scholarly journals Detecting reports of unsafe foods in consumer product reviews

JAMIA Open ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 330-338 ◽  
Author(s):  
Adyasha Maharana ◽  
Kunlin Cai ◽  
Joseph Hellerstein ◽  
Yulin Hswen ◽  
Michael Munsell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Food Safety ◽  
Food Product ◽  
Food Products ◽  
Good Health ◽  
Consumer Product ◽  
Product Reviews ◽  
Applied Machine Learning ◽  
Under Sampling ◽  
Food Recalls

Abstract Objectives Access to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem. Here, we develop a machine learning approach for detecting reports of unsafe food products in consumer product reviews from Amazon.com. Materials and Methods We linked Amazon.com food product reviews to Food and Drug Administration (FDA) food recalls from 2012 to 2014 using text matching approaches in a PostGres relational database. We applied machine learning methods and over- and under-sampling methods to the linked data to automate the detection of reports of unsafe food products. Results Our data consisted of 1 297 156 product reviews from Amazon.com. Only 5149 (0.4%) were linked to recalled food products. Bidirectional Encoder Representation from Transformations performed best in identifying unsafe food reviews, achieving an F1 score, precision and recall of 0.74, 0.78, and 0.71, respectively. We also identified synonyms for terms associated with FDA recalls in more than 20 000 reviews, most of which were associated with nonrecalled products. This might suggest that many more products should have been recalled or investigated. Discussion and Conclusion Challenges to improving food safety include, urbanization which has led to a longer food chain, underreporting of illness and difficulty in linking contaminated food to illness. Our approach can improve food safety by enabling early identification of unsafe foods which can lead to timely recall thereby limiting the health and economic impact on the public.

scholarly journals Juridical Assessment of Food Safety in Packaged Processed Food Product

2021 ◽  
Author(s):  
Dwi Bagus Pambudi ◽  
Rany Ekawati
Keyword(s):  
Food Safety ◽  
Consumer Protection ◽  
Government Regulation ◽  
Food Additives ◽  
Food Product ◽  
Food Products ◽  
The Body ◽  
Processed Food ◽  
Balanced Diet ◽  
The Government

Food safety is one of WHO’s primary concerns during a pandemic. The current Covid-19 pandemic requires us to boost our immune system by eating a healthy and balanced diet. Food consumed by the masses must be free of chemical and biological substances that can be harmful for the body. Nowadays, food products have developed to be more innovative, such as packaged processed food products that can be stored for a long time, generally using Food Additives. The safety of packaged processed food products must be guaranteed by the manufacturers in order to guarantee consumer protection. To ensure this, the government has established the Consumer Protection Law; the Government Regulation on Food Safety, Quality and Nutrition; and the Food and Drug Administration Division. Through the BPOM, the government supervises food products circulating in the community. The supervision carried out by BPOM are preventive and repressive. One form of supervision carried out by BPOM is granting distribution permits for packaged processed food products before they are distributed to the public. Keywords: packaged processed food products, BPOM

scholarly journals Sensory nutrition: The role of taste in the reviews of commercial food products

2019 ◽  
Author(s):  
Danielle. R. Reed ◽  
Joel D. Mainland ◽  
Charles J. Arayata
Keyword(s):  
Customer Service ◽  
Food Product ◽  
Food Products ◽  
Data Sets ◽  
Product Reviews ◽  
Purchasing Decisions ◽  
Consumer Review ◽  
Sensory Responses ◽  
Taste Experience

AbstractMany factors play a role in choosing what to eat or drink. Here we explored the role of sensation to explain these differences, drawing on consumer reviews for commercially available food products sold through an online retailer. We analyzed 393,568 unique food product reviews from Amazon customers with a total of 256,043 reviewers rating 67,553 products. Taste-associated words were mentioned more than words associated with cost, food texture, customer service, nutrition, smell, or those referring to the trigeminal senses, e.g., spicy. We computed the overall number of reviews that mentioned taste qualities: the word taste was mentioned in over 30% of the reviews (N= 142,768), followed by sweet (10.7%, N=42,315), bitter (2.9%, N=11,424), sour (2.1%, N=8,252), and salty (1.4%, N=5,688). We identified 38 phrases used to describe the evaluation of sweetness, finding that ‘too sweet’ was used in nearly 0.8% of the reviews and oversweetness was mentioned over 25 times more often than under-sweetness. We then focused on ‘polarizing’ products, those that elicited a wide difference of opinion (as measured by the ranges of the product ratings). Using the products that had more than 50 reviews, we identified the top 10 most polarizing foods (i.e., those with the largest standard deviation in ratings) and provide representative comments about the polarized taste experience of consumers. Overall, these results support the primacy of taste in real-world food ratings and individualized taste experience, such as whether a product is ‘too sweet’. Analysis of consumer review data sets can provide information about purchasing decisions and customer sensory responses to particular commercially available products and represents a promising methodology for the emerging field of sensory nutrition.

scholarly journals Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics

2016 ◽  
Vol 8s1 ◽  
pp. BII.S37791 ◽  
Author(s):  
Manabu Torii ◽  
Sameer S. Tilak ◽  
Son Doan ◽  
Daniel S. Zisook ◽  
Jung-wei Fan
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Consumer Product ◽  
Future Research ◽  
Product Reviews ◽  
Learning Tools ◽  
Text Analytics ◽  
Related Information ◽  
Online Product Reviews ◽  
Health Related

In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research.

scholarly journals Is that food RTE?

2019 ◽  
Author(s):  
Ella Derby ◽  
BCIT School of Health Sciences, Environmental Health ◽  
Dale Chen ◽  
Lorraine McIntyre
Keyword(s):  
Food Safety ◽  
Product Category ◽  
Food Product ◽  
Food Products ◽  
Primary Data ◽  
Grocery Stores ◽  
Internal Temperature ◽  
Additional Food ◽  
Frozen Food ◽  
Cooking Temperature

  Background: Rates of foodborne illness linked to consumers misinterpreting, or lack of proper cooking instructions on frozen food products continue to rise. With many recalls and outbreaks in the recent years surrounding frozen breaded chicken (FBC) products due to consumers not adequately cooking products and in turn becoming ill. However, it is not just frozen breaded chicken to blame, frozen microwavable entrees have also contributed to this problem. Therefore, the purpose of this project was to determine what was actually being displayed on the packaging of these frozen foods. Identifying whether or not frozen food products have clear, specific and consistent cooking instructions for the consumers is critical in identifying the risk of cooking and eating these foods. Methods: Secondary data was obtained from the British Columbia Centers for Disease Control (BCCDC) of cooking instructions on FBC packaging, and primary data was collected through visiting grocery stores in the Metro Vancouver area by surveying cooking instructions on frozen microwavable entrees packaging. Four categories of data were assessed, 2008 and 2018 raw FBC products, 2018 cooked FBC, and 2019 frozen microwavable entrees. Parameters such as inclusion of internal cooking temperature, thermometer usage, microwave instructions, and additional food safety handling was gathered. Chi-square tests were used to analyze the results with the statistical software NCSS12. Results: Of all categories surveyed 87.1% (n=122) said to cook the product to a minimum of 74°C, and 12.9% (n=18) did not state anything. 2018 raw FBC always stated an internal cooking temperature (100%), whereas 58% of the 2008 raw FBC stated an internal temperature and 89% of both the 2019 frozen entrees and 2018 cooked FBC did. Out of all 140 products surveyed across categories only 8% stated to use a thermometer when cooking to ensure food has reached proper internal temperature. The frequency of categories to display food safety was as follows, the 2018 raw FBC (82%) and the 2008 raw FBC (79%), followed by the 2019 frozen entrees (42%) and the 2018 cooked FBC (21%). For the microwave instructions the frozen entrees almost always stated this (81%), whereas the 2008 and 2018 raw FBC both never stated to use a microwave (0%). There was a significant association between products and the inclusion of the statement of internal cooking temperature and thermometer usage. This was based on the food product category itself, frozen breaded chicken or frozen entrees, or based on manufacturer of the product. Conclusions: It was evident that the major gap lies in the consistency of instructions. Almost every manufacturer had their cooking instructions presented differently, which could in turn confuse the consumer. Instructions also rarely stated to use a thermometer to check the internal temperature, although almost always stated a specific temperature to cook to. A small portion of manufactures are diligent about displaying all necessary information to the consumer such as, Kraft, Conagra foods, and Olymel which adequately met all parameters assessed. In order to fix the gaps of inconsistency of instructions this information can be used as educational tools by the BCCDC to inform customers on what to look for in cooking instructions of frozen  

Allergenomics and analysis of causes of unintentional incorpo‑ ration of substances capable of causing IgE‑mediated food allergy into meat products

2020 ◽  
Vol 5 (3) ◽  
pp. 4-11
Author(s):  
E. V. Kryuchenko ◽  
Yu. A. Kuzlyakina ◽  
V. S. Zamula ◽  
I. M. Chernukha
Keyword(s):  
Food Allergy ◽  
Risk Analysis ◽  
Meat Products ◽  
Food Product ◽  
Food Products ◽  
Legal Regulation ◽  
Comprehensive Assessment ◽  
Ige Mediated

The article discusses the definition and mechanism of IgE‑mediated food allergy, provides an overview of the legal regulation of the production and labeling of allergen-containing food products. In order to prevent the inadvertent appearance of allergens in products during their production, an allergenomics procedure is required — a comprehensive assessment of the allergic potential of a food product: allergenicity of product ingredients, risk analysis, and the procedure for managing allergens in the production.

scholarly journals An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 763
Author(s):  
Ran Yang ◽  
Zhenbo Wang ◽  
Jiajia Chen
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Food Product ◽  
Integrated Approach ◽  
Mechanistic Modeling ◽  
Bayesian Optimization ◽  
Initial Training ◽  
Thickness Optimization ◽  
Heating Uniformity ◽  
Food Design

Mechanistic-modeling has been a useful tool to help food scientists in understanding complicated microwave-food interactions, but it cannot be directly used by the food developers for food design due to its resource-intensive characteristic. This study developed and validated an integrated approach that coupled mechanistic-modeling and machine-learning to achieve efficient food product design (thickness optimization) with better heating uniformity. The mechanistic-modeling that incorporated electromagnetics and heat transfer was previously developed and validated extensively and was used directly in this study. A Bayesian optimization machine-learning algorithm was developed and integrated with the mechanistic-modeling. The integrated approach was validated by comparing the optimization performance with a parametric sweep approach, which is solely based on mechanistic-modeling. The results showed that the integrated approach had the capability and robustness to optimize the thickness of different-shape products using different initial training datasets with higher efficiency (45.9% to 62.1% improvement) than the parametric sweep approach. Three rectangular-shape trays with one optimized thickness (1.56 cm) and two non-optimized thicknesses (1.20 and 2.00 cm) were 3-D printed and used in microwave heating experiments, which confirmed the feasibility of the integrated approach in thickness optimization. The integrated approach can be further developed and extended as a platform to efficiently design complicated microwavable foods with multiple-parameter optimization.

scholarly journals Financial Context News Sentiment Analysis for the Lithuanian Language

2021 ◽  
Vol 11 (10) ◽  
pp. 4443
Author(s):  
Rokas Štrimaitis ◽  
Pavel Stefanovič ◽  
Simona Ramanauskaitė ◽  
Asta Slotkienė
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Experimental Investigations ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Bayes Algorithm ◽  
Website Content

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

Food advertising during children’s television programmes in Italy

2020 ◽  
pp. 1-8
Author(s):  
Silvano Gallus ◽  
Elisa Borroni ◽  
Chiara Stival ◽  
Sharanpreet Kaur ◽  
Sofia Davoli ◽  
...  
Keyword(s):  
Food Product ◽  
Food Products ◽  
Children's Television ◽  
World Health ◽  
Nutritional Values ◽  
International Guidelines ◽  
Food Advertisements ◽  
Health Organization ◽  
Almost All ◽  
The Eu

Abstract Objective: Previous studies from European countries noted that food products promoted on TV for children did not comply with international guidelines, including the World Health Organization European Nutrient Profile Model (WHO-ENPM) and the EU Pledge Nutrition Criteria (EU-PNC, an initiative developed by leading food companies). We aim to provide new data from Italy. Design: Evaluation of Italian TV advertisements. Data on nutritional values for food product advertised were compared with nutritional standards issued by the WHO-ENPM and the EU-PNC. Setting: In total, 180 h of TV programmes from six Italian channels, 2016–2017. Participants: Eight hundred and ten consecutive advertisements during children’s programmes. Results: Out of 810 advertisements, 90 (11·1 %) referred to food products. Among these, 84·5 % of the foods promoted did not meet the WHO-ENPM and 55·6 % the EU-PNC guidelines. Advertisements promoting sweet and salty snacks (i.e. ≥ 70 % of all foods) v. other food products showed higher non-compliance with both the WHO-ENPM (OR: 73·8; 95 % CI: 4·09, 1330) and the EU-PNC (OR: 9·21; 95 % CI: 2·82, 30·1). Conclusions: In Italy, most food advertisements during children’s programmes are not compliant with European nutritional standards. Almost all the advertisements for snacks do not meet international guidelines. As the WHO-ENPM guidelines do not propose standards for all the food products, including meals, there is an urgent need to define independent and easy-to-read guidelines for food advertisements targeting children. As a first step towards the complete ban of food advertisements targeting children recommended by other researchers, these guidelines should be enforced by all the TV broadcasts.

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