Evaluation of novel-extruding ingredients to improve the physicochemical and expansion characteristics of a corn-puffed snack-containing pearled barley

O’Shea, Norah; Gallagher, Eimear

doi:10.1007/s00217-019-03260-w

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Studies have been conducted to correlate sound crispness and mechanical crispness by evaluating the sound in time domain, acoustic signal amplitude, duration, and number of peaks (Akimoto et al, 2019;Dias-Faceto et al, 2020;Gouyo et al, 2020;O'Shea & Gallagher, 2019), This article was published on AA publication on: 22 July 2023 but, nowadays, with the use of artificial intelligence it is possible to predict the crispness of food more quickly, accurately, and advantageously by performing sound analysis (Chen & Ding, 2021;Liu, Cai, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Classification of crispness of food materials by deep neural networks

Lopes,

Dacanal

2023

Journal of Texture Studies

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Crispness is a textural characteristic that influences consumer choices, requiring a comprehensive understanding for product customization. Previous studies employing neural networks focused on acquiring audio through mechanical crushing of crispy samples. This research investigates the representation of crispy sound in time intervals and frequency domains, identifying key parameters to distinguish different foods. Two machine learning architectures, multi‐layer perceptron (MLP) and residual neural network (ResNet), were used to analyze mel frequency cepstral coefficients (MFCC) and discrete Fourier transform (DFT) data, respectively. The models achieved over 95% accuracy “in‐sample” successfully classifying fried chicken, potato chips, and toast using randomly extracted audio from ASMR videos. The MLP (MFCC) model demonstrated superior robustness compared to ResNet and predicted external inputs, such as freshly toasted bread acquired by a microphone or ASMR audio of toast in milk. In contrast, the ResNet model proved to be more responsive to variations in DFT spectrum and unable to predict the similarity of external audio sources, making it useful for classifying pretrained “in‐samples”. These findings are useful for classifying crispness among individual food sources. Additionally, the study explores the promising utilization of ASMR audio from Internet platforms to pretrain artificial neural network models, expanding the dataset for investigating the texture of crispy foods.

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Section: Introductionmentioning

confidence: 99%

Classification of crispness of food materials by deep neural networks

Lopes,

Dacanal

2023

Journal of Texture Studies

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“…Recently, there has been an increasing amount of interest in the replacement of extruded products based on corn grits, which are the most common ones in the market, with nutritional, rich, cereals-based extrudates. Numerous cereals (such as wheat, amaranth and quinoa) have been used to improve the nutritional value and textural properties of extruded snack without reducing product quality in terms of organoleptic properties and consumers acceptability [1][2][3][4]. Thus far, the influence of spelt flour addition on the physical and rheological properties of extruded products based on corn grits has been investigated [5].…”

Section: Introductionmentioning

confidence: 99%

Textural, Color and Sensory Features of Spelt Wholegrain Snack Enriched with Betaine

Kojić

Belović

Krulj

et al. 2022

Foods

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The influence of different extrusion parameters, including screw speed (250–750 rpm), feed rate (15–25 kg/h) and feed moisture content (15–25%), on the textural and color properties of spelt wholegrain snack products produced on a co-rotating twin-screw extruder with added betaine was investigated. In order to determine the relative influence of input variables in the artificial neural network (ANN) model, Yoon’s interpretation method was used, and it was concluded that feed moisture content has the greatest influence on L* values, while screw speed has the greatest influence on a* and b* values. The softest samples were obtained at the lowest moisture content. Sensory analysis was carried out on selected samples, and it showed that betaine addition did not intensify the bitter taste. The sample with the largest expansion exhibited the lowest hardness and chewiness before and after immersion in milk, and this sample is the most suitable for enrichment with betaine.

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“…Even with those challenges involved with fiber and/or protein addition to starch‐based extrudates, several studies have reported success in obtaining extruded snacks with improved nutritional properties while keeping (and sometimes even improving) the physical/textural properties similar to those of conventional starch‐based extrudates, such as by varying fiber and/or protein levels, 67,69,72 fiber particle size, 74,75 ingredients/combinations (e.g. different fiber sources), 76,77 and/or process parameters (e.g. extruder temperatures, moisture content, screw speed) 72,78,79 …”

Section: Introductionmentioning

confidence: 99%

Something to chew on: technological aspects for novel snacks

2021

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Snacks have accompanied people for a long time, meeting our needs for something fast and filling between meals. Societies and technologies have changed, and so have snacks, adapting to people's daily lives, concerns, and demands. Although traditional snacks, such as potato chips, are still ubiquitous and popular worldwide, there is not unanimity around them anymore, since many people have been looking for healthier snacks. Studies have been carried out to propose healthier snack options by changing their composition and/or techniques to produce them, minimizing contents of energy‐dense components and/or maximizing the retention or bioavailability of nutrients. This mini‐review presents the main trends on development of snacks and future perspectives. © 2021 Society of Chemical Industry.

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Evaluation of novel-extruding ingredients to improve the physicochemical and expansion characteristics of a corn-puffed snack-containing pearled barley

Cited by 5 publications

References 26 publications

Classification of crispness of food materials by deep neural networks

Classification of crispness of food materials by deep neural networks

Textural, Color and Sensory Features of Spelt Wholegrain Snack Enriched with Betaine

Something to chew on: technological aspects for novel snacks

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