CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy
Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4815 |
| _version_ | 1827740121938526208 |
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| author | Penghui Sun Jiajia Wang Zhilin Dong |
| author_facet | Penghui Sun Jiajia Wang Zhilin Dong |
| author_sort | Penghui Sun |
| collection | DOAJ |
| description | Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN–LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN–LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN–LSTM neural network combined with IR spectroscopy helps to identify different pasta products. |
| first_indexed | 2024-03-11T03:20:31Z |
| format | Article |
| id | doaj.art-8a18461ecbf84d94a51544a7dc427973 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:31Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8a18461ecbf84d94a51544a7dc4279732023-11-18T03:13:00ZengMDPI AGSensors1424-82202023-05-012310481510.3390/s23104815CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared SpectroscopyPenghui Sun0Jiajia Wang1Zhilin Dong2School of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaInfrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN–LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN–LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN–LSTM neural network combined with IR spectroscopy helps to identify different pasta products.https://www.mdpi.com/1424-8220/23/10/4815infrared spectroscopypre-cooked pastadeep learningconvolutional neural networksLSTM |
| spellingShingle | Penghui Sun Jiajia Wang Zhilin Dong CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy Sensors infrared spectroscopy pre-cooked pasta deep learning convolutional neural networks LSTM |
| title | CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy |
| title_full | CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy |
| title_fullStr | CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy |
| title_full_unstemmed | CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy |
| title_short | CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy |
| title_sort | cnn lstm neural network for identification of pre cooked pasta products in different physical states using infrared spectroscopy |
| topic | infrared spectroscopy pre-cooked pasta deep learning convolutional neural networks LSTM |
| url | https://www.mdpi.com/1424-8220/23/10/4815 |
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