Paper
14 May 2019 Dried red chili peppers pungency assessment by visible and near infrared spectroscopy
Giuseppe Bonifazi, Riccardo Gasbarrone, Silvia Serranti
Author Affiliations +
Abstract
Chili peppers are widely used in many cuisines all around the world for enhancing dishes hotness. For this reason, a fast, reliable, non-destructive and not-invasive method is needed to measure and control the content of hotness in red chili peppers could be quite useful in respect of their use. Visible - Near InfraRed Spectroscopy (Vis-NIRS) fits well this purpose. The work explores the possible utilization of a portable spectroradiometer, to evaluate the spiciness of dried red chili peppers, along others important properties such as moisture content and ash content. An ASD FieldSpec 4™ Standard-Res able to acquire reflectance spectra on “spot” bases in the electromagnetic region (350-2500 nm) was utilized to reach this goal. Different specimen of ground dried chili peppers (i.e. powder) and crushed dried chili peppers, of different characteristics, were analyzed. The collected spectra have been correlated with the pungency, reported in Scoville Heat Units (SHU), of the powder and crushed samples. To reach these goals, a chemometric approach, finalized to set up Partial Least Square (PLS) regression models able to predict red chili peppers characteristics (i.e. ash content, moisture and SHU) was preliminary applied, then a Partial Least Square - Discriminant Analysis (PLS-DA) classification model was calibrated and validated by using reflectance spectra in order to specifically recognize the pungency of the examined samples. Results have been framed in a proximity sensing perspective and in a “on-line” food quality control logic.
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Giuseppe Bonifazi, Riccardo Gasbarrone, and Silvia Serranti "Dried red chili peppers pungency assessment by visible and near infrared spectroscopy", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861S (14 May 2019); https://doi.org/10.1117/12.2517069
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Reflectivity

Calibration

Sensors

Statistical analysis

Statistical modeling

Data modeling

Principal component analysis

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