Recent technology for food and beverage quality assessment: a review

Tan, Wei; Husin, Zulkifli; Yasruddin, Muhammad Luqman; Ismail, Muhammad

doi:10.1007/s13197-022-05439-8

Cited by 17 publications

(11 citation statements)

References 53 publications

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“…However, the traditional techniques for assessing the quality characteristics of baked goods are subjective, destructive, require complex analytical skills, are slow, labor and cost intensive, and are not appropriate for automation (Ali & Hashim, 2022; El‐Mesery et al., 2019). Also, the manual method of sorting and grading and sensory analysis has low accuracy, consistency, and integrity due to factors such as personnel judgement and fatigue; however, the use of machine vision systems has been proven to reduce the identification time and produces a more uniform quality assessment (Andukar et al., 2016; Nithya et al., 2022; Tan et al., 2022).…”

Section: Limitations Of Conventional and Digital Image Processing Tec...mentioning

confidence: 99%

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Olakanmi

Jayas

Paliwal

2023

Comp Rev Food Sci Food Safe

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One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods.These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.

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Section: Limitations Of Conventional and Digital Image Processing Tec...mentioning

confidence: 99%

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Olakanmi

Jayas

Paliwal

2023

Comp Rev Food Sci Food Safe

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“…In this way, once the optimum quality criteria have been established for a product, the intelligent systems will be able to act by making instant decisions in the processing line itself [52]. Various imaging technologies exist such as spectral imaging (also known as spectroscopic imaging or chemical imaging), near-infrared, X-ray imaging, digital and analogue image processing, and odour imaging [53]. These methodologies consist of capturing images in real time, which are displayed on computers and automatically analysed to generate control commands based on the results obtained [54].…”

Section: Imaging Technologymentioning

confidence: 99%

Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Echegaray

Hassoun²,

Jagtap

et al. 2022

Applied Sciences

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Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.

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“…The testing technology of pork freshness in the laboratory has been developed and improved significantly over a long period of time (Tan et al, 2022). However, most of these methods are time-consuming and have high requirements for operators (Baek et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Visualization and prediction of TVB-N content in chilled pork by hyperspectral imaging

Xing

Zhao

et al. 2023

Food Sci. Technol

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TVB-N is one of the important indexes of the freshness of chilled pork. To present the freshness more accurately and completely, a visualization technology of chemical composition was applied and optimized for pork freshness detection. Two hyperspectral cameras covering visible and near-infrared wavebands were used to detect the TVB-N content. GA, SPA, iPLSR, and siPLSR were employed to select characteristic wavebands from the spectra data extracted from the hyperspectral images, and PLSR models were established on full wavebands and characteristic wavebands. According to the models established on the spectral data, TVB-N distribution maps were obtained, and URV and ICV were employed to evaluate the performance of distribution maps. The results showed that, both PLSR models established on the characteristic wavebands and full wavebands achieved good accuracy, and that the characteristic wavebands cluster was in the spectral range of 600-750 nm, 1020-1120 nm, and 1450-1570 nm. To optimize the performance of distribution maps, prediction models were reconstructed under the guidance of URV and ICV. This study provided a method of predicting TVB-N content of pork in both spectral and imaging aspect for online testing the freshness of chilled pork, which improves the efficiency and quality of testing.

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Recent technology for food and beverage quality assessment: a review

Cited by 17 publications

References 53 publications

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Visualization and prediction of TVB-N content in chilled pork by hyperspectral imaging

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