| Potato is the fourth largest crop in the world,which is rich in nutritional value and commercial value.Different processed potato products have different requirements on the dry matter and starch content of raw potatoes.Internal defects,such as blackheart disease,seriously affect the quality of processed products and utilization of raw potatoes.Therefore,the development of online detection equipment that can simultaneously detect internal defects and nutrient content of potatoes is of great significance.It can improve the efficiency of potato detection and classification,add the value of potato products,and accelerate the development of potato detection technology.An online detection system for potato internal quality was developed.Potato dry matter content,starch content,and black heart disease detection models were established and optimized.Performance of the online detection system were evaluated.The main research contents and conclusions were as follows:1.A hardware system and software system for potato online spectral detection were developed and applied to the potato grading line.The optical path was designed for the spectrum detection device,and the diffuse transmission method was used for the internal quality spectrum collection.For the online application of the grading line,the installation of the light source and the optical fiber were designed.The motor speed was optimized to 560r/min to match with the transmission spectrum model.The production capacity reached 2.4 t/h.2.Based on the combined variable selevtion methods,quantitative prediction models of potato absorbance spectrum were established.The effects of 3 different variable selection methods,including the uninformed variable elimination method(UVE),competitive adaptive reweighting sampling algorithm(CARS)and successive projection algorithm(SPA),on the partial least squares(PLS)prediction models of potato dry matter and starch content were studied.The CARS algorithm was superior to UVE and SPA in simplifying the model and improving the accuracy.Compared with the original variable model,after CARS treatment,the dry matter and starch model variables were reduced by 95%and 98%,respectively.The RMSEp were reduced from 1.5206%and 1.3864%to 1.0919%and 1.2249%,respectively.The characteristics of three single variable optimization methods were utilized,and three combined variable screening models,namely CARS-SPA,CARS-UVE,and UVE-SPA were constructed.Among them,CARS-SPA performed the best.Compared with the CARS model,the variables of the dry matter and starch model were further reduced by 52%and 23%,and the RMSEp were further reduced to 1.0418%and 1.2156%.3.Based on energy spectrum.a discriminant model of potato blackheart disease was established.Energy spectrum of blackheart potatoes and healthy potatoes showed obvious difference on the energy ratio of characteristic peaks and the peak area in the range of 657-750 nm.The absorbance spectrum curve of healthy potato showed three peaks at 665,732,and 839 nm,while that of blackheart potato was gentle,without obvious peaks.Around 705 nm,the difference between the absorbance value of blackheart and healthy potatoes reached the maximum.Based on the CARS-SPA combined variable screening method,a qualitative model for the absorbance spectrum of potato black heart disease was established.Nine variables were selected,and the model discrimination accuracy rate reached 98.44%.Based on the energy spectrum,a linear discrimination(LDA)model of potato blackheart disease was established and optimized by 4 different methos,including the normalized peak area method(PA),the normalized peak method(PV),the normalized peak difference method(PDV),and the dual-wavelength correlation analysis method.The result showed that the ratio of energy value at 699 nm and 435 nm(T699/T435),obtained by the correlation coefficient method,reached the highest discrimination rate,97.67%.Although the energy spectrum model was not as accurate as the absorbance model,the discrimination accuracy rate could meet the online screening requirements.However,the energy spectrum model was established with only 2 variables,and the spectrum acquisition was not limited by reference,which was simpler and more stable.4.The optimized models were applied to the potato online detection system for test verification.The hardware reliability of the device was verified in 6 aspects,including baseline noise,and so on.All indicators could meet the online testing requirements.The model accuracy and stability of the online detection device were tested using 20 blackheart potatoes and 20 healthy potatoes which were not involved in the modeling.With the detection speed of 560 r/min,the online non-destructive detection of potato blackheart disease and nutrient contents could be achieved simultaneously.The detection accuracy rate of blackheart potato was 95.00%and the coefficient of variation(CV)was 1.25%.The RMSEp of the dry matter and starch content prediction models were 1.0041%and 1.2660%,respectively.The average CV of 10 replicates were 1.41%and 1.45%,respectively. |
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