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Spectroscopy-Based Sensing Technologies for Food Quality and Safety

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 11697

Special Issue Editors


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Guest Editor
1. Basic Medical Science, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
2. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: spectroscopy; point of detection modalities for pathogen detection; flow cytometry; fluorescence detection
Special Issues, Collections and Topics in MDPI journals
Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: spectroscopy; portable instrument; light scattering; optical diagnostics

Special Issue Information

Dear Colleagues,

The complexity of the international food supply chain and the current stress on the availability of primary products are likely to increase potential spoilage and the need for more accurate and portable evaluation techniques for food safety and authenticity.

Spectroscopic methods ranging from optical to atomic and mass spectrometry offer exciting opportunities, and these methods can be applied to diverse food testing by disseminating elemental, chemical bonds, and optical transducer information via spectral outputs. In addition, the recent development of machine learning algorithms may expedite the extraction of valuable information from large amounts of spectral data, which is crucial for connecting the dots between spectral peaks and food elements. This Special Issue will be soliciting submissions on the following topics related to food analysis via spectroscopic methods:  

  • Food composition analysis by spectroscopic methods;
  • Spectroscopy-based pathogen detection and/or foreign material detection;
  • Food authenticity via spectrometry;
  • Application of machine learning algorithms to the spectroscopic data acquired from food analysis;
  • Field-deployable spectroscopic instrument for food analysis;
  • Evaluation and identification of food spoilage.

Prof. Dr. J. Paul Robinson
Dr. Euiwon Bae
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

  • spectroscopy
  • atomic spectroscopy
  • mass spectrometry
  • authentication
  • food analysis
  • food safety
  • machine learning

Published Papers (6 papers)

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Research

13 pages, 4087 KiB  
Article
A Simple Laser-Induced Breakdown Spectroscopy Method for Quantification and Classification of Edible Sea Salts Assisted by Surface-Hydrophilicity-Enhanced Silicon Wafer Substrates
by Han-Bum Choi, Seung-Hyun Moon, Hyang Kim, Nagaraju Guthikonda, Kyung-Sik Ham, Song-Hee Han, Sang-Ho Nam and Yong-Hoon Lee
Sensors 2023, 23(22), 9280; https://doi.org/10.3390/s23229280 - 20 Nov 2023
Viewed by 764
Abstract
Salt, one of the most commonly consumed food additives worldwide, is produced in many countries. The chemical composition of edible salts is essential information for quality assessment and origin distinction. In this work, a simple laser-induced breakdown spectroscopy instrument was assembled with a [...] Read more.
Salt, one of the most commonly consumed food additives worldwide, is produced in many countries. The chemical composition of edible salts is essential information for quality assessment and origin distinction. In this work, a simple laser-induced breakdown spectroscopy instrument was assembled with a diode-pumped solid-state laser and a miniature spectrometer. Its performances in analyzing Mg and Ca in six popular edible sea salts consumed in South Korea and classification of the products were investigated. Each salt was dissolved in water and a tiny amount of the solution was dropped and dried on the hydrophilicity-enhanced silicon wafer substrate, providing homogeneous distribution of salt crystals. Strong Mg II and Ca II emissions were chosen for both quantification and classification. Calibration curves could be constructed with limits-of-detection of 87 mg/kg for Mg and 45 mg/kg for Ca. Also, the Mg II and Ca II emission peak intensities were used in a k-nearest neighbors model providing 98.6% classification accuracy. In both quantification and classification, intensity normalization using a Na I emission line as a reference signal was effective. A concept of interclass distance was introduced, and the increase in the classification accuracy due to the intensity normalization was rationalized based on it. Our methodology will be useful for analyzing major mineral nutrients in various food materials in liquid phase or soluble in water, including salts. Full article
(This article belongs to the Special Issue Spectroscopy-Based Sensing Technologies for Food Quality and Safety)
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19 pages, 9024 KiB  
Article
Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy
by Rahul Joshi, Lakshmi Priya GG, Mohammad Akbar Faqeerzada, Tanima Bhattacharya, Moon Sung Kim, Insuck Baek and Byoung-Kwan Cho
Sensors 2023, 23(11), 5020; https://doi.org/10.3390/s23115020 - 24 May 2023
Cited by 4 | Viewed by 1705
Abstract
Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection must be developed to address this problem. In conjunction with [...] Read more.
Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection must be developed to address this problem. In conjunction with machine learning and deep learning technique, Fourier transform infrared (FT-IR) spectroscopy was employed in this investigation for the nondestructive quantitative measurement of eight different concentrations of melamine and cyanuric acid added to pet food. The effectiveness of the one-dimensional convolutional neural network (1D CNN) technique was compared with that of partial least squares regression (PLSR), principal component regression (PCR), and a net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO). The 1D CNN model developed for the FT-IR spectra attained correlation coefficients of 0.995 and 0.994 and root mean square error of prediction values of 0.090% and 0.110% for the prediction datasets on the melamine- and cyanuric acid-contaminated pet food samples, respectively, which were superior to those of the PLSR and PCR models. Therefore, when FT-IR spectroscopy is employed in conjunction with a 1D CNN model, it serves as a potentially rapid and nondestructive method for identifying toxic chemicals added to pet food. Full article
(This article belongs to the Special Issue Spectroscopy-Based Sensing Technologies for Food Quality and Safety)
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18 pages, 3073 KiB  
Article
Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns
by Iyll-Joon Doh, Diana Vanessa Sarria Zuniga, Sungho Shin, Robert E. Pruitt, Bartek Rajwa, J. Paul Robinson and Euiwon Bae
Sensors 2023, 23(7), 3485; https://doi.org/10.3390/s23073485 - 27 Mar 2023
Cited by 1 | Viewed by 1408
Abstract
The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and [...] Read more.
The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473–709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%. Full article
(This article belongs to the Special Issue Spectroscopy-Based Sensing Technologies for Food Quality and Safety)
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14 pages, 4187 KiB  
Article
Surface Environment and Energy Density Effects on the Detection and Disinfection of Microorganisms Using a Portable Instrument
by Sungho Shin, Brianna Dowden, Iyll-Joon Doh, Bartek Rajwa, Euiwon Bae and J. Paul Robinson
Sensors 2023, 23(4), 2135; https://doi.org/10.3390/s23042135 - 14 Feb 2023
Cited by 1 | Viewed by 1406
Abstract
Real-time detection and disinfection of foodborne pathogens are important for preventing foodborne outbreaks and for maintaining a safe environment for consumers. There are numerous methods for the disinfection of hazardous organisms, including heat treatment, chemical reaction, filtration, and irradiation. This report evaluated a [...] Read more.
Real-time detection and disinfection of foodborne pathogens are important for preventing foodborne outbreaks and for maintaining a safe environment for consumers. There are numerous methods for the disinfection of hazardous organisms, including heat treatment, chemical reaction, filtration, and irradiation. This report evaluated a portable instrument to validate its simultaneous detection and disinfection capability in typical laboratory situations. In this challenging study, three gram-negative and two gram-positive microorganisms were used. For the detection of contamination, inoculations of various concentrations were dispensed on three different surface types to estimate the performance for minimum-detectable cell concentration. Inoculations higher than 103~104 CFU/mm2 and 0.15 mm of detectable contaminant size were estimated to generate a sufficient level of fluorescence signal. The evaluation of disinfection efficacy was conducted on three distinct types of surfaces, with the energy density of UVC light (275-nm) ranging from 4.5 to 22.5 mJ/cm2 and the exposure time varying from 1 to 5 s. The study determined the optimal energy dose for each of the microorganisms species. In addition, surface characteristics may also be an important factor that results in different inactivation efficacy. These results demonstrate that the proposed portable device could serve as an in-field detection and disinfection unit in various environments, and provide a more efficient and user-friendly way of performing disinfection on large surface areas. Full article
(This article belongs to the Special Issue Spectroscopy-Based Sensing Technologies for Food Quality and Safety)
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18 pages, 7180 KiB  
Article
Non-Destructive Detection of Abnormal Chicken Eggs by Using an Optimized Spectral Analysis System
by Juntae Kim, Dennis Semyalo, Tae-Gyun Rho, Hyungjin Bae and Byoung-Kwan Cho
Sensors 2022, 22(24), 9826; https://doi.org/10.3390/s22249826 - 14 Dec 2022
Cited by 1 | Viewed by 4125
Abstract
Environmental pressures, such as temperature and light intensity, food, and genetic factors, can cause chicken eggs to develop abnormalities. The common types of internal egg abnormalities include bloody and damaged egg yolk. Spectrometers have been recently used in real-time abnormal egg detection research. [...] Read more.
Environmental pressures, such as temperature and light intensity, food, and genetic factors, can cause chicken eggs to develop abnormalities. The common types of internal egg abnormalities include bloody and damaged egg yolk. Spectrometers have been recently used in real-time abnormal egg detection research. However, there are very few studies on the optimization of measurement systems. This study aimed to establish optimum parameters for detecting of internal egg abnormalities (bloody and damaged-yolk eggs) using visible and near-infrared (Vis/NIR) spectrometry (192–1110 nm range) and multivariate data analysis. The detection performance using various system parameters, such as the types of light sources, the configuration of the light, and sensor positions, was investigated. With the help of collected data, a partial least-squares discriminant analysis (PLS-DA) model was developed to classify normal and abnormal eggs. The highest classification accuracy for the various system parameters was 98.7%. Three band selection methods, such as weighted regression coefficient (WRC), sequential feature selection (SFS), and successive projection algorithm (SPA) were used for further model optimization, to reduce the spectral bands from 1028 to less than 7. In conclusion the results indicate that the types of light sources and the configuration design of the sensor and illumination affect the detection accuracy for abnormal eggs. Full article
(This article belongs to the Special Issue Spectroscopy-Based Sensing Technologies for Food Quality and Safety)
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19 pages, 2165 KiB  
Article
Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses
by Anastasia E. Lytou, Panagiotis Tsakanikas, Dimitra Lymperi and George-John E. Nychas
Sensors 2022, 22(18), 7018; https://doi.org/10.3390/s22187018 - 16 Sep 2022
Cited by 2 | Viewed by 1679
Abstract
The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time [...] Read more.
The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time intervals. Microbiological analysis was performed throughout storage to assess the total viable counts (TVC), while in parallel FT-IR spectroscopy, multispectral imaging (MSI) and electronic nose (e-nose) analyses were conducted. Machine learning models (partial least square regression (PLS-R)) were developed to assess any correlations between sensor and microbiological data. Microbial counts ranged from 1.8 to 9.5 log CFU/g, while the microbial growth rate was affected by origin, harvest year and storage temperature. The models developed using FT-IR data indicated a good prediction performance on the external test dataset. The model developed by combining data from both origins resulted in satisfactory prediction performance, exhibiting enhanced robustness from being origin unaware towards microbiological population prediction. The results of the model developed with the MSI data indicated a relatively good prediction performance on the external test dataset in spite of the high RMSE values, whereas while using e-nose data from both MI and SAMS, a poor prediction performance of the model was reported. Full article
(This article belongs to the Special Issue Spectroscopy-Based Sensing Technologies for Food Quality and Safety)
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