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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 (r2) 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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The combustion performance depended on heating value of as-received bamboo which involved elemental components (C, H, N, O, S). The experiment using Micro-NIR spectrometer to investigate of 80 ground bamboo samples and partial least squares regression (PLSR) with cross validation technique was used to develop the model. The reference method was performed on the elemental analyser. It was showed that only C (carbon) was efficiently possible to determine with R2 approximately of 0.6, RMSECV 0.612%, SECV 0.616% and Bias 0.001%. Important peaks in regression coefficient plot at wavelengths 1174 nm and 1927 nm corresponded to C-H overtone and C=O str. 2nd overtone for lignin. At wavelength 1395 nm and 1728 nm were related to 2C-H str.+C-H def. and C-H str. overtone for hemi-cellulose. At wavelength 1580 nm, 1632 nm, 1779 nm, 1824 nm, and 2023 nm conformed respectively to O-H str.1st overtone, C-H str.1st overtone, C-H str. overtone, O-H str.+2C-O str. and 2O-H def.+C-O def. for cellulose.

The aim of this research was to investigate the effect of torrefaction temperature on 4 energy properties as high heating value (HHV), enhancement factor, solid yield, and energy yield of spent coffee ground (SCG). Four different torrefaction temperatures (200, 250, 300, and 350°C) were selected. Torrefaction process was conducted at the heating rate of 10°C/min. HHV and enhancement factor were the highest when SCG was torrefied at the highest temperature of 350°C. However, at this temperature, solid and energy yields were the lowest. Torrefaction temperature highly affected these four energy properties with R of higher than 0.9. Regression models representing the relationship between torrefaction temperature and HHV and energy yield were HHV = 0.0519T+15.917, R2 = 0.9483 and energy yield = -0.2743T+155.1, R2 = 0.9976. These models are helpful for prediction of the energy properties of SCG undergoing torrefaction process in the studied temperature range.

Abstract. The higher heating value (HHV) plays a significant role in determining of the energy potential of biomass. The method to receive this value using a bomb calorimeter takes a long time approximately of 15 minutes. The implementation of near infrared spectroscopy (NIR) technique by generating a prediction model can be replaced the former method in order to reduce time into 2 minutes. Therefore, the precision of both techniques have to be concerned in order to get the reliable values. The repeatability and reproducibility processes were carried out to evaluate the precision of both methods. It was found that the percentage difference of standard deviation (SD) from the mean of repeatability for the NIR was less than 5 % and for reproducibility was slightly over 5 % (but not over than 10%). The SD of repeatability and reproducibility of the bomb calorimeter were 54.6 J/g (0.3 % difference of average HHV value) and 81.3 J/g (0.5 % difference of average HHV value) respectively. The average HHV from the bomb calorimeter was 18,166.77±236.29 J/g. Both techniques showed very low percentage difference of the SD from the mean that revealed very high reliability and precision of measurements.

The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky–Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage.

Near Infrared (NIR) Spectroscopy is widely employed as a rapid technique for the evaluation of properties of biomass materials. Precision and accuracy of the instruments is an important aspect in order to minimize error in the determination of results. The objective of this publication is to determine scanning repeatability and reproducibility of the NIR spectrometer for wheat straw (Triticum aestivum L.), using either a fixed scan or a rotating scan. The former presented marginally better repeatability but worse reproducibility. Samples in equilibrium with the local atmosphere versus samples of controlled and different moisture contents were also compared, and the latter performed better on the precision test but both fixed and rotating scans. As the ultimate objective of this test is the use of this method to determine variations between different moisture content, and as the rotating scan presents better reproducibility, this method was selected as the reference method for further NIR analyses focused on the variation of moisture content.