Freshness estimation of intact frozen fish using fluorescence spectroscopy and chemometrics of excitation–emission matrix

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.

Solid-phase fluorescence: Reproducibility and comparison with the solution states

Solid-phase fluorescence excitation-emission matrix (SPF-EEM) spectroscopy has potential for non-extractive, non-destructive, and non-contact analytical measurements of powder and solid-state samples, as well as front-face EEM spectroscopy for suspensions of high optical density. However, as there is no unified measurement method for SPF spectroscopy, comparing samples measured in different research fields is difficult. Therefore, this study designs a cell that can be created by a 3D printer and examines reproducibility on measuring fluorescent powders. The developed cell is applied to proteins (ovalbumin, BSA, gliadin, gluten, powdered collagen, casein), amino acids (tryptophan, tyrosine, and phenylalanine), soybean ingredients (daidzein, and genistein), and fluorescent chemicals (rhodamine B, fluorescein sodium salt, pyrene, and quinine sulfate dihydrate) and their spectra are compared with those in the solution states. When samples are refilled into the cell three times, the cell exhibits high reproducibility in terms of fluorescence peak wavelength and intensity. The solid proteins exhibit peaks attributed to the fluorescent amino acid residues, and broad peaks which are not detected for the proteins in the solution states. Powdered rhodamine B and fluorescein sodium salt do not exhibit fluorescence, possibly due to the inner-filter effect (IFE). Some non-colored molecules also exhibit loss of fluorescence or a remarkable difference between the solid and solution states, possibly due to the interaction of the fluorescent structure with the surrounding local environment, similar to the solvent effect, which is possibly affected by the molecular proximity, three-dimensional structure, and moisture absorption capacity.

Potential of fluorescence fingerprints for fish meat authentication: Differences in freshness evaluation in white and dark meat at frozen state

As dark meat has a faster deterioration rate and its unintentional mixing occurs during processing, it is crucial to know the status and freshness indicators of dark meat to ensure fishery product quality. In this method, fluorescence fingerprints (FFs) was applied as a rapid and noninvasive quality authentication method to determine differences between white and dark meat in the evaluation of freshness indicators at frozen state. Spotted mackerel (Scomber australasicus) fish chunks with different postmortem conditions (0-40 h ice stored) were obtained and frozen. A new generation of fluorescence spectrophotometer (F-7100) was used to acquire FFs of the frozen fish chunks (containing white and dark meat). Adenosine triphosphate metabolites and pH were determined in both white and dark meat using their relevant biochemical methods. Higher K-values in dark meat might be attributed to a higher accumulation rate of inosine (HxR) in dark meat than in white meat. The pH decrease rate in white meat was higher than that in dark meat during postmortem ice storage periods of fish. Principal component analysis of FFs spectra demonstrated clear discrimination (PC1 + PC2 = 91.7%) between white and dark meat of frozen fish due to the influence of freshness parameters based on the fluorescence features of fish meat. Furthermore, partial least squares regression validation models revealed that freshness indicators of white meat could be predicted more accurately at the frozen state than those of dark meat. This method could be applied during the processing of fishery products, thereby facilitating quality control activities and making it a promising authentication tool for the fisheries industries.

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence

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.

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.

Emerging Approach for Fish Freshness Evaluation: Principle, Application and Challenges

Affected by micro-organisms and endogenous enzymes, fish are highly perishable during storage, processing and transportation. Efficient evaluation of fish freshness to ensure consumer safety and reduce raw material losses has received an increasing amount of attention. Several of the conventional freshness assessment techniques have plenty of shortcomings, such as being destructive, time-consuming and laborious. Recently, various sensors and spectroscopic techniques have shown great potential due to rapid analysis, low sample preparation and cost-effectiveness, and some methods are especially non-destructive and suitable for online or large-scale operations. Non-destructive techniques typically respond to characteristic substances produced by fish during spoilage without destroying the sample. In this review, we summarize, in detail, the principles and applications of emerging approaches for assessing fish freshness including visual indicators derived from intelligent packaging, active sensors, nuclear magnetic resonance (NMR) and optical spectroscopic techniques. Recent developments in emerging technologies have demonstrated their advantages in detecting fish freshness, but some challenges remain in popularization, optimizing sensor selectivity and sensitivity, and the development of algorithms and chemometrics in spectroscopic techniques.

The investigation of storage situation of fish muscle via the analysis of its exudate by MALDI-TOF MS

The potential of MALDI-TOF MS was investigated in terms of its capability to determine the change of exudates of fish muscle with different freeze-thaw cycles (0, 1 and 2), and frozen storage periods. The exudates were collected from dead chilled marine fish species, large yellow croacker (Larimichthys crocea, LC) and freshly slaughtered freshwater fish species, Japanese seabass (Lateolabrax japonicus) to be studied as models. 109 proteins, in which, 32 are extracellular proteins, and 15 are intracellular proteins, were identified by analyzing exudate of LC using MALDI-TOF MS and HPLC-MS/MS. The results show that the present method may be able to determine the change of fish muscle foods in a more sensitive mode than K value indicated quality control. The feasibility of verifying the storage situation of fish sample was performed by analyzing fish samples obtained from the local market. It is promising to estimate the storage situation of fishery products or other animal muscle foods by analyzing their muscle exudates based on the presently developed strategy.

Nondestructive prediction of freshness for bighead carp (Hypophthalmichthys nobilis) head by Excitation-Emission Matrix (EEM) analysis based on fish eye fluid: Comparison of BPNNs and RBFNNs

This study established back-propagation neural networks (BPNNs) and radial basis function neural networks (RBFNNs) models for evaluating the freshness of bighead carp head storage at different temperatures via the characteristic components of Excitation-Emission Matrix (EEM). Two characteristic components of EEM data of fish eye fluid were extracted by parallel factor analysis (PARAFAC) and were the most efficient components to stimulate fluorophores responsible for fish freshness detection during variable temperatures. EEM-RBFNNs and EEM-BPNNs models based on characteristic components of EEM used to predict the fish freshness. The results demonstrated the relative errors of EEM-BPNNs models for hiobarbituric acid reactive substances (TBARS) and total viable bacteria count (TAC) prediction were less than 10% which were better than those of EEM-RBFNNs models. It indicated that EEM-BPNNs model of bighead carp eye fluid by PARAFAC has a high potential for predicting fish freshness under variable storage conditions.

Sensing Technology for Fish Freshness and Safety: A Review

Standard analytical methods for fish freshness assessment are based on the measurement of chemical and physical attributes related to fish appearance, color, meat elasticity or texture, odor, and taste. These methods have plenty of disadvantages, such as being destructive, expensive, and time consuming. All these techniques require highly skilled operators. In the last decade, rapid advances in the development of novel techniques for evaluating food quality attributes have led to the development of non-invasive and non-destructive instrumental techniques, such as biosensors, e-sensors, and spectroscopic methods. The available scientific reports demonstrate that all these new techniques provide a great deal of information with only one test, making them suitable for on-line and/or at-line process control. Moreover, these techniques often require little or no sample preparation and allow sample destruction to be avoided.

Rapid noninvasive monitoring of freshness variation in frozen shrimp using multidimensional fluorescence imaging coupled with chemometrics

Shrimp is one of the most delicious and popular food commodities worldwide due to its exceptional taste and characteristics. Freshness is considered as a key factor for shrimp consumers because freshness has a significant relationship with taste and shelf-life of shrimp. However, post-mortem metabolism of shrimp differs from that of fish as they are highly susceptible to post-harvest quality loss, and it is hard to distinguish the freshness variation of shrimp at frozen state instantly. Thus, instant monitoring of frozen shrimp freshness is challenging for the seafood and aquaculture industries and a reliable, expeditious, and noninvasive technique to estimate shrimp quality is in high demand. Accordingly, this study aimed to visualize changes in post-mortem freshness of frozen shrimp using multidimensional fluorescence imaging. Live coonstripe shrimp (Pandalus hypsinotus) were harvested and instantly killed by beheading, cooled on ice for 0, 6, 24, 48, 72 and 96 h (n = 8), followed by processing into frozen peeled deveined shrimp product and stored at -60 °C. 50% of frozen shrimp were analyzed for excitation-emission matrix (EEM), ATP-related compounds, and pH using a fiber optic supported fluorescence spectrophotometer (F-7100), high performance liquid chromatography (HPLC) and pH meter, respectively at each time point (n = 4). Then, fluorescence images were obtained from the remaining 50% of frozen shrimp (n = 4) by computer vision method equipped with a charge-coupled device (CCD) camera, MAX-303 xenon light source for an excitation light (Ex. 330 nm), and an automatic filter changer for emission band-pass filters (Em. 380-610 nm at 10 nm intervals). Chemical analysis of frozen shrimp revealed that K-value and pH of shrimp increased from 1.61 to 66.56% and 6.49-7.31, respectively, during storage on ice. Repeated partial least squares regression (PLSR) models of EEM for K-value prediction suggested an efficient excitation wavelength (330 nm) and its corresponding emission wavelengths (380-610 nm) to produce fluorescence images. Spatial-temporal changes of K-value and pH were visualized successfully in frozen shrimp by fluorescence imaging. K-value visualization was then validated effectively using another group of frozen shrimp (0-72 h ice stored) with different killing method (super chilling) and the prediction accuracy was R² = 0.80. This novel approach using a CCD camera coupled with EEM provides a state-of-the-art authentication method for practical assessment of frozen seafood freshness.

New method for rapid identification and quantification of fungal biomass using ergosterol autofluorescence

This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasound-assisted procedure showed the best results. Following an experimental design, various quantities of the dried mycelium of the fungus Schizophyllum commune were mixed with the starchy solid residue (BBR) from the babassu (Orbignya sp.) oil industry, and these samples were subjected to several ergosterol extraction methods. The EEM spectral data of the samples were subjected to Principal Component Analysis (PCA), which showed the possibility to qualitatively evaluate the presence of ergosterol in the samples by ergosterol autofluorescence without the addition of any reagent. In order to assess the feasibility of quantifying fungal biomass using ergosterol autofluorescence, the EEM spectral data and known amounts of fungal biomass were modeled using partial least squares (PLS) regression and a procedure of backward selection of predictors (AutoPLS) was applied to select the Excitation-Emission wavelength pairs that provide the lowest prediction error. The results revealed that the amount of fungal biomass in samples containing interfering substances (BBR) can be accurately predicted with R²CV = 0.939, R²P = 0.936, RPDcv = 4.07, RPDp = 4.06, RMSECV = 0.0731 and RMSEP = 0.0797. In order to obtain an easy-to-understand equation that expresses the relationship between fungal biomass and fluorescence intensity, multiple linear regression (MLR) was applied to the VIP variables selected by the AutoPLS method. The MLR model selected only 2 variables and showed a very good performance, with R²CV = 0.862, R²P = 0.809, RPDcv = 2.18, RPDp = 2.35, RMSECV = 0.137 and RMSEP = 0.138. This study demonstrated that ergosterol autofluorescence can be successfully used to quantify fungal biomass even when mixed with agroindustrial residues, in this case BBR.

A comprehensive review on freshness of fish and assessment: Analytical methods and recent innovations

Fish, a highly nutritious, containing a good amount of protein and fatty acids, has TMA and TVB-N present as major factors responsible for quality deterioration during storage and maintaining of fish freshness. Freshness is one of the most important parameters in the fish market. There are many methods of estimating fish freshness, out of which some are very costly while others are not user-friendly. However, with more technological innovations, there have been efforts to make a more reliable method of calculating and analyzing freshness. Parameters chosen for assessing the freshness are sensory, physical, chemical and microbiological including the recent trends such as SDS-PAGE, fast protein liquid chromatography, hyper Spectral Imaging Technique, etc. focused on reducing time, destruction and labor. Traditional and recent methods of evaluation of freshness along with their comparison based on several parameters are needed to link them and making it convenient for upcoming researchers to have a detailed study for having a universal indicator for assessing the freshness of fish. Information in the present article has all the methods of assessing the fish freshness been discussed in detail. There has also been focus on bringing the readers knowledge about the comprehensive information related to recent developments. The recommended limit for different indicators signifies the time period for which the particular fish can be stored and it depends upon several factors like species, surrounding environment and enzymatic and non-enzymatic actions. Based on these demands, this paper is uniquely worked upon to review the different literature which brought all the discussions from the past including the recent innovations in assessing the freshness of different fishes with the help of various indicators as well as a complete study of spoilage and toxicity mechanism leading to deterioration in quality, making it easy for the reader and researchers to have quick glance over the trends and innovations.

Applications of Photonics in Agriculture Sector: A Review

The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

Expeditious prediction of post-mortem changes in frozen fish meat using three-dimensional fluorescence fingerprints

The present study was conducted to characterize fluorophores in the fish body using three-dimensional fluorescence fingerprints (3D-FFs) and to utilize these 3D-FFs obtained from frozen horse mackerel (Trachurus japonicus) fillets to predict early post-mortem changes. Alive fish were sacrificed instantly, preserved in ice until 2 days, and then filleted, vacuum packed, and frozen. Subsequently, 3D-FFs of the frozen fillets were acquired using F-7000 aided with a fiber probe. Post-mortem freshness changes were tracked by measuring adenylate energy charge (AEC) values and nicotinamide adenine dinucleotide (NAD and NADH) content. Partial least squares regression models for predicting AEC values and NADH content in frozen fish meat showed good fittings, with R2 of 0.90 and 0.85, by utilizing eight and five excitation wavelengths, respectively, based on their fluorescence features acquired from standard fluorophores. This novel approach of 3D-FFs could be utilized as an efficient technique for at-line monitoring of frozen fish quality.

Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds

BACKGROUND

The traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. Whilst proven to be effective, these approaches can be criticized as being destructive, time consuming, labor intensive and requiring experienced seed analysts. Thus, the objective of this study was to investigate the potential of computer vision and multispectral imaging systems supported with multivariate analysis for high-throughput classification of cowpea (Vigna unguiculata) seeds. An automated computer-vision germination system was utilized for uninterrupted monitoring of seeds during imbibition and germination to identify different categories of all individual seeds. By using spectral signatures of single cowpea seeds extracted from multispectral images, different multivariate analysis models based on linear discriminant analysis (LDA) were developed for classifying the seeds into different categories according to ageing, viability, seedling condition and speed of germination.

RESULTS

The results revealed that the LDA models had good accuracy in distinguishing 'Aged' and 'Non-aged' seeds with an overall correct classification (OCC) of 97.51, 96.76 and 97%, 'Germinated' and 'Non-germinated' seeds with OCC of 81.80, 79.05 and 81.0%, 'Early germinated', 'Medium germinated' and 'Dead' seeds with OCC of 77.21, 74.93 and 68.00% and among seeds that give 'Normal' and 'Abnormal' seedlings with OCC of 68.08, 64.34 and 62.00% in training, cross-validation and independent validation data sets, respectively. Image processing routines were also developed to exploit the full power of the multispectral imaging system in visualizing the difference among seed categories by applying the discriminant model in a pixel-wise manner.

CONCLUSION

The results demonstrated the capability of the multispectral imaging system in the ultraviolet, visible and shortwave near infrared range to provide the required information necessary for the discrimination of individual cowpea seeds to different classes. Considering the short time of image acquisition and limited sample preparation, this stat-of-the art multispectral imaging method and chemometric analysis in classifying seeds could be a valuable tool for on-line classification protocols in cost-effective real-time sorting and grading processes as it provides not only morphological and physical features but also chemical information for the seeds being examined. Implementing image processing algorithms specific for seed quality assessment along with the declining cost and increasing power of computer hardware is very efficient to make the development of such computer-integrated systems more attractive in automatic inspection of seed quality.

Visualize the quality of frozen fish using fluorescence imaging aided with excitation-emission matrix

The quality monitoring of frozen marine products has become essential in the fishery industry, where efficient and effective quality assurance is becoming increasingly important. In this study, we proposed a novel method of evaluating fish quality by combining the fluorescence excitation-emission matrix (EEM) with imaging techniques to visualize the spatial-temporal changes of freshness indices such as K-value and taste component IMP content. The result showed that the distribution of K-value and IMP content could be visualized with accuracy of R² = 0.78 and R² = 0.83, respectively. Furthermore, this innovative approach was applied to differentiate burnt meat, which is a type of abnormal meat found in many types of fish, and it was found that burnt meat could be detected even when in a frozen condition.

Fluorescence Spectroscopy for the Monitoring of Food Processes

Different analytical techniques have been used to examine the complexity of food samples. Among them, fluorescence spectroscopy cannot be ignored in developing rapid and non-invasive analytical methodologies. It is one of the most sensitive spectroscopic approaches employed in identification, classification, authentication, quantification, and optimization of different parameters during food handling, processing, and storage and uses different chemometric tools. Chemometrics helps to retrieve useful information from spectral data utilized in the characterization of food samples. This contribution discusses in detail the potential of fluorescence spectroscopy of different foods, such as dairy, meat, fish, eggs, edible oil, cereals, fruit, vegetables, etc., for qualitative and quantitative analysis with different chemometric approaches.