Rapid Evaluation of Biomass Properties Used for Energy Purposes Using Near-Infrared Spectroscopy

The parameters corresponding to combustion and pyrolysis such as proximate parameter (emissions), calorific value, elemental component, pyrolysis characteristics (temperature), and thermal properties are necessary to the thermal conversion process and the trading of biomass. Traditionally, these parameters of wood chips, milled wood, and biomass pellets are determined with chemicals, time-consuming, and required technical experts, such as thermogravimetry, bomb calorimetry, dry oven, muffle furnace, and so on. The near-infrared (NIR) spectroscopy is a rapid, noncontact no-chemical measurement. For NIR spectroscopy, only 2–3 seconds are used for evaluation, and it could be used for online measurement. The application of NIR spectroscopy in the estimation of the biomass characteristics of wood chips, milled wood, and biomass pellets is described in this chapter

Effects of Waxy Types of a Sugarcane Stalk Surface on the Spectral Characteristics of Visible-Shortwave Near Infrared Measurement

Precision of spectroscopic methods were frequently affected by the identity of the inhomogeneous materials, especially for direct scanning. This research aimed to investigated effects of waxy types, naturally founded on cane surface, on spectral characteristic. A portable Vis/SWNIR instrument with interactance mode across wavelength of 570-1031 nm were used for direct scanning on cane stalk. Principle component analysis (PCA) was applied to examine the differences of spectra scanned from 180 samples including 3 types of waxy type; white, black, and mixed black and white. Seven widespread pretreatments were employed to reduce the effect of waxy types. Results show that spectra of samples with each waxy type was separated in groups and SNV pretreatment gave the best results but was not able to eliminate the effect compared to the wax-removed samples.  Meanwhile, the standard deviation of absorbance values, at the wavelength of 760, 904 and 970 nm of 3 samples, was used for assessing the repeatability and reproducibility. The samples with removing waxy cover provided lower the standard deviation of absorbance values of spectra than the best pretreated spectra using standard normal variate (SNV) of the samples without removing waxy cover by one to six times. Thus, the waxy material on cane surface should be removed before collect spectra

Two Different Portables Visible-Near Infrared and Shortwave Infrared Region for On-Tree Measurement of Soluble Solid Content of Marian Plum Fruit

The goal of this study was to predict the soluble solid content (SSC) of on-tree Marian plum fruit using two different wavelength range and algorithm. One of these was the commercial dispersion NIR spectrometer (MicroNIR 1700), providing shortwave infrared (SWIR), while the other was a making diode array spectrometer giving visible-near infrared (Vis-NIR). To search optimal model, the analytical ability of the two wavelength ranges spectrometers coupled with two algorithms: i.e. partial least squares regression (PLSR) and support vector machine regression (SVR), were investigated. Different spectral pre-processing methods were tested. The model providing the lowest root mean square errors of prediction (RMSEP) was selected. Overall, the proposed outcome was that the performance of SWIR was more accurate than Vis-NIR spectrometer, and that both SWIR and Vis-NIR coupled with PLSR algorithm had a higher accuracy than SVR algorithm. The best model for on-tree evaluation SSC was the SWIR constructed using the PLSR algorithm with the spectral pre-processing of the 2nd derivative, providing a coefficient of determination of calibration set (R2) of 0.81, a coefficient of determination of validation set (r2) of 0.76, RMSEP of 0.69 °Brix, and a relative standard error of prediction (RSEP) of 4.43%. The outcome showed that a portable SWIR spectrometer developed with PLSR could be used for monitoring the SSC of individual Marian plum fruit on-tree for quality assurance

Estimation of Sugar Content in Sugarcane (Saccharum spp.) Variety Lumpang 92-11 (LK 92-11) and Khon Kaen 3 (KK 3) by Near Infrared Spectroscopy

In this study, a non-destructive measuring method, near-Infrared (NIR) technique was used to evaluate the quality of sugarcane. Two sugarcane (Saccharum spp.) varieties viz., Lumpang 92-11 and Khon Kaen 3 were chosen for the test. The samples were collected for 3 years. The sugar contents were measured in terms of °Brix, %Pol, %Fiber, and Commercial Cane Sugar (CCS) values using the NIR technique and conventional laboratory testing for comparison. The Partial Least Squares Regression (PLSR) model was performed using 400 samples for each variety. The NIR models showed the coefficient of determination (R2) of 0.97, 0.90, 086 and 0.82 for °Brix, %Pol, %Fiber and CCS, respectively with the corresponding root mean square error of prediction (RMSEP) of 0.246, 0.512, 0.353 and 0.542. The results indicated that the modelling using °Brix gave the best estimation with the highest R2 and lowest RMSEP, indicating high accuracy and reliability. The modelling with %Pol and %Fiber gave the moderate estimations and that with CCS value gave the lowest accuracy. However, all the four modelling predictions were within the acceptable range and could thus be used in crops trading instead of the traditional method. It was more reliable, quicker, more comfortable and more environmentally friendly than the traditional method as it did not involve the use of the chemical

Cassava stalk detection for a cassava harvesting robot based on YOLO v4 and Mask R-CNN

The quality of fresh cassava roots can be increased through the use of precision equipment. As a first step towards developing an automatic cassava root cutting system, this study demonstrates the use of a computer vision system with deep learning for cassava stalk detection. An RGB image of a cassava tree mounted on a cassava-pulling machine was captured, and the YOLO v4 model and two Mask R-CNN models with ResNet 101 and ResNet 50 base architectures were employed to train the weights to predict the position of the cassava stalk. One hundred test images of stalks of various shapes and sizes were used to determine the grasping point and inclination, and the results from manual annotation were compared with the predicted results. Regarding localisation, Mask R-CNN with ResNet 101 gave a significantly higher performance than the other models, with an F1 score and a mean IoU of 0.81 and 0.70, respectively. YOLO v4 showed the highest correlation for the x- and y-coordinates for the prediction of the grasping point, with values for R2 of 0.89 and 0.53, respectively. For inclination prediction, Mask R-CNN with ResNet 101 and Mask R-CNN with ResNet 50 gave the same level of correlation, with values for R2 of 0.50 and 0.61, respectively. These results were acceptable for use as design criteria for developing a cassava rootcutting robot

Prediction of elemental components of ground bamboo using micro-NIR spectrometer

Abstract 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.</jats:p

Comparison of Analytical Ability of PLS and SVM Algorithm in Estimation of Moisture Content, Higher Heating Value, and Lower Heating Value of Cassava Rhizome Ground using FT-NIR Spectroscopy

Abstract FT-NIR spectroscopy coupled with chemometrics analysis was used for nondestructive estimation of moisture content (MC), higher heating value (HHV) and lower heating value (LHV) of cassava rhizome ground. The goal of this study was compared to the analytical ability of both algorithm between PLS and SVM. The purpose was to find the effective modelling technique. The outcome was found that PLS and SVM provided good accuracy in evaluation of energy properties, and could be utilized for quality assurance. PLS algorithm gave slightly higher accuracy than SVM algorithm for the prediction of MC, HHV, and LHV. PLS regression generated no difference between measured and predicted value. PLS and SVM regression showed R2 between 0.90-0.98 and 0.84-0.90 for all parameters, respectively. The pre-processing of 2nd derivative was suitable for the PLS and SVM regression to the modelling.</jats:p