Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks

Ahn, DaeHan; Choi, Ji‐Young; Kim, Hee-Chul; Cho, Jeong-Seok; Moon, Kwang-Deog; Park, Taejoon

doi:10.3390/s19071560

Cited by 22 publications

(7 citation statements)

References 24 publications

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“…Thus, innovative healthcare for them is rapidly drawing public attention. For instance, those sensitive to food intake and weight changes may want to keep track of the calories and amounts of carbohydrates, proteins, fats, and other nutrients ( 20 ). One approach used to provide that information in a simple and accessible way is by FELs ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning accurately predicts food exchange list and the exchangeable portion

Hernández-Hernández,

Perez-Lizaur,

Palacios-González

et al. 2023

Front. Nutr.

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IntroductionFood Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning algorithms could be a tool that facilitates this process and allows for updated FELs—the present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion.MethodsData mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost.ResultsThe algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents.DiscussionAccurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, machine learning is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.

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Section: Discussionmentioning

confidence: 99%

Machine learning accurately predicts food exchange list and the exchangeable portion

Hernández-Hernández,

Perez-Lizaur,

Palacios-González

et al. 2023

Front. Nutr.

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“…The chemical composition of food products meets human needs primarily, and the chemical content directly affects human health. Therefore, the content of chemical components is regarded as an essential indicator for assessing the quality and safety of food products [87]. In research, food ingredients are usually classified into endogenous and exogenous substance components, with endogenous referring to the intrinsic ingredients of food and exogenous referring to the ingredients ingested during the whole food process from processing to ingestion.…”

Section: Quantitative and Aualitative Analysis Of Chemical Compositionmentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

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“…To overcome these limitations, food analysis based on multispectral analysis has been widely adopted [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. The basic principle is that each individual ingredient of a food has a unique absorption spectrum.…”

Section: Introductionmentioning

confidence: 99%

Multi-Spectral Food Classification and Caloric Estimation Using Predicted Images

Lee

2024

Foods

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In nutrition science, methods that accomplish continuous recognition of ingested foods with minimal user intervention have great utility. Our recent study showed that using images taken at a variety of wavelengths, including ultraviolet (UV) and near-infrared (NIR) bands, improves the accuracy of food classification and caloric estimation. With this approach, however, analysis time increases as the number of wavelengths increases, and there are practical implementation issues associated with a large number of light sources. To alleviate these problems, we proposed a method that used only standard red-green-blue (RGB) images to achieve performance that approximates the use of multi-wavelength images. This method used RGB images to predict the images at each wavelength (including UV and NIR bands), instead of using the images actually acquired with a camera. Deep neural networks (DNN) were used to predict the images at each wavelength from the RGB images. To validate the effectiveness of the proposed method, feasibility tests were carried out on 101 foods. The experimental results showed maximum recognition rates of 99.45 and 98.24% using the actual and predicted images, respectively. Those rates were significantly higher than using only the RGB images, which returned a recognition rate of only 86.3%. For caloric estimation, the minimum values for mean absolute percentage error (MAPE) were 11.67 and 12.13 when using the actual and predicted images, respectively. These results confirmed that the use of RGB images alone achieves performance that is similar to multi-wavelength imaging techniques.

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Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks

Cited by 22 publications

References 24 publications

Machine learning accurately predicts food exchange list and the exchangeable portion

Machine learning accurately predicts food exchange list and the exchangeable portion

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Multi-Spectral Food Classification and Caloric Estimation Using Predicted Images

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