Dataset Information

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

ABSTRACT: Reflectance Visible (Vis) and Short Wave Infrared (SWIR) spectra of pre-cooked pasta products were collected in a non-invasive way by using an ASD FieldSpec® 4 Standard-Res portable spectrophotoradiometer (350-2500 nm). Vis-SWIR data were collected on 6 samples of Pennette 72 and 6 samples of Mezze Penne with different salting levels with the aid of a contact probe in two different physical conditions: i) frozen and ii) thawed. Fifty Vis-SWIR spectra were collected per measurement time from each sample resulting in 1200 raw spectra. The dataset presented in this descriptor can be used to explore the possibilities to develop automated methods to perform pre-cooked pasta analysis. Vis-SWIR data have potential reuse for follow-up studies finalized to develop pre-cooked pasta quality control applications by using similar devices or to test the ability of different chemometric algorithms.

SUBMITTER: Bonifazi G

PROVIDER: S-EPMC8050707 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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

Bonifazi Giuseppe G Gasbarrone Riccardo R Capobianco Giuseppe G Serranti Silvia S

Data in brief 20210323

Reflectance Visible (Vis) and Short Wave Infrared (SWIR) spectra of pre-cooked pasta products were collected in a non-invasive way by using an ASD FieldSpec® 4 Standard-Res portable spectrophotoradiometer (350-2500 nm). Vis-SWIR data were collected on 6 samples of <i>Pennette 72</i> and 6 samples of <i>Mezze Penne</i> with different salting levels with the aid of a contact probe in two different physical conditions: i) frozen and ii) thawed. Fifty Vis-SWIR spectra were collected per measurement ...[more]

PMID: 33889694

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Project description:If a non-destructive and rapid technique to determine the textural properties of cooked germinated brown rice (GBR) was developed, it would hold immense potential for the enhancement of the quality control process in large-scale commercial rice production. We combined the Fourier transform near-infrared (NIR) spectral data of uncooked whole grain GBR with partial least squares (PLS) regression and an artificial neural network (ANN) for an evaluation of the textural properties of cooked germinated brown rice (GBR); in addition, data separation and spectral pretreatment methods were investigated. The ANN was outperformed in the evaluation of hardness by a back extrusion test of cooked GBR using the smoothing combined with the standard normal variate pretreated NIR spectra of 188 whole grain samples in the range of 4000-12,500 cm-1. The calibration sample set was separated from the prediction set by the Kennard-Stone method. The best ANN model for hardness, toughness, and adhesiveness provided R2, r2, RMSEC, RMSEP, Bias, and RPD values of 1.00, 0.94, 0.10 N, 0.77 N, 0.02 N, and 4.3; 1.00, 0.92, 1.40 Nmm, 9.98 Nmm, 1.6 Nmm, and 3.5; and 0.97, 0.91, 1.35 Nmm, 2.63 Nmm, -0.08 Nmm, and 3.4, respectively. The PLS regression of the 64-sample KDML GBR group and the 64-sample GBR group of various varieties provided the optimized models for the hardness of the former and the toughness of the latter. The hardness model was developed by using 5446.3-7506 and 4242.9-4605.4 cm-1, which included the amylose vibration band at 6834.0 cm-1, while the toughness model was from 6094.3 to 9403.8 cm-1 and included the 6834.0 and 8316.0 cm-1 vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r2 values of 0.85 and 0.82 and the RPDs of 2.9 and 2.4, respectively. The ANN model for the hardness, toughness, and adhesiveness of cooked GBR could be implemented for practical use in GBR production factories for product formulation and quality assurance and for further updating using more samples and several brands to obtain the robust models.

Dataset Information

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

Publications

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

Similar Datasets

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