Modelling approaches for predicting moisture transfer during baking of chhana podo (milk cake) incorporated with tikhur (Curcuma angustifolia) starch

The particular interest in biomodifications of underutilised but nutritionally distinct whole grains is vital to promote diet diversity, nutrition transition and food security. This study investigated the use of shortterm solid-state fermentation and germination to ease processability and improve the quality characteristics of whole grain (WG) cowpea and quinoa flours. The fermented and germinated WG flours were prepared at 28 °C for 48 h. The biochemical, nutritional quality and techno-functional properties of the obtained flours were determined. The macromolecules' biomodification by microbial metabolism and endogenous enzymes activation influenced quality variations in the biomodified flours. The cowpea sourdough flour (CSF) presented lower acidity (pH 4.72), higher total flavonoid (29.63 mg QE/g), total phenolic (8.21 mg GAE/g) and antioxidant activity. The flour also showed high contents of fibre (5.30%), ash (4.42%), calcium (864.49 mg/kg), potassium (12848.64 mg/kg), zinc (33.83 mg/kg), good protein (21.43%) and a moderate fat level (2.65%). Higher oil absorption and water solubility indices were also noted for CSF. In contrast, malted quinoa flour (MQF) exhibited higher swelling power, increased dispersibility and improved final, peak and trough viscosities. The CSF displayed higher redness and browning index, whereas MQF had greater lightness. The results suggest that CSF and MQF had the best complementary quality attributes. Their formulation as gluten-free, whole and multigrain ingredients may promote healthy choices for individualised growing dietary needs.

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“…Further to this, the browning index was estimated according to Emerald et al . (2020), using Equations 9 and 10.

Browning index = \frac{(][, 100 ()(, y ‐ 0.31))}{0.17}

y = \frac{(a^{*} + 1.75 L^{*})}{()(, 5.645 L^{*} + a^{*} ‐ 3.012 b^{*})}

…”

Section: Methodsmentioning

confidence: 99%

Biochemical properties, nutritional quality, colour profile and techno‐functional properties of whole grain sourdough and malted cowpea and quinoa flours

Kewuyemi

Kesa

Adebo

2021

Int J of Food Sci Tech

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“…The moisture content of biscuits was determined using an air oven (Jeto Tech, OF-O2G) at the temperature of 130 °C for 1 h 30 . The moisture was calculated as follows ( 3 ): …”

Section: Methodsmentioning

confidence: 99%

Investigation of physicochemical and sensory characteristics of low calorie sponge cake made from flaxseed mucilage and flaxseed flour

Ahmadinia,

Mohtarami,

Esmaiili

et al. 2023

Sci Rep

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This research aimed to extract flaxseed mucilage (FM) and investigate its rheological properties (static and dynamic tests) compared with animal oil. In the next stage, the D-optimal design was applied to investigate the effect of FM (0–60%) and FF (0–30%) replacements with animal oil and cake flour, respectively on the sponge cake's physicochemical, textural, and sensory properties. According to the flow behavior test, FM showed Newtonian behavior while animal oil had pseudoplastic behavior. The results of the dynamic test showed with an increase in frequency, the loss modulus (G״) and storage modulus (Gʹ) of samples increased. However, Gʹ was higher than G״ in all samples. By replacement of FM and FF, the moisture content, water activity, antioxidant capacity, crumb hardness, and cohesiveness of the samples increased while springiness, crust hardness, and specific volume decreased (P < 0.05). Lightness of samples with replacement of FF decreased (P < 0.05). The overall acceptance score was enhanced with an increase in FM substitution while it was decreased with the replacement of FF (P < 0.05). The amounts of fat, calories, and peroxide values were decreased in sponge cake with the incorporation of FF and FM (P < 0.05). In general, the substitution of FM (60%) and FF (28%) in the cake formulation as an optimized sample to make new products with low-calorie content is possible without significant decreases in product quality.

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“…ML‐based modeling is an exciting option to predict baking kinetics and control the quality of the product without much difficulty. Considering the benefits of ML‐based predictive modeling, recently researchers have attempted to apply ML in many areas of food baking such as the baking of biscuits/cookies (Broyart & Trystram, 2003; Isleroglu & Beyhan, 2020; Olawoye et al., 2020), milk cake (Emerald et al., 2020), bread (Banooni et al., 2009; Sablani et al., 2002), and soft cake (Goyal & Goyal, 2011).…”

Section: In Food Processing Applicationsmentioning

confidence: 99%

Machine learning‐based modeling in food processing applications: State of the art

Khan

Sablani

Nayak

et al. 2022

Comp Rev Food Sci Food Safe

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Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better‐quality products. The development of a machine learning (ML)‐based approach to food processing applications is an exciting and innovative idea for optimizing process parameters and process kinetics to reduce energy consumption, processing time, and ensure better‐quality products; however, developing such a novel approach requires significant scientific effort. This paper presents and evaluates ML‐based approaches to various food processing operations such as drying, frying, baking, canning, extrusion, encapsulation, and fermentation to predict process kinetics. A step‐by‐step procedure to develop an ML‐based model and its practical implementation is presented. The key challenges of neural network training and testing algorithms and their limitations are discussed to assist readers in selecting algorithms for solving problems specific to food processing. In addition, this paper presents the potential and challenges of applying ML‐based techniques to hybrid food processing operations. The potential of physics‐informed ML modeling techniques for food processing applications and their strategies is also discussed. It is expected that the potential information of this paper will be valuable in advancing the ML‐based technology for food processing applications.

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Modelling approaches for predicting moisture transfer during baking of chhana podo (milk cake) incorporated with tikhur (Curcuma angustifolia) starch

Cited by 10 publications

References 49 publications

Biochemical properties, nutritional quality, colour profile and techno‐functional properties of whole grain sourdough and malted cowpea and quinoa flours

Biochemical properties, nutritional quality, colour profile and techno‐functional properties of whole grain sourdough and malted cowpea and quinoa flours

Investigation of physicochemical and sensory characteristics of low calorie sponge cake made from flaxseed mucilage and flaxseed flour

Machine learning‐based modeling in food processing applications: State of the art

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