Color Measurement of Tea Leaves at Different Drying Periods Using Hyperspectral Imaging Technique

Xie, Chuanqi; Liu, Yuanyuan; Shao, Yimin; He, Yong

doi:10.1371/journal.pone.0113422

Cited by 42 publications

(19 citation statements)

References 35 publications

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“…Effective wavelengths selection is a significant step in spectral analysis. Usually, the selected wavelengths can simplify the model and produce equivalent or improved models compared with full spectral wavelengths [22,23] . PCA-loading was carried out to select the most useful wavelengths in this study.…”

Section: Wavelengths Selectionmentioning

confidence: 99%

Spectral reflectance response to nitrogen fertilization in field grown corn

Xie¹,

Yang²,

Hummel³

et al. 2018

International Journal of Agricultural and Biological Engineering

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This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn crops. Four different nitrogen treatments of 0%, 80%, 100% and 120% BMP (best management practice) were studied. Principal component analysis-loading (PCA-loading) was used to identify the effective wavelengths. Partial least squares (PLS) and multiple linear regression (MLR) models were built to predict different nitrogen values. Vegetation indices (VIs) were calculated and then used to build more prediction models. Both full and selected wavelengths-based models showed similar prediction trends. The overall PLS model obtained the coefficient of determination (R 2 ) of 0.6535 with a root mean square error (RMSE) of 0.2681 in the prediction set. The selected wavelengths for overall MLR model obtained the R 2 of 0.6735 and RMSE of 0.3457 in the prediction set. The results showed that the wavelengths in visible and near infrared region (350-1000 nm) performed better than the two either spectral regions (1001-1350/1425-1800 nm and 2000-2400 nm). For each data set, the wavelengths around 555 nm and 730 nm were identified to be the most important to predict nitrogen rates. The vogelmann red edge index 2 (VOG 2) performed the best among all VIs. It demonstrated that spectral reflectance has the potential to be used for analyzing nitrogen response in corn.

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Section: Wavelengths Selectionmentioning

confidence: 99%

Spectral reflectance response to nitrogen fertilization in field grown corn

Xie¹,

Yang²,

Hummel³

et al. 2018

International Journal of Agricultural and Biological Engineering

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“…The final aim of selecting effective wavelengths is to establish a subset of spectral wavelengths to replace the whole ones. The selected wavelengths can be equal to or even more effective than the full wavelengths [26] . Also, these wavelengths can not only simplify the model but also be used to develop a multispectral imaging classification system.…”

Section: Wavelength Selectionmentioning

confidence: 99%

Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging

Xie

2018

International Journal of Agricultural and Biological Engineering

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This study was carried out to investigate the feasibility of using visible and near infrared hyperspectral imaging for the variety classification of mung beans. Raw hyperspectral images of mung beans were acquired in the wavelengths of 380-1023 nm, and all images were calibrated by the white and dark reference images. The spectral reflectance values were extracted from the region of interest (ROI) of each calibrated hyperspectral image, and then they were treated as the independent variables. The dependent variables of four varieties of mung beans were set as 1, 2, 3 and 4, respectively. The extreme learning machine (ELM) model was established using full spectral wavelengths for classification. Modified gram-schmidt (MGS) method was used to identify effective wavelengths. Based on the selected wavelengths, the ELM and linear discriminant analysis (LDA) models were built. All models performed excellently with the correct classification rates (CCRs) covering 99.17%-99.58% in the training sets and 99.17%-100% in the testing sets. Fifteen wavelengths (432 nm,

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“…They highlight different contaminants that are critical for food safety and show how hyperspectral imaging can be used for early detection of contamination. Detailed work on the use of spectral and hyperspectral imaging in drying has been performed on tea [6], carrots [7], soy beans [8], and apples [9][10][11][12]. The use of hyperspectral imaging in the drying of mangoes has been studied by Pu and Sun, who investigated the changes in moisture content when drying mango slices in a microwave [13].…”

Section: Introductionmentioning

confidence: 99%

High End Quality Measuring in Mango Drying through Multi-Spectral Imaging Systems

et al. 2020

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In modern fruit processing technology, non-destructive quality measuring techniques are sought for determining and controlling changes in the optical, structural, and chemical properties of the products. In this context, changes inside the product can be measured during processing. Especially for industrial use, fast, precise, but robust methods are particularly important to obtain high-quality products. In this work, a newly developed multi-spectral imaging system was implemented and adapted for drying processes. Further it was investigated if the system could be used to link changes in the surface spectral reflectance during mango drying with changes in moisture content and contents of chemical components. This was achieved by recovering the spectral reflectance from multi-spectral image data and comparing the spectral changes with changes of the total soluble solids (TSS), pH-value and the relative moisture content xwb of the products. In a first step, the camera was modified to be used in drying, then the changes in the spectra and quality criteria during mango drying were measured. For this, mango slices were dried at air temperatures of 40–80 °C and relative air humidities of 5%–30%. Samples were analyzed and pictures were taken with the multi-spectral imaging system. The quality criteria were then predicted from spectral data. It could be shown that the newly developed multi-spectral imaging system can be used for quality control in fruit drying. There are strong indications as well, that it can be employed for the prediction of chemical quality criteria of mangoes during drying. This way, quality changes can be monitored inline during the process using only one single measuring device.

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Color Measurement of Tea Leaves at Different Drying Periods Using Hyperspectral Imaging Technique

Cited by 42 publications

References 35 publications

Spectral reflectance response to nitrogen fertilization in field grown corn

Spectral reflectance response to nitrogen fertilization in field grown corn

Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging

High End Quality Measuring in Mango Drying through Multi-Spectral Imaging Systems

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