Physical and microstructural quality of extruded snacks made from blends of barley and green lentil flours

Abstract

Background and objectives Most puffed snacks in the market are made from refined cereal flours which allow greater expansion and better texture but are nutritionally inferior as they lack protein and dietary fiber. Whole barley and green lentil flours at several blending ratios were extruded as a function of temperature and moisture content to optimize the physical and microstructural quality of fiber and protein‐enriched snacks. Findings High extrusion temperature significantly improved overall expansion and texture. The effects of feed moisture depended on the blending ratio, parallel with the total protein and dietary fiber content of extrudates. Barley:green lentil of 45:55 showed the highest extrudate expansion (~1.9 mm/mm on average) and lowest hardness (~29 N on average), followed by the blend 60:40. X‐ray microtomography showed that this blending ratio also produced a larger mean cell size (~1.6 mm), lower mean wall thickness (~0.3 mm), and higher overall connectivity between cells. Conclusion Barley and green lentil when blended at the ratios of 45:55 and 60:40, and extruded at higher temperature resulted in optimal extrudate physical and microstructural properties including higher expansion and crispness, thinner cell walls, reduced hardness, and crunchiness. Significance and novelty Blending cereal and pulse flours in snack food applications will allow development of fiber and protein‐enriched options that are also texturally and structurally appealing.

Butterfly Pea Flower as a Novel Ingredient to Produce Antioxidant-Enriched Yellow Pea-Based Breakfast Cereals

“…The decrease in expansion with the incorporation of BP may be attributed to the higher dietary fiber content of BP ( Table 1 ). A decrease in expansion index of extrudates obtained from higher dietary fiber containing blends was also reported by Li, Guillermic, Nadimi, Paliwal, and Koksel [ 32 ] at similar extrusion processing conditions. As the dietary fiber content of BP incorporated extrudates increase at the expense of their starch content and since expansion index primarily depends on the starch properties [ 58 ], this starch dilution effect of dietary fiber may be responsible for the observed decrease in the expansion of 5% and 10% BP extrudates.…”

“…Overall, all extrusion treatments produced well-expanded extrudates, with expansion index values ranging from 2.6 to 3.5. Comparing with literature works on producing high-protein extruded snacks [ 32 , 37 , 53 ], the protein content (22.4–22.7 g protein/100 g, db) and expansion index (2.6–3.5) of extrudates produced in the present study were relatively high. For instance, Muñoz-Pabon, Parra-Polanco, Roa-Acosta, Hoyos-Concha, and Bravo-Gomez [ 53 ] reported an expansion index in the range of 1.2–2.3 for multi-cereal blends having protein content between 7.0 and 16.2 g protein/100 g, db, and Anton, Fulcher, and Arntfield [ 37 ] produced expanded puffed snacks from common bean–corn starch blends having protein contents between 3.0 and 10.1 g protein/100 g, db and expansion indices between 1.7 and 2.2.…”

“…The raw flours and extrudates were first milled and then filled into a cylindrical glass measurement container, and the values for different color parameters, i.e., L* (higher values depict lighter color), a* (positive a* value depicts redness and negative a* value depicts greenness), b* (positive b* value depicts yellowness and negative b* value depicts blueness) were obtained using Spectra Magic (Version 3.60) software. ΔE, i.e., total color change between extrudates and their raw counterparts, was calculated using the equation given by [ 32 ].…”

Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications

“…The authors believe that the proposed methodology can revolutionize intelligent crop/weed classifiers. An interesting topic for future work could be to examine the capability of the proposed approach on other machine vision-based applications, such as fruit maturity detection [69,70], fruit grading [71], agri-food product microstructural evaluation [65,72,73], crop disease identification [74], and crop growth and yield monitoring [75][76][77].…”

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios

“…Over the past decade, and with the growing market demand for superior produce, the food industry has been actively looking for rapid, objective, non-destructive, and intelligent tools for the maturity detection of agricultural fruit and vegetables. In this regard, scholars have explored various tools such as near-infrared spectroscopy [ 16 , 17 , 18 , 19 ], or imaging techniques [ 20 , 21 , 22 , 23 ] to predict the ripeness levels of various agriproducts and/or to evaluate their quality parameters [ 24 , 25 , 26 ]. For example, the maturity of persimmon blueberry [ 27 , 28 , 29 ], tomato [ 30 ], apple [ 31 , 32 ], citrus [ 33 ], mulberry [ 34 ], and oil palm fruit [ 35 ] have been estimated using imaging and machine vision algorithms.…”