NIR Hyperspectral Imaging for Plastics Classification

Introduction N ear infrared (NIR) hyperspectral imaging (HSI) has been widely used in the pharmaceutical and food industries for quality monitoring and process control. This spread in applications has been mainly due to the development of instrumentation, computational ability in handling large amounts of data and data processing methods that can extract relevant information from the images. Hyperspectral images are threedimensional images in which a complete spectrum is measured for each spatial pixel. When the spectrum is recorded in the NIR region, an NIR hyperspectral image is obtained. Partial Least Squares-Discriminant Analysis (PLS-DA) is one of the most commonly used data analysis methods for classification of samples on the basis of conventional spectroscopy. When NIR hyperspectral images of different samples are recorded, PLS-DA may be applied to the spectral profiles of the different samples in order to classify them. As usual, a previous calibration step involving known samples is necessary so that PLS-DA is able to classify unknown samples. Plastic classification is an important field in which NIR HSI could be applied; correct identification and classification of plastics would enhance recycling efficiencies. Recently, some attempts at plastic classification by optical methods such as NIR, Raman spectroscopy, Fourier transform infrared (FT-IR) or differential scanning calorimetry (DSC) have been reported, but the existing literature on HSI-based methods is limited. HSI offers the important added benefit of being able to spatially map the location of various different plastic objects in a scene on the basis of their spectral signatures. In this study, we considered the potential of NIR HSI combined with PLS-DA for the classification of different types of polymers. Materials and methods The polymers studied were low-density polyethylene (LDPE), high-density polyethylene (HDPE), polypropylene (PP) and polystyrene PS. Three images have been analysed, denoted sample 1, sample 2 and sample 3. In Figure 1, a false colour image of sample 1 is shown. Samples 2 and 3 can be seen in Figure 2. Each of the images has two sides, the left side consists of a piece of each polymer type (individual polymers) and the right side consists of a mixture of seven pieces of each polymer (polymer mixture). In two of the images (samples 2 and 3), some of the mixed pieces of polymers are overlapped whereas in the other image (sample 1) the pieces are mixed but not overlapped. The initial dimensions of each image were 320 rows × 580 columns × 121 bands, where the first two dimensions (320 × 580) refer to the pixel or spatial dimension of the surface and the last dimension (121) refers to the number of wavelengths of each spectrum measured at each of the pixels. The NIR wavelength interval recorded spanned the range from 880 nm to 1720 nm with a resolution of 7 nm. PLS-DA is widely-used and known for sample classification. In this application, it consisted of a PLS2-based regression in which the response variables were categorical, expressing the class membership of the statistical units. It is a supervised method and it requires an initial calibration step involving some samples belonging to each known class. 2