1
|
Cavallini N, Cavallini E, Savorani F. Monitoring the homemade fermentation of readymade malt extract using the SCiO NIR sensor: A convergence of technology and tradition. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025; 325:125126. [PMID: 39326188 DOI: 10.1016/j.saa.2024.125126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 09/06/2024] [Accepted: 09/10/2024] [Indexed: 09/28/2024]
Abstract
Miniaturized near-infrared (NIR) instruments are launched on the market to satisfy the need of both researchers and consumers for quick ways of analysing stuff, especially in the food field. Their portability and ease of operation are at the centre of the "do-it-yourself" idea behind providing virtually anyone with the ability of making analytical measurements on their own. In this study, we highlighted the convergence of technology and tradition in two seemingly disparate domains: spectroscopy and homebrewing. Homebrewing is in fact a leisure activity which conceptually shares the do-it-yourself approach: the consumer becomes the producer of his/her own beer. Hence, in our study, we investigated the analytical possibilities provided by a handheld NIR spectrometer to monitor the homemade fermentation process of a commercial malt extract, over the course of one week. The spectroscopic data, acquired using the SCiO sensor (Consumer Physics), were analysed via Principal Component Analysis (PCA) to investigate temporal trends and relationships with brewing parameters. Our findings reveal that discernible trends, reflective of brewing stage progression and temperature variations, can be captured and unveiled with simple data analysis approaches. At the same time, the detection of scattering effects that can be attributed to bubble formation on the spectrometer's acquisition window confirm the need for robust and reasoned experimental procedures, but also for proper data preprocessing methods. This rather simple and basic analytical approach could provide the homebrewer the possibility to qualitatively monitor the advancement of the brewing process.
Collapse
Affiliation(s)
- Nicola Cavallini
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi, 24 - 10129 Torino (TO), Italy.
| | | | - Francesco Savorani
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi, 24 - 10129 Torino (TO), Italy
| |
Collapse
|
2
|
He HJ, da Silva Ferreira MV, Wu Q, Karami H, Kamruzzaman M. Portable and miniature sensors in supply chain for food authentication: a review. Crit Rev Food Sci Nutr 2024:1-21. [PMID: 39066550 DOI: 10.1080/10408398.2024.2380837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
Abstract
Food fraud, a pervasive issue in the global food industry, poses significant challenges to consumer health, trust, and economic stability, costing an estimated $10-15 billion annually. Therefore, there is a rising demand for developing portable and miniature sensors that facilitate food authentication throughout the supply chain. This review explores the recent advancements and applications of portable and miniature sensors, including portable/miniature near-infrared (NIR) spectroscopy, e-nose and colorimetric sensors based on nanozyme for food authentication within the supply chain. After briefly presenting the architecture and mechanism, this review discusses the application of these portable and miniature sensors in food authentication, addressing the challenges and opportunities in integrating and deploying these sensors to ensure authenticity. This review reveals the enhanced utility of portable/miniature NIR spectroscopy, e-nose, and nanozyme-based colorimetric sensors in ensuring food authenticity and enabling informed decision-making throughout the food supply chain.
Collapse
Affiliation(s)
- Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang, China
| | | | - Qianyi Wu
- Department of Agriculture and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Hamed Karami
- Department of Petroleum Engineering, Collage of Engineering, Knowledge University, Erbil, Iraq
| | - Mohammed Kamruzzaman
- School of Food Science, Henan Institute of Science and Technology, Xinxiang, China
| |
Collapse
|
3
|
Ozen B, Cavdaroglu C, Tokatli F. Trends in authentication of edible oils using vibrational spectroscopic techniques. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:4216-4233. [PMID: 38899503 DOI: 10.1039/d4ay00562g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.
Collapse
Affiliation(s)
- Banu Ozen
- Izmir Institute of Technology, Department of Food Engineering, Urla, Izmir, Turkiye.
| | - Cagri Cavdaroglu
- Izmir Institute of Technology, Department of Food Engineering, Urla, Izmir, Turkiye.
| | - Figen Tokatli
- Izmir Institute of Technology, Department of Food Engineering, Urla, Izmir, Turkiye.
| |
Collapse
|
4
|
Vega-Castellote M, Sánchez MT, Torres-Rodríguez I, Entrenas JA, Pérez-Marín D. NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review. Foods 2024; 13:1612. [PMID: 38890841 PMCID: PMC11172355 DOI: 10.3390/foods13111612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 05/18/2024] [Accepted: 05/20/2024] [Indexed: 06/20/2024] Open
Abstract
Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.
Collapse
Affiliation(s)
- Miguel Vega-Castellote
- Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain;
| | - María-Teresa Sánchez
- Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain;
| | - Irina Torres-Rodríguez
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| | - José-Antonio Entrenas
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| | - Dolores Pérez-Marín
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| |
Collapse
|
5
|
Babatunde HA, Collins J, Lukman R, Saxton R, Andersen T, McDougal OM. SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk Using MIR. Foods 2024; 13:166. [PMID: 38201194 PMCID: PMC10778881 DOI: 10.3390/foods13010166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Protein content variation in milk can impact the quality and consistency of dairy products, necessitating access to in-line real time monitoring. Here, we present a chemometric approach for the qualitative and quantitative monitoring of β-lactoglobulin and α-lactalbumin, using mid-infrared spectroscopy (MIR). In this study, we employed Hotelling T2 and Q-residual for outlier detection, automated preprocessing using nippy, conducted wavenumber selection with genetic algorithms, and evaluated four chemometric models, including partial least squares, support vector regression (SVR), ridge, and logistic regression to accurately predict the concentrations of β-lactoglobulin and α-lactalbumin in milk. For the quantitative analysis of these two whey proteins, SVR performed the best to interpret protein concentration from 197 MIR spectra originating from 42 Cornell University samples of preserved pasteurized modified milk. The R2 values obtained for β-lactoglobulin and α-lactalbumin using leave one out cross-validation (LOOCV) are 92.8% and 92.7%, respectively, which is the highest correlation reported to date. Our approach introduced a combination of preprocessing automation, genetic algorithm-based wavenumber selection, and used Optuna to optimize the framework for tuning hyperparameters of the chemometric models, resulting in the best chemometric analysis of MIR data to quantitate β-lactoglobulin and α-lactalbumin to date.
Collapse
Affiliation(s)
| | - Joseph Collins
- Biomolecular Sciences Graduate Program, Boise State University, Boise, ID 83725, USA;
| | - Rianat Lukman
- Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA; (R.L.); (R.S.)
| | - Rose Saxton
- Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA; (R.L.); (R.S.)
| | | | - Owen M. McDougal
- Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA; (R.L.); (R.S.)
| |
Collapse
|
6
|
Neutens P, Zinoviev K, Jimenez Valencia AM, Woronoff G, Jansen R, Hosseini N, Uribe AJ, Goheen J, Kryszak LA, Stakenborg T, Clarke WA, Van Roy W. Toward point-of-care diagnostics: Running enzymatic assays on a photonic waveguide-based sensor chip with a portable, benchtop measurement system. JOURNAL OF BIOPHOTONICS 2024; 17:e202300279. [PMID: 37703421 DOI: 10.1002/jbio.202300279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/15/2023]
Abstract
We demonstrate a portable, compact system to perform absorption-based enzymatic assays at a visible wavelength of 639 nm on a photonic waveguide-based sensor chip, suitable for lab-on-a-chip applications. The photonic design and fabrication of the sensor are described, and a detailed overview of the portable measurement system is presented. In this publication, we use an integrated photonic waveguide-based absorbance sensor to run a full enzymatic assay. An assay to detect creatinine in plasma is simultaneously performed on both the photonic sensor on the portable setup and on a commercial microplate reader for a clinically relevant creatinine concentration range. We observed a high correlation between the measured waveguide propagation loss and the optical density measurement from the plate reader and measured a limit-of-detection of 4.5 μM creatinine in the sensor well, covering the relevant clinical range for creatinine detection.
Collapse
Affiliation(s)
- Pieter Neutens
- Life Science Technologies Department, Imec, Leuven, Belgium
| | | | - Angela M Jimenez Valencia
- Laboratory for Integrated Nanodiagnostics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Roelof Jansen
- Life Science Technologies Department, Imec, Leuven, Belgium
| | - Naser Hosseini
- Life Science Technologies Department, Imec, Leuven, Belgium
| | | | - Joshua Goheen
- Laboratory for Integrated Nanodiagnostics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Lindsay A Kryszak
- Laboratory for Integrated Nanodiagnostics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tim Stakenborg
- Life Science Technologies Department, Imec, Leuven, Belgium
| | - William A Clarke
- Laboratory for Integrated Nanodiagnostics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Willem Van Roy
- Life Science Technologies Department, Imec, Leuven, Belgium
| |
Collapse
|
7
|
Kashani Zadeh H, Hardy M, Sueker M, Li Y, Tzouchas A, MacKinnon N, Bearman G, Haughey SA, Akhbardeh A, Baek I, Hwang C, Qin J, Tabb AM, Hellberg RS, Ismail S, Reza H, Vasefi F, Kim M, Tavakolian K, Elliott CT. Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115149. [PMID: 37299875 DOI: 10.3390/s23115149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/04/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.
Collapse
Affiliation(s)
| | - Mike Hardy
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
| | - Mitchell Sueker
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA
| | - Yicong Li
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
| | | | | | | | - Simon A Haughey
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
| | | | - Insuck Baek
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Chansong Hwang
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Jianwei Qin
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Amanda M Tabb
- Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Rosalee S Hellberg
- Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Shereen Ismail
- School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
| | - Hassan Reza
- School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
| | | | - Moon Kim
- USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA
| | - Kouhyar Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA
| | - Christopher T Elliott
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, UK
- School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, Khong Luang 12120, Thailand
| |
Collapse
|
8
|
Sudarsh S, Müller-Maatsch J. Evaluation of on-site testing methods with a novel 3-in-1 miniaturized spectroscopic device for cinnamon screening. Talanta 2023; 256:124195. [PMID: 36736268 DOI: 10.1016/j.talanta.2022.124195] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/21/2022] [Accepted: 12/10/2022] [Indexed: 12/16/2022]
Abstract
"True cinnamon" is often fraudulently replaced by other varieties for economic reasons. In the powdered form, it is not possible to distinguish the varieties visually, but they differ in their sensory profile, in particular in the aromatic compound coumarin content which has also been deemed hepatotoxic in animal models. Molecular and analytical techniques exist which can be used for authentication but are expensive, time-consuming, and destructive. As an alternative, we tested three different miniaturized spectroscopic techniques namely, ultraviolet-visible (UV-Vis), near-infrared (NIR) and fluorescence (FLUO) to authenticate cinnamon samples. Out of the three, UV-Vis and NIR were superior to FLUO. The separation with UV-Vis and FLUO could be visually identified after pre-processing the spectral data and subsequently submitting it to principal component analysis (PCA). When chemometrics were applied a correct classification rate by variety of 89%, 90% and 89% for UV-Vis, NIR, and fluorescence spectroscopy, respectively, was observed. The usage of miniaturized spectrophotometers combined with PCA and classification algorithms was found promising to authenticate cinnamon.
Collapse
Affiliation(s)
- Subrath Sudarsh
- Wageningen Food Safety Research (WFSR) Part of Wageningen University and Research, Akkermaalsbos 2, 6708 WB Wageningen, the Netherlands
| | - Judith Müller-Maatsch
- Wageningen Food Safety Research (WFSR) Part of Wageningen University and Research, Akkermaalsbos 2, 6708 WB Wageningen, the Netherlands.
| |
Collapse
|
9
|
Authenticity of almond flour using handheld near infrared instruments and one class classifiers. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
10
|
Untargeted metabolomic analysis of honey mixtures: discrimination opportunities based on ATR-FTIR data and machine learning algorithms. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
|
11
|
Bogomolov A, Evseeva A, Ignatiev E, Korneev V. New approaches to data processing and analysis in optical sensing. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
|
12
|
Hassoun A, Jagtap S, Garcia-Garcia G, Trollman H, Pateiro M, Lorenzo JM, Trif M, Rusu AV, Aadil RM, Šimat V, Cropotova J, Câmara JS. Food quality 4.0: From traditional approaches to digitalized automated analysis. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
13
|
Ali A, Nettey-Oppong EE, Effah E, Yu CY, Muhammad R, Soomro TA, Byun KM, Choi SH. Miniaturized Raman Instruments for SERS-Based Point-of-Care Testing on Respiratory Viruses. BIOSENSORS 2022; 12:bios12080590. [PMID: 36004986 PMCID: PMC9405795 DOI: 10.3390/bios12080590] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/24/2022] [Accepted: 07/26/2022] [Indexed: 06/12/2023]
Abstract
As surface-enhanced Raman scattering (SERS) has been used to diagnose several respiratory viruses (e.g., influenza A virus subtypes such as H1N1 and the new coronavirus SARS-CoV-2), SERS is gaining popularity as a method for diagnosing viruses at the point-of-care. Although the prior and quick diagnosis of respiratory viruses is critical in the outbreak of infectious disease, ELISA, PCR, and RT-PCR have been used to detect respiratory viruses for pandemic control that are limited for point-of-care testing. SERS provides quantitative data with high specificity and sensitivity in a real-time, label-free, and multiplex manner recognizing molecular fingerprints. Recently, the design of Raman spectroscopy system was simplified from a complicated design to a small and easily accessible form that enables point-of-care testing. We review the optical design (e.g., laser wavelength/power and detectors) of commercialized and customized handheld Raman instruments. As respiratory viruses have prominent risk on the pandemic, we review the applications of handheld Raman devices for detecting respiratory viruses. By instrumentation and commercialization advancements, the advent of the portable SERS device creates a fast, accurate, practical, and cost-effective analytical method for virus detection, and would continue to attract more attention in point-of-care testing.
Collapse
Affiliation(s)
- Ahmed Ali
- Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan;
| | - Ezekiel Edward Nettey-Oppong
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
| | - Elijah Effah
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
| | - Chan Yeong Yu
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
| | - Riaz Muhammad
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
| | - Toufique Ahmed Soomro
- Department of Electronic Engineering, Quid-e-Awam University of Engineering, Science and Technology, Larkana 77150, Pakistan;
| | - Kyung Min Byun
- Department of Biomedical Engineering, Kyung Hee University, Yongin 17104, Korea
- Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Korea
| | - Seung Ho Choi
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
- Department of Integrative Medicine, Major in Digital Healthcare, Yonsei University College of Medicine, Seoul 06229, Korea
| |
Collapse
|
14
|
Beć KB, Grabska J, Huck CW. Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives. Foods 2022; 11:foods11101465. [PMID: 35627034 PMCID: PMC9140213 DOI: 10.3390/foods11101465] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/13/2022] [Indexed: 01/27/2023] Open
Abstract
The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.
Collapse
|
15
|
Delatour T, Becker F, Krause J, Romero R, Gruna R, Längle T, Panchaud A. Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder. Foods 2021; 11:foods11010075. [PMID: 35010202 PMCID: PMC8750415 DOI: 10.3390/foods11010075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/21/2021] [Accepted: 12/23/2021] [Indexed: 11/16/2022] Open
Abstract
With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5-10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.
Collapse
Affiliation(s)
- Thierry Delatour
- Société des Produits Nestlé S.A., Nestlé Research, Route du Jorat 57, 1000 Lausanne, Switzerland; (R.R.); (A.P.)
- Correspondence:
| | - Florian Becker
- Fraunhofer IOSB, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Fraunhoferstrasse 1, 76131 Karlsruhe, Germany; (F.B.); (J.K.); (R.G.); (T.L.)
| | - Julius Krause
- Fraunhofer IOSB, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Fraunhoferstrasse 1, 76131 Karlsruhe, Germany; (F.B.); (J.K.); (R.G.); (T.L.)
| | - Roman Romero
- Société des Produits Nestlé S.A., Nestlé Research, Route du Jorat 57, 1000 Lausanne, Switzerland; (R.R.); (A.P.)
| | - Robin Gruna
- Fraunhofer IOSB, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Fraunhoferstrasse 1, 76131 Karlsruhe, Germany; (F.B.); (J.K.); (R.G.); (T.L.)
| | - Thomas Längle
- Fraunhofer IOSB, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Fraunhoferstrasse 1, 76131 Karlsruhe, Germany; (F.B.); (J.K.); (R.G.); (T.L.)
| | - Alexandre Panchaud
- Société des Produits Nestlé S.A., Nestlé Research, Route du Jorat 57, 1000 Lausanne, Switzerland; (R.R.); (A.P.)
| |
Collapse
|