Study On The Detection Method Of Spray-dried Porcine Plasma (SDPP) Based On Electronic Nose And Near-Infrared Spectroscopy

African swine fever is a global outbreak of infectious disease with a mortality rate of 100%.Since African swine fever(ASF)was first found in China in August 2018,31 provinces have experienced outbreaks of the epidemic,with a total of more than 1,200,000 pigs culled and a direct economic loss of 60 billion yuan,according to statistical data from the Ministry of Agriculture and Rural Affairs released on January 31,2020.Recently,researchers adopted gene sequencing analysis indicated that the ASF virus(ASFV)derived from the spray-dried porcine plasma(SDPP)of pig feed were the same strain with the first found in China.The Canadian Food Inspection Agency has claimed that livestock feed was one of the four transmission routes of the ASFVs.To respond to the extremely grim situation in China,two regulations were issued by the Ministry of Agriculture and Rural Affairs,mandated that the use of SDPP in creep feed must be detected and monitoring strictly to prevent and control the spread of ASF.Restricting the use of SDPP is an effective way to control the spread of ASF.Therefore,it is necessary to develop rapid,high-efficiency analytical methods capable of detecting SDPP in creep feed.In this study,SDPP and whey protein concentrate(WPC)which is the best substitute for SDPP both were selected as research objects,180 qualitative test samples(including 90 pure samples and 90 impure samples)and 180 quantitative test samples(9 samples×20 SDPP increments between 1%and 20%),an electronic nose sensor array and nearinfrared spectroscopy(NIRS)were first adopted to establish a detection method for SDPP through discrimination experiments and mixture concentration experiments.Various preprocessing algorithms coupled with multivariate data analysis were implemented to select the optimal discrimination and prediction model for both the electronic nose and NIRS.The main research work of this paper is as follows:(1)To investigate the feasibility of detecting SDPP based on utilizing electronic nose and NIRS technologies.There were significant differences showed by the original data of pure and impure samples.For electronic nose,the response of sensor R7 and R9 were obvious.There were obvious difference in the specific bands for NIRS too.Cluster analysis for electronic nose data and NIRS data were performed with principal component analysis(PCA).The first two principal components(PC)accounted for 95.64%of the variance in the electronic nose data.The majority of the variance in the NIRS data was captured by the first two PCs,accounting for 99.56%.The result showed that both electronic nose and NIRS technologies could be utilized by detecting SDPP in mix samples.(2)To develop a robust classification model for SDPP discrimination based on electronic nose data and NIRS data.Three kinds of sample partitioning methods were adopted.The multiplicative scattering correction(MSC),Mean-center and SavitzkyGolay first derivative&Mean-center data preprocessing methods were found helpful for increasing the accuracy of partial least squares discriminant analysis(PLS-DA).The result showed that the highest recognition rates of the model based on electronic nose and NIRS were 93%and 100%,respectively,which demonstrated that both the two techniques had good performances in SDPP detection,compared with the electronic nose technique,the NIRS technique established a more accurate discrimination model with a much smaller confidence interval range.(3)To investigate the optimum regression model of electronic nose and NIRS for the quantitative prediction of SDPP in mixed samples.The qualitative test samples were detected by electronic nose and NIRS platform,Hotelling’s T2 method which at 95%confidence level was adopted to eliminate abnormal sample data.Then,a continuum regression(CR)model was employed to explore the appropriate regression model for the quantitative prediction of SDPP with the two technologies.From the CR PRESS surfaces of the electric nose data in the test set,the optimal model parameters were located in the region between MLR and PLSR.However,the minimum PRESS of the NIR data in the test set was found in the region between PCR and PLSR.On this basis,different regression models based on electronic nose data and NIRS data were established to verify the correctness of the CR analysis.The results showed that the optimal algorithm for the electronic nose was the MLR algorithm and the optimal algorithm for NIRS was the PLSR algorithm which again confirmed the accuracy of the CR analysis.Thus,basing on the research result,both of the two techniques can successfully applied for detecting SDPP.The NIRS technique with multivariate data analysis methods showed great potential than electronic nose,with the optimal regression model adopted partial least squares regression(PLSR)with the Savitzky-Golay first derivative and mean-center preprocessing for the NIRS dataset(Rp2=0.999,RMSEP=0.1905).In this research,both the electronic nose and NIRS techniques were first applied for SDPP detection in mixed samples.The results demonstrated that both the electronic nose and NIRS were mature enough to provide rapid and accurate techniques for the detection and quantification of the usage of SDPP.The results of the electronic nose experiment indicated that the vital volatile substances of SDPP enabled the detection of SDPP in mixed samples by electronic nose technology was the sulfur-containing organics which was odorous compound and sensitive to sensor R7 and R9.The results of the NIRS experiment implied that there exist significantly different components between SDPP and WPC,which may explain the spectral absorption variations.Compared with traditional detection methods,these two electronic technologies could provide faster,low-cost,highthroughput and real-time detection methods for SDPP,it shows great potential value in SDPP detection which is effective measure for the prevention and control of ASF.