Abstract
An investigation dedicated to evaluating a big issue in biorefineries, solid impurity in raw sugarcane, is presented. This relevant industrial sector requests a high-frequency, low-cost, and noninvasive method. Then, the developed method uses the averaged color values of ten color-scale descriptors: R (red), G (green), B (blue), their relative colors (r, g, and b), H (hue), S (saturation), V (value) and L (luminosity) from digital images acquired from 146 solid mixtures among sugarcane stalks and solid impurity � vegetal parts (green and dry leaves) and soil. The solid mixture of samples was prepared considering desirable and undesirable scenarios for the solid impurity amounts. The outstanding result was revealed by an artificial neural network (ANN), achieving 100% of accurate classifications for two ranges of raw sugarcane in the samples: from 90 to 100 wt% and from 41 to 87 wt%. Low-computational cost and a simple setup for image acquisition method could screen solid impurity in sugarcane shipments as a promising application.
Highlights
Image and color information has played an important role in analytical chemistry and can help solve many issues, mainly because of its versatility and availability of many low-cost devices for in loco or laboratory analysis (Capitán-Vallvey et al, 2007; Diniz, 2020; Pereira and Bueno, 2007; Pereira et al, 2011).Our research group has developed analytical methods to evaluate raw sugarcane to help the mills or biorefineries manufacturing process of this material routinely monitored as a consignment for payment purposes
The developed method uses the averaged color values of ten color-scale descriptors: R, G, B, their relative colors (r, g, and b), H, S, V and L from digital images acquired from 146 solid mixtures among sugarcane stalks and solid impurity — vegetal parts and soil
The outstanding result was revealed by an artificial neural network (ANN), achieving 100% of accurate classifications for two ranges of raw sugarcane in the samples: from 90 to 100 wt% and from 41 to 87 wt%
Summary
Image and color information has played an important role in analytical chemistry and can help solve many issues, mainly because of its versatility and availability of many low-cost devices for in loco or laboratory analysis (Capitán-Vallvey et al, 2007; Diniz, 2020; Pereira and Bueno, 2007; Pereira et al, 2011). Solid impurity in raw sugarcane is defined as the plant presence (tops, green, brown, and dry leaves) and the soil (Eggleston et al, 2010). This issue is impacted by the type of harvesting process, as harvesting green or burnt cane. According to approximately 0.97 of receiver operating characteristic (ROC) area curves for sensibility and specificity using PLS-DA and 1 for SIMCA and kNN These results were promising, the average color values were tested with no successful results. The main goal was to classify raw sugarcane in the presence of solid impurity using the ANN method, as the last part of series of investigations dedicated to this critical issue for sugar mills and biorefineries
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