Hyperspectral system trade-offs for illumination, hardware and analysis methods: a case study of seed mix ingredient discrimination

Incluye material complementarioThe discrimination power of a hyperspectral imaging system for image segmentation or object detection is determined by the illumination, the camera spatial–spectral resolution, and both the pre-processing and analysis methods used for image processing. In this study, we methodically reviewed the alternatives for each of those factors for a case study from the food industry to provide guidance in the construction and configuration of hyperspectral imaging systems in the visible near infrared range for food quality inspection. We investigated both halogen-and LED-based illuminations and considered cameras with different spatial–spectral resolution trade-offs. At the level of the data analysis, we evaluated the impact of binning, median filtering and bilateral filtering as pre-or post-processing and compared pixel-based classifiers with convolutional neural networks for a challenging application in the food industry, namely ingredient identification in a flour–seed mix. Starting from a basic configuration and by modifying the combination of system aspects we were able to increase the mean accuracy by at least 25%. In addition, different trade-offs in performance-complexity were identified for different combinations of system parameters, allowing adaptation to diverse application requirements.This work was carried out in the context of the iFAST project with the support from Flanders’ FOOD and VLAIO (Agentschap Innoveren & Ondernemen), research and innovation program under grant agreement No. 140992

Deep learning for food instance segmentation

Food object detection and instance segmentation are critical in many applications such as dietary management or food intake monitoring. Food image recognition poses different challenges, such as the existence of a large number of classes, a high inter-class similarity, and high intra-class variance. This, along with the traditional problems associated with object detection and instance segmentation make this a very complex computer vision task. Real-world food datasets generally suffer from long-tailed and fine-grained distributions. However, the recent literature fails to address food detection in this regard. In this research, we propose a novel two-stage object detector, which we call Strong LOng-tailed Food object Detection and instance Segmentation (SLOF-DS), to tackle the long-tailed nature of food images. In addition, a multi-task based framework, which exploits different sources of prior information, was proposed to improve the classification of fine-grained classes. Lastly, we also propose a new module based on Graph Neural Neworks, we call Graph Confidence Propagation (GCP) that additionally improves the performance of both object detection and instance segmentation modules by combining all the model outputs considering the global image context. Exhaustive quantitive and qualitative analysis performed on two open source food benchmarks, namely the UECFood-256 (object detection) and the AiCrowd Food Recognition Challenge 2022 dataset (instance segmentation) using different baseline algorithms prove the robust improvements introduced by the different components proposed in this thesis. More concretely, we outperformed the state-of-the-art performance on both public datasets

Enhancing Customer Purchase Intentions through Service Brand Credibility

Purpose – The basic aim of present study was to test the construct of brand credibility and its impact on purchase intentions with moderation effect of brand image in service sector of Pakistan.Design/methodology/approach – for the sake of data collection, A questionnaire was used from the customers of fast food users from the city of Lahore Pakistan.  Regression and correlation analysis along with others were used for data analysis and hypothesis testing.  Findings – Based of the statistical evidences of present study it is found that brand credibility and purchase intentions are positively related whereas brand image moderates this relationship.Practical implications – This study will help survey marketers and mangers to understand the importance of brand credibility for enhancing customer purchase intentions, furthermore it will guide them to incorporate the brand image in crafting different marketing and branding strategies to increase brand royalty.Originality/value – According to researcher, this is a pioneer study to propose the impact of service-brand-credibility and its impact on customer purchase intentions with moderating effect of brand image in the context of Pakistan. Keywords: Brand credibility, Purchase intentions, Brand image, Brands, Pakistan. Paper type: Research pape

Political Appetites: Food as Rhetoric in American Politics

Food is mobilized as a site of political communication. The framing of food as politically relevant is possible because food is deeply rooted in a particular cultural context; because food is symbolic of its culinary community, therefore, it can be deployed as a form of strategic messaging. For that reason food has played a role in political campaigning since the earliest American elections. However major changes to the conditions under which politics is undertaken have altered the messages sent through food. Specifically, the emergence of image-based campaigning and a taste-based notion of elitism has created an environment in which food politics is designed to demonstrate a political figure\u27s connection to, or disconnection from, middle class American culture. This qualitative study investigates three sites--diner politics, food faux pas, and the regulation of food--where food and politics intersect. Data for this analysis consists of textual analysis of over 400 articles published in newspapers and magazines; semi-structured interviews with public health advocates, political officials, and strategists; and candidate speeches and peripheral campaign materials. Analysis of these data demonstrates that political strategists deploy food tastes commonly associated with down-home culinary culture--namely tastes for diners, bars, and local restaurants--as a way to present their candidate as in touch with average Americans. Conversely, food faux pas committed by presidential candidates are treated by their opponents and the press as evidence of the erring candidate\u27s elite food tastes. But food tastes do not carry the same symbolic weight in legislative contexts as they do in campaign contexts. This is because food tastes invoke little symbolism for legislators. Even so, proposed food policy legislation can nonetheless be framed by the press as a site of symbolic conflict if and when oppositional voices adopt the food police narrative. In sum, the mobilization of food\u27s symbolic value is motivated by the desire to frame political figures according to their food tastes. This is the case because such a narrative maps onto the increasing role of personal tastes in the cultural organizing of the American public

Hyperspectral imaging logics: efficient strategies for agri-food products quality control

The increasingly normative severity and market competitiveness have led the agriculture sector and the food industry to constantly look for logic improvements that can be applied in processes monitoring systems. In a context where fast, non-destructive and reliable techniques are required, image analysis-based methods have gained interest, thanks to their ability to spatially characterize heterogeneous samples. In such a scenario, HyperSpectral Imaging (HSI) is an emerging technique that provides not only spatial information of imaging systems, but even spectral information of spectroscopy. The utilization of the HSI approach opens new interesting scenario to quality control logics in agricultural and food processing/manufacturing sectors. Three different case studies are presented in this paper. In particular, the utilization of an HSI system, working in SWIR range, was applied for: i) detecting contaminants in dried fruits to be packaged, ii) identifying olive fruits attacked by olive fruit flies and iii) recognizing flour type. In particular, the proposed approach is based on the application of Partial Least Squares – Discriminant Analysis (PLS-DA) classification method to HyperSpectral images in Short Wave InfraRed (SWIR) range (1000-2500 nm). The proposed case studies demonstrate that this logic can be successfully utilized as a quality control system on agri-food products coming from different manufacturing stages, but it can even be seen as an analytical core for sorting engines

Making Sense and Talking Sense: A Case Study of the Correlations Between Sensemaking, Identity and Image in the New Zealand Functional Food Industry

Functional foods are purported by scientists to provide consumers with health benefits over and above food’s most basic uses: providing energy and sustaining life. Western nations, including New Zealand, face significant health challenges as their populations suffer from unprecedented rates of chronic illnesses like cancer and obesity, and health-conscious consumers appear willing and able to purchase these products. The functional food industry has been growing rapidly for the last decade and is widely tipped to continue this growth. However, there is concern that the market is largely unregulated and consumers are confused by the sheer volume of news and information about functional food and health issues. The purpose of this study is to examine the way that a functional food producer makes sense of its role in this complex social, political and economic context, particularly regarding its contribution to public health. The study takes a communication perspective and uses primarily a thematic analysis. Theories of organisational sensemaking, identity and image provide a framework for the case study analysis focusing on organisational communication with stakeholders and attempts to manage contextual issues that affect both the case study organisation and the whole industry. Data was gathered by interviewing higher-level managers from a range of divisions in the organisation, and by collecting a selection of corporate communication documents produced by the organisation for consumers. The study found that the case study organisation’s identity was heavily influenced by health values that align with the product’s proven health benefits. However, the organisation promotes the product as a premium food product, which prices a number of consumers out of the market, and illustrates the limitations this particular product has for improving consumer health. At the same time, the organisational identity comes under threat from challenges to the sustainability of the organisation’s production methods. Analysing the way organisational members respond to these threats provides an interesting picture of the way sensemaking processes are affected by external influences as internal stakeholders re-assess the organisation’s identity

Scraping social media photos posted in Kenya and elsewhere to detect and analyze food types

Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sources can be used for monitoring dietary behavior. We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We used it to collect 3.56 million images over a period of 20 days in March 2019. We also propose a scrape-by-keywords methodology and used it to scrape ∼30,000 images and their captions of 38 Kenyan food types. We publish two datasets of 104,000 and 8,174 image/caption pairs, respectively. With the first dataset, Kenya104K, we train a Kenyan Food Classifier, called KenyanFC, to distinguish Kenyan food from non-food images posted in Kenya. We used the second dataset, KenyanFood13, to train a classifier KenyanFTR, short for Kenyan Food Type Recognizer, to recognize 13 popular food types in Kenya. The KenyanFTR is a multimodal deep neural network that can identify 13 types of Kenyan foods using both images and their corresponding captions. Experiments show that the average top-1 accuracy of KenyanFC is 99% over 10,400 tested Instagram images and of KenyanFTR is 81% over 8,174 tested data points. Ablation studies show that three of the 13 food types are particularly difficult to categorize based on image content only and that adding analysis of captions to the image analysis yields a classifier that is 9 percent points more accurate than a classifier that relies only on images. Our food trend analysis revealed that cakes and roasted meats were the most popular foods in photographs on Instagram in Kenya in March 2019.Accepted manuscrip

Variational recurrent sequence-to-sequence retrieval for stepwise illustration

We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query. This new task extends the traditional cross-modal retrieval, where each image-text pair is treated independently ignoring broader context. We propose a novel variational recurrent seq2seq (VRSS) retrieval model for this seq2seq task. Unlike most cross-modal methods, we generate an image vector corresponding to the latent topic obtained from combining the text semantics and context. This synthetic image embedding point associated with every text embedding point can then be employed for either image generation or image retrieval as desired. We evaluate the model for the application of stepwise illustration of recipes, where a sequence of relevant images are retrieved to best match the steps described in the text. To this end, we build and release a new Stepwise Recipe dataset for research purposes, containing 10K recipes (sequences of image-text pairs) having a total of 67K image-text pairs. To our knowledge, it is the first publicly available dataset to offer rich semantic descriptions in a focused category such as food or recipes. Our model is shown to outperform several competitive and relevant baselines in the experiments. We also provide qualitative analysis of how semantically meaningful the results produced by our model are through human evaluation and comparison with relevant existing methods