Nondestructive Detection Of Chinese Pecans(Carya Cathavensis)Internal Quality Based On Electronic Nose

Nuts are important food supplement which contains high content of unsaturated fatty acids,abundant protein,dietary fiber and mineral substance.Additionaly,the unique nutty aroma will be produced during the roasting and baking process,which is loved by comsumers,and the economic value of nut products is increasingly prominent.In the field of food detection,the research of nondestructive detection method for the internal quality of nut products becomes increasingly important.In this paper,electronic nose combined with chemometrics qualitative and quantitative anlaysis methods was introduced to detect the Chinese pecans(Carya Cathayensis),and the detection of the fast,nondestructive detection method of internal quality of Chinese pecans were explored.The main research results and conclusions are as follows:1.The influence of storage time for the internal quality of raw Chinese pecans was studied in this paper.Duirng the storage,the contens of main 6 fatty acids(i.e.,oleic acid,linoleic acid,palmitoleic acid,linolenic acid,palmitic acid and stearic acid)were decreased significantly,which caused changes of main volatile compounds emitted from Chinese pecans(total content of 291.67ng/g to 2270.40 ng/g).For the qualitative classification:in this paper,a new classification method(voting method)which combined the advantages of multi features was proposed to classify different pecan samples,and the classification accuracy rate was 96.00%.For the quantitative prediction:different prediction models of quality indexes were built based on different chemometric methods.Partial least squares regression(PLSR)was suitable for build the prediction models of physicochemical indexes,Back propagation neural network(BPNN)was suitable for building prediction models of fatty acid contents and storage time.The built prediction models could nondestructively and efficiently predict acid values(R2>0.89),peroxide values(R2>0.90),contents of six main fatty acids(R2>0.88)and storage time(R2>0.98).2.The influence of roasting time for the internal quality of Chinese pecans was studied in this paper.During the roasting process,five main volatile compounds(i.e.,hexanal,furfuryl alcohol,benzaldehyde,2-pentylfuran and nonanal)were changed significantly and were selected to characterize roasted Chinese pecans.For the qualitative classification:the classificaiton performance of BPNN model based on response value in stationary stage was better than those of models based on other single feature,and the highest classification accuracy rate and the average classification accuracy rate were 92.00%and 84.67%,respectively.The proposed novel voting method exhibited a good classification performance with 92.00%classification accuracy rate which was higher than those of other models.For the quantitative prediction:in this paper,the architecture of BPNN was optimized by genetic algorithm(GA).The GA-BPNN model could predict the main volatile compounds more accurately(R2>0.93)than BPNN model.3.The influence of storage time before roasting for roasted pecans were analyzed.The volatile compounds of roasted pecans in this study were significantly different,and the total contents of volatile compounds ranged from 1080.94ng/g to 4514.57ng/g.Ten main volatile compounds were selected to characterize the aroma of roasted pecans.The content of selected ten volatile compounds accounted for 65.40%-85.01%of total content of volatile compounds.For the qualitative classification:K-nearest neighbors(KNN),BPNN and RoF-BPNN(RoF,rotation forest)were applied to classify different pecan samples with different roasting times.RoF-BPNN model had the best classification performance with the highest classification accuracy rate of 94.0%and the average classification accuracy rate of 90.4%.For the quantitative prediction:the RoF-BPNN could predict the main volatile compounds more accurately(R2>0.949 in calibration set,R2>0.890 in validation set)than BPNN model.4.The internal quality of roasted pecans during shelf-life was analyzed.The internal quality of roasted pecans began to deteriorate when the shelf-life was more than 90 days.Ten main volatile compounds were selected to characterize the aroma of roasted pecans during shelf-life,and the content of selected main volatile compounds accounted for 70.68%-81.87%of total content.Duirng shelf-life(0 day-180 days),contents of six main fatty acids all had decrease tendency,and the content of linolenic acid was decreased by 30.46%.For the qualitative classification:RoF-BPNN model had the best classification performance with the highest classification accuracy rate of 97.14%and the average classification accuracy rate of 92.49%.For the quantitative prediction:the RoF-BPNN could more accurately predict the main volatile compounds(R2>0.934 in calibration set,R2>0.916 in validation set)and contents of main fatty acids(R2>0.986 in calibration set,R2>0.969 in validation set)than BPNN models which were based on single-feature.