Rapid Methods For Monitoring Fish Quality And Safety By Near-infrared And Surface-enhanced Raman Spectroscopic Techniques

Fish acts as an essential food and raw material for many regions across the globe.Given the enormous demand for fish for food,medicine,pharmaceutical,cosmetics,and other fields around the world,successful control of its quality and safety criteria is vital to maintaining public health,global trade,and a healthier climate.The quality and safety of fish have been estimated using traditional and analytical approaches.This research was motivated by the expectation that these methods can solve the specific safety and quality problems associated with fish.While these methods are highly sensitive,precise and accurate,they have the downside of being costly,environmentally unfriendly,requiring intricate sample pretreatments,and highly skilled workers.It is also time-consuming,making it impracticable for farmers to assess,leading to extreme gluts and reducing fish grading.The focus of this academic work was to established and utilize near-infrared techniques and SERS based nanoprobes fused with chemometric algorithms for rapid assessment of vital quality index of fish consisting of K-value,trimethylamine;and safety indices including heavy metals-mercury(Hg²⁺),antibiotic residue-norfloxacin and persistent organic pollutants-dieldrin geared towards guaranteeing safety and quality.The study was motivated with the hope that unique problems often faced in the fish food chain can be solved adequately by such systems.The key accomplishments of this academic contribution are outlined as follows:1.Fast detection of fish freshness index(k value)using near-infrared(NIR)spectroscopy technology.This technique was explored to address the problems of fish freshness detection,including complex methods,high cost,and field detection difficulty.In this chapter,the near-infrared spectra of fish with different freshness were obtained by a spectrometer.Afterward,the original spectrum was preprocessed by standard ortho Pacific variable transform(SNV)and multiple scattering correction(MSC)to improve the signal ratio.The collected fish's freshness index(k value)was determined by high-performance liquid chromatography(HPLC).Finally,the chemometric model of near-infrared spectroscopy and fish freshness index(k value)was constructed using variable selection and partial least squares regression.The results show that the Si-PLS model recorded the best detection effect.The correlation coefficient of the sample in the prediction set was Rp=0.9606,the root mean square error of prediction is RMSEP=3.8218,and the relative analysis error is RPD=3.8903 and a LOD of 0.0024%.The outcome of this study shows that the Si PLS chemometric model constructed by near-infrared spectroscopy can accurately and quickly detect the K value of fish freshness index.2.Fast determination of TMA-N concentration in fish using NIR spectroscopy technology.It is difficult to monitor the spoilage degree of fish in real-time and to use traditional analysis methods for detection requires high expenditures and time-consuming.In this chapter,the near-infrared spectrum of fish muscle with different spoilage degrees was collected.Then,the original spectrum was preprocessed by SNV and MSC to remove the noise and interference information in the spectrum to improve the spectrum's analysis.Then,the actual concentration of TMA-N in the spoilt fish's collected spectrum was determined by chemical analysis.Finally,the variable screening algorithm(GA and Si)and multiple linear regression model(PLS)were combined to select the correlation between the information components in NIR and TMA-N of the spoilt fish muscle,and the detection model was constructed.The results show that the GA PLS model's detection effect was the best.The correlation coefficient of the prediction concentration sample recorded Rp=0.9695,the error of RMSEP=5.5300,and the relative analysis error RPD=4.30.The study's outcome suggests that the near-infrared spectroscopy combined with the GA-PLS chemometrics model can be deployed to monitor the degree of fish spoilage in real-time.The method has high accuracy,high speed and limit of detection of 0.0014 mg N/100g.3.Rapid detection of Hg²⁺in fish using Ag nano substrate-based SERS sensor.In this chapter,to realize the green application of nanosensor technology,different concentrations of fish waste were deployed as a green reducing agent to fabricate Ag nano SERS substrate,which can detect Hg²⁺by combining quantitative analysis coupled with chemometric models.Firstly,Ag nano SERS substrates were synthesized with different fish waste concentrations as a reducing agent,and the SERS substrates were optimized.Subsequently,the optimized SERS substrates were mixed with different concentrations of Hg²⁺to collect the corresponding SERS spectra.It was observed that the selective interaction between Hg²⁺and Ag NP occurred in a relatively short time,suggesting an amalgam formation due to the reduction of Hg²⁺leading to a decrease in SERS intensity.Finally,after preprocessing the acquired SERS spectra,combined with chemometrics algorithm competitive adaptive weighted sampling algorithm partial least squares method CARS-PLS and GA-PLS were used to establish the quantitative model between SERS spectral intensity and Hg²⁺concentration in fish.The engineered Ag NP-50%nanosensor achieved limits of detection(LOD)of 3.42×10^-6?g/kg,which was well below the allowable limits of 0.5 mg/kg fixed by the European Commission regulation for fish.The results show that the Ag nano substrate SERS sensing technology combined with the chemometrics method can effectively predict Hg²⁺in the range of 1.0×10³-1.0×10^-4?g/m L and a LOD of 3.97×10^-6?g/kg with the CARS-PLS model registering the best detection effect.The sample correlation coefficient of prediction concentration had Rp=0.9880,the root means square error of prediction,RMSEP=0.0590,and the relative analysis error,RPD=7.4.4.Rapid determination of norfloxacin in fish using Au@Ag nano substrate-based SERS sensor.In order to further enhance the detection sensitivity of SERS,this chapter explores a new method based on the SERS substrate in the previous chapter Au@Ag NP label free method for rapid detection of norfloxacin was proposed.Initially,Ag NPs were synthesized using the Au@Ag seed growth process.SERS spectra of different concentrations of norfloxacin were acquired based on the Au@Ag core-shell nanoparticles,and the model was analyzed and predicted by GA-PLS.A detection limit of 2.36×10^-5?g/kg was reported in the spiked fish muscle sample compared to the European Commission's maximum threshold level of 100?g/kg.The results show that the Au@Ag core-shell nano SERS substrate combined with GA-PLS can detect norfloxacin in the concentration range of 10³-10^-4?g/m L.The correlation coefficient of predicted samples had an Rp=0.9756,the root means square error of prediction recorded RMSEP=0.5058,and the relative analysis error had RPD=8.42.The research outcome indicates that the foundation established in this chapter is feasible.The Au@Ag core-shell nano SERS sensor combined with GA-PLS can realize the rapid detection of norfloxacin residues in fish.5.Rapid detection of dieldrin in fish using Ag nano/carbon dots composite substrate(AGNPS/CDs)SERS sensor.To expand the detection application of Ag nano SERS substrate,this chapter innovatively combines Ag nano and carbon dots(CDS)to construct an Ag NPs CDs composite SERS substrate,which can effectively and rapidly detect dieldrin when coupled with chemometric models.The Ag NPs/CDs nanocomposites were formed by the chemical reaction of CDs with Ag NPs and used as SERS substrate.Firstly,Ag NPs CDs composite SERS substrate was synthesized,where Ag NPs were used as SERS element and CDs as capture element.Based on the substrate,SERS spectra of dieldrin with different concentrations in fish were collected,and the prediction model was established by combining the CARS-PLS algorithm.The results show that Ag NPs CDs composite SERS substrate has better stability,and combined with the CARS-PLS algorithm,it significantly improves the model's prediction performance.The prediction set's sample correlation coefficient had a Rp=0.9895,the root means square error of prediction RMSEP=0.3618,and the relative analysis error had RPD=8.27.The developed sensor achieved a limit of detection of 3.97×10^-6?g/kg,suggesting a high sensitivity and robust reproducibility of the developed technique.The results indicate that the CARS-PLS hybrid method combined with the SERS sensor based on Ag NPs CDs nano mixture can realize the sensitive,rapid and stable detection of dieldrin residues in fish.