A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies

Akter, Tanjima; Bhattacharya, Tanima; Kim, Jung-Hyeon; Kim, Moon S.; Baek, Insuck; Chan, Diane E.; Cho, Byoung-Kwan

doi:10.1016/j.jafr.2024.101068

Cited by 5 publications

(1 citation statement)

References 132 publications

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“…Vegetables are a significant part of the human diet and provide many nutrients-including fiber, vitamins, and minerals [1]. Vegetables can be divided into root vegetables, legumes, and fruits.…”

Section: Introductionmentioning

confidence: 99%

Valorization of Red Beetroot (Beta vulgaris L.) Pomace Combined with Golden Linseed (Lini semen) for the Development of Vegetable Crispbreads as Gluten-Free Snacks Rich in Bioactive Compounds

Niemira,

Galus

2024

Molecules

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This work aimed to develop gluten-free snacks such as crispbread based on beetroot pomace (Beta vulgaris L.) and golden linseed (Lini semen). Beetroot is attracting more and more consumer attention because of its nutritional and health properties. The use of beet pomace contributes to waste management. Linseed, known as a superfood with many health-promoting properties, was used to produce crispbreads as an alternative to cereals, which are allergens. Beetroot pomace and whole or ground linseed were used in different proportions to produce crispbread snacks. Chemical and physical analyses were performed including water activity, dry matter, betalains, and polyphenols content, as well as Fourier transform infrared spectroscopy (FTIR). A sensory evaluation and microstructure observations were also performed. The obtained snacks were characterized by low water activity (0.290–0.395) and a high dry matter content (93.43–97.53%), which ensures their microbiological stability and enables longer storage. Beetroot pomace provided betalains—red (14.59–51.44 mg betanin/100 g d.m.) and yellow dyes (50.02–171.12 mg betanin/100 g d.m.)—while using linseed enriched the product with polyphenols (730–948 mg chlorogenic acid/100 g d.m.). FTIR analysis showed the presence of functional groups such as the following: -OH, -C-O, -COOH, and -NH. The most desired overall consumer acceptability was achieved for snacks containing 50% beetroot pomace and 50% linseed seeds. The obtained results confirmed that beetroot pomace combined with linseed can be used in the production of vegetable crispbread snacks.

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Section: Introductionmentioning

confidence: 99%

Valorization of Red Beetroot (Beta vulgaris L.) Pomace Combined with Golden Linseed (Lini semen) for the Development of Vegetable Crispbreads as Gluten-Free Snacks Rich in Bioactive Compounds

Niemira,

Galus

2024

Molecules

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On-Site Intelligent Grading System for Fruits and Vegetables: Case Studies on Tomato and Apple

Yun,

Ma,

et al. 2024

Smart Agriculture

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Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights

Shen,

Wang,

Nawazish

et al. 2024

Comp Rev Food Sci Food Safe

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Food fraud undermines consumer trust, creates economic risk, and jeopardizes human health. Therefore, it is essential to develop efficient technologies for rapid and reliable analysis of food quality and safety for food authentication. Machine vision–based methods have emerged as promising solutions for the rapid and nondestructive analysis of food authenticity and quality. The Industry 4.0 revolution has introduced new trends in this field, including the use of deep learning (DL), a subset of artificial intelligence, which demonstrates robust performance and generalization capabilities, effectively extracting features, and processing extensive data. This paper reviews recent advances in machine vision and various DL‐based algorithms for food authentication, including DL and lightweight DL, used for food authenticity analysis such as adulteration identification, variety identification, freshness detection, and food quality identification by combining them with a machine vision system or with smartphones and portable devices. This review explores the limitations of machine vision and the challenges of DL, which include overfitting, interpretability, accessibility, data privacy, algorithmic bias, and design and deployment of lightweight DLs, and miniaturization of sensing devices. Finally, future developments and trends in this field are discussed, including the development of real‐time detection systems that incorporate a combination of machine vision and DL methods and the expansion of databases. Overall, the combination of vision‐based techniques and DL is expected to enable faster, more affordable, and more accurate food authentication methods.

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A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies

Cited by 5 publications

References 132 publications

Valorization of Red Beetroot (Beta vulgaris L.) Pomace Combined with Golden Linseed (Lini semen) for the Development of Vegetable Crispbreads as Gluten-Free Snacks Rich in Bioactive Compounds

Valorization of Red Beetroot (Beta vulgaris L.) Pomace Combined with Golden Linseed (Lini semen) for the Development of Vegetable Crispbreads as Gluten-Free Snacks Rich in Bioactive Compounds

On-Site Intelligent Grading System for Fruits and Vegetables: Case Studies on Tomato and Apple

Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights

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