Starch degradation in pasta induced by extrusion speed.
The structural deformation of starch during pasta extrusion leads to significant effects on product quality. In this context, a recent study, carried out by a team of Australian researchers (Jia et al., 2023), investigated the impact of the shearing force on the starch structure by varying the extruder speed (in the range of 100 to 600 rpm), over a temperature range of 25 to 50°C (in 5°C increments from the feeding zone to the die zone).
The results show that a higher screw speed results in a lower pasta viscosity due to the loss of starch molecular order and crystallinity. Furthermore, size-exclusion chromatography revealed that the samples produced at 600 rpm have a lower amylopectin size distribution than the other samples. The authors attribute this result to the significant molecular breakdown occurred during extrusion under severe conditions.
As a result, pasta produced at 600 rpm (both raw and cooked) had higher in vitro starch hydrolysis than the pasta made at 100 rpm. In conclusion, the results reported in the study are useful to the pasta industry as they provide interesting information on the relationship existing between extrusion process speed, structure and nutritional functionality of pasta.
Use of hyperspectral imaging technology for online monitoring of the soy flour content in functional pasta.
Pasta enriched with soy flour can be considered as a functional food, due to its significant content in nutraceutical compounds, including isoflavones, carotenoids and other antioxidants. Therefore, the quantification of the amount of a functional ingredient is important for food authenticity. The availability of non-destructive techniques for analyses of food is desirable for the industry in the sector.
In this context, a recent study, carried out by a group of Italian researchers (Romaniello et al., 2023), aimed to investigate the performance of hyperspectral imaging technology in reflectance mode as an online tool, for the evaluation of the soy flour content in pasta. For the test, samples of pasta in shape of spaghetti were produced with a variable soy flour percentage (in the range between 0 and 50 %).
The raw data acquired by the imaging system was processed using the FS-MRMR algorithm (Feature Selection Minimum Redundancy Maximum Relevance). The study identified the most influent wavelengths, developing a 6-term Gaussian model. The identified function was able to predict the percentage of soy flour in the samples with high accuracy (with an R2adj value of 0.98 and a Root Mean Square Error of 1.31). In conclusion, the authors argue that the proposed method is suitable to be used directly on production lines to control the process of this class of functional products in a continuous mode.
References: Jia et al., Food Chemistry, 426, 2023, 136524; Romaniello et al., Journal of Agricultural Engineering, 54, 2023, 1535.
