Development of new measurement methods to determine sugarcane quality from stalk samples

Recently, there has been a growing interest within the Australian sugarcane industry to measure sugarcane quality in the field to further improve product quality and value. However, conventional technologies for measuring sugarcane quality in a laboratory have limitations for uses in the field because they require sugarcane to be prepared as either juice or fibrated samples. In-field samples processing is very difficult and time-consuming, especially during harvest. Thus, the development of a rapid and efficient measurement technique which can be performed directly on stalk samples is highly desirable. In this thesis, a new quality measurement method for fresh sugarcane stalk samples was developed using a visible and shortwave near infrared spectroradiometer (VNIRS) with the wavelength ranging from 350 to 1075 nm. A light-proof measurement box was developed and used as an instrument platform to evaluate the capability of the VNIRS to measure quality parameters of sugarcane samples. The box was used to determine quality parameters using two newly proposed scanning methods: the skin scanning method (SSM) and the cross sectional scanning method (CSSM). These methods were applied on both whole stalk and internode samples. No preparation mechanism was required prior to the quality measurement on stalk samples. The selection of chemometrics methods used to optimise the regression models between spectral data and sugar content were also investigated. Partial least square (PLS) regression analysis with full cross validation (leave-one-out) technique was chosen to establish regression models between the spectral data and quality parameters. To improve the accuracy of the regression models, the spectral data was first pre-processed using the multiplicative scatter correction (MSC) method. Principal component analysis (PCA) was then used to extract useful information from the spectral data, decrease the noise and determine the optimum number of latent variables (LVs). The pre-processing methods, PLS and PCA exercises were run using Unscrambler V 9.6 software. The RPD (ratio of prediction to deviation) value was also used to evaluate the performance of the models. For whole stalk samples, it was found that the R2 for SSM and CSSM were 0.82 and 0.68, respectively. The calibration models for the fibrated, juice and whole stalk samples were developed using quality values obtained by standard industry procedures. For internode samples, the R2 for SSM and CSSM were 0.91 and 0.87, respectively. The calibration models for internode samples were developed using °Brix values obtained from a handheld refractometer. The RPD values of the prediction models for internode samples by both SSM and CSSM were 2, indicating that these newly proposed methods can be used for coarse quantitative prediction purposes. The variation of sugar content (°Brix) along the length of the stalks and internode samples were also assessed. The understanding of these variations can provide a foundation toward the design and development of the quality measurement system in the field. In this study, sugar content was found to vary significantly between the first and last internodes, with their average °Brix values being 22.2 and 7.6, respectively. The variation of sugar content between node and internode areas was 7.6% (SSM method) and 8.7% (CSSM method), respectively. To demonstrate the possible applications of the proposed methods on a harvester, a basic calculation and conceptual design for a proposed in-field quality measurement system was outlined using the VNIRS mounted on top of the elevator conveyor. The proposed system had the potential to sense billet samples based on SSM either by directly scanning the moving billets on the elevator or by scanning the billets supplied by a sampling mechanism using a vacuum system. This theoretical design has shown that it is technically possible to develop a quality measurement system on a sugarcane harvester. However, more work needs to be done before this proposed method can be successfully mounted on a harvester. Overall, it is concluded that the accuracy of the new measurement methods based on stalk samples using portable and low-cost VNIRS developed in this thesis is adequate. The proposed methods have significant potential uses as a tool for measuring sugarcane quality parameters from stalk samples in the field

Application of spectroscopic method to predict sugar content of sugarcane internodes

The aim of this study was to investigate the potential of near-infrared (NIR) reflectance spectroscopy for predicting sugar content of sugarcane from internode samples. NIR spectral data were measured using a full-range spectroradiometer (FRS) in the wavelength region between 350 and 2,500 nm based on cross sectional scanning method (CSSM) and skin scanning method (SSM). Statistical models were developed using the partial least square (PLS) to interpret the spectral data and develop calibration model for the sugar content (Brix) of sugarcane. Both CSSM and SSM had good prediction accuracies in predicting Brix values, with the corresponding correlation of determination (R2) values of 0.92 and 0.82 and root mean square error of prediction (RMSEP) of 1.03 and 1.50 Brix respectively. These results showed that the FRS can be used to predict the sugar content from internode samples using CSSM or SSM. However, CSSM was found to give better prediction accuracy than SSM. These findings showed that spectroscopic methods have the potential to be applied for rapid determination of sugar content from stalk samples in the fields

Prediction of total soluble solids and pH in banana using near infrared spectroscopy

The potential application of near infrared (NIR) spectroscopy in the range of wavelength from 1000 to 2500 nm to non-destructively determine total soluble solids (Brix) and pH values of bananas were evaluated. Thirty banana samples were measured at five different maturity stages. Each banana sample was scanned at three different locations (top, middle and bottom). The Brix and pH values were associated with the absorbance spectral data for the model development which were split into prediction and calibration sets. The partial least squares (PLS) model was built based on both data sets of banana samples. The prediction model for the Brix values obtained a coefficient of determination of 0.81 and root means square error of predictions of 3.91 Brix. The prediction model for pH values had an R2 of 0.69 and RMSEP of 0.36 pH. These findings proposed that near infrared spectroscopy has great potential to predict sugar content in bananas

Evaluation of a suitable thin layer model for drying of pumpkin under forced air convection

The thin layer drying kinetics of pumpkin slices (Cucurbita moschata) were experimentally investigated in a convective hot air dryer. In order to select the appropriate model for predicting the drying kinetics of pumpkin (Cucurbita moschata), twelve thin layer semi theoretical, theoretical and empirical models, widely used in describing the drying behaviour of agricultural products were fitted to the experimental data. The Page and Two term exponential models showed the best fit under certain drying conditions. The Hii et al. (2009) model, which was adopted from a combination of the Page and Two term models was compared to the other 11 selected thin layer models based on the coefficient of determination (R2) and sum of squares error (SSE). Comparison was made between the experimental and model predicted moisture ratio by non-linear regression analysis. Furthermore, the effect of drying temperature and slice thickness on the best model constants was evaluated. Consequently, the Hii et al. (2009) model showed an excellent fit with the experimental data (R2 > 0.99 and SSE < 0.012) for the drying temperatures of 50, 60, 70 and 80 °C and at different sample thicknesses of 3 mm, 5 mm and 7 mm respectively. Thus, the Hii et al. (2009) model can adequately predict the drying kinetics of pumpkin

Preliminary study to predict moisture content of jackfruit skin using shortwave near infrared spectroscopy

Moisture content of a jackfruit is one of the main attributes used by farmers to determine the maturity level of the fruit. The objective of this preliminary research was to explore the potential application of low-cost shortwave near infrared (VSWNIR) spectroscopy to non-destructively predict moisture content of jackfruit from their outer skin. A total of 870 skin portions collected from twenty-nine jackfruit samples were used in this study. After the spectral measurement, the skin portions were dried in the oven in order to measure their moisture content (%, wet basis, w.b.). Partial least square (PLS) method was used to develop both calibration and prediction models for calibrating the spectral data with the moisture content. This study found that the value of coefficient of determination (R2) and root means square error of calibration (RMSEC)were 0.65 and 2.17, respectively. For the prediction model, the value of R2and root mean square error of prediction (RMSEP) were 0.64 and 2.81, respectively. These results indicated the VSWNIR spectrometer is a promising technology for non-destructively predicting moisture content of jackfruits

Modelling the convective drying process of pumpkin (Cucurbita moschata) using an artificial neural network

This study investigated the drying kinetic of pumpkin under different drying temperatures (50, 60, 70 and 80°C), samples thickness (3, 4, 5 and 7mm), air velocity (1.2m/s) and relative humidity (40 - 50%). Kinetic models were developed using semi-theoretical thin layer models and multi-layer feed-forward artificial neural network (ANN) method. The Hii et al. (2009) semi-theoretical model was found to be the most suitable thin layer model while two hidden layers with 20 neurons was the best for the ANN method. The selections were based on the statistical indicators of coefficient of determination (R2), root mean square error (RMSE) and sum of squares error (SSE). Results indicated that the ANN demonstrated better prediction than those of the theoretical models with R2, RMSE and SSE values of 0.992, 0.036 and 0.207 as compared to the Hii et al. (2009) model values of 0.902, 0.088 and 1.734 respectively. The validation result also showed good agreement between the predicted values obtained from the ANN model and the experimental moisture ratio data. This indicates that an ANN can effectively describe the drying process of pumpkin

Prediction of sugarcane quality parameters using visible-shortwave near infrared spectroradiometer

This study was undertaken to explore the potential of spectroscopic method to predict sugarcane quality parameters by directly scanning the internode samples. Spectral data was collected from 125 internode using a visible-shortwave near infrared spectroradiometer (VNIRS). The spectral data was calibrated using Partial Least Square (PLS) method against the reference values of Brix, fibre content (FC) and moisture content (MC). The prediction results for Brix, FC and MC as represented by coefficient of determination (R2) were 0.88, 0.93 and 0.90, respectively. These results suggested that the spectroscopic method could be used to predict sugarcane quality parameters with good accuracy

Determination of tensile properties for twisted fibre bundles of oil palm empty fruit bunch at different diameters

The potential use of natural fibre extracted from oil palm empty fruit bunches has gained wide attention among researchers. This natural fibre comes from fibrous strands which form fibre bundle after shredding process at a mill. The measurement of tensile properties is important to understand the mechanical performance of this fibre bundle. This study was undertaken to determine the tensile properties of the fibre bundle from oil palm empty fruit bunch (OPEFB). Fibrous strands of the OPEFB extracted from shredded empty fruit bunches were twisted to form fibre bundle specimens at different diameters varying from 1 to 5 mm. The tensile properties measured in this study including tensile strength, tensile load and tensile modulus. The measurements were performed using Instron Universal Test Machine (IUTM) model 5000. From the results, it was found that the specimens at 1 and 5 mm in diameter required 71.25 and 429.68 N of the tensile load to break, respectively. The specimen with 1 mm in diameter recorded the highest tensile strength of 90.72 MPa while the specimen with 5 mm in diameter recorded only 21.88 MPa. The highest tensile modulus with value of 662.50 MPa was obtained from the specimen with 1 mm in diameter while the specimen with 5 mm in diameter had the tensile modulus value of 157.47 MPa. It was also found that the tensile strength and tensile modulus decreased when the diameter of the specimens increased. The findings reported in this study can serve as an engineering basis for the design specification in the development of the future in-silo composting machine

PREDICTION OF TOTAL SOLUBLE SOLIDS AND PH IN BANANA USING NEAR INFRARED SPECTROSCOPY

The potential application of near infrared (NIR) spectroscopy in the range of wavelength from 1000 to 2500 nm to non-destructively determine total soluble solids (Brix) and pH values of bananas were evaluated. Thirty banana samples were measured at five different maturity stages. Each banana sample was scanned at three different locations (top, middle and bottom). The Brix and pH values were associated with the absorbance spectral data for the model development which were split into prediction and calibration sets. The partial least squares (PLS) model was built based on both data sets of banana samples. The prediction model for the Brix values obtained a coefficient of determination of 0.81 and root means square error of predictions of 3.91 Brix. The prediction model for pH values had an R2 of 0.69 and RMSEP of 0.36 pH. These findings proposed that near infrared spectroscopy has great potential to predict sugar content in bananas

The effect of crop parameters on mechanical properties of oil palm fruitlets

Oil palm fruitlets are highly susceptible to mechanical damage during harvesting and handling. In this study, the effect of crop parameters and two loading orientations on mechanical properties of oil palm fruitlets were investigated. The investigated crop parameters were four mass categories (11–11.9, 12–12.9, 13–13.9 and 14–14.9 g), two varieties (Tenera and Dura) and three ripeness stages (underripe, ripe and overripe). For impact loading, both vertical and horizontal loading orientations were studied. The investigated mechanical properties were rupture force, rupture energy, deformation at rupture and specific deformation. The mechanical properties were measured using a pendulum impact load test device. A total of 540 fruitlet samples extracted from 18 fresh fruit bunches (FFB) were employed. The results showed that the values of rupture force and energy for all mass categories under the vertical loading orientation were lower than their values under the horizontal loading orientation. The maximum rupture forces of 697.1 and 939.4 N were recorded for the highest mass category (14–14.9 g) under the vertical and horizontal loading orientations, respectively. The results also showed that the values of all mechanical properties belong to Tenera variety were higher than that of Dura variety under the both loading orientations. In terms of the ripeness stage, the rupture force and energy of oil palm fruitlets decreased as the ripeness stage increased. The maximum rupture force and energy of 940 N and 2.95 Nm were recorded for ripe stage under the horizontal loading orientation, respectively. The results from this study would be very useful in designing a proper harvesting and in-field transportation systems to minimize crop damage due to impact loading