and Emadi, 2018 ; Ren et al, 2021 ). The GLCM measures the probability that a pixel of a particular gray level occurs at a specified direction and a distance from its neighboring pixels.…”

Section: Methodsmentioning

confidence: 99%

Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods

Wang

Huang²,

Tian³

et al. 2022

Front. Plant Sci.

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The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000–2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment.

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Section: Methodsmentioning

confidence: 99%

Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods

Wang

Huang²,

Tian³

et al. 2022

Front. Plant Sci.

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“…As for the spectral data distributed in high dimensional space, some of the dimensions are useless or even disturbed, which will influence subsequent modeling results (Zhang et al, 2020). Relevant studies have shown that CARS and IRIV have been widely used in feature selection of spectral data due to their good dimensionality reduction effect and high correlation between selected variable combinations and indicators (Ahmad et al, 2021; Ren et al, 2020). Therefore, CARS and IRIV were adopted to extract the feature wavelengths from the full spectral data.…”

Section: Methodsmentioning

confidence: 99%

Nondestructive detection for adulteration of panax notoginseng powder based on hyperspectral imaging combined with arithmetic optimization algorithm‐support vector regression

Zhang

Shi

et al. 2022

J Food Process Engineering

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Illegal merchants adulterate the powder of panax notoginseng rhizome and main root with the powder of panax notoginseng fibrous root, which reduces the efficacy and hygiene of panax notoginseng powder. To detect the adulteration of panax notoginseng powder nondestructively and rapidly, in this study, the hyperspectral images of 80 samples of adulterated rhizome powder and adulterated main root powder (AR & AM) were collected by using a hyperspectral image acquisition system (400–1000 nm). Then, Savitzky–Golay and standard normalized variable (SG‐SNV) were used to preprocess the spectrum and improve the signal‐to‐noise ratio of the data. Next, competitive adaptive reweighed sampling (CARS) and iteratively retains informative variables (IRIV) methods were respectively adopted to select feature wavelengths from the spectra data after pretreatment. Subsequently, support vector regression (SVR) was used to establish the prediction models based on the selected variables and the full spectra data. Also, the arithmetic optimization algorithm (AOA) was used to optimize the parameters of the SVR model. The results showed that CARS‐AOA‐SVR was the optimal model for AR & AM, achieving normalRnormalP2 of .9693 and .9667, RMSEP of .0266 and .0264, respectively. Therefore, the hyperspectral imaging combined with the CARS‐AOA‐SVR is a feasible method to detect the adulteration concentration of panax notoginseng powder. Practical Applications The traditional detection methods for adulteration of panax notoginseng powder depended on manual detection and chemical analysis. In order to detect the adulterated concentration of panax notoginseng powder rapidly and accurately, the adulterated panax notoginseng powder was studied based on hyperspectral imaging. The results showed that hyperspectral imaging combined with CARS‐AOA‐SVR model could be used to quantitatively detect the adulteration concentration of panax notoginseng powder, which could be a fast, accurate, and nondestructive method for adulteration detection of panax notoginseng powder and other powdery materials.

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“…The substitution phenomenon is mainly found in agricultural products such as meat, tea, and cereals that have essential effects on the overall attributes due to factors such as variety, grade, and origin, and their prices and popularity can vary greatly depending on the characteristics and consumer preferences of the food products [2,4]. Traditional classifiers such as SVM and PLS-DA primarily identify the varieties, origins, and grades of agricultural products such as tea [74,73,76], coffee beans [78], cocoa beans [79], and chia seed [83] for adulteration and the accuracy of the models are above 90%. However, the sample size for establishing the traditional classification model is relatively small.…”

Section: Identification Of Food Adulterationmentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

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Evaluation of Dianhong black tea quality using near‐infrared hyperspectral imaging technology

Cited by 18 publications

References 35 publications

Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods

Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods

Nondestructive detection for adulteration of panax notoginseng powder based on hyperspectral imaging combined with arithmetic optimization algorithm‐support vector regression

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

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