Evaluation of the moisture content of Jatropha curcas kernels and the heating value of the oil-extracted residue using near-infrared spectroscopy

Posom, Jetsada; Sirisomboon, Panmanas

doi:10.1016/j.biosystemseng.2014.12.003

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…As reported by Posom and Sirisomboon [9], the band of cellulose and fiber had high impact to the forecast of the HV of the oil extracted residue of Jatropha curcas kernels. In addition, Zhang et al [10] also reported that the important band for estimating HHV of sorghum was the C-H stretching and CH 3 structure.…”

Section: Reference Data Of the Gcvmentioning

confidence: 79%

“…Near infrared (NIR) spectroscopy is a nondestructive and rapid technique for estimating a quality of food and agricultural products., In addition, there have been some reports that demonstrated the use of NIR spectroscopy for the evaluation of the GCV and other properties of biomass such as the evaluation of heating value of bamboo [1], the evaluation of moisture content and GCV of Leucaena leucocephala pellets [2], the prediction of GCV of Miscanthus [3], the GCV prediction of straw [4], the evaluation of moisture content and the GCV of oil-extracted residue of Jatropha curcas [9] and the analysis of GCV and elemental compositions of sorghum [10]. The results of their studies suggested that NIR spectroscopy had the potential to estimate the calorific value of biomass fuel.…”

Section: Introductionmentioning

confidence: 99%

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Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy

Posom

Nakawajana

2018

MATEC Web Conf.

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Abstract. The maize cob biomass is one of important biomass crops in Thailand. Nowadays, the use of the biomass as renewable resource is increasing, especially residue agriculture waste. As we know that the biomass properties impact combustion, in order to achieve boiler efficiency, its energy characteristics of biomass was required immediately before burning. This work uses the FT-near infrared spectroscopy to estimate gross calorific value (GCV) of maize cob as the rapid method. Each sample was scanned using diffuse reflectance mode at a wavenumber range between 12500-3600 cm and completed 32 scans per sample, then averaged to be one spectrum. The results showed that this technique could decrease a processing time to 1-2 minutes per sample to determine GCV whereas alternatively the current method used a processing time of 25-30 minutes per sample. The capacity of the model gave root mean square error of cross validation (RMSECV) of 91.1 Jg -1 , which was low. Hence, the model was acceptable and cloud be used for screening.

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Section: Reference Data Of the Gcvmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy

Posom

Nakawajana

2018

MATEC Web Conf.

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“…As a result, it is urgent to establish a fast, nondestructive, and accurate measurement method for moisture content prediction. e near-infrared (NIR) spectroscopy technique has been widely used for material content prediction, such as moisture content [6], nitrogen content [7], and chlorophyll content [8] of plants. To date, many researchers have constructed different NIR spectral calibration models to detect the moisture content of different plants such as sunflower [9], populous tree [10], and eucalyptus tree [11].…”

Section: Introductionmentioning

confidence: 99%

Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy

Zhang

Wang

2018

Journal of Electrical and Computer Engineering

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Masson pine is widely planted in southern China, and moisture content of the pine seedling leaves is an important index for evaluating the vigor of seedlings. For precisely predicting leaf moisture content, near-infrared spectroscopy analysis is applied in the experiment, which is a cost-effective, high-speed, and noninvasive material content prediction tool. To further improve the spectroscopy analysis accuracy, in this study, a new analysis model is proposed which integrates a stacked autoencoder for extracting hierarchical output-related features layer by layer and a support vector regression model to leverage these features for precisely predicting moisture contents. Compared with traditional spectroscopy analysis method like partial least squares regression and basic support vector regression, the proposed model shows great superiority for leaf moisture content prediction, with R2 value 0.9946 and root-mean squared error (RMSE) value 0.1636 in calibration set and R2 value 0.9621 and RMSE 0.4249 in prediction set.

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“…NIR spectroscopy was applied for the evaluation of moisture content of rice straw by Jin and Chen [17], Miscanthus x giganteus and short rotational coppice willow by Fagan et al [18], biomass pellet by Gillespie et al [19]. NIR spectroscopy has been used not only to measure the moisture content of biomass but also the composition of biomass like cellulose, hemicellulose, lignin [20,21], ash [18], heating value [22]. However, to this date no study has reported about potential of NIR spectroscopy to predict the moisture content of bamboo wood chips.…”

Section: Introductionmentioning

confidence: 99%

Rapid evaluation of moisture content in bamboo chips using diode array near infrared spectroscopy

Shrestha

Sirisomboon

2018

MATEC Web Conf.

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Moisture in biomass plays important role during storage, combustion and pelletization. In order to measure moisture content in bamboo chips, two diode array near-infrared instruments, NIR-Gun (600-1100 nm at 2 nm intervals) and Micro-NIR (1150-2150 nm at 7 nm intervals), were used for scanning bamboo chips. Total number of samples used for developing model after removing outliers was 252. The circumference and moisture content of bamboos used were in the range between 16-39 cm and 39-86% wet basis (wb) respectively. Partial least squares regression technique was used to develop the model to predict the moisture content in bamboo chips. The R2, SECV, SEP, bias and RPD of optimum model of NIR-Gun were found to be 0.924, 2.871% wb, 2.385% wb, -0.250% wb and 3.656, while for Micro-NIR model the values were found to be 0.743, 4.349% wb, 4.499% wb, 0.026% wb and 1.972 respectively. In prediction of moisture content in bamboo chip, both models show the effect of different constituents of bamboo more than moisture. This study indicates that the results are suitable for screening the moisture content in bamboo chips. This would be helpful for process controlling using the moisture parameter during drying, pelletization and thermochemical conversion.

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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

Cited by 18 publications

References 19 publications

Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy

Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy

Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy

Rapid evaluation of moisture content in bamboo chips using diode array near infrared spectroscopy

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