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Background: Recent developments in molecular genetics lead to addressing certain poultry diseases via breeding for disease resistance. The present study was carried to identify and compare the genetic polymorphism in Chicken Mx1 and TVB genes among the indigenous and Giriraja chicken using PCR-RFLP technique. Methods: Blood samples were collected from 50 indigenous and 50 Giriraja birds and DNA isolation was done by Phenol: Chloroform: Isoamyl alcohol method. PCR amplification of Chicken Mx1 (exon 14) and Chicken TVB (exon 3) genes was carried out followed by RFLP analysis. Result: PCR product sizes of 301 bp and 303 bp of Mx1 and TVB genes, respectively were successfully amplified. RFLP analysis of Mx1 gene with Hyp8I restriction enzyme revealed three genotypes AA, AB and BB. In indigenous birds genotypic frequencies of AA, AB and BB were 0.314, 0.493 and 0.194, respectively and gene frequencies were 0.56 and 0.44 for alleles A and B, respectively. In Giriraja birds, genotypic frequencies for AA, AB and BB were 0.27, 0.499 and 0.23, respectively and gene frequencies were 0.52 and 0.48 for alleles A and B, respectively. RFLP analysis of TVB gene with NlaIII restriction enzyme revealed two genotypes viz., AA and AB. In indigenous birds genotypic frequencies of AA and AB were 0.81 and 0.18, respectively and gene frequencies were 0.9 and 0.1 for alleles A and B, respectively. In Giriraja birds genotypic frequencies for AA and AB were 0.774 and 0.211, respectively and gene frequencies were 0.88 and 0.12 for alleles A and B, respectively. [ABSTRACT FROM AUTHOR]

Background: The total volatile basic nitrogen (TVB-N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB-N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB-N, in this article, a self-developed electronic nose (e-nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB-N value was established to complete the freshness detection., Results: The TVB-N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short-term memory (LSTM) models, and finally, the final TVB-N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD-LSTM model optimized by SSA in predicting TVB-N. The coefficient of determination (R 2 ), the root-mean-squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%., Conclusions: This method improves the performance of e-nose in detecting the freshness of fish meal and provides a reference for the quality detection of e-nose in other materials. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Product quality is the physical condition, function, and nature of a product that can meet the tastes and needs of consumers, therefore there needs to be maximum efforts that can be made to maintain the quality of raw materials, especially fresh fish and the example is tuna fish. One of the parameters of fish chemical is total volatile base nitrogen (TVB-N). Testing the levels of TVB-N at the Technical Implementation Unit for Quality Testing and Development of Marine and Fishery Product Banyuwangi begins with sample characterization, including: complete weight, central temperature, pH and water content. The method used is guided by SNI 2354.8:2009 which consists of the preparation, distillation and titration stages. The standard level of TVB-N for fresh fish suitable for consumption according to SNI 2354.8:2009 is 20-30 mgN/100 g. The results of testing the levels of TVB-N of tuna fish samples ranged from 16.29-29.57 mgN/100 g, which means it is still within the acceptable level of consumption, that is suitable to SNI 2354.8:2009 is 20-30 mgN/100 g.

The edible coating is one alternative that has the potential to extend the shelf life of sausages. This study aimed to determine the effect of the type of packaging and the storage times of beef sausages at frozen storage. The study using the factorial completely randomized design (CRD) with two factors. The first factor is the type of packaging (K1: edible coating durian seeds starch-chitosan; K2: edible coating durian seeds starch-chitosan with kesum leave extract; K3: plastic LDPE). The second factor is storage time (L0: 0 months; L1: 1 month; L2: 2 months; L3: 3 months; L4: 4 months) with four replication. The research variable observed was the sausage quality like TPC (Total Plate Count) and TVB-N (Total Volatile Bases). The result showed that the treatment of type packaging, storage time, and the interaction had a very significant effect on TPC with the lowest value K3L0 (2.31 x 104 CFU/g) and the highest value K1L4 (11.79 x 104 CFU/g). And, it had a significant effect on TVB-N with the lowest K1L0 (9.19 mg/N/100 g) and the highest value K1L4 (50.93 mg/N/100 g). Â Edible coating of durian-chitosan seed starch with kesum leaf extract can extend the shelf life of beef sausage for four months at freezing temperature based on the TPC value of 9.65.104 cfu/g and TVB-N value 23.96 mg N/100 g.

Penaeus vannamei is an important farm-raised shrimp species in China. However, the shrimp deteriorates quickly and has a short shelf life. The surface colour will change with the increase of spoilage, which can reflect its freshness. Therefore, a method for predicting Total Volatile Basic Nitrogen (TVB-N) content using a combination of a chromatic value and Near-Infrared (NIR) spectra is provided. This study explores which chromatic parameter is most efficient for freshness prediction from a data analysis perspective. The Levenberg–Marquardt Optimized Artificial Neural Network (LM-Optimised ANN), the Quadratic Support Vector Machine (Quadratic SVM), and Partial Least Squares Regression (PLSR) algorithms verified the idea. The combination of spectra and chromatic value b* can achieve a more accurate prediction of TVB-N content with RMSEP of 1.660 in Quadratic SVM, 1.337 in PLSR, and R pred 2 of 0.964 in LM-Optimised ANN, better than for other methods. As the polyphenol oxidase blackens the colour of the shrimp, the fusion of NIR spectra and b* can improve the prediction of TVB-N content with fewer parameters. Finally, we constructed an easy and fast TVB-N content prediction system. [ABSTRACT FROM AUTHOR]

Total volatile basic nitrogen (TVB-N) is one of the key indicators for assessing fish freshness. This research employed near-infrared (NIR) and Raman spectroscopy methods to detect the TVB-N content in snakehead fillets. We extracted feature variables associated with TVB-N from NIR and Raman spectroscopy using Variable Crossover Point Arithmetic - Improved Reduced-Input Vector (VCPA-IRIV). Using these features, we established partial least squares (PLS) and One-dimensional Convolutional Neural Network (1D-CNN) models. Subsequently, data fusion strategies were employed to predict the TVB-N content. Notably, feature-level fusion in conjunction with Bayesian-optimized 1D-CNN, reached the best results, as evidenced by calibration and predictive correlation coefficients of 0.9677 and 0.9676 for TVB-N. These findings underscore the effectiveness of both NIR and Raman spectroscopy in evaluating fish freshness. The fusion of these two vibrational spectroscopy techniques enables a more rapid, efficient and comprehensive quantification of fish freshness., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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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., (© 2023 Society of Chemical Industry.)

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 x 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. [ABSTRACT FROM AUTHOR]

In order to study the variation patterns of the main spoilage bacteria and total volatile basic nitrogen (TVB-N) content of fresh pork with time at different storage temperatures, and to find out their variation function models. Four storage temperature groups, namely (-3±0.5)°C, (0±0.5)°C, (4±0.5)°C, and (10±0.5)°C, were selected, and 7 different storage time, including 0 h, 10 h, 20 h, 34 h, 48 h, 72 h, and 96 h were set in each group, to detect six microbial indexes such as the total number of bacterial colonies and, Escherichia coli, and TVB-N content of fresh pork. Paired T-tests were conducted on the time series values of each index among the four storage temperature groups, and a logistic growth curve function model was established. The results showed that there were extremely significant differences between various indicators of fresh pork in different storage temperature groups. Each observation index had a highly significant logistic growth curve function with the independent variable time t at different storage temperatures, and the coefficient of determination R² was greater than 0.9. Taking the total volatile salt base nitrogen content as the index, it is theoretically concluded that fresh meat storage should not exceed 76 h at (0±0.5)°C, while the storage time for sub-fresh meat should be between 76 h to 121 h. Comprehensive analysis, it is recommended to store fresh pork at (0±0.5)°C, and storage time should not exceed 121 h. The research results can provide reference for the storage and preservation of fresh pork. [ABSTRACT FROM AUTHOR]