Developing a machine vision system for simultaneous prediction of freshness indicators based on tilapia ( Oreochromis niloticus ) pupil and gill color during storage at 4 °C

Antibacterial activity of zinc oxide nanoparticles against Shewanella putrefaciens and its application in preservation of large yellow croaker (Pseudosciaena crocea)

Specific spoilage organisms (SSOs) are the key factors affecting the deterioration of large yellow croaker. This study investigated the antibacterial activity and mechanism of Zinc oxide nanoparticles (ZnO-NPs) against Shewanella putrefaciens. The effects of different concentrations of ZnO-NPs (0.5, 1, 2 mg/mL) combined with seawater slurry ice preservation on storage quality and microbial community of large yellow croaker were further investigated. The results showed that ZnO-NPs had a strong antibacterial effect on Shewanella putrefaciens, which destroyed the integrity of the cell membrane, resulting in nucleic acid leakage and increased electrical conductivity. In addition, ZnO-NPs could effectively inhibit the proliferation of microorganisms, slow down the rate of lipid oxidation, delay the rise of pH value and total volatile basic nitrogen, and maintain the color of fish. Among them, 2 mg/mL ZnO-NPs treatment showed the best preservation effect on large yellow croaker. High-throughput sequencing results showed that Pseudoalteromonas and Shewanella became the dominant spoilage bacteria with the extension of storage time. ZnO-NPs significantly reduced the relative abundance of dominant spoilage bacteria and changed the microbial composition of fish. Inhibition of the growth of SSOs was important for delaying spoilage and prolonging the shelf-life of large yellow croaker. Therefore, ZnO-NPs combined with seawater slurry ice preservation could be used as a new storage method, which provides a new idea for food quality and safety control.

Nondestructive freshness prediction of large yellow croaker (Pseudosciaena crocea) using computer vision and machine learning techniques based on pupil color

Conventional methods for evaluating of fish freshness based on physiological and biochemical methods are often destructive, complicated, and costly. This study aimed to predict the freshness of large yellow croaker which was sampled every second day in 9 consecutive days at 4°C, using computer vision technology combined with pupil color parameters and different machine learning algorithms (back propagation neural network, BPNN; radial basis function neural network; support vector regression; and random forest regression, RFR). In the process of model building, the RFR model provided the most accurate prediction for the value of total volatile basic nitrogen (TVB-N), with the R-square of the test set (R p 2 $R^{2}_p $ ) of 0.993. The BPNN model exhibited the best fit for predicting the value of thiobarbituric acid (TBA), withR p 2 $R^{2}_p $ of 0.959. Additionally, the RFR model was the most effective in forecasting total viable count (TVC), withR p 2 $R^{2}_p $ of 0.935. After validation, the root mean square error values of the RFR model for predicting TVB-N value, TBA value, and TVC value were the lowest, which were 0.764, 0.067, and 0.219, respectively. It demonstrated the applicability and good predictive performance of the RFR model for predicting biochemical and microbiological indicators. These findings also demonstrated that monitoring the changes in pupil color could successfully predict the freshness of chilled fish. PRACTICAL APPLICATION: Quality inspectors detect changes in the freshness of large yellow croaker in real time from the beginning of distribution to the selling site.

Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning

BACKGROUND

It is important to monitor and control the moisture content throughout the Tencha drying processing procedure so that its quality is ensured. Workers often rely on their senses to perceive the moisture content, leading to relative subjectivity and low reproducibility. Traditional drying methods, which are used for measuring moisture content, are destructive to samples. This research was conducted using computer vision combined with deep learning to detect moisture content during the Tencha drying process. Different color space components of Tencha drying sample images were first extracted by computer vision. The color components were preprocessed using MinMax and Z score. Subsequently, one-dimensional convolutional neural networks (1D-CNN), partial least squares, and backpropagation artificial neural networks models were built and compared.

RESULTS

The 1D-CNN model and Z score preprocessing achieved superior predictive accuracy, with correlation coefficient of prediction (Rp) = 0.9548 for moisture content. The migration of moisture content during the Tencha drying process was eventually visualized by mapping its spatial and temporal distributions.

CONCLUSION

The results indicated that computer vision combined with 1D-CNN was feasible for moisture prediction during the Tencha drying process. This study provides technical support for the industrial and intelligent production of Tencha. © 2024 Society of Chemical Industry.

Current applications and future trends of artificial senses in fish freshness determination: A review

Fish is a highly demanding food product and the determination of fish freshness is crucial as it is a fundamental factor in fish quality. Therefore, the fishery industry has been working on developing rapid fish freshness determination methods to monitor freshness levels. Artificial senses that mimic human senses are developed as convenient emerging technologies for fish freshness determination. Computer vision, electronic nose (e-nose), and electronic tongue (e-tongue) are the emerging artificial senses for fish freshness determination. This review article is uniquely worked upon to investigate the current applications of the artificial senses in fish freshness determination while describing the steps, and fundamental principles behind each artificial sense, comparing them with their advantages and limitations, and future trends related to fish freshness determination. Among the artificial senses, computer vision determines the freshness of fish in a completely nondestructive way while the e-tongue determines the freshness of fish in a completely destructive way. There are developed e-noses for fish freshness determination in both destructive and nondestructive ways. By analyzing visual cues such as color, computer vision systems can assess fish quality without the need for physical contact and it makes computer vision suitable for large-scale industrial fish quality assessing applications. Overall, this review study reveals artificial senses as a proven replacement for traditional sensory panels in determining fish freshness precisely and conveniently. As future trends, there is a demand for developing applications for consumers to determine fish freshness based on artificial senses.

Current State of Milk, Dairy Products, Meat and Meat Products, Eggs, Fish and Fishery Products Authentication and Chemometrics

Food fraud is a matter of major concern as many foods and beverages do not follow their labelling. Because of economic interests, as well as consumers' health protection, the related topics, food adulteration, counterfeiting, substitution and inaccurate labelling, have become top issues and priorities in food safety and quality. In addition, globalized and complex food supply chains have increased rapidly and contribute to a growing problem affecting local, regional and global food systems. Animal origin food products such as milk, dairy products, meat and meat products, eggs and fish and fishery products are included in the most commonly adulterated food items. In order to prevent unfair competition and protect the rights of consumers, it is vital to detect any kind of adulteration to them. Geographical origin, production methods and farming systems, species identification, processing treatments and the detection of adulterants are among the important authenticity problems for these foods. The existence of accurate and automated analytical techniques in combination with available chemometric tools provides reliable information about adulteration and fraud. Therefore, the purpose of this review is to present the advances made through recent studies in terms of the analytical techniques and chemometric approaches that have been developed to address the authenticity issues in animal origin food products.

Insights into electrospun pullulan-carboxymethyl chitosan/PEO core-shell nanofibers loaded with nanogels for food antibacterial packaging

Nisin, a natural substance from Lactococcus lactis, displays a promising antibacterial ability against the gram-positive bacteria. However, it is susceptible to the external environment, i.e. temperature, pH, and food composition. In this study, a dual stabilization method, coaxial electrospinning, was applied to protect nisin in food packaging materials and the effect of nisin concentration on the properties of the nanofibers was investigated. The core-shell nanofibers with pullulan as a core layer and carboxymethyl chitosan (CMCS)/polyethylene oxide (PEO) as shell layer were prepared, and then the prepared CMCS-nisin nanogels (CNNGs) using a self-assembly method were loaded into the core layer of the nanofibers as antibacterial agents. The result revealed that the smooth surface can be observed on the nanofibers by microstructure characterization. The CNNGs-loaded nanofibers exhibited enhanced thermal stability and mechanical strength, as well as excellent antibacterial activity. Importantly, the as-formed nanofibers were applied to preserve bass fish and found that the shelf life of bass fish packed by CNNGSs with nisin at a concentration of 8 mg/mL was effectively extended from 9 days to 15 days. Taken together, the CNNGs can be well stabilized with the core-shell nanofibers, thus exerting significantly improved antimicrobial stability and bioactivity. This special structure exerts a great potential for application as food packaging materials to preserve aquatic products.

A comprehensive review of the principles, key factors, application, and assessment of thawing technologies for muscle foods

For years, various thawing technologies based on pressure, ultrasound, electromagnetic energy, and electric field energy have been actively investigated to minimize the amount of drip and reduce the quality deterioration of muscle foods during thawing. However, existing thawing technologies have limitations in practical applications due to their high costs and technical defects. Therefore, key factors of thawing technologies must be comprehensively analyzed, and their effects must be systematically evaluated by the quality indexes of muscle foods. In this review, the principles and key factors of thawing techniques are discussed, with an emphasis on combinations of thawing technologies. Furthermore, the application effects of thawing technologies in muscle foods are systematically evaluated from the viewpoints of eating quality and microbial and chemical stability. Finally, the disadvantages of the existing thawing technologies and the development prospects of tempering technologies are highlighted. This review can be highly instrumental in achieving more ideal thawing goals.

Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.

Quality changes and deterioration mechanisms in three parts (belly, dorsal and tail muscle) of tilapia fillets during partial freezing storage

The quality changes in tilapia belly muscle (BM), dorsal muscle (DM) and tail muscle (TM) were studied and the hypothesis of browning of the fillets was revealed during partial freezing. Compared with DM and TM groups, BM samples had higher thiobarbituric acid reactive substances (TBARS) (0.41 mg malondialdehyde eq/kg at 49 d) and K values (61.81% at 42 d) (P < 0.05). The microstructure of the BM group deteriorated most obviously during storage. Therefore, the BM group was considered to be the fastest to oxidize and deteriorate. In addition, 54 different micromolecular metabolites were identified from tilapia fillets by UHPLC-Q-TOF-MS analysis, and there were significant differences in the micromolecular metabolites in the three parts of tilapia. Therefore, proteins and lipids were degraded by the action of enzymes and microorganisms to produce some amines and small molecular acids, leading to the deterioration of the quality of tilapia fillets.

Monitoring dynamic changes in chicken freshness at 4 °C and 25 °C using pH-sensitive intelligent films based on sodium alginate and purple sweet potato peel extracts

A pH-sensitive intelligent indicator film was developed and used for monitoring dynamic changes in chicken freshness at 4 °C and 25 °C by immobilizing 0.2 %-1.0 % purple sweet potato peel extracts (PPE) with sodium alginate (SA). The films presented a wide range of colors from red-pink to green-yellow at 2-13, and the films with less PPE were more sensitive to ammonia. The color of films with 0.6 % PPE changed from pink to blue when used in monitoring chicken freshness at 4 °C (5 d) and 25 °C (60 h), which corresponded to changes in total volatile base nitrogen from 5.35 (5.35) mg/100 g to 16.2 (19.9) mg/100 g. Scanning electron microscopy and X-ray diffraction revealed that PPE improved the compactness and crystallinity of SA films, while Fourier transform infrared spectroscopy revealed hydrogen bonds between SA and PPE. Compared to SA films, the water vapor and light barrier abilities of films with 0.6 % were significantly improved (P < 0.05), there was no significant effect on tensile strength (P > 0.05), and the elongation of 0.6 % PPE films (P < 0.05) was decreased. Thus, PPE can serve as an excellent indicator of intelligent films for monitoring the freshness of meat products.

EGCG-gelatin biofilm improved the protein degradation, flavor and micromolecule metabolites of tilapia fillets during chilled storage

The protein degradation, flavor and micromolecule metabolites changes of (-)-epigallocatechin gallate (EGCG)-gelatin biofilm treatment (EGT) on chilled tilapia fillets in 21 days were investigated. Morphology observations revealed EGT protected good connective myofibrillar protein. It maintained protein secondary structure by significantly increasing the proportion of α-helix (15.20%) and decreasing the ratio of random coils (22.02%) in the EGT group compared to the control (CON) group (P < 0.05). Metabolomics with UHPLC-Q-TOF/MS analysis indicated a distinct separation between the CON and treatment groups at the end of storage. Small peptides analysis demonstrated that the EGT group increased the level of sweet peptides. Additionally, the EGT group significantly reduced the formation of amino acid derivatives and esters and off-flavor development. Overall, EGT effectively improved flavor, inhibited fish protein oxidation/degradation, and verified metabolomics results. This study unveiled the potential of metabolomics to analyze metabolites determined by tilapia and monitor the changes during storage.

Establish intelligent detection system to evaluate the sugar smoking of chicken thighs

The objective of this study was to establish a standardized color detection method to achieve low-cost, rapid, nonintrusive and accurate characterization of the color change of smoked chicken thighs during the smoking process. This study was based on machine vision technology using the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm to establish 3 colorimetric cards for the color of sugar-smoked chicken thighs. The accuracy of the 3 colorimetric cards was verified by the K-medoids algorithm and sensory analysis, respectively. Results showed that all 3 colorimetric cards had significant color gradient changes. From the K-medoids algorithm, the accuracy of the colorimetric card produced by the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm was 87.2, 95.1, and 96.7%, respectively. Meanwhile, the verification results of the sensory analysis showed that the accuracy of the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm colorimetric card was 69.4, 80.9, and 79.2%, respectively. A comparative analysis found that the colorimetric cards produced by the K-means algorithm and K-means algorithm + image noise reduction have excellent accuracy. These 2 colorimetric cards could become a suitable method for rapid, low-cost, and accurate online color monitoring of smoked chicken.

Comparison of freshness prediction method for salmon fillet during different storage temperatures

BACKGROUND

Many new forecasting models have been applied to fish freshness prediction like support vector regression (SVR) and radial basis function neural network (RBFNN). In this study, RBFNN, SVR, and Arrhenius models were established and compared for predicting and evaluating the quality of salmon fillets during storage at different temperatures, based on thiobarbituric acid (TBA), total volatile basic nitrogen (TVB-N), total viable counts (TVCs), K value, and sensory assessment (SA).

RESULTS

The TBA, TVB-N, TVC, and K values increased during storage whereas SA decreased. Residuals of the three models are random and irregular, indicating that these models were suitable for predicting the freshness of salmon fillets. The RBFNN predicted quality of salmon fillets stored at different temperatures with relative errors all within ±5% (except for the TVC value at day 6). Relative errors of the SVR model for predicting TVB-N and K value were within 10%, while the relative errors of the Arrhenius model fluctuated greatly (ranging from ±0.46 to ±38.29%) and most of it exceeded 10%. RBFNN model had the best predictive performance by comparing the residual and relative errors of the three models.

CONCLUSION

RBFNN is a promising method for predicting the freshness of salmon fillets stored at -2 to 10 °C in the cold chain. © 2021 Society of Chemical Industry.

Accurate prediction and genome-wide association analysis of digital intramuscular fat content in longissimus muscle of pigs

Intramuscular fat (IMF) content is a critical indicator of pork quality that affects directly the purchasing desire of consumers. However, to measure IMF content is both laborious and costly, preventing our understanding of its genetic determinants and improvement. In the present study, we constructed an accurate and fast image acquisition and analysis system, to extract and calculate the digital IMF content, the proportion of fat areas in the image (PFAI) of the longissimus muscle of 1709 animals from multiple pig populations. PFAI was highly significantly correlated with marbling scores (MS; 0.95, r²  = 0.90), and also with IMF contents chemically defined for 80 samples (0.79, r²  = 0.63; more accurate than direct analysis between IMF contents and MS). The processing time for one image is only 2.31 s. Genome-wide association analysis on PFAI for all 1709 animals identified 14 suggestive significant SNPs and 1 genome-wide significant SNP. On MS, we identified nine suggestive significant SNPs, and seven of them were also identified in PFAI. Furthermore, the significance (-log P) values of the seven common SNPs are higher in PFAI than in MS. Novel candidate genes of biological importance for IMF content were also discovered. Our imaging systems developed for prediction of digital IMF content is closer to IMF measured by Soxhlet extraction and slightly more accurate than MS. It can achieve fast and high-throughput IMF phenotype, which can be used in improvement of pork quality.

Hyperspectral imaging in combination with data fusion for rapid evaluation of tilapia fillet freshness

The potential of two different hyperspectral imaging systems (visible near infrared spectroscopy (Vis-NIR) and NIR) was investigated to determine TVB-N contents in tilapia fillets during cold storage. With Vis-NIR and NIR data, calibration models were established between the average spectra of tilapia fillets in the hyperspectral image and their corresponding TVB-N contents and optimized with various variable selection and data fusion methods. Superior models were obtained with variable selection methods based on low-level fusion data when compared with the corresponding methods based on single data blocks. Mid-level fusion data achieved the best model based on CARS, in comparison with all others. Finally, the respective optimized models of single Vis-NIR and NIR data were employed to visualize TVB-N contents distribution in tilapia fillets. In general, the results showed the great feasibility of hyperspectral imaging in combination with data fusion analysis in the nondestructive evaluation of tilapia fillet freshness.

Total volatile basic nitrogen and trimethylamine in muscle foods: Potential formation pathways and effects on human health

The use of total volatile basic nitrogen (TVB-N) as a quality parameter for fish is rapidly growing to include other types of meat. Investigations of meat quality have recently focused on TVB-N as an index of freshness, but little is known on the biochemical pathways involved in its generation. Furthermore, TVB-N and methylated amines have been reported to exert deterimental health effects, but the relationship between these compounds and human health has not been critically reviewed. Here, literature on the formative pathways of TVB-N has been reviewed in depth. The association of methylated amines and human health has been critically evaluated. Interventions to mitigate the effects of TVB-N on human health are discussed. TVB-N levels in meat can be influenced by the diet of an animal, which calls for careful consideration when using TVB-N thresholds for regulatory purposes. Bacterial contamination and temperature abuse contribute to significant levels of post-mortem TVB-N increases. Therefore, controlling spoilage factors through a good level of hygiene during processing and preservation techniques may contribute to a substantial reduction of TVB-N. Trimethylamine (TMA) constitutes a significant part of TVB-N. TMA and trimethylamine oxide (TMA-N-O) have been related to the pathogenesis of noncommunicable diseases, including atherosclerosis, cancers, and diabetes. Proposed methods for mitigation of TMA and TMA-N-O accumulation are discussed, which include a reduction in their daily dietary intake, control of internal production pathways by targeting gut microbiota, and inhibition of flavin monooxygenase 3 enzymes. The levels of TMA and TMA-N-O have significant health effects, and this should, therefore, be considered when evaluating meat quality and acceptability. Agreed international values for TVB-N and TMA in meat products are required. The role of feed, gut microbiota, and translocation of methylated amines to muscles in farmed animals requires further investigation.

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.

Battery-Free and Noninvasive Estimation of Food pH and CO2 Concentration for Food Monitoring Based on Pressure Measurement

In this paper, we developed a battery-free system that can be used to estimate food pH level and carbon dioxide (CO₂) concentration in a food package from headspace pressure measurement. While being stored, food quality degrades gradually as a function of time and storage conditions. A food monitoring system is, therefore, essential to prevent the detrimental problems of food waste and eating spoilt food. Since conventional works that invasively measure food pH level and CO₂ concentration in food packages have shown several disadvantages in terms of power consumption, system size, cost, and reliability, our study proposes a system utilizing package headspace pressure to accurately and noninvasively extract food pH level and CO₂ concentration, which reflection food quality. To read pressure data in the food container, a 2.5 cm × 2.5 cm smart sensor tag was designed and integrated with near-field communication (NFC)-based energy harvesting technology for battery-free operation. To validate the reliability of the proposed extraction method, various experiments were conducted with different foods, such as pork, chicken, and fish, in two storage environments. The experimental results show that the designed system can operate in a fully passive mode to communicate with an NFC-enabled smartphone. High correlation coefficients of the headspace pressure with the food pH level and the headspace CO₂ concentration were observed in all experiments, demonstrating the ability of the proposed system to estimate food pH level and CO₂ concentration with high accuracy. A linear regression model was then trained to linearly fit the sensor data. To display the estimated results, we also developed an Android mobile application with an easy-to-use interface.

Fish Quality Index Method: Principles, weaknesses, validation, and alternatives-A review

Fish is a high nutritional value matrix of which production and consumption have been increasing in the last years. Advancements in the efficient evaluation of freshness are essential to optimize the quality assessment, to improve consumer safety, and to reduce raw material losses. Therefore, it is necessary to use rapid, nondestructive, and objective methodologies to evaluate the quality of this matrix. Quality Index Method (QIM) is a tool applied to indicate fish freshness through a sensory evaluation performed by a group of assessors. However, the use of QIM as an official method for quality assessment is limited by the protocol, sampling size, specificities of the species, storage conditions, and assessor's experience, which make this method subjective. Also, QIM may present divergences regarding the development of microorganisms and chemical analysis. In this way, novel quality evaluation methods such as electronic noses, electronic tongues, machine vision system, and colorimetric sensors have been proposed, and novel technologies such as proteomics and mitochondrial analysis have been developed. In this review, the weaknesses of QIM were exposed, and novel methodologies for quality evaluation were presented. The consolidation of these novel methodologies and their use as methods of quality assessment are an alternative to sensory methods, and their understanding enables a more effective fish quality control.

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.

Prediction of various freshness indicators in fish fillets by one multispectral imaging system

In current study, a simple multispectral imaging (430–1010 nm) system along with linear and non-linear regressions were used to assess the various fish spoilage indicators during 12 days storage at 4 ± 2 °C. The indicators included Total-Volatile Basic Nitrogen (TVB-N) and Psychrotrophic Plate Count (PPC) and sensory score in fish fillets. immediately, after hyperspectral imaging, the reference values (TVB-N, PPC and sensory score) of samples were obtained by traditional method. To simplify the calibration models, nine optimal wavelengths were selected by genetic algorithm. The prediction performance of various chemometric models including partial least-squares regression (PLSR), multiple-linear regression (MLR), least-squares support vector machine (LS-SVM) and back-propagation artificial neural network (BP-ANN) were compared. All models showed acceptable performance for simultaneous predicting of PPC, TVB-N and sensory score (R²_P ≥ 0.853 and RPD ≥ 2.603). Non-linear models were considered better quantitative model to predict all of three freshness indicators in fish fillets. Among the three spoilage indices, the best predictive power was obtained for PPC value and the weakest one was acquired for TVB-N content prediction. The best model for prediction TVB-N (R²_p = 0.862; RMSEP = 3.542 and RPD = 2.678) and sensory score (R²_p = 0.912; RMSEP = 1.802 and RPD = 3.33) belonged to GA-LS-SVM and for prediction of PPC value was BP-ANN (R²_p = 0.921; RMSEP = 0.504 and RPD = 3.64). Therefore, developing multispectral imaging system based on LS-SVM model seems to be suitable for simultaneous prediction of all three indicators (R²_P > 0.862 and RPD > 2.678). Further studies needed to improve the accuracy and applicability of HSI system for predicting freshness of rainbow-trout fish.

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

Non destructive monitoring of the yoghurt fermentation phase by an image analysis of laser-diffraction patterns: Characterization of cow's, goat's and sheep's milk

Monitoring yogurt fermentation by the image analysis of diffraction patterns generated by the laser-milk interaction was explored. Cow's, goat's and sheep's milks were tested. Destructive physico-chemical analyses were done after capturing images during the processes to study the relationships between data blocks. Information from images was explored by applying a spectral phasor from which regions of interest were determined in each image channel. The histograms of frequencies from each region were extracted, which showed evolution according to textural modifications. Examining the image data by multivariate analyses allowed us to know that the captured variance from the diffraction patterns affected both milk type and texture changes. When regression studies were performed to model the physico-chemical parameters, satisfactory quantifications were obtained (from R² = 0.82 to 0.99) for each milk type and for a hybrid model that included them all. This proved that the studied patterns had a common fraction of variance during this processing, independently of milk type.