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Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment

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A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Engineering and Technology".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 32653

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Special Issue Editor

Assoc. Prof. Sigfredo Fuentes

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School of Agriculture and Food Sciences, University of Melbourne, Australia; Faculty of Veterinary and Agricultural Sciences, Parkville, Australia
Interests: digital agriculture; food and wine sciences; plant physiology; remote sensing; climate change; robotics applied to agriculture and computer programming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The implementation of artificial intelligence (AI) in the food industry has been growing in the past few years. New technologies involving the use of any branch of AI, such as Machine Learning, have changed and improved the different food science areas, such as the production process, quality assessment, and methods for understanding consumers acceptability. The application of AI in the food industry has led to the development of more reliable, objective, cost-effective, nondestructive, and less time-consuming techniques compared to traditional methods available to the industry.

This Special Issue is a good opportunity for colleagues working in these areas to submit high-quality manuscripts related to the implementation of artificial intelligence or any of its subdivisions such as, but not limited to: computer vision, machine learning, deep learning, biometrics and robotics, sensor technology and internet of things (IoT), among others applied to assess food quality, food production, and sensory and consumer acceptability. Preference will be given to research producing non-invasive quick and accurate tools that could be easily implemented in the food industry.

Prof. Sigfredo Fuentes
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Artificial intelligence
Machine learning
Deep learning
Computer vision
Biometrics
Robotics
Food production
Sensory analysis
Quality assessment
Food chemistry

Published Papers (7 papers)

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Editorial

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2 pages, 173 KiB

Open AccessEditorial

Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment

by Sigfredo Fuentes

Foods 2022, 11(9), 1192; https://doi.org/10.3390/foods11091192 - 20 Apr 2022

Cited by 1 | Viewed by 1919

Abstract

In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries [...] Full article

(This article belongs to the Special Issue Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment)

Research

Jump to: Editorial

16 pages, 4052 KiB

Open AccessArticle

Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning

by Chreston Miller, Leah Hamilton and Jacob Lahne

Foods 2021, 10(7), 1633; https://doi.org/10.3390/foods10071633 - 14 Jul 2021

Cited by 5 | Viewed by 3173

Abstract

This paper is concerned with extracting relevant terms from a text corpus on whisk(e)y. “Relevant” terms are usually contextually defined in their domain of use. Arguably, every domain has a specialized vocabulary used for describing things. For example, the field of Sensory Science, a sub-field of Food Science, investigates human responses to food products and differentiates “descriptive” terms for flavors from “ordinary”, non-descriptive language. Within the field, descriptors are generated through Descriptive Analysis, a method wherein a human panel of experts tastes multiple food products and defines descriptors. This process is both time-consuming and expensive. However, one could leverage existing data to identify and build a flavor language automatically. For example, there are thousands of professional and semi-professional reviews of whisk(e)y published on the internet, providing abundant descriptors interspersed with non-descriptive language. The aim, then, is to be able to automatically identify descriptive terms in unstructured reviews for later use in product flavor characterization. We created two systems to perform this task. The first is an interactive visual tool that can be used to tag examples of descriptive terms from thousands of whisky reviews. This creates a training dataset that we use to perform transfer learning using GloVe word embeddings and a Long Short-Term Memory deep learning model architecture. The result is a model that can accurately identify descriptors within a corpus of whisky review texts with a train/test accuracy of 99% and precision, recall, and F1-scores of 0.99. We tested for overfitting by comparing the training and validation loss for divergence. Our results show that the language structure for descriptive terms can be programmatically learned. Full article

(This article belongs to the Special Issue Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment)

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20 pages, 3199 KiB

Open AccessArticle

Comparative Study of Several Machine Learning Algorithms for Classification of Unifloral Honeys

by Fernando Mateo, Andrea Tarazona and Eva María Mateo

Foods 2021, 10(7), 1543; https://doi.org/10.3390/foods10071543 - 3 Jul 2021

Cited by 13 | Viewed by 3447

Abstract

Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst. Full article

(This article belongs to the Special Issue Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment)

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21 pages, 2486 KiB

Open AccessArticle

Insights into Drivers of Liking for Avocado Pulp (Persea americana): Integration of Descriptive Variables and Predictive Modeling

by Luis Martín Marín-Obispo, Raúl Villarreal-Lara, Dariana Graciela Rodríguez-Sánchez, Armando Del Follo-Martínez, María de la Cruz Espíndola Barquera, Jesús Salvador Jaramillo-De la Garza, Rocío I. Díaz de la Garza and Carmen Hernández-Brenes

Foods 2021, 10(1), 99; https://doi.org/10.3390/foods10010099 - 6 Jan 2021

Cited by 12 | Viewed by 3738

Abstract

Trends in new food products focus on low-carbohydrate ingredients rich in healthy fats, proteins, and micronutrients; thus, avocado has gained worldwide attention. This study aimed to use predictive modeling to identify the potential sensory drivers of liking for avocado pulp by evaluating acceptability scores and sensory descriptive profiles of two commercial and five non-commercial cultivars. Macronutrient composition, instrumental texture, and color were also characterized. Trained panelists performed a descriptive profile of nineteen sensory attributes. Affective data from frequent avocado adult consumers (n = 116) were collected for predictive modeling of an external preference map (R² = 0.98), which provided insight into sensory descriptors that drove preference for particular avocado pulps. The descriptive map explained 67.6% of the variance in sensory profiles. Most accepted pulps were from Hass and Colin V-33; the latter had sweet and green flavor notes. Descriptive flavor attributes related to liking were global impact, oily, and creamy. Sensory drivers of texture liking included creamy/oily, lipid residue, firmness, and cohesiveness. Instrumental stickiness was disliked and inversely correlated to dry-matter and lipids (r = −0.87 and −0.79, respectively). Color differences (∆E_ab*) also contributed to dislike. Sensory-guided selection of avocado fruits and ingredients can develop products with high acceptability in breeding and industrialization strategies. Full article

(This article belongs to the Special Issue Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment)

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19 pages, 14657 KiB

Open AccessArticle

A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products

by Víctor M. Álvarez-Pato, Claudia N. Sánchez, Julieta Domínguez-Soberanes, David E. Méndoza-Pérez and Ramiro Velázquez

Foods 2020, 9(6), 774; https://doi.org/10.3390/foods9060774 - 11 Jun 2020

Cited by 34 | Viewed by 4607

Abstract

Sensory experiences play an important role in consumer response, purchase decision, and fidelity towards food products. Consumer studies when launching new food products must incorporate physiological response assessment to be more precise and, thus, increase their chances of success in the market. This paper introduces a novel sensory analysis system that incorporates facial emotion recognition (FER), galvanic skin response (GSR), and cardiac pulse to determine consumer acceptance of food samples. Taste and smell experiments were conducted with 120 participants recording facial images, biometric signals, and reported liking when trying a set of pleasant and unpleasant flavors and odors. Data fusion and analysis by machine learning models allow predicting the acceptance elicited by the samples. Results confirm that FER alone is not sufficient to determine consumers’ acceptance. However, when combined with GSR and, to a lesser extent, with pulse signals, acceptance prediction can be improved. This research targets predicting consumer’s acceptance without the continuous use of liking scores. In addition, the findings of this work may be used to explore the relationships between facial expressions and physiological reactions for non-rational decision-making when interacting with new food products. Full article

(This article belongs to the Special Issue Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment)

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Graphical abstract

14 pages, 3120 KiB

Open AccessArticle

Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence

by Sigfredo Fuentes, Eden Tongson, Damir D. Torrico and Claudia Gonzalez Viejo

Foods 2020, 9(1), 33; https://doi.org/10.3390/foods9010033 - 30 Dec 2019

Cited by 15 | Viewed by 4349

Abstract

Wine aroma profiles are determinant for the specific style and quality characteristics of final wines. These are dependent on the seasonality, mainly weather conditions, such as solar exposure and temperatures and water management strategies from veraison to harvest. This paper presents machine learning modeling strategies using weather and water management information from a Pinot noir vineyard from 2008 to 2016 vintages as inputs and aroma profiles from wines from the same vintages assessed using gas chromatography and chemometric analyses of wines as targets. The results showed that artificial neural network (ANN) models rendered the high accuracy in the prediction of aroma profiles (Model 1; R = 0.99) and chemometric wine parameters (Model 2; R = 0.94) with no indication of overfitting. These models could offer powerful tools to winemakers to assess the aroma profiles of wines before winemaking, which could help adjust some techniques to maintain/increase the quality of wines or wine styles that are characteristic of specific vineyards or regions. These models can be modified for different cultivars and regions by including more data from vertical vintages to implement artificial intelligence in winemaking. Full article

(This article belongs to the Special Issue Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment)

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11 pages, 1127 KiB

Open AccessArticle

Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling

by Thejani M. Gunaratne, Claudia Gonzalez Viejo, Nadeesha M. Gunaratne, Damir D. Torrico, Frank R. Dunshea and Sigfredo Fuentes

Foods 2019, 8(10), 426; https://doi.org/10.3390/foods8100426 - 20 Sep 2019

Cited by 17 | Viewed by 5440

Abstract

Chocolates are the most common confectionery and most popular dessert and snack across the globe. The quality of chocolate plays a major role in sensory evaluation. In this study, a rapid and non-destructive method was developed to predict the quality of chocolate based on physicochemical data, and sensory properties, using the five basic tastes. Data for physicochemical analysis (pH, Brix, viscosity, and color), and sensory properties (basic taste intensities) of chocolate were recorded. These data and results obtained from near-infrared spectroscopy were used to develop two machine learning models to predict the physicochemical parameters (Model 1) and sensory descriptors (Model 2) of chocolate. The results show that the models developed had high accuracy, with R = 0.99 for Model 1 and R = 0.93 for Model 2. The thus-developed models can be used as an alternative to consumer panels to determine the sensory properties of chocolate more accurately with lower cost using the chemical parameters. Full article

(This article belongs to the Special Issue Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment)

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