Quinoa starch nanocrystals (QSNCs), obtained by acid hydrolysis, were used as a reinforcing filler in cassava starch films. The influence of QSNC concentrations (0, 2.5, 5.0, 7.5 and 10%, w/w) on the film’s physical and surface properties was investigated. QSNCs exhibited conical and parallelepiped shapes. An increase of the QSNC concentration, from 0 to 5%, improved the film’s tensile strength from 6.5 to 16.5 MPa, but at 7.5%, it decreased to 11.85 MPa. Adequate exfoliation of QSNCs in the starch matrix also decreased the water vapor permeability (~17%) up to a 5% concentration. At 5.0% and 7.5% concentrations, the films increased in roughness, water contact angle, and opacity, whereas the brightness decreased. Furthermore, at these concentrations, the film’s hydrophilic nature changed (water contact angle values of >65°). The SNC addition increased the film opacity without causing major changes in color. Other film properties, such as thickness, moisture content and solubility, were not affected by the QSNC concentration. The DSC (differential scanning calorimetry) results indicated that greater QSNC concentrations increased the second glass transition temperature (related to the biopolymer-rich phase) and the melting enthalpy. However, the film’s thermal stability was not altered by the QSNC addition. These findings contribute to overcoming the starch-based films’ limitations through the development of nanocomposite materials for future food packaging applications.

The aim of this study was to build a model to predict the beef marbling using HSI and Partial Least Squares Regression (PLSR). Totally 58 samples of longissmus dorsi muscle were scanned by a HSI system (400 - 1000 nm) in reflectance mode, using 44 samples to build the PLSR model and 14 samples to model validation. The Japanese Beef Marbling Standard (BMS) was used as reference by 15 middle-trained judges for the samples evaluation. The scores were assigned as continuous values and varied from 1.2 to 5.3 BMS. The PLSR model showed a high correlation coefficient in the prediction (r = 0.95), a low Standard Error of Calibration (SEC) of 0.2 BMS score, and a low Standard Error of Prediction (SEP) of 0.3 BMS score.

Background: Hyperspectral image analysis is a fast and non-destructive technique that is being used to measure quality attributes of food products. This research investigated the feasibility of predicting internal quality attributes, such as Total Soluble Solids (TSS), pH, Titratable Acidity (TA), and maturity index (TSS/TA); and external quality attributes such as color components (L*, a*, b*) as well as Color Index (CI) of Valencia orange fruit using hyperspectral reflectance imaging in the range of 400-1000 nm. Methods: Oranges were scanned by the system in order to build full models for predicting quality attributes using partial least squares regression. Optimal wavelengths were identified using the regression coefficients from full models, which were used to build simplified models by multiple linear regression. The coefficient of determination of prediction (R2p) and the Standard Error of Prediction (SEP) were used to measure the performance of the models obtained. Results: Full models for internal quality attributes had low performance (R2p50%). Full models for external quality attributes presented a high performance for L* (R2p=0.898, SEP=19%), a* (R2p=0.952, SEP=13%), b* (R2p=0.922, SEP=20%), and CI (R2p=0.972, SEP=12%). The simplified models presented similar performance to those obtained for external quality attributes. Conclusion: Hyperspectral reflectance imaging has potential for predicting color of oranges in an objective and noncontact way.