Early warning modeling and analysis based on analytic hierarchy process integrated extreme learning machine (AHP-ELM): Application to food safety

Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning

The Protected Designation of Origin (PDO) indication for foods intends to guarantee the conditions of production and the geographical origin of regional products within the European Union. Honey products are widely consumed due to their health-promoting properties and there is a general interest in tracing their authenticity. In this regard, metagenomics sequencing and machine learning (ML) have been proposed as complementary technologies to improve the traceability methods of foods. Therefore, the aim of this study was to analyze the metagenomic profiles of Spanish honeys from three different PDOs (Granada, Tenerife and Villuercas-Ibores), and compare them with non-PDO honeys using ML models (PLS, RF, LOGITBOOST, and NNET). According to the results obtained, non-PDO honeys and Granada PDO showed higher beta diversity values than Tenerife and Villuercas-Ibores PDOs. ML classification of honey products allowed the identification of different microbial biomarkers of the geographical origin of honeys: Lactobacillus kunkeei, Parasaccharibacter apium and Lactobacillus helsingborgensis for PDO honeys and Paenibacillus larvae, Lactobacillus apinorum and Klebsiella pneumoniae for non-PDO honeys. In addition, potential microbial biomarkers of some honey varieties including L. kunkeei for Albaida and Retama del Teide varieties, and P. apium for Tajinaste variety, were identified. ML models were validated on an independent set of samples leading to high accuracy rates (above 90 %). This work demonstrates the potential of ML to differentiate different types of honey using metagenome-based methods, leading to high performance metrics. In addition, ML models discriminate both the geographical origin and variety of products corresponding to different PDOs and non-PDO products. Results here presented may contribute to develop enhanced traceability and authenticity methods that could be applied to a wide range of foods.

An explainable unsupervised risk early warning framework based on the empirical cumulative distribution function: Application to dairy safety

Efficient food safety risk assessment significantly affects food safety supervision. However, food detection data of different types and batches show different feature distributions, resulting in unstable detection results of most risk assessment models, lack of interpretability of risk classification, and insufficient risk traceability. This study aims to explore an efficient food safety risk assessment model that takes into account robustness, interpretability and traceability. Therefore, the Explainable unsupervised risk Warning Framework based on the Empirical cumulative Distribution function (EWFED) was proposed. Firstly, the detection data's underlying distribution is estimated as non-parametric by calculating each testing indicator's empirical cumulative distribution. Next, the tail probabilities of each testing indicator are estimated based on these distributions and summarized to obtain the sample risk value. Finally, the "3σ Rule" is used to achieve explainable risk classification of qualified samples, and the reasons for unqualified samples are tracked according to the risk score of each testing indicator. The experiments of the EWFED model on two types of dairy product detection data in actual application scenarios have verified its effectiveness, achieving interpretable risk division and risk tracing of unqualified samples. Therefore, this study provides a more robust and systematic food safety risk assessment method to promote precise management and control of food safety risks effectively.

Advancing food safety risk assessment in China: development of new approach methodologies (NAMs)

Novel techniques and methodologies are being developed to advance food safety risk assessment into the next-generation. Considering the shortcomings of traditional animal testing, new approach methodologies (NAMs) will be the main tools for the next-generation risk assessment (NGRA), using non-animal methodologies such as in vitro and in silico approaches. The United States Environmental Protection Agency and the European Food Safety Authority have established work plans to encourage the development and application of NAMs in NGRA. Currently, NAMs are more commonly used in research than in regulatory risk assessment. China is also developing NAMs for NGRA but without a comprehensive review of the current work. This review summarizes major NAM-related research articles from China and highlights the China National Center for Food Safety Risk Assessment (CFSA) as the primary institution leading the implementation of NAMs in NGRA in China. The projects of CFSA on NAMs such as the Food Toxicology Program and the strategies for implementing NAMs in NGRA are outlined. Key issues and recommendations, such as discipline development and team building, are also presented to promote NAMs development in China and worldwide.

Review of visual analytics methods for food safety risks

With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.

Prediction and Visual Analysis of Food Safety Risk Based on TabNet-GRA

Food safety risk prediction is crucial for timely hazard detection and effective control. This study proposes a novel risk prediction method for food safety called TabNet-GRA, which combines a specialized deep learning architecture for tabular data (TabNet) with a grey relational analysis (GRA) to predict food safety risk. Initially, this study employed a GRA to derive comprehensive risk values from fused detection data. Subsequently, a food safety risk prediction model was constructed based on TabNet, and training was performed using the detection data as inputs and the comprehensive risk values calculated via the GRA as the expected outputs. Comparative experiments with six typical models demonstrated the superior fitting ability of the TabNet-based prediction model. Moreover, a food safety risk prediction and visualization system (FSRvis system) was designed and implemented based on TabNet-GRA to facilitate risk prediction and visual analysis. A case study in which our method was applied to a dataset of cooked meat products from a Chinese province further validated the effectiveness of the TabNet-GRA method and the FSRvis system. The method can be applied to targeted risk assessment, hazard identification, and early warning systems to strengthen decision making and safeguard public health by proactively addressing food safety risks.

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.

CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample's contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.

A review on extreme learning machine

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk

Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel’s efficiency, whereas the panel enhances the model’s reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

Impact and prospect of the fourth industrial revolution in food safety: Mini-review

The fourth industrial revolution represented by big data and artificial intelligence (AI), already had a significant impact on the food industry. In this review, the impacts and prospects of the 4th industrial revolution in food safety were discussed. First, the general process and characteristics of AI application from data collection to visualization are covered. Additionally, various data collection and analysis methods are discussed, with emphasis on the collection of high variety, volume, and velocity data and visualization. Available literature presents examples of machine learning applications in food samples that are mostly associated with the classification of agricultural food items through convolutional neural networks. Based on these examples, the prospects of the 4th industrial revolution in food safety are categorized as follows: prediction of food safety risk, detection of foodborne pathogens, and food safety management. This mini-review will help understand the relationship between the 4th industrial revolution and food safety.

Application of machine learning to the monitoring and prediction of food safety: A review

Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.

Risk Assessment of Veterinary Drug Residues in Meat Products

With the improvement of the global food safety regulatory system, there is an increasing importance for food safety risk assessment. Veterinary drugs are widely used in poultry and livestock products. The abuse of veterinary drugs seriously threatens human health. This article explains the necessity of risk assessment for veterinary drug residues in meat products, describes the principles and functions of risk assessment, then summarizes the risk assessment process of veterinary drug residues, and then outlines the qualitative and quantitative risk assessment methods used in this field. We propose the establishment of a new meat product safety supervision model with a view to improve the current meat product safety supervision system.

Integration of a low-cost electronic nose and a voltammetric electronic tongue for red wines identification

The purpose of this present study was to develop a rapid and effective approach for identification of red wines that differ in geographical origins, brands, and grape varieties, a multi-sensor fusion technology based on a novel cost-effective electronic nose (E-nose) and a voltammetric electronic tongue (E-tongue) was proposed. The E-nose sensors was created using porphyrins or metalloporphyrins, pH indicators and Nile red printed on a C2 reverse phase silica gel plate. The voltammetric E-Tongue with six metallic working electrodes, namely platinum, gold, palladium, tungsten, titanium, and silver was employed to sense the taste of red wines. Principal component analysis (PCA) was utilized for dimensionality reduction and decorrelation of the raw sensors datasets. The fusion models derived from extreme learning machine (ELM) were built with PCA scores of E-nose and tongue as the inputs. Results showed superior performance (100% recognition rate) using combination of odor and taste sensors than individual artificial systems. The results suggested that fusion of the novel cost-effective E-nose created and voltammetric E-tongue coupled with ELM has a powerful potential in rapid quality evaluation of red wine.

Development of a Holistic Assessment Framework for Industrial Organizations

The evaluation and selection among the best production practices beyond the conventional linear models is, nowadays, concerned with those holistic approaches drawn toward environmental assessment in industry. Therefore, researchers need to develop an analysis that can evaluate the performance of industrial organization in the light of their environmental viewpoint. This study implemented a pilot co-integrated scheme based on an innovative in-house Holistic Assessment Performance Index for Environment (HAPI-E) industry tool while assimilating the principles of circular economy through the Eco-innovation Development and Implementation Tool (EDIT). For the latter, nine qualitative indicators were motivated and enriched the weighting criteria of the questionnaire. The decomposition of the complexity and preferences mapping was accompanied by a multi-criteria holistic hierarchical analysis methodology in order to synthesize a single index upon a need-driven scoring. This multi-criteria decision approach in industry can quantify the material and process flows, thus enhancing the existing knowledge of manipulating internal resources. The key-criteria were based on administrative, energy, water, emissions, and waste strategies. Subsequently, the HAPI-E industry tool was modeled on the food industry, being particularly focused on pasta-based industrial production. Then, the parameters of this tool were modeled, measured, and evaluated in terms of the environmental impact awareness. The magnitude of necessary improvements was unveiled, while future research orientations were discussed. The HAPI-E industry tool can be utilized as a precautionary methodology on sustainable assessment while incorporating multifaceted and quantification advantages.

Prediction model of PSO-BP neural network on coliform amount in special food

Special food safety supervision by means of intelligent models and methods is of great significance for the health of local people and tourists. Models like BP neural network have the problems of low accuracy and poor robustness in food safety prediction. So, firstly, the principal component analysis was used to extract the key factors that influenced the amount of coliform communities, which was applied to reduce the dimension of this model as the input variable of BP neural network. Secondly, both the particle swarm optimization (PSO) and BP neural network were implemented to optimize initial weights and threshold to obtain the optimal parameter, and a model was constructed to predict the amount of coliform bacteria in Dai Special Snacks, Sa pie, based on PSO-BP neural network model. Finally, the predicted value of the model is verified. The results show that MSE is 0.0097, MAPE is 0.3198 and MAE is 0.0079, respectively. It was clear that PSO-BP model was better accuracy and robustness. That means, this model can effectively predict the amount of coliform. The research has important guiding significance for the quality and the production of Sa pie.

Post hoc support vector machine learning for impedimetric biosensors based on weak protein–ligand interactions

We develop a simple, open source machine learning algorithm for analyzing impedimetric biosensor data using a mobile phone.