Visual detection of the moisture content of tea leaves with hyperspectral imaging technology

Wei, Yuzhen; Wu, Feiyue; Xu, Jie; Sha, Junjing; Zhao, Zhijia; He, Yong; Liu, Yuanyuan

doi:10.1016/j.jfoodeng.2019.01.004

Cited by 80 publications

(32 citation statements)

References 37 publications

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“…Random frog (RF) algorithm -SPA (Wei et al, 2019) hyperspectral detection is realized by capturing the spectral signal reflected from the aflatoxin, which is relatively sensitive. In addition, the data collection does not result in the consumption of aflatoxin.…”

Section: Modeling Methodsmentioning

confidence: 99%

Application of hyperspectral technology in detection of agricultural products and food: A Review

Zhu

Huang

et al. 2020

Food Science & Nutrition

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China is the third largest country with land area in the world and is also a big agricultural country. Every year, the total amount of agricultural products is in the forefront of the world. Although the output of all kinds of food has increased, a problem that cannot be ignored has aroused people's attention, that is, food safety. In particular, major food safety incidents in recent years, such as "red heart" duck eggs, "melamine" incident, and "lean meat powder" pork, once made China's food industry into a state of decline. The whole industrial chain from primary agricultural products to commodity was greatly affected. These problems not only appear in China, but also in other countries of the world. For example in 2005, the food standard agency of the UK found that salmon sold in

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Section: Modeling Methodsmentioning

confidence: 99%

Application of hyperspectral technology in detection of agricultural products and food: A Review

Zhu

Huang

et al. 2020

Food Science & Nutrition

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“…Spectral derivation can eliminate spectral overlapping translation, enhance spectral characteristics, improve spectral resolution and highlight the changes of the spectra based on slope waveform [31]. The effects of spectral derivation are shown in Figure 3, where Figure 3 Generally, all the samples were divided into a calibration set and a prediction set [7], Kennard-Stone (KS) algorithm and sample set partitioning based on joint x-y distance (SPXY) algorithm were adopted to select a calibration set with 120 samples and a prediction set with 40 samples for analysis in this work [32].…”

Section: Spectral Pretreatmentmentioning

confidence: 99%

“…As moisture content generally varies throughout the production process, the traditional detection method (the gravimetric oven method) is time-consuming, and cumbersome. Several detection methods reported in literature such as nuclear magnetic resonance, microwave, capacitance, near-infrared spectroscopy, and hyperspectral imaging (HSI) have been used for rapid detection of moisture content in food [7,8]. Among them, HSI has attracted much attention from researchers due to its fast measurements, high accuracy, and non-destructive and non-toxic properties.…”

Section: Introductionmentioning

confidence: 99%

“…To solve the problems of processing high-dimensional data, researchers developed feature extraction methods used to reduce the dimensionality [14]. Wei [7] selected 12 and 11 feature bands for the front side and back side, respectively of tea using the random frog (RF) and successive projection algorithm (SPA) algorithm; Sun [15] used four feature selection algorithms to extract the most effective wavelengths of the tea. Zhou [16] used the partial least squares regression (PLSR) algorithm to extract characteristic wavelengths of lettuce leaves.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the moisture content of Daqu with hyperspectral imaging

Chen

Tian

et al. 2020

International Journal of Food Engineering

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Daqu, a Chinese liquor fermentation starter, contains all kinds of microorganisms and enzymes for Chinese liquor fermentation. The moisture content of Daqu significantly influence on the reproduction of microorganisms in Daqu. This work presents for the first time that determination of moisture content of Daqu with hyperspectral imaging. The characteristic spectrum of water is extracted based on comparative experiments with varying moisture content. The molds based on the full bands and feature bands were established by the support vector regression (SVR) method, which is used to predict moisture content of Daqu during fermentation process. The performance of the model based on the feature bands (R2 = 0.9870, root mean square error (RMSE) = 0.0091) is comparable to the full bands and the dimensions of the spectral information were significantly reduced. This work presents a novel, rapid and nondestructive approach for detecting the moisture content in Daqu and lays a foundation for the application of hyperspectral imaging.

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“…Based on the mass of the leaves at different dehydration stages, the water content is expressed as Fuel Moisture Content (FMC) with freshf -or dry basisd - [39,[52][53][54] and Equivalent Water Thickness (EWT) [52,53,[55][56][57][58]. The former depends only on the leaf mass (Equations (1) and (2)), while the latter also requires its area (Equation (3)):…”

Section: Water Content and Vegetation Indexesmentioning

confidence: 99%

Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile

Villacrés

Arevalo-Ramirez

Fuentes

et al. 2019

Sensors

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Fuel moisture content (FMC) proved to be one of the most relevant parameters for controlling fire behavior and risk, particularly at the wildland-urban interface (WUI). Data relating FMC to spectral indexes for different species are an important requirement identified by the wildfire safety community. In Valparaíso, the WUI is mainly composed of Eucalyptus Globulus and Pinus Radiata—commonly found in Mediterranean WUI areas—which represent the 97.51% of the forests plantation inventory. In this work we study the spectral signature of these species under different levels of FMC. In particular, we analyze the behavior of the spectral reflectance per each species at five dehydration stages, obtaining eighteen spectral indexes related to water content and, for Eucalyptus Globulus, the area of each leave—associated with the water content—is also computed. As the main outcome of this research, we provide a validated linear regression model associated with each spectral index and the fuel moisture content and moisture loss, per each species studied.

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Visual detection of the moisture content of tea leaves with hyperspectral imaging technology

Cited by 80 publications

References 37 publications

Application of hyperspectral technology in detection of agricultural products and food: A Review

Application of hyperspectral technology in detection of agricultural products and food: A Review

Predicting the moisture content of Daqu with hyperspectral imaging

Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile

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