Rice Grain Freshness Measurement Using Rapid Visco Analyzer and Chemometrics
© 2017, Springer Science+Business Media, LLC. This research developed a rapid and accurate method based on the use of rapid visco analyzer (RVA) for predicting the storage time of rice grain. Freshly harvested rice samples, five waxy and five non-waxy rice grains, were stored in paddy form at ambien...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Journal |
| Published: |
2018
|
| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85029156476&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/58118 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Chiang Mai University |
| Summary: | © 2017, Springer Science+Business Media, LLC. This research developed a rapid and accurate method based on the use of rapid visco analyzer (RVA) for predicting the storage time of rice grain. Freshly harvested rice samples, five waxy and five non-waxy rice grains, were stored in paddy form at ambient room temperature (28–32 °C) for 1 year. During storage, the RVA profiles of the rice samples were recorded every month. In addition, physicochemical properties, such as alkali spreading value (ASV), amylose content, gel consistency, stickiness, and hardness, were measured. Chemometric models including partial least squares (PLS) regression and supervised self-organizing map (supervised SOM) were employed for predicting the storage time based on the use of the RVA profiles, the physicochemical parameters, and both of the datasets simultaneously. In most cases, PLS outperformed supervised SOM. The PLS models established using the RVA profiles provided the best predictive results with root mean square error of cross validation (RMSECV) = 1.2, cross-validated explained variance (Q2) = 0.90, and the ratio of prediction to deviation (RPD) = 3.2. Based on partial least squares-variable influence on projection (PLS-VIP), pasting properties, including peak viscosity (PV) and final viscosity (FV), were identified as the parameters having strong influence on the prediction models. The developed method detecting the rheological change of the stored rice samples was simple and could be performed quickly with no additional chemicals required. |
|---|