Development of the partial least-squares model to determine the soluble solids content of sugarcane billets on an elevator conveyor

Abstract This study aimed to determine the optimum multivariate model for monitoring the soluble solids content (SSC) of sugarcane billets being transferred on a conveyor. The study covered two main issues: the exploration of an appropriate spectral range (450–900 nm versus 700–900 nm) and the assessment of the influence of different levels of cane billets on an elevator via modelling to predict the SSC values. Partial least squares regression (PLSR) was used for model development. Modelling using the range of 450–900 nm employed 4 latent variables (LVs) and showed the coefficient of determination (R2) and root mean squares error of prediction (RMSEP) of 0.83 and 0.29 °Brix, respectively. This caused the model established using the range of 700–900 nm, employed 3 LVs and provided the R2 and RMSEP values of 0.81 and 0.31 °Brix, respectively, seems more appropriate. In case of assessing the different cane levels on the conveyor, the outcomes presented model performance of the full and half cane levels in predicting half and full cane datasets with R2 and RMSEP of 0.52 and 0.55 °Brix and 0.53 and 0.48 °Brix, respectively. This showed that the different levels affected the SSC predictive accuracy of the model. The combined model was developed to cover variations of this difference and was used to predict two external sets. The predictions of ninety and thirty samples that were collected from the same and different growing seasons as the samples for the modelling presented the R2, RMSEP and RPD of 0.70, 0.42 °Brix and 1.83 and 0.56, 0.42 °Brix and 2.00, respectively.

[1]  Tomoko Watanabe,et al.  The effect of path length, light intensity and co-added time on the detection limit associated with NIR spectroscopy of potassium hydrogen phthalate in aqueous solution , 2017, PloS one.

[2]  Robert G. V. Bramley,et al.  Opportunities for improving the management of sugarcane production through the adoption of precision agriculture: An australian perspective , 2001 .

[3]  E. Pourbasheer,et al.  The comparison of partial least squares and principal component regression in simultaneous spectrophotometric determination of ascorbic acid, dopamine and uric acid in real samples , 2017 .

[4]  Qin Zhang,et al.  Sugarcane Harvester Technology: A Critical Overview , 2014 .

[5]  Knut Kvaal,et al.  Analysing Complex Sensory Data by Non-Linear Artificial Neural Networks , 1996 .

[6]  Antonella Zanobetti,et al.  The Impact of Sugar Cane–Burning Emissions on the Respiratory System of Children and the Elderly , 2006, Environmental health perspectives.

[7]  V. Giovenzana,et al.  Use of visible and near infrared spectroscopy with a view to on-line evaluation of oil content during olive processing , 2018, Biosystems Engineering.

[8]  Trygve Helgerud,et al.  Towards on-line prediction of dry matter content in whole unpeeled potatoes using near-infrared spectroscopy. , 2015, Talanta.

[9]  Sergey V. Kucheryavskiy,et al.  Predicting pear (cv. Clara Frijs) dry matter and soluble solids content with near infrared spectroscopy , 2014 .

[10]  A. Gitelson,et al.  Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit , 2003 .

[11]  John Alderman,et al.  Issues in development of NIR micro spectrometer system for on-line process monitoring of milk product , 2003 .

[12]  J. Guthrie,et al.  Application of commercially available, low-cost, miniaturised NIR spectrometers to the assessment of the sugar content of intact fruit , 2000 .

[13]  Guangnan Chen,et al.  In-field measurement and sampling technologies for monitoring quality in the sugarcane industry: a review , 2014, Precision Agriculture.

[14]  Zou Xiaobo,et al.  Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of ‘Fuji’ apple based on different PLS models , 2007 .

[15]  J. Hussain,et al.  Fluorescence spectroscopy-partial least square regression method for the quantification of quercetin in Euphorbia masirahensis , 2018, Measurement.

[16]  Tormod Næs,et al.  A user-friendly guide to multivariate calibration and classification , 2002 .

[17]  Tom Fearn,et al.  Practical Nir Spectroscopy With Applications in Food and Beverage Analysis , 1993 .

[18]  John Billingsley,et al.  A sensor for the sugar cane harvester topper , 2002 .

[19]  Xiuqin Rao,et al.  Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system , 2014 .

[20]  S. Shah,et al.  Modeling Microwave Drying Kinetics of Sugarcane Bagasse , 2010 .

[21]  Nazmi Mat Nawi,et al.  Development of new measurement methods to determine sugarcane quality from stalk samples , 2014 .

[22]  S. Wongpichet,et al.  A portable near infrared spectrometer as a non-destructive tool for rapid screening of solid density stalk in a sugarcane breeding program , 2018, Sensing and Bio-Sensing Research.

[24]  Guangnan Chen,et al.  Visible and Shortwave near Infrared Spectroscopy for Predicting Sugar Content of Sugarcane Based on a Cross-Sectional Scanning Method , 2013 .

[25]  A. Peirs,et al.  Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .

[26]  Jiewen Zhao,et al.  Color compensation and comparison of shortwave near infrared and long wave near infrared spectroscopy for determination of soluble solids content of ‘Fuji’ apple , 2016 .

[27]  J. Posom,et al.  On-line measurement of activation energy of ground bamboo using near infrared spectroscopy , 2019, Renewable Energy.

[28]  Ulrich Klute.  Microwave measuring technology for the sugar industry. , 2007 .

[29]  T. Suwonsichon,et al.  Installation design of on-line near infrared spectroscopy for the production of compound fertilizer , 2020 .

[30]  Panmanas Sirisomboon,et al.  Evaluation of pyrolysis characteristics of milled bamboo using near-infrared spectroscopy , 2017 .

[31]  Stuart O. Nelson.  Potential Agricultural Applications for RF and Microwave Energy , 1987 .

[32]  Vasu Udompetaikul,et al.  An online visible and near-infrared spectroscopic technique for the real-time evaluation of the soluble solids content of sugarcane billets on an elevator conveyor , 2018, Comput. Electron. Agric..

[33]  K. Walsh,et al.  Short-Wavelength Near-Infrared Spectra of Sucrose, Glucose, and Fructose with Respect to Sugar Concentration and Temperature , 2003, Applied spectroscopy.

[34]  Guangnan Chen,et al.  Prediction and classification of sugar content of sugarcane based on skin scanning using visible and shortwave near infrared , 2013 .

[35]  G. Montague,et al.  Towards online Near-Infrared spectroscopy to optimise food product mixing , 2019 .

[36]  D. Cozzolino,et al.  The Effect of Path Length on the Measurement Accuracies of Wine Chemical Parameters by UV, Visible, and Near-Infrared Spectroscopy , 2017, Food Analytical Methods.

[37]  Xuesong Jiang,et al.  On-line detection of toxigenic fungal infection in wheat by visible/near infrared spectroscopy , 2019, LWT.