Query: Ingredient food

title: You Are What You Eat: Exploring Rich Recipe Information for Cross Region Food Analysis
abstract: Cuisine is a style of cooking and usually associated with a specific geographic region. Recipes from different cuisines shared on the web are an indicator of culinary cultures in different countries. Therefore, analysis of these recipes can lead to deep understanding of food from the cultural perspective. In this paper, we perform the first cross region recipe analysis by jointly using the recipe ingredients, food images, and attributes such as the cuisine and course (e.g. main dish and dessert) For that solution, we propose a culinary culture analysis framework to discover the topics of ingredient bases and visualize them to enable various applications. We first propose a probabilistic topic model to discover cuisine course specific topics. The manifold ranking method is then utilized to incorporate deep visual features to retrieve food images for topic visualization. At last, we applied the topic modeling and visualization method for three applications: 1) multimodal cuisine summarization with both recipe ingredients and images, 2) cuisine course pattern analysis including topic specific cuisine distribution and cuisine specific course distribution of topics, and 3) cuisine recommendation for both cuisine oriented and ingredient oriented queries. Through these three applications, we can analyze the culinary cultures at both macro and micro levels. We conduct the experiment on a recipe database Yummly 66K with 66,615 recipes from 10 cuisines in Yummly. Qualitative and quantitative evaluation results have validated the effectiveness of topic modeling and visualization, and demonstrated the advantage of the framework in utilizing rich recipe information to analyze and interpret the culinary cultures from different regions.

title: Consumers' associative networks of plant based food product communications
abstract: Food producers respond to the current consumer trend of clean label products and reducing meat consumption by increasingly offering plant based food products and transparent, understandable ingredient lists. However, consumer interest can be driven by various motives and food producers face the challenge of identifying the most effective motive to address. We analyze concept maps of 90 consumers who received information that positioned plant based food products as sustainable, healthy, or with a transparent ingredient focus. To assess the applicability of text mining with a view to reducing coder bias and the duration of qualitative data analysis, we compared the results of text mining versus a human coder approach. Our results show that human coder analysis results in more detail, however the advantage of the text mining procedure is that it can run independently and analyze qualitative data more objectively. When a high degree of control and depth of analysis is necessary to satisfy the study objective, human coding might have its rewards. For the current study, both approaches draw a similar picture of the associative networks and are therefore equally suitable to satisfy the study objective. When plant based diets are communicated solely based on the ingredient used for substituting animal based ingredients, associative networks are less complex and associations are primarily concerned with taste. A health communication perspective results in more complex networks with a focus on other food product properties such as processing degree and nutrition. A sustainability communication also results in higher complexity, with fewer associations concerning the product properties itself, but rather with the environmental impact and the authenticity of the product. The in depth understanding of consumers' associations evoked by communicating different perspectives of plant based food products can be used by practitioners in tailoring their marketing activities to the characteristics of their product offerings.

title: Food Product Design: A Hybrid Machine Learning and Mechanistic Modeling Approach
abstract: At present, food products are designed by trial and error and the sensorial ratings are determined by a tasting panel. To expedite the development of new food products, a hybrid machine learning and mechanistic modeling approach is proposed. Sensorial ratings are predicted using a machine learning model trained with historical data for the food under consideration. The approach starts by identifying a set of food ingredient candidates and the key operating conditions in food processing based on heuristics, databases, etc. Food characteristics such as color, crispness, and flavors are related to these ingredients and processing conditions (which are design variables) using mechanistic models. The desired food characteristics are optimized by varying the design variables to obtain the highest sensorial ratings. To solve this gray box optimization problem, a genetic algorithm is utilized where the design constraints (representing the desired food characteristics) are handled as penalty functions. A chocolate.

title: Food Constituent Estimation for Lifestyle Disease Prevention by Multi Task CNN
abstract: Unbalanced nutrition due to an unhealthy diet may increase the risk of developing lifestyle diseases. Many mobile applications have been released to record everyday meals for the health conscious to enable them to improve their dietary habits. Most of these applications only base their food classification on an image of the food, requiring the user to manually input information about the ingredients such as the calories and salinity. To address this problem, food ingredient estimation from food images has been attracting increasing attention. Automatic ingredient estimation could possibly strongly alleviate the process of food intake estimation and dietary assessment. In this paper, we propose an automatic food ingredient estimation method from food images by using multi task CNN. We focus on classification of the food category and estimation of the calorie content and salinity for lifestyle disease prevention. Two stage transfer learning using a large number of food category recognition image databases is applied to train our multi task CNN for improved ingredient estimation. We experimentally analyze the relationship between the food category and salinity by using multi task CNN.

title: CHAPTER 14:Food Fraud Prevention Selecting the Right Test, Method, and Sampling Plan
abstract: Food fraud is one of the most urgent food industry topics and prevention must consider the entire fraud opportunity. Food fraud is more than just ingredient adulterant substances and can cause harm from all types of fraud and for all products throughout the food supply chain. While the most complex and challenging activity will be authenticity testing, the selection of the right test, method, sampling plan, and test frequency must consider not only a single incident but the vulnerability of the entire system. This chapter focuses on identifying the root cause created by the human adversaries and the reduction of the vulnerability by focusing on criminology and specifically Situational Crime Prevention. A case study is provided that reviews the criminology concepts of "hot products" and "hot spots" to help support resource allocation decision making. By focusing on the root cause, very direct and holistic countermeasures and control systems can be identified and very precisely applied. By focusing on the precise hot product and hot spot, there can be an effective selection of the right test, method, and sampling plan.

title: Application of Computational Methods for the Safety Assessment of Food Ingredients
abstract: At the Office of Food Additive Safety (OFAS) in the Center for Food Safety and Applied Nutrition at the United States Food and Drug Administration, scientists review toxicological data submitted by industry or published in scientific journals as a part of premarket safety assessments of food ingredients. OFAS also reviews relevant safety data during postmarket assessments of food ingredients as new toxicological data or exposure information become available. OFAS is committed to maintaining a high standard of science based safety reviews and to staying abreast of novel computational approaches used by industry that could add value to improve safety assessments of food ingredients. In this chapter, we discuss some computational approaches, including quantitative structure activity relationships, toxicokinetic modeling and simulation, and bioinformatics, as well as OFAS's in house food ingredient knowledgebase. We describe the scientific utility of these computational approaches for improving the efficiency of the review process and reducing uncertainties in decisions about the safe use of food ingredients and highlight some challenges with their use for food ingredient safety assessments.

title: Learning Using Privileged Information for Food Recognition
abstract: Food recognition for user uploaded images is crucial in visual diet tracking, an emerging application linking multimedia and healthcare domains. However, it is challenging due to the various visual appearances of food images. This is caused by different conditions when taking the photos, such as angles, distances, light conditions, food containers, and background scenes. To alleviate such a semantic gap, this paper presents a cross modal alignment and transfer network (ATNet) which is motivated by the paradigm of learning using privileged information (LUPI) It additionally utilizes the ingredients in food images as an "intelligent teacher" in the training stage to facilitate cross modal information passing. Specifically, ATNet first uses a pair of synchronized autoencoders to build the base image and ingredient channels for information flow. Subsequently, the information passing is enabled through a two stage cross modal interaction. The first stage of interaction adopts a two step method, called partial heterogeneous transfer, to 1) alleviate the intrinsic heterogeneity between images and ingredients and 2) align them in a shared space to make their carried information about food classes interact. In the second stage, ATNet learns to map the visual embeddings of images to the ingredient channel for food recognition from the view of "teacher' This leads a refined recognition by a multi view fusion. Experiments on two real world datasets show that ATNet can be incorporated with any state of the art CNN models to consistently improve their performance.

title: Overview on Chitosan as a valuable ingredient and biostimulant in pharmaceutical industries and agricultural products
abstract: Chitosan is a polysaccharide extracted from the shells of crustaceans, such as shrimp, crab, and cell walls of fungi. Chitosan is formed from chitin, a co polymer of N acetyl D glucosamine and Dglucosamine, when over 80% of the acetyl groups of the N acetyl D glucosamine residues are removed. Chitosan and its derivatives have been shown to possess diverse biological activities, including antioxidant, anti hypertensive, anti coagulant, anti diabetic, anti obesity, anti allergic, antiinflammatory, anti microbial, and anti cancer. Chitosan has been studied in recent years as a natural agrochemical biostimulant for applications in areas such as seed coating, preservation of fruits and vegetables, fungicides, growth biostimulants, elicitors, inducer of resistance to pathogens, etc. Chitosan as a biostimulant has biological properties and effective uses in the pharmaceutical industries, food industries, and agricultural products. This paper is a brief review on characterization, modification, and applications of chitosan.

title: Food categories classification and Ingredients estimation using CNNs on Raspberry Pi 3
abstract: Foods are important things to human lives, especially for elderly or diabetics. Tradition nutrition book is not the effective way for people to use and not cover all kind of foods. Most of the food nutrition in the book focused on Western dishes not Asian dishes. This research proposed the new way to categorized Thai fast food dishes, classified and localized the ingredients in each dish. Convolutional Neural Networks (CNNs) are used to achieve these tasks. MobileNet is used as food categorizer while You Only Look Once (YOLO) network works as the ingredients classifier and localizer. Then, ingredients in the pictures are cropped and passed through traditional image processing to calculate area and compared with real ingredient's dimension. Non uniform shape ingredients are segmented, then, the nutrition of the dish can be calculated. Finally, the networks are transferred in to Raspberry Pi 3 platform to simulate limited resources and calculation power platform likes in a mobile phone. The networks in Raspberry Pi 3 produce good prediction accuracy but slow speed. PeachPy is introduced to speed up the network and it can run at 3.3 seconds per food image.

title: Ontology Based Food Safety Counseling System
abstract: The lack of knowledge about food ingredients can be risky and lead to serious complications for those with chronic diseases and food allergies. This paper proposes ontology based Food Safety Counseling System (FSCS) that help the users with chronic diseases and food allergies to select their food. FSCS' aim to investigate the level of appropriate food products chosen by the users. The proposed models aim is to measure semantic relations between effected elements (The Main Item) of food product and the personal health status of users to provide professional advice about risky ingredient through six level of risk factor. It contains the Egypt food safety ontology (EFSO) and set of rules created based on knowledge elicited from experts in food safety domain to discover the tacit knowledge and allow semantic integration by applying forward chaining deduction method and inference mechanism to knowledge base. The results indicate the model works effectively with high accuracy.