Prediction in the Dynamics and Spoilage of Shewanella putrefaciens in Bigeye Tuna (Thunnus obesus) by Gas Sensors Stored at Different Refrigeration Temperatures

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Changes in the Quality and Microflora of Yellowtail Seriola quinqueradiata Muscles during Cold Storage

We evaluated the changes in the quality and microflora of yellowtail flesh cold-stored until spoilage. Based on the sensory evaluation, odor palatability was deemed unacceptable for dark muscle (DM) and the dorsal part of the ordinary muscle (OD) after >10 days and 14 of storage, respectively. Log 7 CFU/g in DM as well as OD was obtained on days 10 (Aeromonas spp.) and 14 (Enterobacteriaceae and Pseudomonas spp.) of storage, whereas log 5 (Brocothrix thermosphacta) and 6 (H2S-producing bacteria) CFU/g in them were obtained on day 14 of storage. In these bacteria, the viable bacterial counts of Pseudomonas spp. and Aeromonas spp. in DM were significantly higher than those in OD only at some storage times. Amplicon sequencing revealed that in both muscles, Pseudomonas became predominant after storage, with greater than 90% recorded after more than 10 days of storage. The relative abundances of Acinetobacter, Unclassified Gammaproteobacter, and Shewanella were relatively high in both muscles after more than 10 days of storage; however, these values were less than 5%. Ethyl butyrate in the OD and DM and 2,3-butanedione in the OD were first detected on days 14 and 10 of storage, respectively. Acetoin in the OD increased by 81-fold after 14 days of storage and was significantly increased in the DM after more than 10 days compared with the amount detected pre-storage. Volatiles, such as (E)-2-pentenal in the OD and 1-pentanol in the DM, decreased and increased linearly, respectively, throughout the 14-day storage period. Altogether, these volatile components may cause quality deterioration due to spoilage and/or lipid oxidation during cold storage of the OD and DM.

Quantification of Lactobacillus reuteri ProTectis in MRS Broth Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy and Chemometrics

There is an increasing global demand for probiotics because of their numerous health benefits. However, a significant percentage of commercially available probiotic products have microbial quantities that are not in accordance with their product labels. In quantifying bacteria, the viable plate count is the standard method but is considered laborious and time-consuming. We demonstrate the use of an attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics to quantify a pure culture of Lactobacillus reuteri (L. reuteri) ProTectis grown in deMan, Rogosa, and Sharpe broth. The chemometric partial least-squares model generated was able to accurately quantify viable plate count (VPC) (root-mean-square error of cross-validation (RMSECV) = 0.115 log CFU mL-1, root-mean-square error of prediction (RMSEP) = 0.145 log CFU mL-1, R2 = 0.982). These results provide proof of concept for this quantification technique and can potentially be developed and applied for the quantification of L. reuteri ProTectis in food products.

Detection of Mildewed Nutmeg Internal Quality during Storage Using an Electronic Nose Combined with Chemical Profile Analysis

Internal mildewed nutmeg is difficult to perceive without cutting the nutmeg open and examining it carefully, which poses a significant risk to public health. At present, macroscopic identification and chromatographic analysis are applied to determine whether nutmeg is moldy or not. However, the former relies on a human panel, with the disadvantages of subjectivity and empirical dependence, whilst the latter is generally time-consuming and requires organic solvents. Therefore, it is urgent to develop a rapid and feasible approach for evaluating the quality and predicting mildew in nutmeg. In this study, the quality and odor characteristics of five groups of nutmeg samples with different degrees of mildew were analyzed by using the responses of an electronic nose combined with chemical profiling. The main physicochemical indicators, such as the levels of α-pinene, β-pinene, elemicin, and dehydro-di-isoeugenol, were determined. The results revealed that the contents of α-pinene, β-pinene, and elemicin changed significantly with the extension of storage time. Through the use of an electronic nose and HS-GC-MS technology to assess the overall odor characteristics of nutmeg samples, it was found that the production of volatile organic compounds (VOCs) such as ammonia/organic amines, carbon monoxide, ethanol, and hydrogen sulfide, as well as changes in the terpene and phenylpropene components of the nutmeg itself, may be the material basis for the changes in odor. The accuracy of the qualitative classification model for the degree of mildew in nutmeg was higher than 90% according to the electronic nose data combined with different machine learning algorithms. Quantitative models were established for predicting the contents of the chemical components, and models based on a BP neural network (BPNN), the support vector machine (SVM), and the random forest algorithm (RF) all showed good performance in predicting the concentrations of these chemical components, except for dehydro-di-isoeugenol. The BPNN performed effectively in predicting the storage time of nutmeg on the basis of the E-nose's responses, with an RMSE and R2 of 0.268 and 0.996 for the training set, and 0.317 and 0.993 for the testing set, respectively. The results demonstrated that the responses of the electronic nose (E-nose) had a high correlation with the internal quality of nutmeg. This work proposes a quick and non-destructive evaluation method for the quality of nutmeg, which has high accuracy in discriminating between different degrees of mold in nutmeg and is conducive to early detection and warning of moldy phenomena.

Dynamic Modelling and Simulation of Food Systems: Recent Trends and Applications

Several factors influence consumers' choices of food products [...].

Genomic Analysis of Two Representative Strains of Shewanella putrefaciens Isolated from Bigeye Tuna: Biofilm and Spoilage-Associated Behavior

Shewanella putrefaciens can cause the spoilage of seafood and shorten its shelf life. In this study, both strains of S. putrefaciens (YZ08 and YZ-J) isolated from spoiled bigeye tuna were subjected to in-depth phenotypic and genotypic characterization to better understand their roles in seafood spoilage. The complete genome sequences of strains YZ08 and YZ-J were reported. Unique genes of the two S. putrefaciens strains were identified by pan-genomic analysis. In vitro experiments revealed that YZ08 and YZ-J could adapt to various environmental stresses, including cold-shock temperature, pH, NaCl, and nutrient stresses. YZ08 was better at adapting to NaCl stress, and its genome possessed more NaCl stress-related genes compared with the YZ-J strain. YZ-J was a higher biofilm and exopolysaccharide producer than YZ08 at 4 and 30 °C, while YZ08 showed greater motility and enhanced capacity for biogenic amine metabolism, trimethylamine metabolism, and sulfur metabolism compared with YZ-J at both temperatures. That YZ08 produced low biofilm and exopolysaccharide contents and displayed high motility may be associated with the presence of more a greater number of genes encoding chemotaxis-related proteins (cheX) and low expression of the bpfA operon. This study provided novel molecular targets for the development of new antiseptic antisepsis strategies.

Freshness monitoring of raw fish by detecting biogenic amines using a gold nanoparticle-based colorimetric sensor array

We herein report the quantitative detection of biogenic amines using a gold nanoparticle-based colorimetric chemosensor array for food analysis. The gold nanoparticles are functionalized with carboxylate derivatives, which capture target amines through hydrogen bonds and electrostatic interactions. The simultaneous discrimination of 10 amine derivatives was achieved by a linear discriminant analysis with a 100% correct classification based on the multi-colorimetric response pattern of structural differences. Furthermore, a real sample analysis for raw fish (i.e., tuna) demonstrated highly accurate determination of histamine concentrations by a support vector machine, the result of which was matched with high-performance liquid chromatography. Most importantly, the chemosensor array succeeded in detecting the time-dependent concentration change of histamine in the raw fish, meaning that the decomposition of the fish could be monitored by the colorimetric changes. Hence, the proposed chemosensor array combined with pattern recognition techniques can be a user-friendly analytical method for food freshness monitoring.

A gold nanoparticle-based chemosensor array functionalized with carboxylate derivatives performed freshness monitoring of amines in a fish sample.