Exploring the potential of laser photoacoustic spectroscopy (LPAS) for predicting amylose content in rice flour

• Published in: Exploration of Foods and Foodomics Vol. 2; no. 6; pp. 542 - 554
• Main Authors: Florinda Artuso, Claudio Ciceroni, Luca Fiorani, Isabella Giardina, Massimo Francucci, Antonia Lai, Ivano Menicucci, Marcello Nuvoli, Fabio Pollastrone
• Format: Journal Article
• Language: English
• Published: Open Exploration Publishing Inc 10.09.2024
• Subjects: amylose; laser photoacoustic spectroscopy; non-destructive analysis; rice flour
• ISSN: 2837-9020
• DOI: 10.37349/eff.2024.00050

Aim: Rice, one of the most widely consumed staple foods globally, relies on amylose content for its quality, impacting cooking, digestibility, and health properties. Conventional amylose determination methods are time-consuming and involve complex chemical treatments. Thus, there is growing interest in rapid, non-destructive techniques for food quality control. This study explores the potential of laser photoacoustic spectroscopy (LPAS) for predicting amylose content in rice flour. Methods: Certified rice flour standards of varying amylose levels have been analyzed using a quantum-cascade LPAS system. Preliminary analysis utilized Fourier transform infrared/attenuated total reflectance (FTIR/ATR) to identify rice starch spectral features in the IR region. Multivariate data tools like principal component analysis (PCA) and partial least squares (PLS) regression have been combined with LPAS measurements to extract information from the complex spectral data set and to demonstrate the ability of the system to predict their amylose content. Results: LPAS spectra, recorded between 7.0–11.0 μm, displayed two broad bands, showing a linear increase in signal with amylose content, especially notable in the specific fingerprint region within 8.5–10.0 μm. The prominent peak at 9.3 μm exhibited a high linear correlation with amylose levels (R2 > 0.99). PCA effectively differentiated rice flour samples, while PLS accurately predicted amylose content. The difference between predicted and actual amylose is significantly less than the statistical error of the measurement. Conclusions: LPAS combined with chemometric analysis emerges as a promising non-destructive method for rapidly assessing rice amylose content, potentially supplementing or replacing current standard methods. Its advantages, limitations, and future prospects in rice quality analysis are discussed, highlighting its role in preliminary screening.