Development of a classification algorithm for efficient handling of multiple classes in sorting systems based on hyperspectral imaging

Abstract

When dealing with practical applications of hyperspectral imaging, the development of efficient, fast and flexible classification algorithms is of the utmost importance. Indeed, the optimal classification method should be able, in a reasonable time, to maximise the separation between the classes of interest and, at the same time, to correctly reject possible outlier samples. To this aim, a new extension of Partial Least Squares Discriminant Analysis (PLS-DA), namely Soft PLS-DA, has been implemented. The basic engine of Soft PLS-DA is the same as PLS-DA, but class assignment is subjected to some additional criteria which allow samples not belonging to the target classes to be identified and rejected. The proposed approach was tested on a real case study of plastic waste sorting based on near infrared hyperspectral imaging. Household plastic waste objects made of the six recyclable plastic polymers commonly used for packaging were collected and imaged using a hyperspectral camera mounted on an industrial sorting system. In addition, paper and not recyclable plastics were also considered as potential foreign materials that are commonly found in plastic waste. For classification purposes, the Soft PLS-DA algorithm was integrated into a hierarchical classification tree for the discrimination of the different plastic polymers. Furthermore, Soft PLS-DA was also coupled with sparse-based variable selection to identify the relevant variables involved in the classification and to speed up the sorting process. The tree- structured classification model was successfully validated both on a test set of representative spectra of each material for a quantitative evaluation, and at the pixel level on a set of hyperspectral images for a qualitative assessment.

Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection

“…Different preprocessing strategies, according to the most applied to infrared spectral data [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ], including those related to plastic samples [ 10 , 15 , 17 , 48 , 49 , 50 , 51 , 52 , 53 ], were selected to build each pretreatment sequence, that is: Standard Normal Variate (SNV): SNV was applied to reduce the scattering effects in the spectral data and to obtain a general linearization of the relationship between signal and concentration [ 10 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]; Savitzky–Golay (SG) derivative: Derivatives are a common method used to remove unimportant baseline signal from data. SG first derivative filter was applied to emphasize the spectral differences with second polynomial order and 15 points window [ 10 , 33 , 34 , 35 , 36 , 37 , 40 ]; Multiplicative Scatter Correction (MSC): MSC is widely used for infrared data (such as SNV and derivation).…”

“…Different preprocessing strategies, according to the most applied to infrared spectral data [33][34][35][36][37][38][39][40][41][42][43], including those related to plastic samples [10,15,17,[48][49][50][51][52][53], were selected to build each pretreatment sequence, that is:…”

Evaluation of the effect of factors related to preparation and composition of grated Parmigiano Reggiano cheese using NIR hyperspectral imaging

“…Indeed, hyperspectral images (also named hypercubes) are three-dimensional data arrays with two spatial dimensions (x pixel rows and y pixel columns), accounting for pixel location on the sample surface, and one spectral dimension, corresponding to the λ wavelengths acquired in a specific spectral range (Amigo et al, 2015;Gowen et al, 2007Gowen et al, , 2008. Thanks to the possibility of coupling both spectral and spatial information, the applications of NIR-HSI are continuously growing in different research and technical areas, including food quality control (Huang et al, 2014;Lorente et al, 2012;Shan et al, 2020;Wu & Sun, 2013), implementation of online sorting systems for waste management (Bonifazi et al, 2018;Calvini et al, 2018), pharmaceutical analysis (Roggo et al, 2005), and many others (Caballero et al, 2020;Calvini, Ulrici, & Amigo, 2020). Thus, NIR-HSI is particularly suitable for the analysis of heterogenous food matrices, like P-R grated cheese samples, which are composed by particles derived from both cheese pulp and rind with different chemical and physical properties.…”

Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

“…Multivariate approach, HIS based, was largely adopted in many research fields [29][30][31][32] to manage the huge amount of data and to utilize the information to identify, to characterize and to sort ACM. The aim of the chemometric approach was to obtain a data dimensionality reduction for a better data spectral evaluation and to develop classification algorithm for an efficient handling of multiple classes when hyperspectral imaging [33][34][35][36][37][38][39][40][41] sorting strategies have to be set up. Furthermore, the proposed approach also represents a step forward with respect to safety, being applied with a minor exposure risk for workers.…”