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Author: search.filters.author.Posom, JetsadaSubject: search.filters.subject.Moisture content

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    A low-cost system for moisture content detection of bagasse upon a conveyor belt with multispectral image and various machine learning methods
    (2021-05-01)
    Nakawajana, Natrapee
    ;
    Lerdwattanakitti, Patchara
    ;
    Saechua, Wanphut
    ;
    Posom, Jetsada
    ;
    Saengprachatanarug, Khwantri
    This research aimed to propose an online system based on multispectral images for the real-time estimation of the moisture content (MC) of sugarcane bagasse. The system consisted of a conveyor belt, four halogen bulbs, and a multispectral camera. The MC models were developed using machine learning algorithms, i.e., multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), PCA-ANN, Gaussian process regression (GPR), PCA-GPR, random forest regression (RFR), and PCA-GPR. The models were developed using 150 samples (calibration set) meanwhile the remaining 50 samples were applied as a validation set. The comparison of all developed models showed that the PCA-RFR model achieved better detection with a higher accuracy of MC prediction. The PCA-RFR model showed the best results which were a coefficient of determination of prediction (r<sup>2</sup> ) 0.72, root mean square error of prediction (RMSEP) 11.82 wt%, and a ratio of the standard error of prediction to standard deviation (RPD) of 1.85. The results show that this technique was very useful for MC rapid screening of the sugarcane bagasse.
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    Rapid non-destructive evaluation of moisture content and higher heating value of Leucaena leucocephala pellets using near infrared spectroscopy
    (2016-07-15)
    Posom, Jetsada
    ;
    Shrestha, Amrit
    ;
    Saechua, Wanphut
    ;
    Sirisomboon, Panmanas
    The MC (moisture content) and HHV (higher heating value) of Leucaena leucocephala pellets using NIR (near infrared) spectroscopy was investigated in this study. The MC of the pellets was adjusted by subjecting the samples to different relative humidity environments. The samples were scanned in diffuse reflection mode at wavenumbers of 12,500-4000 cm<sup>-1</sup>. Partial least squares regression models correlating the MC and HHV with the NIR spectra were developed and validated by full cross validation. The model for MC and HHV provided coefficients of determination (R<sup>2</sup>) of 0.995 and 0.964, a root mean square error of cross validation (RMSECV) of 0.187%wb and 79.2 J g<sup>-1</sup>, bias of -0.0008%wb and 1.29 J g<sup>-1</sup> and a RPD (ratio of prediction to deviation) of 13.9 and 5.30, respectively. The models had excellent accuracy. This rapid quality evaluation method may be used for trading of biomass pellets. An equation related MC and HHV was also developed.
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    Evaluation of the moisture content of Jatropha curcas kernels and the heating value of the oil-extracted residue using near-infrared spectroscopy
    (2015-02-01)
    Posom, Jetsada
    ;
    Sirisomboon, Panmanas
    The use of near-infrared spectroscopy for evaluation of moisture content of Jatropha curcas kernels and heating value of its residue after oil extraction were studied. In total, 100 samples of whole kernels from green, yellow and black fruits and oven-dried kernels scanned in diffuse reflection mode using a Fourier transform NIR spectrometer at wave numbers of 1,250,000-400,000m<sup>-1</sup> were used to develop moisture-predicting models. The corresponding residues after the oil extraction of the samples scanned in transflection mode using the same spectrometer and wave number range were used to develop the heating-value-predicting models. The models correlating the spectral data and the corresponding values measured using the reference method were developed by partial least squares regression and were validated using a test set. For the moisture content and heating value, coefficients of determination (R<sup>2</sup>) were 0.969 and 0.860, root mean square errors of prediction (RMSEP) were 4.0%wb and 360Jg<sup>-1</sup>, biases were -0.7%wb and -17Jg<sup>-1</sup> and ratios of prediction to deviation (RPD) were 5.7 and 2.6, respectively. In addition, vibration bands of fibre and cellulose had important effects on the prediction of the heating value.