Evaluating Japanese dace (Tribolodon hakonensis) fish freshness during storage using multispectral images from visible and UV excited fluorescence

Fusion of visible and fluorescence imaging through deep neural network for color value prediction of pelletized red peppers

A non-destructive method for determining the color value of pelletized red peppers is crucial for pepper processing factories. This study aimed to investigate the potentiality of visible and fluorescence images for the determination of color value of pelletized red pepper. The imaging problem, caused by the cylindrical shape and irregular cross-sectional features of the pelletized red peppers, was reduced through the extraction of an approximate plane region. To integrate the information in the visible and fluorescence images, a baseline convolutional neural network (CNN) architecture was designed and low level, middle level, and high level fusion models (denoted as LL-CNN, ML-CNN, and HL-CNN, respectively) were developed upon the baseline CNN. The effects of input image size and color space were examined. According to the training result, CNN fusion models were developed using visible image in L*a*b* color space and fluorescence image in RGB color space using 56 × 56 input image size. Among the three types of CNN fusion models, the HL-CNN obtained the best performance, resulting in Rv 2 of 0.828 and RMSEV of 0.351. This study suggests that the fusion of visible and fluorescence image through CNN is a practical approach to save testing time and replace traditional destructive method. The low cost and compact structure of the imaging systems can maintain the commercial appeal of pepper industry.

Deep learning models with optimized fluorescence spectroscopy to advance freshness of rainbow trout predicting under nonisothermal storage conditions

This study established long short-term memory (LSTM), convolution neural network long short-term memory (CNN_LSTM), and radial basis function neural network (RBFNN) based on optimized excitation-emission matrix (EEM) from fish eye fluid to predict freshness changes of rainbow trout under nonisothermal storage conditions. The method of residual analysis, core consistency diagnostics, and split-half analysis of parallel factor analysis was used to optimize EEM data, and two characteristic components were extracted. LSTM, CNN_LSTM, and RBFNN models based on characteristic components of EEM used to predict the freshness indices. The results demonstrated the relative errors of RBFNN models with an R2 above 0.96 and relative errors less than 10% for K-value, total viable counts, and volatile base nitrogen, which were better than those of LSTM and CNN_LSTM models. This study presents a novel approach for predicting the freshness of rainbow trout under nonisothermal storage conditions.

Deep machine learning identified fish flesh using multispectral imaging

Food fraud is widespread in the aquatic food market, hence fast and non-destructive methods of identification of fish flesh are needed. In this study, multispectral imaging (MSI) was used to screen flesh slices from 20 edible fish species commonly found in the sea around Yantai, China, by combining identification based on the mitochondrial COI gene. We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species. We then employed eight models to compare their prediction performances based on the hold-out method with 70% training and 30% test sets. Convolutional neural network (CNN), quadratic discriminant analysis (QDA), support vector machine (SVM), and linear discriminant analysis (LDA) models perform well on cross-validation and test data. CNN and QDA achieved more than 99% accuracy on the test set. By extracting the CNN features for optimization, a very high degree of separation was obtained for all species. Furthermore, based on the Gini index in RF, 11 bands were selected as key classification features for CNN, and an accuracy of 98% was achieved. Our study developed a successful pipeline for employing machine learning models (especially CNN) on MSI identification of fish flesh, and provided a convenient and non-destructive method to determine the marketing of fish flesh in the future.

Non-destructive prediction of TVB-N using color-texture features of UV-induced fluorescence image for freeze-thaw treated frozen-whole-round tilapia

BACKGROUND

The investigation of UV-induced fluorescence imaging coupled with machine learning was conducted to non-destructively detect the total volatile basic nitrogen (TVB-N) of frozen-whole-round tilapia (FWRT) during freezing and thawing. The UV-induced fluorescence images of FWRT at the wavelength of 365 nm were acquired by self-developed fluorescence image acquisition system. In total, 169 color and texture features based on RGB, hue-saturation-intensity and L*a*b* color spaces and gray level co-occurrence matrix were extracted, respectively. Successive projections algorithm (SPA) was employed to select the optimal 16 features to achieve feature dimension reduction modeling. With full and extracted features as input, the models of partial least squares regression (PLSR), least-squares support vector machine (LSSVM) and convolutional neural network (CNN) were established for TVB-N prediction.

RESULTS

Results indicated that the full features-based CNN performed better than SPA based prediction models (SPA-PLSR and SPA-LSSVM). The CNN model was determined to be the optimal with an RP2 value of 0.9779, RMSEP value of 1.1502 × 10-2  g N kg-1 and RPD value of 6.721 for TVB-N content predictiin.

CONCLUSION

The CNN method based on UV fluorescence imaging technology has potential for quality and safety detection of FWRT. © 2023 Society of Chemical Industry.

Investigating the characteristics of fluorescence features on sweet peppers using UV light excitation

Sweet peppers are popular worldwide due to their nutrition and taste. Conventional vegetable tracing methods have been trialed, but the application of such labels or tags can be laborious and expensive, making their commercial application impractical. What is needed is a label-free method that can identify features unique to each individual fruit. Our research team has noted that sweet peppers have unique textural fluorescence features when observed under UV light that could potentially be used as a label-free signature for identification of individual fruit as it travels through the postharvest supply chain. The objective of this research was to assess the feature of these sweet pepper features for identification purposes. The macroscopic and microscopic images were taken to characterize the fluorescence. The results indicate that all sweet peppers possess dot-like fluorescence features on their surface. Furthermore, it was observed that 93.60% of these features exhibited changes in fluorescence intensity within the cuticle layer during the growth of a pepper. These features on the macro-image are visible under 365 nm UV light, but challenging to be seen under white LEDs and to be classified from the fluorescence spectrum under 365 nm light. This research reported the fluorescence feature on the sweet pepper, which is invisible under white light. The results show that the uniqueness of fluorescent features on the surface of sweet peppers has the potential to become a traceability technology due to the presence of its unique physical modality.

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

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.

Japanese dace (Tribolodon hakonensis) fish freshness estimation using front-face fluorescence spectroscopy coupled with chemometric analysis

Although fish and its related products are good sources of protein and unsaturated fatty acids, like omega-3 in the human diet, their shelf-life is limited by biochemical and microbial changes. In this study, a front-face fluorescence spectroscopy technique was used to acquire Excitation-emission matrices (EEM) to monitor Japanese dace (Tribolodon hakonensis) fish freshness degradation during storage. EEM of Japanese dace fish parts (intact eyeball and surface-containing scales), excitation from 220 to 585 nm and emissions from 250 to 600 nm, were measured at different times during storage. To simplify the acquired complex spectra datasets from each fish part, the variables were reduced to those that were only significant/important (those with higher positive or negative correlation) for K value prediction, and as an index of freshness. Partial least square regression (PLSR) results demonstrated that combining the fluorescence EEM of the eyeball and surface-containing scales the best monitoring of fish freshness; excitation at 280 and 350 nm for both the eyeball and surface-containing scales, with 2.84 and 0.96 as RMSE and R², respectively. These findings demonstrate that multiple excitation fluorescence approaches can be convenient for the freshness evaluation of fish.

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

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.