Deep transfer learning to verify quality and safety of ground coffee

Data integrity of food and machine learning: Strategies, advances and prospective

Data integrity is an emerging concept aimed at recording real food properties in the form of data throughout the food lifecycle. However, due to the one-sided nature of current food control data, the comprehensive implementation of data integrity has not been fully achieved. Cause food data integrity realization is required to establish the connection of data-algorithm-application. Machine learning (ML) provides a possibility for the practical carrier of food data integrity. Despite ML is one of top-trend in food quality and safety, ML applications are floating on the surface. The current review does not reveal the relationships behind different algorithms and data patterns. Similarly, due to the rapid development of ML, the current advanced concepts and data explanation tools have not been systematically reviewed. This paper expounds the feasibility of machine learning to achieve data integrity and looks forward to the future vision brought about by artificial intelligence to data integrity.

Enhanced food authenticity control using machine learning-assisted elemental analysis

With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namely food traceability and food quality control. More explicitly, they are the traceability of food origin and organic food, detection of food adulteration and heavy metals. It also points out the limitations of the commonly used morphology and organic compound detection methods, and highlights the advantages of combining the elements in food as detection indicators using machine learning technology to solve the problem of food authenticity. Taking elements as detection objects has the significant advantages of stability, machine learning technology can combine large data samples, ensuring both the accuracy and efficiency. In addition, the most suitable algorithm can be found by comparing their accuracy.

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

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.

The emerging role of biotechnological advances and artificial intelligence in tackling gluten sensitivity

Gluten comprises an intricate network of hundreds of related but distinct proteins, mainly "gliadins" and "glutenins," which play a vital role in determining the rheological properties of wheat dough. However, ingesting gluten can trigger severe conditions in susceptible individuals, including celiac disease, wheat allergy, or non-celiac gluten sensitivity, collectively known as gluten-related disorders. This review provides a panoramic view, delving into the various aspects of gluten-triggered disorders, including symptoms, diagnosis, mechanism, and management. Though a gluten-free diet remains the primary option to manage gluten-related disorders, the emerging microbial and plant biotechnology tools are playing a transformative role in reducing the immunotoxicity of gluten. The enzymatic hydrolysis of gluten and the development of gluten-reduced/free wheat lines using RNAi and CRISPR/Cas technology are laying the foundation for creating safer wheat products. In addition to biotechnological interventions, the emerging artificial intelligence technologies are also bringing about a paradigm shift in the diagnosis and management of gluten-related disorders. Here, we provide a comprehensive overview of the latest developments and the potential these technologies hold for tackling gluten sensitivity.

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

Neural network in food analytics

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.

Is my food safe? - AI-based classification of lentil flour samples with trace levels of gluten or nuts

An artificial intelligence-based method to rapidly detect adulterated lentil flour in real time is presented. Mathematical models based on convolutional neural networks and transfer learning (viz., ResNet34) have been trained to identify lentil flour samples that contain trace levels of wheat (gluten) or pistachios (nuts), aiding two relevant populations (people with celiac disease and with nut allergies, respectively). The technique is based on the analysis of photographs taken by a simple reflex camera and further classification into groups assigned to adulterant type and amount (up to 50 ppm). Two different algorithms were trained, one per adulterant, using a total of 2200 images for each neural network. Using blind sets of data (10% of the collected images; initially and randomly separated) to evaluate the performance of the models led to strong performances, as 99.1% of lentil flour samples containing ground pistachio were correctly classified, while 96.4% accuracy was reached to classify the samples containing wheat flour.

Deep learning for the quality control of thermoforming food packages

Quality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied. This automation is challenging since specific strategies designed according to the application scenario are required. Focusing on the quality control of the sealing and closure of matrix-shaped thermoforming food packages, the aim of the article is to propose a deep-learning-based solution designed to automatically perform the quality control while satisfying production cadence and ensuring 100% inline inspection of the products. Particularly, the designed computer vision system and the image-based criteria defined to determine when a product has to be accepted or rejected are presented. In addition, the vision control software is described with special emphasis on the different convolutional neural network (CNN) architectures that have been considered (ResNet18, ResNet50, Vgg19 and DenseNet161, non-pre-trained and pre-trained on ImageNet) and on the specifically designed dataset. To test the solution, different experiments are carried out in the laboratory and also in a real scenario, concluding that the proposed CNN-based approach improves the efficiency and security of the quality control process. Optimal results are obtained with the pre-trained DenseNet161, achieving false positive rates that range from 0.03 to 0.30% and false negative rates that range from 0 to 0.07%, with a rejection rate between 0.64 and 5.09% of production, and being able to detect at least 99.93% of the sealing defects that occur in any production. The modular design of our solution as well as the provided description allow it to adapt to similar scenarios and to new deep-learning models to prevent the arrival of faulty products to end consumers by removing them from the automated production line.