Convolutional neural network for identi cation of plastic solid waste using near infrared spectroscopy
Jingjing Xia China Agricultural University Yue Huang China Agricultural University Qianqian Li Beijing University of Chinese Medicine Yanmei Xiong China Agricultural University Shungeng Min ( [email protected] ) China Agricultural University
Research Article
Keywords: Plastic solid waste, identi cation model, CNN, NIR
Posted Date: March 30th, 2021
DOI: https://doi.org/10.21203/rs.3.rs-245885/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Convolutional neural network with near infrared for plastic waste 2 classification
3 Jingjing Xiaa, Yue Huangb, Qianqian Lic, Yanmei Xionga*, Shungeng Mina**
4 a College of Science, China Agricultural University, Beijing 100193, PR China 5 b College of Food Science and Nutritional Engineering, China Agricultural University, 6 Beijing 100193, PR China 7 c School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing 8 100029, China 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 * Corresponding author. 28 ** Corresponding author. 29 Email addresses: [email protected] (Yanmei Xiong), [email protected] (Shungeng 30 Min).
1 31 Abstract: Plastic waste is a relevant challenge for waste management sector and further
32 technological means has to be urgently researched. Near infrared spectroscopy (NIR), as
33 a non-destructive, cost-effective and mature technology, is currently operational in some
34 waste-sorting facilities. However, NIR remains challenging to discriminate different
35 black plastics because black targets have low reflectance in the its spectral region.
36 Therefore, this study aimed to address the problem in black plastic classification. Seven
37 kinds of plastics: High Impact Polystyrene (HIPS), Acrylonitrile Butadiene Styrene
38 (ABS), High Density Polyethylene (HDPE), Polyethylene terephthalate (PET),
39 Polyamide 66 (PA66), Polycarbonate (PC) and Polypropylene (PP), were prepared for
40 analysis, in which black plastic accounted for half of the total samples (84/159). Some
41 methods such as partial least squares discrimination analysis (PLS-DA), soft independent
42 modelling of class analogy (SIMCA), linear discriminant analysis (LDA) and
43 convolutional neural network (CNN) were used to classify the plastics. Coupling with
44 proper preprocessing methods, PLS-DA and LDA could provide an accuracy of 57.00%,
45 and SIMCA of 69.98%. Meanwhile, the NIR spectra fed into the CNN model as one-
46 dimensional data, the accuracy of CNN model could reach 98.00%. Compared to the
47 SIMCA, PLS-DA and LDA, the accuracy of CNN provided an average improvement of
48 28-41%. Results demonstrated that NIR spectra combined with CNN gave an excellent
49 accuracy which was potential to solve the bottleneck problem of black plastic
50 discrimination.
51 Key words: plastic solid waste; identification model; CNN; NIR
52 53 54 55 56
2 57 1. Introduction
58 Plastic material is widely used in manufacturing and daily applications, with a global
59 annual production of 335 million tons in 2016 (Belmokaddem et al. 2020). One of the
60 main concerns associated with the extensive applications of plastics comes from the large
61 amount of generated waste. Plastic waste embodies 11% of the total waste; where about
62 31% was disposed in landfills (Alassali et al. 2018). Much of this plastic waste is
63 mismanaged, leading to its dispersal in the environment. In order to contribute to the
64 optimization of plastic waste management, an integrated strategy of waste minimization,
65 reuse and recovery should be considered, since the processes of recycling and recovery
66 enable waste to become a resource (Worrell and Reuter 2014). As we all know, the prior
67 step in waste removal is always waste identification (Padervand et al. 2021; Padervand,
68 Rhimi, et al. 2020). Plastic mixing generally leads to decreased mechanical properties as
69 most of them are incompatible. For example, almost each plastic has personal melting
70 point, heating several plastics with various melting points will cause unsure changes, or
71 even degrade, especially between crystalline and amorphous plastics (Ayeleru et al. 2020).
72 Thus, the main technological obstacle to recycling is an efficient and accurate plastic
73 sorting scheme (Signoret et al. 2019).
74 There are several techniques have been used for the fast separation and identification of
75 plastic wastes. In the study of Mohsen Padervand, reviewed the microplastic occurrence,
76 transport, raw polymers and additives, toxicity and methods of removal (Padervand,
77 Lichtfouse, et al. 2020). Zhang proposed a novel magnetic separation method, which is
78 based on particles of different densities that are driven by the magnetic force, and finally
79 landed in different collection regions (Zhang et al. 2019). In the study of La Marca, the
80 fluid dynamic conditions were investigated by an image analysis technique, separating
81 plastic waste under different geometric configuration (La Marca et al. 2012). Shen
82 investigated the floatability of seven plastics in the presence of nonionic surfactant, but
3 83 was likely to distinguish ABS from PS (Shen et al. 2002). For most cases, the processes
84 were slow (La Marca et al. 2012; Li et al. 2015; Zhang et al. 2019), needed to smash
85 samples before examination (Li et al. 2015), did not require high purity polymer streams
86 for varieties of plastics (Shen et al. 2002), even existed the danger posed by inefficient
87 chemical waste management (Zulkifley et al. 2014).
88 Infrared spectroscopy is an essential method for the automatic sorting of polymers as it is
89 a non-destructive and fast detection technique (Becker et al. 2017; Zheng et al. 2018; Zhu
90 et al. 2019). Especially for NIR, has been used extensively to sort plastic automatically
91 because of its non-destructive remote high-speed measurements, high penetration depth
92 of the NIR radiation, and high signal-to-noise ratio (Amigo 2015; Vázquez-Guardado et
93 al. 2015; Voron et al. 2015; Macho and Larrechi 2002).
94 The middle infrared spectrum (MIR) using photon up-conversion technique was applied
95 to sort black polymer parts in the research of Becker (Becker et al. 2017). Kassouf tested
96 the potential of combing MIR with independent component analysis to rapidly
97 discriminate the plastics packaging materials (Kassouf et al. 2014). Rani demonstrated
98 that a miniaturized handheld NIR can be used to successful fingerprint and classify the
99 different plastic polymers (Rani et al. 2019). NIR, MIR and differential scanning
100 calorimetry (DSC), as quantitative methods, were used to obtained polymers from
101 recycled mixed plastic waste (Camacho and Karlsson 2001). Zhu presented an
102 identification system of plastic solid waste based on NIR using support vector machine
103 (SVM), and the model’s accuracy could reach to 97.50% (Zhu et al. 2019). Zheng
104 proposed a discrimination model using NIR hyperspectral imaging system, and the
105 yielded the accuracy of 100% (Zheng et al. 2018). Regretfully, NIR spectroscopy is
106 limited to samples with dark color that are difficult to identify (Rozenstein et al. 2017).
107 Carbon black strongly absorbs NIR rays, consequently making NIR inappropriate for
108 sorting these plastics (Froelich et al. 2007). Hence in the most studies, samples were
4 109 usually transparent or in a bright color but excluded black (Zheng et al. 2018; Zhu et al.
110 2019). Generally, one solution is to improve the instrument performance as the
111 development of technology, the other is to build a more reliable and robust model.
112 CNN has been the popular solution for different machine learning tasks, including object
113 detection (Yang et al. 2019), image classification (Qin et al. 2020) and natural language
114 processing (Ravi et al. 2019), etc. It is common to employ the CNN to classify the waste.
115 In order to analyze the possibilities for automatic waste sorting, the CNN was used for
116 image classification, and the best validation accuracy was 87.80% (Gyawali et al. 2020).
117 In the study of (Zheng and Gu 2021), proposed a novel ensemble learning model called
118 EnCNN-UPMWS to classify the household solid waste via waste images. The results
119 demonstrate the effectiveness of the proposed strategy in terms of its accuracy and F1-
120 scores. The research used a CNN-based algorithm which deployed a high-resolution
121 camera to capture waste image and sensors to detect feature information. After training
122 and validating, the classification accuracy could achieve above 90.00% (Chu et al. 2018).
123 Above the researches all focused on the images, however, it is still available to explore
124 the possibility which classify the plastic waste using the CNN combine the spectral data.
125 In recent years, a few examples on implementation of CNN for spectroscopic analysis
126 started to appear, which attracted a lot of attention due to CNN excellent advantages. (1)
127 Compared with classical methods, CNN is less dependent on preprocessing than the
128 standard modeling methods for vibrational spectroscopy data and achieves excellent
129 performance (Cui and Fearn 2018). (2) CNN exploits spatially-local correlation by
130 enforcing a local connectivity pattern between neurons of adjacent layers, in most cases,
131 has fewer parameters than traditional neural network (Acquarelli et al. 2017). (3) Based
132 on the study of Ng (Ng et al. 2019), compared with PLSR and Cubist models, CNN could
133 not only give a higher accuracy, but also maintain output correlation.
134 Therefore, in this study, a total of 159 plastic samples were selected, among which the
5 135 black plastics accounted for half more (84/159), including seven kinds of plastics (HIPS,
136 ABS, HDPE, PET, PA66, PC and PP). We used of NIR spectra as one-dimensional data
137 to feed CNN, then built model to identify the plastic after optimizing network parameters.
138 Meanwhile, in order to verify the performance of CNN model, traditional algorithms were
139 involved as a comparison.
140 2. Materials and methods
141 2.1 Sample sets
142 The plastic samples provided by China Nanchang Customs. All of samples were detected
143 by standard methods and confirmed the compounds. The number of plastic samples with
144 more than 10 were chosen in terms of the statistical theory, including HIPS, ABS, HDPE,
145 PET, PA66, PC and PP. Out of 159 plastic samples, 84 were black, 48 were transparent or
146 white and 27 were other colors of plastic (Table 1). The shapes for all samples are included
147 cylindrical (about 1mm in diameter, about 4mm in height), oblate (about 3mm in diameter)
148 and sphere (about 3mm diameter). For each sample, the size of plastic particles is same
149 and well-proportioned.
150 Each of the sample was scanned five times after being reshuffled and compacted. Thus,
151 there were a total of 795 spectra. The data set split into a training set and a test set by
152 systematic sampling, and the distance was four. Hence, 596 spectra, as training set which
153 were used to build the model. For other 199 spectra (test set), were used as unknown
154 samples to verify the accuracy of the model.
155 Table 1. Detail of plastic samples
Black White Other Sum
HIPS 27 10 0 37
ABS 23 8 4 35
HDPE 9 4 5 18
6 PET 3 8 3 14
PA66 7 6 7 20
PC 6 8 2 16
PP 9 4 6 19
Total 84 48 27 159
156 2.2 Near infrared spectroscopy analysis
157 Both background and samples were measured by MicroNIR Pro ES 1700 (VIAVI
158 Solutions Inc., USA), at a data interval of 7 nm. The spectral range covered the NIR
159 window, from 950 nm till 1650 nm and the measurements were obtained in absorbance
160 units. The NIR was controlled by a computer equipped with MicroNIR v1.5.7 software.
161 The free classification_toolbox_4.0 was applied with Matlab R2019a to develop PCA,
162 SIMCA, PLS-DA and LDA models. CNN model was built on Tensorflow (Intel (R) Core
163 (TM) i5-8265U, version 2.0.0).
164 2.3 Analytical Method
165 2.3.1 Principal Component Analysis (PCA)
166 PCA can be used to reveal the hidden structure within large data sets. It provides a visual
167 representation of the relationships between samples and variables and provides insights
168 into how measured variables cause some samples to be similar, or how they differ from
169 each other. PCA is also known as a projection method, because it takes information
170 carried by the original variables and projects them onto a smaller number of latent
171 variables called principal components (PCs) (Fuentes-García et al. 2018). Each PC
172 explains a certain amount of the total information contained in the original data and the
173 first PC contains the greatest source of information in the data set. Each subsequent PC
174 contains, in order, less information than the previous one. In representation, the PCA
7 175 model with a given number of components has the following equation (1):
176 (1) 푇 177 Where X is predictors matrix, T푋 is = the 푇푃 scores+ 퐸 matrix, P the loadings matrix and E the
178 error matrix.
179 2.3.2 Soft Independent Modeling of Class Analogy
180 SIMCA is known as a supervised pattern recognition method, identify different classes of
181 samples based on the value that provided by known samples (training set). Unknown
182 samples (test set) are then compared to the class models and assigned to classes according
183 to their proximity to the training set (Durante et al. 2011). SIMCA requires building one
184 PCA model for each class which describes the structure of that class as well as possible.
185 The optimal number of PCs should be chosen for each model separately, according to a
186 suitable validation procedure (Vanden Branden and Hubert 2005). In this study, based on
187 cross-validation, the optimal number of PC is determined for each class.
188 2.3.3 Partial Least Squares-discriminate Analysis
189 PLS-DA is a classification method is derived from PLS regression, incorporates
190 dimension reduction by combining predictors to generate latent variables (LV) which
191 maximally correlate with the targeted outcomes. PLS-DA implements in dependent
192 variable Y by utilizing independent variables X, provides the ability to construct a
193 multidimensional model for the prediction of the features. The basis of PLS-DA is to
194 reduce the size of original data X and replaced it with the matrix of scores and loadings
195 maximizing the covariance between X and Y. The level of reduction is described by the
196 number of significant LV, the optimal number of LV is also decided by cross-validation
197 in the data analysis (Bevilacqua and Marini 2014).
8 198 2.3.4 Linear Discriminant Analysis
199 LDA, is the simplest of all possible classification methods that are based on Bayes’
200 formula, provides a linear transformation of n-dimensional feature vectors (or samples)
201 into an m-dimensional space (m < n), so that samples belonging to the same class are
202 close together but samples from different classes are far apart from each other (Gerhardt
203 et al. 2019). LDA is a supervised classification method, as the categories to which objects
204 are to be classified is known before the model is created. The objective of LDA is to
205 determine the best fit parameters for classification of samples by a developed model. The
206 model can then be used to classify unknown samples.
207 2.3.5 Convolutional neural network
208 A typical CNN model would divide layers into 3 sets: input, hidden and output layers.
209 Among them, hidden layers include convolution, pooling, and fully connected (FC) layers.
210 The representation of the CNN architecture using spectral input is included in Supplement
211 Fig.1. The input layer is the first layer and it generally has a liner activation function. The
212 output layer is the last layer and it usually has the number of neurons, which is equal to
213 the number of class. In our case, the initial input was spectral variables, and final output
214 was the prediction of the target classes. Rectified linear unit (ReLU) was employed in the
215 all layers because the network was able to converge faster compared with sigmoid or tanh
216 functions (Ng et al. 2019).
217 2.4 Model Evaluation
218 To evaluate the predictive ability of the models, the accuracy and loss function were used.
219 Accuracy is the ratio of correctly assigned samples:
220 Accuracy= 퐺 (2) ∑푔=1 푛푔푔 9푛 221 Where is the number of samples belonging to class g and correctly assigned to class
푔푔 222 g. And n푛 represents the total number of samples.
223 The loss function adopted in this research is the Cross-entropy loss function.
224 Loss= (3) 1 푁 푁 ∑푛=1 [ 푛 푛 ( 푛) ( 푛)] 225 Where − , 푦 is the푙표푔푦 activê + function,1 − 푦 푙표푔 1 is − the 푦̂ n-th sample, w is output
푛 푛 푛 226 weights 푦̂and = 휑( 푤 is ∙the 푥 )target휑(∙ )label. 푥
푦푛 227 3. Results and discussion
228 3.1 PCA analysis
229 Using PCA to observe the distribution of samples. Fig.1 (a) showed a two-dimensional
230 score diagram. The abscissa was PC1 which contained 69.00% of data information, and
231 the ordinate was PC2 which included 18.00%. Fig.1 (b) gave the three-dimensional score
232 diagram with PC3 contained 5.00% of information. Different kinds of plastic samples
233 were grouped together, and was impossible to classify all kinds of samples only by PCA.
234 Fig.1 Two-dimensional diagram of PC scores (a); Three-dimensional diagram of scores (b).
10 235 3.2 Modeling
236 3.2.1 Spectral pre-processing
237 Spectral pre-processing is used to correct sample variations and noisy spectra. For CNN
238 model, the required pre-processing is normalization (Acquarelli et al. 2017; Cui and Fearn
239 2018). For example, each variable has a mean of zero and standard deviation of 1 along
240 the column axis (Cui and Fearn 2018). However, for PLS-DA, SIMCA and LDA, there is
241 no standard rule on choosing proper preprocessing methods (Gerretzen et al. 2015).
242 Savitzky-Golay (S-G) smoothing and standard normal variate (SNV) transformation
243 which are commonly applied in preprocessing methods. Thus, in this study, we obtained
244 the best preprocessing combinations (the one achieving the highest 10-fold cross-
245 validation accuracy on the test set was selected) of the methods from above mentioned.
246 Illustrations for the pre-processing spectra were included Supplementary Material table.1.
247 3.2.2 Optimization of models
248 For PLS-DA, SIMCA and LDA, usually, the optimal PC that needs to be confirmed by
249 10-fold cross-validation. According to Fig.2 (a) (b), the abscissa was the number of PC,
250 the ordinate was error rate. The optimal PC for PLS-DA, LDA is 10, 8 and the
251 corresponding error rate is 0.41, 0.45, respectively. PCA model for each class was
252 constructed independently for SIMCA calculation. Training set was used to determine the
253 optimal PC by cross-validation, Fig.2 (c) showed the result in SIMCA, and Fig.2 (d)
254 presented the optimal PCs were 3, 6, 3, 7, 3, 6 and 3 for PC, PP, HIPS, ABS, HDPE, PET
255 and PA66.
11 256 Fig.2. Performance of the training set with different number of PC by cross-validation, (a)
257 was PLS-DA, (b) was LDA, wherein the green rectangles in (a), (b) represent the optimal
258 PC respectively. (c) and (d) belonged to SIMCA, (c) showed the processing for each plastic
259 by cross-validation, (d) gave the optimal PCs information for each plastic in the SIMCA
260 model.
261
262 In most cases, CNN has fewer parameters than traditional neural network, and with
263 embedded regularization and dropout techniques, it is more robust to overfitting
264 (Acquarelli et al. 2017). For each CNN model, there are several parameters need to be
265 considered.
266 Fig.3 (a): The structure of model (Layers). Different network structures directly impact
267 the accuracy and reliability of result and the speed of model. Therefore, different-layer
268 convolutions were trained and compared. From Fig.3 (a), two-layer, three-layer and four-
269 layer convolution were showed. When epoch was 3500, the accuracy was 80.00%, and
270 the loss was 0.50 of test set in two-layer convolution. However, the accuracy was 90.00%,
271 and the loss near 0.25 in three-layer. Demonstrated that the structure of three-layer not
12 272 only could converge faster, also the result was more reliable. Compared with three-layer,
273 although, the accuracy could reach 0.90, and the loss was below 0.25 at 2000, more
274 glitches were showed in four-layer. Glitch is normal for loss if the model includes shuffle.
275 However, we tend a steady model when the results are near. In summary, the model with
276 three-layer convolution was chosen in this study.
277 Fig.3 (b): Learning rate (lr). Learning rate determines when the objective function can
278 converge the minimum. Learning rate is too small, and the update speed is slow, learning
279 rate is too large, the speed is quick, however, it may skip the optimal value. In this research,
280 learning rates (0.0005, 0.0001 and 0.00005) were considered when the model structure
281 was same. As can be seen from Fig.3 (b), when lr was 0.0005, the speed of converge was
282 very fast (the accuracy was close to 100.00% and the loss was near 0.00), but the glitch
283 was obvious. When lr was 0.0001, the loss began to rise after epoch 4000. For lr was
284 0.00005, although the speed was slow, the result was acceptable. The accuracy could
285 reach 98.00% and the loss was 0.12. Thus, 0.00005 was choose as learning rate value.
286 Fig.3 (c): Kernel size, kernel is a weight matrix used for features detection. When epoch
287 was 5000, we could see from Fig.3 (c), kernel size set 2 and the model was steady, and
288 the highest accuracy of model reached 85.00%. But when kernel size set 3 and 4, the
289 accuracy began to increase as the epoch increased. And the best accuracy could acquire
290 98.00%. Compared with kernel size 4, it was obviously seen that the loss value was more
291 stable when the kernel size was 3. Thus, 3 was choose as a value of kernel size.
292 Fig.3 (d): Strides, Stride is the number of kernels shifts over the input data. Fig.3 (d) gave
293 the comparison chart of different strides. The accuracies were approximative whatever
294 the stride set 1, 2 or 3. However, compared with stride 2 and 3, when the stride was 1, the
295 loss was more stable. So, stride set 1 in this study.
13 296
14 297 Fig.3 Adjusting the network parameters: (a) with different layers convolutions; (b) under
298 different learning rates; (c) with different kernel sizes; (d) under different strides.
299
300 CNN was implemented when used the spectra as one-dimensional input. This network
301 consisted of three convolutional layers. The output of the convolutional layer was passed
302 to the pooling layer. Aimed on the structure of model, the convolution layer contained 32
303 filters with a kernel size of 3, stride as 1 and zero paddings (‘Same’ way). The number of
304 filters at each subsequent convolutional layer was increased by a factor of two, while the
305 other parameters were kept the same. The detailed architecture of the model is
306 summarized in Supply Material Table.2.
307 4. Results of modeling
308 Accuracies of prediction of seven kinds of plastic using conventional PLS-DA, SIMCA
309 and LDA techniques were evaluated respectively. The resulting preprocessing strategy for
310 PLS-DA was S-G smoothing with the second order polynomial, followed by SNV.
311 SIMCA with S-G smoothing with a first order polynomial, followed by SNV, could bring
312 in good result. And for LDA, SNV was chosen as preprocessing. Aimed on CNN, only
313 normalization was enough.
314 The spectra (199) in test set were not participate in modeling, but as unknow samples to
315 predict the performance of model. The confusion matrixes of test set were used to show
316 the results of PLS-DA, SIMCA, LDA and CNN models. In confusion matrix, the larger
317 the number, the darker of the grid, the correctly classified samples were on the diagonal
318 while the misclassified samples fell into the other areas. Fig.4 (a) gave the confusion
319 matrix of PLS-DA, the result was badly, almost a half samples were mistaken. Obviously,
320 the SIMCA result was a little bit better than PLS-DA from the comparison between Fig.4
321 (a) and (b). LDA concluded the similar result (Fig.4 (c)) with PLS-DA. When all the
322 samples were involved to build the CNN model, only four samples misclassified based
15 323 on the confusion matrix (Fig.4 (d)). The results obtained using the various methods were
324 summarized in Table.2. The accuracies of PLS-DA and LDA were close, about 57.00%.
325 SIMCA gave a high accuracy relatively, which was 69.98%. For CNN model, the
326 accuracies both training set and test set were 98.00%. The result of CNN yielded far
327 superior to traditional techniques for distinguishing the types of plastic.
328 Fig.4 The classification results of models. (a) was the confusion matrix of PLS-DA; (b) was
329 the confusion matrix of SIMCA; (c) was the confusion matrix of LDA; (d) was the
330 confusion matrix of CNN.
331
332 Table.2 The result of various algorithms
PLS-DA SIMCA LDA CNN Accuracy（%） Preprocessing S-G→SNV S-G→SNV SNV Normalization Training set 59.73 76.28 56.47 98.00 Test set 57.79 69.98 54.36 98.00 333
334 NIR is currently operational in some waste-sorting facilities. However, NIR remains
335 challenging to discriminate different black plastics. The reason for this limitation is that 16 336 black targets have low reflectance in the NIR spectral region. In this research, black
337 plastic accounted for half of the total samples (84/159). CNN still could give an
338 outstanding result (98.00% accuracy). From the algorithm of view, made up for the
339 shortcomings of NIR. To some extent, the result we may explain from followed theories.
340 On the one hand, focused on black plastic from different categories, the compositions of
341 carbon black in plastics are slightly difference (Becker et al. 2017; Rozenstein et al. 2017).
342 Hence, the NIR spectra is similar, but the absorption intensity is not same. On the other
343 hand, CNN can learn the characteristics of plastic. Just like the study of Ng (Ng et al.
344 2019), CNN model also could produce an excellent result for soil properties. After
345 analyzing of correlation between soil properties, CNN may learn inherent structure from
346 data to improve prediction (Ng et al. 2019; Xu et al.).
347 5. Conclusion
348 This study mainly focused on the problem of black plastic classification. Several
349 traditional algorithms including PLS-DA, SIMCA and LDA were employed to classify
350 plastics. After preprocessing, the accuracies of PLS-DA and LDA were similar, both
351 reached to 57.00%. The accuracy of SIMCA outputted 69.98%, which was higher than
352 those of PLS-DA and LDA. CNN, which had strong learning ability, after data
353 normalization, provided an excellent accuracy of 98.00%. The main contribution of this
354 work was the identification of the black plastics by a novel model, thus overcoming some
355 of the detection limitation currently experienced in the NIR region to identify the plastics.
356 This paper offered a potential approach for industrial sorting of plastic waste using NIR
357 technology.
358 In this study, mainly focused on the seven kinds of plastic, as the other kinds of plastic
359 collecting, we will update the model with more categories of plastic samples. Additionally,
360 the plastics used were basically clear and purity samples. However, the environment
361 plastics in soil, water and air which may reduce the accuracy, given the excellent effect 17 362 of the CNN model. In the next study, we will move to the actual samples from the daily
363 life and build a more broaden and robust model. Meanwhile, although CNN had little
364 concern on preprocessing and time-saving, the parameters in network were manually
365 adjusted. The following novelty is to find a solution to optimize parameters automatically,
366 further widening the application of CNN in waste management.
367 Acknowledgement
368 Thanks to the China Nanchang Customs providing the plastic samples and the 369 corresponding chemistry information. And the authors would like to thank Yue Huang 370 and Qianqian Li for the English language review. This research did not receive any 371 specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
372 6. References
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20 Figures
Figure 1
Two-dimensional diagram of PC scores (a); Three-dimensional diagram of scores (b).
Figure 2 Performance of the training set with different number of PC by cross-validation, (a) was PLS-DA, (b) was LDA, wherein the green rectangles in (a), (b) represent the optimal PC respectively. (c) and (d) belonged to SIMCA, (c) showed the processing for each plastic by cross-validation, (d) gave the optimal PCs information for each plastic in the SIMCA model.
Figure 3 Adjusting the network parameters: (a) with different layers convolutions; (b) under different learning rates; (c) with different kernel sizes; (d) under different strides.
Figure 4
The classi cation results of models. (a) was the confusion matrix of PLS-DA; (b) was the confusion matrix of SIMCA; (c) was the confusion matrix of LDA; (d) was the confusion matrix of CNN
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