Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries

Wang, Dayuan; Zhang, Min; Mujumdar, Arun S.

doi:10.1007/s12393-021-09298-5

Cited by 28 publications

(16 citation statements)

References 173 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Among them, the training set is used to establish the model, the validation set is used to adjust the parameters that control the complexity of the model and monitor whether the model appears to be overfitted, and the test set verifies the generalization ability of the optimal model. Machine learning contains subsets of traditional machine learning, chemometrics, artificial neural network (ANN), and deep learning [31], and the interrelationships among the subsets are shown in Fig. 3.…”

Section: Machine Learning and Data Processing Processesmentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

View full text Add to dashboard Cite

Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

show abstract

Section: Machine Learning and Data Processing Processesmentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Currently, the degree of automation and intelligence in the tree nut industry (particularly in postharvest processing) is still behind some other food industries. Integration of the conventional processing technologies with Internet of Things (IoT) technologies such as real‐time sensors (Boz, 2021), artificial intelligence (Wang et al., 2021), simulation and modeling (Chen & Pan, 2021), cloud computing and data analysis (Režek Jambrak et al., 2021), and so forth will benefit the smart and precise processing of foods for higher efficiency, product quality and safety, and reduced cost. There are increased research interests and opportunities in these areas, which need extensive efforts and explorations.…”

Section: Future Research Needsmentioning

confidence: 99%

Postharvest processing of tree nuts: Current status and future prospects—A comprehensive review

Chen

Pan

2022

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

Tree nuts are important economic crops and are consumed as healthy snacks worldwide. In recent years, the increasing needs for more efficient and effective postharvest processing technologies have been driven by the growing production, higher quality standards, stricter food safety requirements, development of new harvesting methods, and demand to achieve energy saving and carbon neutralization. Among all, the technologies related to drying, disinfection, and disinfestation and downstream processes, such as blanching, kernel peeling, and roasting, are the most important processes influencing the quality and safety of the products. These processes make up the largest contribution to the energy consumptions and environmental impacts stemming from tree nut production. Although many studies have been conducted to improve the processing efficiency and sustainability, and preserve the product quality and safety, information from these studies is fragmented and a centralized review highlighting the important technology advancements of postharvest processing of tree nuts would benefit the industry. In this comprehensive review, almonds, walnuts, and pistachios are selected as the representative crops of tree nuts. Current statuses, recent advances, and ongoing challenges in the scientific research as well as in the industrial processing practices of these tree nuts are summarized. Some new perspectives and applications of tree nut processing waste and by‐products (such as the hulls and shells) are also discussed. In addition, future trends and research needs are highlighted. The material presented here will help both stakeholders and scientists to better understand postharvest tree nut processing and provide technological recommendations to improve the efficiency and sustainability, product quality and safety, and competitiveness of the industry.

show abstract

“…2D and 3D images showing different levels of contrast are created based on the response of the sample tissue to high magnetic fields and radio frequency waves (Adedeji et al., 2020). The MRl is used to detect the moisture content, moisture distribution, and flowability of fruits and vegetables during storage, and observe the changes among various tissue structures, which can be used to determine the maturity of fruits and vegetables as well as the degree of damage and decay, thereby achieving the purpose of quickly and dynamically predicting and controlling the quality of fruits and vegetables (Wang et al., 2021). High‐resolution MRI systems using 5.9 to 21.1T have been effectively used to assess the quality of fruits and vegetables, such as physicochemical properties (moisture content, TSS, sugar to acid ratio, and maturity) and physical defects (internal browning, rot, insect pests, and bruises and chilling injury) (Hussain et al., 2018; Pathmanaban et al., 2019).…”

Section: Imaging Technologymentioning

confidence: 99%

“…Results of identification of lettuce browning using single waveband imaging (SWI) algorithm, ratio imaging (RI) algorithm, and two‐band subtraction imaging (SI) algorithm (Mo et al., 2015) (a); Spatial distribution diagram of insoluble (IDF) and soluble dietary fiber (SDF) content in fresh‐cut celery (Yan et al., 2017) (b); Spatial distribution diagram of starch content in fresh‐cut potato (Wang et al., 2021) (c)…”

Section: Application Of Various Imaging Technologies In Fresh‐cutting...mentioning

confidence: 99%

Intelligent detection for fresh‐cut fruit and vegetable processing: Imaging technology

Tang

Zhang

Mujumdar

2022

Comp Rev Food Sci Food Safe

Self Cite

View full text Add to dashboard Cite

Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.

show abstract

Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries

Cited by 28 publications

References 173 publications

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Postharvest processing of tree nuts: Current status and future prospects—A comprehensive review

Intelligent detection for fresh‐cut fruit and vegetable processing: Imaging technology

Contact Info

Product

Resources

About