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6 pages, 943 KiB

Open AccessEditorial

Applications of Artificial Intelligence in Food Industry

by Heera Jayan, Weiqing Min and Zhiming Guo

Foods 2025, 14(7), 1241; https://doi.org/10.3390/foods14071241 - 1 Apr 2025

Viewed by 1407

Abstract

With breakthroughs in artificial intelligence (AI) brought by the fourth industrial revolution, intelligent applications are providing innovative solutions across food industry [...] Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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Research

Jump to: Editorial, Review, Other

24 pages, 6387 KiB

Open AccessArticle

Beyond Traditional Methods: Deep-Learning Machines Empower Fingerroot (Boesenbergia rotunda)-Extract Production with Superior Antioxidant Activity

by Padej Pao-la-or, Kakanang Posridee, Pussarat Buranakon, Jittra Singthong, Jirawan Oonmetta-Aree, Ratchadaporn Oonsivilai and Anant Oonsivilai

Foods 2024, 13(17), 2676; https://doi.org/10.3390/foods13172676 - 25 Aug 2024

Cited by 1 | Viewed by 1088

Abstract

This study investigated the impact of drying parameters on the quality of fingerroot (Boesenbergia rotunda) extract, focusing on phenolic compounds, flavonoids, and antioxidant activity. A Box–Behngen design was employed to evaluate the effects of maltodextrin concentration, inlet temperature, and outlet temperature [...] Read more.

This study investigated the impact of drying parameters on the quality of fingerroot (Boesenbergia rotunda) extract, focusing on phenolic compounds, flavonoids, and antioxidant activity. A Box–Behngen design was employed to evaluate the effects of maltodextrin concentration, inlet temperature, and outlet temperature on the extract’s properties. The highest total phenolic content (18.96 µg of GAE/mg extract) and total flavonoid content (33.52 µg of GE/mg extract) were achieved using 20% maltodextrin, a 160 °C inlet temperature, and an 80 °C outlet temperature. Antioxidant activity, measured by DPPH and FRAP assays, was also influenced by drying parameters. Stepwise regression analysis revealed that maltodextrin concentration significantly affected all responses, while the inlet temperature had no significant effect. The outlet temperature significantly influenced FRAP activity. The developed mathematical models accurately predicted experimental values, validating the effectiveness of the RSM and Deep-Learning Machine. Optimal drying conditions for maximizing phenolic compounds were determined to be 20% maltodextrin, a 150 °C inlet temperature, and a 70 °C outlet temperature, resulting in TPC 15.33 µg of GAE/mg extract, TF 28.75 µg of GE/mg extract, IC₅₀ value of 3.99 µg/mL, FRAP value at 4.44 µmoL Fe²⁺/mg extract of phenolic content, and 18.96 µg of the GAE/mg extract. Similar conditions were found to be optimal for maximizing flavonoid content. These findings provide valuable insights for optimizing the drying process of fingerroot extract to preserve its bioactive compounds and enhance its potential applications. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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19 pages, 12972 KiB

Open AccessArticle

Integrating Image Analysis and Machine Learning for Moisture Prediction and Appearance Quality Evaluation: A Case Study of Kiwifruit Drying Pretreatment

by Shuai Yu, Haoran Zheng, David I. Wilson, Wei Yu and Brent R. Young

Foods 2024, 13(12), 1789; https://doi.org/10.3390/foods13121789 - 7 Jun 2024

Cited by 1 | Viewed by 1331

Abstract

The appearance of dried fruit clearly influences the consumer’s perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess [...] Read more.

The appearance of dried fruit clearly influences the consumer’s perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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15 pages, 3309 KiB

Open AccessArticle

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms

by Zhiming Guo, Xuan Chen, Yiyin Zhang, Chanjun Sun, Heera Jayan, Usman Majeed, Nicholas J. Watson and Xiaobo Zou

Foods 2024, 13(11), 1698; https://doi.org/10.3390/foods13111698 - 28 May 2024

Cited by 7 | Viewed by 2019

Abstract

Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were [...] Read more.

Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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16 pages, 1071 KiB

Open AccessArticle

Disambiguity and Alignment: An Effective Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval

by Zhuoyang Zou, Xinghui Zhu, Qinying Zhu, Hongyan Zhang and Lei Zhu

Foods 2024, 13(11), 1628; https://doi.org/10.3390/foods13111628 - 23 May 2024

Cited by 1 | Viewed by 1937

Abstract

As a prominent topic in food computing, cross-modal recipe retrieval has garnered substantial attention. However, the semantic alignment across food images and recipes cannot be further enhanced due to the lack of intra-modal alignment in existing solutions. Additionally, a critical issue named food [...] Read more.

As a prominent topic in food computing, cross-modal recipe retrieval has garnered substantial attention. However, the semantic alignment across food images and recipes cannot be further enhanced due to the lack of intra-modal alignment in existing solutions. Additionally, a critical issue named food image ambiguity is overlooked, which disrupts the convergence of models. To these ends, we propose a novel Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval (MMACMR). To consider inter-modal and intra-modal alignment together, this method measures the ambiguous food image similarity under the guidance of their corresponding recipes. Additionally, we enhance recipe semantic representation learning by involving a cross-attention module between ingredients and instructions, which is effective in supporting food image similarity measurement. We conduct experiments on the challenging public dataset Recipe1M; as a result, our method outperforms several state-of-the-art methods in commonly used evaluation criteria. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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13 pages, 2107 KiB

Open AccessArticle

Exploration of Convective and Infrared Drying Effect on Image Texture Parameters of ‘Mejhoul’ and ‘Boufeggous’ Date Palm Fruit Using Machine Learning Models

by Younes Noutfia and Ewa Ropelewska

Foods 2024, 13(11), 1602; https://doi.org/10.3390/foods13111602 - 21 May 2024

Cited by 3 | Viewed by 1728

Abstract

Date palm (Phoenix dactylifera L.) fruit samples belonging to the ‘Mejhoul’ and ‘Boufeggous’ cultivars were harvested at the Tamar stage and used in our experiments. Before scanning, date samples were dried using convective drying at 60 °C and infrared drying at 60 [...] Read more.

Date palm (Phoenix dactylifera L.) fruit samples belonging to the ‘Mejhoul’ and ‘Boufeggous’ cultivars were harvested at the Tamar stage and used in our experiments. Before scanning, date samples were dried using convective drying at 60 °C and infrared drying at 60 °C with a frequency of 50 Hz, and then they were scanned. The scanning trials were performed for two hundred date palm fruit in fresh, convective-dried, and infrared-dried forms of each cultivar using a flatbed scanner. The image-texture parameters of date fruit were extracted from images converted to individual color channels in RGB, Lab, XYZ, and UVS color models. The models to classify fresh and dried samples were developed based on selected image textures using machine learning algorithms belonging to the groups of Bayes, Trees, Lazy, Functions, and Meta. For both the ‘Mejhoul’ and ‘Boufeggous’ cultivars, models built using Random Forest from the group of Trees turned out to be accurate and successful. The average classification accuracy for fresh, convective-dried, and infrared-dried ‘Mejhoul’ reached 99.33%, whereas fresh, convective-dried, and infrared-dried samples of ‘Boufeggous’ were distinguished with an average accuracy of 94.33%. In the case of both cultivars and each model, the higher correctness of discrimination was between fresh and infrared-dried samples, whereas the highest number of misclassified cases occurred between fresh and convective-dried fruit. Thus, the developed procedure may be considered an innovative approach to the non-destructive assessment of drying impact on the external quality characteristics of date palm fruit. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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13 pages, 1891 KiB

Open AccessArticle

Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning

by Yanqiu Zhu, Shuxiang Fan, Min Zuo, Baohua Zhang, Qingzhen Zhu and Jianlei Kong

Foods 2024, 13(10), 1570; https://doi.org/10.3390/foods13101570 - 17 May 2024

Cited by 8 | Viewed by 1489

Abstract

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize [...] Read more.

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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17 pages, 2572 KiB

Open AccessArticle

Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm

by Milana Pribić, Ilija Kamenko, Saša Despotović, Milan Mirosavljević and Jelena Pejin

Foods 2024, 13(2), 343; https://doi.org/10.3390/foods13020343 - 22 Jan 2024

Cited by 6 | Viewed by 1777

Abstract

Triticale grain, a wheat–rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes [...] Read more.

Triticale grain, a wheat–rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme^® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with objective function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which confirms the potential to enhance the quality and efficiency of triticale wort production. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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13 pages, 2299 KiB

Open AccessArticle

Boosting Purnica granatum L. Seed Oil Yield: An Adaptive Neuro-Fuzzy Interference System Fuels SC-CO₂ Extraction Breakthrough

by Padej Pao-la-or, Boonruang Marungsri, Pornariya Chirinang, Kakanang Posridee, Ratchadaporn Oonsivilai and Anant Oonsivilai

Foods 2024, 13(1), 161; https://doi.org/10.3390/foods13010161 - 2 Jan 2024

Cited by 2 | Viewed by 1730

Abstract

This study used supercritical fluid extraction to successfully enhance the conditions for extracting oil from pomegranate seeds. To determine the optimal extraction conditions for maximizing pomegranate oil yield, the researchers employed a Box–Behnken design experimental strategy, involving three parameters with three levels each: [...] Read more.

This study used supercritical fluid extraction to successfully enhance the conditions for extracting oil from pomegranate seeds. To determine the optimal extraction conditions for maximizing pomegranate oil yield, the researchers employed a Box–Behnken design experimental strategy, involving three parameters with three levels each: extraction pressure, extraction temperature, and extraction time. To determine the optimal optimization conditions, the Response Surface Method (RSM) and the Artificial Neural Fuzzy Intelligent System (ANFIS) were also used. The results revealed a strong correlation with the experimental data, demonstrating that both strategies were helpful in optimizing the extraction process. The ideal extraction parameters, according to this study, were an extraction pressure of 40 MPa, an extraction temperature of 55 °C, and an extraction time of 120 min with a CO₂ flow rate of 21.3 L/h. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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12 pages, 4583 KiB

Open AccessArticle

Development of a Low-Cost Artificial Vision System as an Alternative for the Automatic Classification of Persian Lemon: Prototype Test Simulation

by Bridget V. Granados-Vega, Carlos Maldonado-Flores, Camila S. Gómez-Navarro, Walter M. Warren-Vega, Armando Campos-Rodríguez and Luis A. Romero-Cano

Foods 2023, 12(20), 3829; https://doi.org/10.3390/foods12203829 - 19 Oct 2023

Cited by 3 | Viewed by 2032

Abstract

In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, [...] Read more.

In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, thickness of the peel, diameter, length, and color) of 90 lemons selected from the company Esperanza de San José Ornelas SPR de RL (Jalisco, Mexico) were studied, which were divided into three groups (Category “extra”, Category I, and Category II) according to their characteristics. The parameters of weight (26.50 ± 3.00 g), diameter/length (0.92 ± 0.08) and thickness of the peel (1.50 ± 0.29 mm) did not present significant differences between groups. On the other hand, the color (determined by the RGB and HSV models) presents statistically significant changes between groups. Due to the above, the proposed ANN correctly classifies 96.60% of the data obtained for each of the groups studied. Once the ANN was trained, its application was tested in an automatic classification process. For this purpose, a prototype based on the operation of a stepper motor was simulated using Simulink from Matlab, which is connected to three ideal switches powered by three variable pulse generators that receive the information from an ANN and provide the corresponding signal for the motor to turn to a specific position. Manual classification is a process that requires expert personnel and is prone to human error. The scientific development presented shows an alternative for the automation of the process using low-cost computational tools as a potential alternative. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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15 pages, 1543 KiB

Open AccessArticle

Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation

by Tianhao Li, Wensong Wei, Shujuan Xing, Weiqing Min, Chunjiang Zhang and Shuqiang Jiang

Foods 2023, 12(17), 3145; https://doi.org/10.3390/foods12173145 - 22 Aug 2023

Cited by 16 | Viewed by 3773

Abstract

The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis [...] Read more.

The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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15 pages, 3242 KiB

Open AccessArticle

A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media

by Dandan Tao, Ruofan Hu, Dongyu Zhang, Jasmine Laber, Anne Lapsley, Timothy Kwan, Liam Rathke, Elke Rundensteiner and Hao Feng

Foods 2023, 12(14), 2769; https://doi.org/10.3390/foods12142769 - 20 Jul 2023

Cited by 9 | Viewed by 3635

Abstract

Foodborne diseases and outbreaks are significant threats to public health, resulting in millions of illnesses and deaths worldwide each year. Traditional foodborne disease surveillance systems rely on data from healthcare facilities, laboratories, and government agencies to monitor and control outbreaks. Recently, there is [...] Read more.

Foodborne diseases and outbreaks are significant threats to public health, resulting in millions of illnesses and deaths worldwide each year. Traditional foodborne disease surveillance systems rely on data from healthcare facilities, laboratories, and government agencies to monitor and control outbreaks. Recently, there is a growing recognition of the potential value of incorporating social media data into surveillance systems. This paper explores the use of social media data as an alternative surveillance tool for foodborne diseases by collecting large-scale Twitter data, building food safety data storage models, and developing a novel frontend foodborne illness surveillance system. Descriptive and predictive analyses of the collected data were conducted in comparison with ground truth data reported by the U.S. Centers for Disease Control and Prevention (CDC). The results indicate that the most implicated food categories and the distributions from both Twitter and the CDC were similar. The system developed with Twitter data could complement traditional foodborne disease surveillance systems by providing near-real-time information on foodborne illnesses, implicated foods, symptoms, locations, and other information critical for detecting a potential foodborne outbreak. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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14 pages, 1461 KiB

Open AccessArticle

The Estimation of Chemical Properties of Pepper Treated with Natural Fertilizers Based on Image Texture Parameters

by Ewa Ropelewska and Justyna Szwejda-Grzybowska

Foods 2023, 12(11), 2123; https://doi.org/10.3390/foods12112123 - 24 May 2023

Cited by 4 | Viewed by 1581

Abstract

The cultivar and fertilization can affect the physicochemical properties of pepper fruit. This study aimed at estimating the content of α-carotene, β-carotene, total carotenoids, and the total sugars of unfertilized pepper and samples treated with natural fertilizers based on texture parameters determined using [...] Read more.

The cultivar and fertilization can affect the physicochemical properties of pepper fruit. This study aimed at estimating the content of α-carotene, β-carotene, total carotenoids, and the total sugars of unfertilized pepper and samples treated with natural fertilizers based on texture parameters determined using image analysis. Pearson’s correlation coefficients, scatter plots, regression equations, and coefficients of determination were determined. For red pepper Sprinter F₁, the correlation coefficient (R) reached 0.9999 for a texture from color channel B and −0.9999 for a texture from channel Y for the content of α-carotene, −0.9998 (channel a) for β-carotene, 0.9999 (channel a) and −0.9999 (channel L) for total carotenoids, as well as 0.9998 (channel R) and −0.9998 (channel a) for total sugars. The image textures of yellow pepper Devito F₁ were correlated with the content of total carotenoids and total sugars with the correlation coefficient reaching −0.9993 (channel b) and 0.9999 (channel Y), respectively. The coefficient of determination (R²) of up to 0.9999 for α-carotene content and the texture from color channel Y for pepper Sprinter F₁ and 0.9998 for total sugars and the texture from color channel Y for pepper Devito F₁ were found. Furthermore, very high coefficients of correlation and determination, as well as successful regression equations regardless of the cultivar were determined. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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22 pages, 2606 KiB

Open AccessEditor’s ChoiceArticle

Health-Aware Food Recommendation Based on Knowledge Graph and Multi-Task Learning

by Yi Chen, Yandi Guo, Qiuxu Fan, Qinghui Zhang and Yu Dong

Foods 2023, 12(10), 2079; https://doi.org/10.3390/foods12102079 - 22 May 2023

Cited by 18 | Viewed by 6698

Abstract

Current food recommender systems tend to prioritize either the user’s dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account [...] Read more.

Current food recommender systems tend to prioritize either the user’s dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account the user’s personalized health requirements, in addition to their dietary preferences. Our work comprises three perspectives. Firstly, we propose a collaborative recipe knowledge graph (

C R K G

) with millions of triplets, containing user–recipe interactions, recipe–ingredient associations, and other food-related information. Secondly, we define a score-based method for evaluating the healthiness match between recipes and user preferences. Based on these two prior perspectives, we develop a novel health-aware food recommendation model (

F K G M

) using knowledge graph embedding and multi-task learning.

F K G M

employs a knowledge-aware attention graph convolutional neural network to capture the semantic associations between users and recipes on the collaborative knowledge graph and learns the user’s requirements in both preference and health by fusing the losses of these two learning tasks. We conducted experiments to demonstrate that

F K G M

outperformed four competing baseline models in integrating users’ dietary preferences and personalized health requirements in food recommendations and performed best on the health task. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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Review

Jump to: Editorial, Research, Other

32 pages, 2198 KiB

Open AccessReview

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review

by Hind Raki, Yahya Aalaila, Ayoub Taktour and Diego H. Peluffo-Ordóñez

Foods 2024, 13(1), 11; https://doi.org/10.3390/foods13010011 - 19 Dec 2023

Cited by 10 | Viewed by 3060

Abstract

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food’s supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food’s safety is [...] Read more.

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food’s supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food’s safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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29 pages, 4609 KiB

Open AccessReview

The Application of Artificial Intelligence and Big Data in the Food Industry

by Haohan Ding, Jiawei Tian, Wei Yu, David I. Wilson, Brent R. Young, Xiaohui Cui, Xing Xin, Zhenyu Wang and Wei Li

Foods 2023, 12(24), 4511; https://doi.org/10.3390/foods12244511 - 18 Dec 2023

Cited by 59 | Viewed by 24044

Abstract

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous [...] Read more.

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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Other

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13 pages, 30203 KiB

Open AccessPerspective

The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants

by Longlong Zhang, Qiuping Yang and Zhiyuan Zhu

Foods 2024, 13(12), 1936; https://doi.org/10.3390/foods13121936 - 19 Jun 2024

Cited by 6 | Viewed by 2555

Abstract

Against the backdrop of continuous socio-economic development, there is a growing concern among people about food quality and safety. Individuals are increasingly realizing the critical importance of healthy eating for bodily health; hence the continuous rise in demand for detecting food pollution. Simultaneously, [...] Read more.

Against the backdrop of continuous socio-economic development, there is a growing concern among people about food quality and safety. Individuals are increasingly realizing the critical importance of healthy eating for bodily health; hence the continuous rise in demand for detecting food pollution. Simultaneously, the rapid expansion of global food trade has made people’s pursuit of high-quality food more urgent. However, traditional methods of food analysis have certain limitations, mainly manifested in the high degree of reliance on personal subjective judgment for assessing food quality. In this context, the emergence of artificial intelligence and biosensors has provided new possibilities for the evaluation of food quality. This paper proposes a comprehensive approach that involves aggregating data relevant to food quality indices and developing corresponding evaluation models to highlight the effectiveness and comprehensiveness of artificial intelligence and biosensors in food quality evaluation. The potential prospects and challenges of this method in the field of food safety are comprehensively discussed, aiming to provide valuable references for future research and practice. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

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