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Novel Contactless Sensors for Food, Beverage and Packaging Evaluation

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (10 July 2023) | Viewed by 28469

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Special Issue Editors

Dr. Claudia Gonzalez Viejo

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Guest Editor

Digital Agriculture Food and Wine, School of Agriculture and Food, Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC 3010, Australia
Interests: food science and engineering; sensory science; computer vision; sensors; robotics; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sigfredo Fuentes

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Guest Editor

Digital Agriculture Food and Wine, School of Agriculture and Food, Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC 3010, 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

Dr. Damir Torrico

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Guest Editor

Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln University, Lincoln 7647, New Zealand
Interests: sensory evaluation; consumer science; product development; statistical analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent digital developments in food, beverage, and packaging analysis have been focused on using contactless sensors, such as remote sensing and biometrics, to assess the quality traits of produces, detect packaging defects during the production process, and understand perception through sensory analysis based on consumer biometrics. The implementation of these novel techniques is aiding the food, beverage, and packaging industries in the rapid, efficient, cost-effective, and reliable evaluation and assessment of products at any stage of the production chain for the early detection of potential faults within the process and consumer evaluation of the final product and packaging.

This Special Issue will bring together high-quality papers focused on these technologies and their applications in the food and beverage industries, including packaging assessments. These contactless sensors incorporate electronic noses and tongues, the use of infrared thermal cameras, UV-Vis, mid- and near-infrared spectroscopy, and computer vision analysis, among others. These technologies may be applied to assess food, beverages, and packaging quality traits such as sensory attributes, physicochemical components, nutritional values, and defects, at any stage in the production chain, which aid in the quality assessment of products.

Dr. Claudia Gonzalez Viejo
Dr. Sigfredo Fuentes
Dr. Damir Torrico
Guest Editors

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. Sensors 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 2600 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

electronic nose
electronic tongue
infrared thermal cameras
computer vision (video and image analysis)
mid- and near-infrared spectroscopy
UV-Vis spectroscopy
hyperspectral images
artificial intelligence
machine learning

Published Papers (10 papers)

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Editorial

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3 pages, 179 KiB

Open AccessEditorial

Novel Contactless Sensors for Food, Beverage and Packaging Evaluation

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

Sensors 2023, 23(19), 8082; https://0-doi-org.brum.beds.ac.uk/10.3390/s23198082 - 26 Sep 2023

Viewed by 696

Abstract

The use of traditional methods to evaluate food, beverages, and packaging tends to be time-consuming, labour-intensive, and usually involves high costs due to the need for expensive equipment, regular refill of consumables, skilled personnel and, in the case of sensory evaluation, incentives or [...] Read more.

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

Research

Jump to: Editorial

16 pages, 1878 KiB

Open AccessArticle

Semi-Autonomic AI LF-NMR Sensor for Industrial Prediction of Edible Oil Oxidation Status

by Tatiana Osheter, Salvatore Campisi Pinto, Cristian Randieri, Andrea Perrotta, Charles Linder and Zeev Weisman

Sensors 2023, 23(4), 2125; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042125 - 13 Feb 2023

Cited by 3 | Viewed by 1913

Abstract

The evaluation of an oil’s oxidation status during industrial production is highly important for monitoring the oil’s purity and nutritional value during production, transportation, storage, and cooking. The oil and food industry is seeking a real-time, non-destructive, rapid, robust, and low-cost sensor for nutritional oil’s material characterization. Towards this goal, a ¹H LF-NMR relaxation sensor application based on the chemical and structural profiling of non-oxidized and oxidized oils was developed. This study dealt with a relatively large-scale oil oxidation database, which included crude data of a ¹H LF-NMR relaxation curve, and its reconstruction into T₁ and T₂ spectral fingerprints, self-diffusion coefficient D, and conventional standard chemical test results. This study used a convolutional neural network (CNN) that was trained to classify T₂ relaxation curves into three ordinal classes representing three different oil oxidation levels (non-oxidized, partial oxidation, and high level of oxidation). Supervised learning was used on the T₂ signals paired with the ground-truth labels of oxidation values as per conventional chemical lab oxidation tests. The test data results (not used for training) show a high classification accuracy (95%). The proposed AI method integrates a large training set, an LF-NMR sensor, and a machine learning program that meets the requirements of the oil and food industry and can be further developed for other applications. Full article

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

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15 pages, 5795 KiB

Open AccessArticle

rGO-PDMS Flexible Sensors Enabled Survival Decision System for Live Oysters

by Pengfei Liu, Luwei Zhang, You Li, Huanhuan Feng, Xiaoshuan Zhang and Mengjie Zhang

Sensors 2023, 23(3), 1308; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031308 - 23 Jan 2023

Cited by 4 | Viewed by 1574

Abstract

The shell-closing strength (SCS) of oysters is the main parameter for physiological activities. The aim of this study was to evaluate the applicability of SCS as an indicator of live oyster health. This study developed a flexible pressure sensor system with polydimethylsiloxane (PDMS) as the substrate and reduced graphene oxide (rGO) as the sensitive layer to monitor SCS in live oysters (rGO-PDMS). In the experiment, oysters of superior, medium and inferior grades were selected as research objects, and the change characteristics of SCS were monitored at 4 °C and 25 °C. At the same time, the time series model was used to predict the survival rate of live oyster on the basis of changes in their SCS characteristics. The survival times of superior, medium and inferior oysters at 4 °C and 25 °C were 31/25/18 days and 12/10/7 days, respectively, and the best prediction accuracies for survival rate were 89.32%/82.17%/79.19%. The results indicate that SCS is a key physiological indicator of oyster survival. The dynamic monitoring of oyster vitality by means of flexible pressure sensors is an important means of improving oyster survival rate. Superior oysters have a higher survival rate in low-temperature environments, and our method can provide effective and reliable survival prediction and management for the oyster industry. Full article

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

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12 pages, 4549 KiB

Open AccessArticle

An Infrared Laser Sensor for Monitoring Gas-Phase CO₂ in the Headspace of Champagne Glasses under Wine Swirling Conditions

by Florian Lecasse, Raphaël Vallon, Frédéric Polak, Clara Cilindre, Bertrand Parvitte, Gérard Liger-Belair and Virginie Zéninari

Sensors 2022, 22(15), 5764; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155764 - 02 Aug 2022

Cited by 2 | Viewed by 1693

Abstract

In wine tasting, tasters commonly swirl their glasses before inhaling the headspace above the wine. However, the consequences of wine swirling on the chemical gaseous headspace inhaled by tasters are barely known. In champagne or sparkling wine tasting, starting from the pouring step, [...] Read more.

_{2}

) is the main gaseous species that progressively invades the glass headspace. We report the development of a homemade orbital shaker to replicate wine swirling and the upgrade of a diode laser sensor (DLS) dedicated to monitoring gas-phase CO

_{2}

in the headspace of champagne glasses under swirling conditions. We conduct a first overview of gas-phase CO

_{2}

monitoring in the headspace of a champagne glass, starting from the pouring step and continuing for the next 5 min, with several 5 s swirling steps to replicate the natural orbital movement of champagne tasters. The first results show a sudden drop in the CO

_{2}

concentration in the glass headspace, probably triggered by the liquid wave traveling along the glass wall following the action of swirling the glass. Full article

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

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8 pages, 1044 KiB

Open AccessArticle

Unscrambling the Provenance of Eggs by Combining Chemometrics and Near-Infrared Reflectance Spectroscopy

by Louwrens Christiaan Hoffman, Dongdong Ni, Buddhi Dayananda, N Abdul Ghafar and Daniel Cozzolino

Sensors 2022, 22(13), 4988; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134988 - 01 Jul 2022

Cited by 5 | Viewed by 2049

Abstract

Issues related to food authenticity, traceability, and fraud have increased in recent decades as a consequence of the deliberate and intentional substitution, addition, tampering, or misrepresentation of food ingredients, where false or misleading statements are made about a product for economic gains. This study aimed to evaluate the ability of a portable NIR instrument to classify egg samples sourced from different provenances or production systems (e.g., cage and free-range) in Australia. Whole egg samples (n: 100) were purchased from local supermarkets where the label in each of the packages was used as identification of the layers’ feeding system as per the Australian legislation and standards. The spectra of the albumin and yolk were collected using a portable NIR spectrophotometer (950–1600 nm). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data. The results obtained in this study showed how the combination of chemometrics and NIR spectroscopy allowed for the classification of egg albumin and yolk samples according to the system of production (cage and free range). The proposed method is simple, fast, environmentally friendly and avoids laborious sample pre-treatment, and is expected to become an alternative to commonly used techniques for egg quality assessment. Full article

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

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13 pages, 3869 KiB

Open AccessArticle

Influence of Label Design and Country of Origin Information in Wines on Consumers’ Visual, Sensory, and Emotional Responses

by Chang Liu, Chetan Sharma, Qiqi Xu, Claudia Gonzalez Viejo, Sigfredo Fuentes and Damir D. Torrico

Sensors 2022, 22(6), 2158; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062158 - 10 Mar 2022

Cited by 10 | Viewed by 3501

Abstract

This study aimed to evaluate the influence of origin information on Pinot Noir wine labels using eye-tracking and its associations with purchase intent, and hedonic and subconscious emotional responses. Two studies were carried out on untrained university staff and students aged 20–60 years old. Study 1 was conducted to assess consumers’ (n = 55; 55% males, and 45% females) self-reported and subconscious responses towards four design labels (with and without New Zealand origin name/script or origin logo) using eye-tracking and video analysis to evaluate emotions of participants. In study 2, participants (n = 72, 56% males, and 44% females) blind-tasted the same wine sample from different labels while recording their self-reported responses. In study 1, no significant differences were found in fixations between origin name/script and origin logo. However, participants paid more attention to the image and the brand name on the wine labels. In study 2, no significant effects on emotional responses were found with or without the origin name/script or logo. Nonetheless, a multiple factor analysis showed either negative or no associations between the baseline (wine with no label) and the samples showing the different labels, even though the taste of the wine samples was the same, which confirmed an influence of the label on the wine appreciation. Among results from studies 1 and 2, origin information affected the purchase intent and hedonic responses marginally. These findings can be used to design wine labels for e-commerce. Full article

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

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13 pages, 2073 KiB

Open AccessArticle

Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis

by Sigfredo Fuentes, Claudia Gonzalez Viejo, Damir D. Torrico and Frank R. Dunshea

Sensors 2021, 21(22), 7641; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227641 - 17 Nov 2021

Cited by 6 | Viewed by 2333

Abstract

New and emerging non-invasive digital tools, such as eye-tracking, facial expression and physiological biometrics, have been implemented to extract more objective sensory responses by panelists from packaging and, specifically, labels. However, integrating these technologies from different company providers and software for data acquisition and analysis makes their practical application difficult for research and the industry. This study proposed a prototype integration between eye tracking and emotional biometrics using the BioSensory computer application for three sample labels: Stevia, Potato chips, and Spaghetti. Multivariate data analyses are presented, showing the integrative analysis approach of the proposed prototype system. Further studies can be conducted with this system and integrating other biometrics available, such as physiological response with heart rate, blood, pressure, and temperature changes analyzed while focusing on different label components or packaging features. By maximizing data extraction from various components of packaging and labels, smart predictive systems can also be implemented, such as machine learning to assess liking and other parameters of interest from the whole package and specific components. Full article

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

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18 pages, 6023 KiB

Open AccessArticle

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

by Aimi Aznan, Claudia Gonzalez Viejo, Alexis Pang and Sigfredo Fuentes

Sensors 2021, 21(19), 6354; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196354 - 23 Sep 2021

Cited by 14 | Viewed by 4086

Abstract

Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies. Full article

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

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11 pages, 1105 KiB

Open AccessArticle

Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy

by Ahmed Menevseoglu, Didem P. Aykas, Beatriz Hatta-Sakoda, Victor Hugo Toledo-Herrera and Luis E. Rodriguez-Saona

Sensors 2021, 21(18), 6278; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186278 - 18 Sep 2021

Cited by 6 | Viewed by 2763

Abstract

Handheld Raman and portable FT-IR spectroscopy devices were evaluated for fast and non-invasive determination of methanol and ethanol levels in Peruvian Pisco. Commercial Peruvian Pisco (n = 171) samples were kindly provided by the UNALM Alliance for Research in Alcohol and its Derivatives (Lima, Peru) and supplemented by purchases at grocery and online stores. Pisco spectra were collected on handheld Raman spectrometers equipped with either a 1064 nm or a 785 nm excitation laser and a portable infrared unit operating in transmission mode. The alcohol levels were determined by GC–MS. Calibration models used partial least-squares regression (PLSR) to develop prediction algorithms. GC–MS data revealed that 10% of Pisco samples had ethanol levels lower than 38%, indicating possible water dilution. Methanol levels ranged from 10 to 130 mg/100 mL, well below the maximum levels allowed for fruit brandies. Handheld Raman equipped with a 1064 nm excitation laser gave the best results for determining ethanol (SEP = 1.2%; RPre = 0.95) and methanol (SEP = 1.8 mg/100 mL; RPre = 0.93). Randomly selected Pisco samples were spiked with methanol (75 to 2800 mg/100 mL), and their Raman spectra were collected through their genuine commercial bottles. The prediction models gave an excellent performance (SEP = 98 mg/100 mL; RPre = 0.97), allowing for the non-destructive and non-contact determination of methanol and ethanol concentrations without opening the bottles. Full article

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

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15 pages, 16032 KiB

Open AccessEditor’s ChoiceArticle

Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity

by Claudia Gonzalez Viejo, Eden Tongson and Sigfredo Fuentes

Sensors 2021, 21(6), 2016; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062016 - 12 Mar 2021

Cited by 54 | Viewed by 5109

Abstract

Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on–time and ensure high-quality products. Full article

(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)

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