A dataset of visible – Short wave InfraRed reflectance spectra collected on pre-cooked pasta products

Bonifazi, Giuseppe; Gasbarrone, Riccardo; Capobianco, Giuseppe; Serranti, Silvia

doi:10.1016/j.dib.2021.106989

Cited by 1 publication

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References 14 publications

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“…The Vis–SWIR data were collected on 6 Pennette72 samples and 6 Mezze Penne samples with different salt contents under two physical conditions (frozen vs. thawed) with the help of contact probes. Fifty Vis–SWIR spectra were collected from each sample at each measurement time, yielding 1200 raw spectra [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy

Sun

Wang

Dong

2023

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%