Browsing by Author "Yang, Haiqing Q."

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    Prediction of soil TN and TC at a farm-scale using VIS-NIR spectroscopy
    (2011-12-31T00:00:00Z) Yang, Haiqing Q.; Kuang, Boyan Y.; Mouazen, Abdul Mounem
    Building cost-effective models is of academic and practical value for fast measurement of soil properties, especially at a farm-scale. The aim of this study is to build quantitative models for soil total nitrogen (TN) and total carbon (TC) using visible and near infrared (VIS-NIR) spectroscopy. Dried samples (n=122) collected from an experimental farm, at Silsoe, Bedfordshire, United Kingdom, were scanned from 350 to 2500 nm at 1-nm intervals. Samples were divided into a calibration set (75%) and an independent validation set (25%). A partial least squares regression (PLSR) with leave-one-out cross validation was carried out based on different spectral ranges. Result shows that the best predictions (R2>0.90 and RPD>3.3) are achieved for TN using the VIS range (400- 700nm) and for TC using the VIS-NIR range (400-2500nm). It is concluded that VIS-NIR spectroscopy coupled with PLSR can be adopted for the prediction of soil TN and TC at a farm-scale.
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    Size estimation of tomato fruits based on spectroscopic analysis
    (2011-12-31T00:00:00Z) Yang, Haiqing Q.; Kuang, Boyan Y.; Mouazen, Abdul Mounem
    This study used visible and near-infrared (VIS-NIR) spectroscopy for size estimation of tomato fruits of three cultivars. A mobile, fibre-type, VIS-NIR spectrophotometer (AgroSpec, Tec 5, Germany) with spectral range of 350-2200 nm, was used to measure reflectance spectra of on-vine tomatoes growing from July to September 2010. Spectra were divided into a calibration set (75%) and an independent validation set (25%). A partial least squares regression (PLSR) with leave-one-out cross validation was adopted to establish calibration models between fruit diameter and spectra. Furthermore, the latent variables (LVs) obtained from PLS regression was used as input to back-propagation artificial neural network (BPANN) analysis. Result shows that the prediction of PLSR model can produce good performance with coefficient of determination (R2) of 0.82, root-mean-square error of prediction (RMSEP) of 4.87 mm and residual prediction deviation (RPD) of 2.35. Compared to the PLSR model, the PLS-BPANN model provides considerably higher prediction performance with R2 of 0.88, RMSEP of 3.98 mm and RPD of 2.89. It is concluded that VIS-NIR spectroscopy coupled with PLS-BPANN can be adopted successfully for size estimation of tomato fruits.