Fish freshness monitoring using UV-fluorescence imaging on Japanese dace (Tribolodon hakonensis) fisheye

Omwange, Ken Abamba; Riza, Dimas Firmanda Al; Nie, Shuai; Shiigi, Tomoo; Kuramoto, Makoto; Ogawa, Yuichi; Kondo, Naoshi; Suzuki, Tetsuhito

doi:10.1016/j.jfoodeng.2020.110111

Cited by 20 publications

(8 citation statements)

References 21 publications

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“…Similarly, Omwange et al. (2020) used EEM in combination with PLSR to select excitation wavelengths for developing LED‐IF imaging systems for fish freshness estimation (using K ‐value). Three main peaks, denoted A, B, and C, with excitation wavelengths of 230, 290, and 350 nm, respectively, were observed in the EEM of the fish eyeball.…”

Section: Analysis Related To Lif and Led‐if Techniques At Laboratory ...mentioning

confidence: 99%

Toward commercial applications of LED and laser‐induced fluorescence techniques for food identity, quality, and safety monitoring: A review

Peng

et al. 2023

Comp Rev Food Sci Food Safe

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The assessment of food safety and quality is a matter of paramount importance, especially considering the challenges posed by climate change. Convenient, eco‐friendly, and non‐destructive techniques have attracted extensive attention in the food industry because they can retain food safety and quality. Fluorescence radiation, the process by which fluorophore emits light upon the absorption of ultraviolet or visible light, offers the advantages of high sensitivity and selectivity. The use of excitation–emission matrix (EEM) has been extensively explored in the food industry, but on‐site detection of EEMs remain a challenge. To address this limitation, laser‐induced fluorescence (LIF) and light emitting diode‐induced fluorescence (LED‐IF) have been implemented in many cases to facilitate the transition of fluorescence measurements from the laboratory to commercial applications. This review provides an overview of the application of commercially available LIF/LED‐IF devices for non‐destructive food measurement and recent studies that focus on the development of LIF/LED‐IF devices for commercial applications. These studies were categorized into two stages: the preliminary exploration stage, which emphasizes the selection of an appropriate excitation wavelength based on the combination of EEM and chemometrics, and the pre‐application stage, where experiments were conducted on scouting with specific excitation wavelength. Although commercially available devices have emerged in many research fields, only a limited number have been reported for use in the food industry. Future studies should focus on enhancing the diversity of test samples and parameters that can be measured by a single device, exploring the application of LIF techniques for detecting low‐concentration substances in food, investigating more quantitative approaches, and developing embedded computing devices.

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Section: Analysis Related To Lif and Led‐if Techniques At Laboratory ...mentioning

confidence: 99%

Toward commercial applications of LED and laser‐induced fluorescence techniques for food identity, quality, and safety monitoring: A review

Peng

et al. 2023

Comp Rev Food Sci Food Safe

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“…Besides red snapper, the fluorescence properties of Japanese dace (Tribolodon hakonensis) has also been investigated. Omwange et al [ 46 ] developed a K value “standard freshness index” prediction model by extracting color components from the fluorescent images of the pupil and iris, and achieved good results with an RMSECV of 3.5% and R 2 of 0.92. When studying mackerel (Trachurus japonicus), Rahman et al [ 48 ] used three-dimensional fluorescence fingerprints (3D-FFs) to characterize the fluorophores in the fish.…”

Section: Quality Evaluation Of White Meatmentioning

confidence: 99%

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

Fan

2022

Biosensors

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Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.

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“…The coefficient of determination assessed the performance of these models for predicting K value and FFA for test sets, 𝑅 (equation ( 4)), root mean square error of test 𝑅𝑀𝑆𝐸 (equation ( 5)) (Omwange et al, 2020), and residual prediction deviation for tests (𝑅𝑃𝐷 ) (Forouzangohar et al, 2008). An RPD value lower than 2.0 will give irrelevant prediction results, whereas any value between 2.0-3.0 is considered satisfactory for rough screening.…”

Section: ．Fluorescence Measurement (Excitation-emission Matrix)mentioning

confidence: 99%

Estimation of <i>K</i> Value and Free Fatty Acids of Adulterated Olive Oil Using Fluorescence Spectroscopy Coupled with Multivariate Analysis and Convolutional Neural Network Models

Omwange

Saito

Itakura

et al. 2022

Engineering in Agriculture, Environment and Food

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Adulterating extra-virgin olive oils (EVOO) with lower grade olive oils, like virgin olive oil (VOO), and selling it as EVOO to unsuspecting consumers has sparked concern in the recent years. Developing inexpensive and quick adulteration detection methods to unravel such acts will promote trust in the industry. This study focused on the quality degradation of EVOO when adulterated by different proportions of VOO. Excitation emission matrices (EEMs) and fluorescence images were taken for analysis. Partial least square regression (PLSR), support vector machine (SVM), decision tree and convolutional neural network (CNN) models were used to explore both the EEMs and fluorescence images of adulterated oils, which indicate the extent of adulteration of extra virgin olive oils can be detected.

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Fish freshness monitoring using UV-fluorescence imaging on Japanese dace (Tribolodon hakonensis) fisheye

Cited by 20 publications

References 21 publications

Toward commercial applications of LED and laser‐induced fluorescence techniques for food identity, quality, and safety monitoring: A review

Toward commercial applications of LED and laser‐induced fluorescence techniques for food identity, quality, and safety monitoring: A review

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

Estimation of <i>K</i> Value and Free Fatty Acids of Adulterated Olive Oil Using Fluorescence Spectroscopy Coupled with Multivariate Analysis and Convolutional Neural Network Models

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