Model for estimating the weight-loss ratio of damaged Korla fragrant pears

To predict the weight-loss ratio of Korla fragrant pears effectively,improve commodity value and study the variation laws of the weight-loss ratio of damaged fragrant pears during storage,this study predicted the weight-loss ratio of fragrant pears by utilizing the generalized regression neural network(GRNN),support vector regression(SVR),partial least squares regression(PLSR)and error back propagation neural network(BPNN).The prediction performances of GRNN,SVR,PLSR and BPNN models were compared comprehensively,and the optimal model was determined.In addition,the optimal prediction model was verified.The results show that weight-loss ratio of fragrant pears increases gradually with the extension of storage time.During storage,the weight-loss ratio of fragrant pears is positively related to the degree of damage.The trained GRNN,SVR,PLSR and BPNN models can be used to predict the weight-loss ratio of fragrant pears.The BPNN model is the most accurate in predicting the weight-loss ratio of damaged fragrant pears(R^(2)=0.9929;RMSE=0.2138).It has also been proved to have good predictive effect in production practice(R^(2)=0.9377,RMSE=0.7138).The research findings can provide references to predict the delivery quality and time of delivery of Korla fragrant pears.

Comprehensive evaluation of Korla fragrant pears and optimization of plucking time during the harvest period

To optimize the harvest of Korla fragrant pears and to provide a theoretical basis for post-harvest processing,a total of 26 basic indices of Korla fragrant pears,including their morphology,quality,and softening age,were investigated.The harvest period ranged from August 22nd to October 6th,samples were collected every 5 d(totally 10 plucking days,indicated as H1-H10).The results indicated that changes in pectin content were the main causes of softening in fragrant pears.The scavenging of free radicals mainly occurred because of the collaborative effects of superoxide dismutase(SOD)and catalase(CAT).In the harvest periods H1-H3,the hardness,titratable acid(TA),chlorophyll content,density,and diameter of the stone cells,as well as the cellulose and hemicellulose content of the Korla fragrant pears were at their highest.During the periods H7-H10,the single-fruit weight,fruit horizontal and vertical diameter,color coordinates L,a*,b*,soluble-solids content(SSC),SOD activity,CAT activity,and water-soluble pectin were higher than in the other plucking periods.The highest vitamin C(VC)content and moderate values for a variety of indicators were observed during H3-H7.Variations in the Korla fragrant pears during H1-H3 mainly manifested through changes in softening-related parameters.During H3-H10,changes in the softening-related,aging-related,color-related,and quality indices had a dominant role.On this basis,some suggestions for the post-harvest processing of fragrant pears have been proposed.Fruit,during H1-H3,are suitable for transportation and storage;during H7-H10 are suitable for fresh-eating and further processing;and during H3-H7,exhibited moderate values for a variety of indicators and had the highest commercial value.This research provides a systematic evaluation of the characteristics of mature Korla fragrant pears during the harvest period and can form the basis for fruit quality control and processing of Korla fragrant pears.

Transcriptome analysis to identify candidate genes related to chlorogenic acid biosynthesis during development of Korla fragrant pear in Xinjiang

Korla fragrant pear(KFP)with special fragrance is a unique cultivar in Xinjiang,China.In order to explore the biosynthesis molecular mechanism of chlorogenic acid(CGA)in KFP,the samples at different development periods were collected for transcriptome analysis.High performance liquid chromatography analysis showed that CGA contents of KFP at 88,118 and 163 days after full bloom were(20.96±1.84),(12.01±0.91)and(7.16±0.41)mg/100 g,respectively,and decreased with the fruit development.Pears from these typical 3 periods were selected for de novo transcriptome assemble and 68059 unigenes were assembled from 444037960 clean reads.One‘phenylpropanoid biosynthesis’pathway including 57 unigenes,11 PALs,1 PTAL,64CLs,9 C4Hs,25 HCTs and 5 C3’Hs related to CGA biosynthesis was determined.It was found that the expression levels of 11 differentially expressed genes including 1 PAL,2 C4Hs,34CLs and 5 HCTs were consistent with the change of CGA content.Quantitative polymerase chain reaction analysis further showed that 8 unigenes involved in CGA biosynthesis were consistent with the RNA-seq data.These findings will provide a comprehensive understanding and valuable information on the genetic engineering and molecular breeding in KFP.

Response of Leaf Tissue Structure to Exogenous ABA in Korla Fragrant Pear

To study the effects of different concentrations of Abscisic Acid(ABA) on leaf tissue structure of Korla fragrant pear and lay the foundation for the cultivation and regulation of Korla fragrant pear. Using the paraffin section method, the leaf thickness, the epidermis thickness, the palisade tissue thickness and the sponge tissue thickness were measured; palisade spongy ratio, Cell Tightness(CT) and Cell Looseness(CL) were examined after treatments with exogenous ABA. Leaf thickness, leaf palisade spongy ratio and palisade tissue thickness increased significantly after exogenous ABA treatment compared with CK, while the effect on leaf epidermis was not obvious. The leaf thickness and palisade tissue thickness increased evidently after 70 mg · LABA treatment by 24.89% and 41.10%, respectively. The tissue structures of leaves were more compact, while CL was lower after 70 and 90 mg · LABA treatments. The effects of different concentrations of exogenous ABA could increase the leaf thickness, palisade tissue thickness of Korla fragrant pear and CT, reduced the thickness of spongy tissue and CL. Among those concentrations of exogenous ABA, the effect of spraying 70 mg · Lexogenous ABA was suitable for improving cold resistance of Korla fragrant pear.

Changes in Soil Organic Matter,Nitrogen and Phosphorus Contents during Decomposition of Pear Branches

[Objectives]To investigate the changes in soil organic matter,nitrogen and phosphorus content in the decomposition process of Korla fragrant pear branches by indoor mixed culture.[Methods]The branches of Korla fragrant pear in the orchard were collected and returned to the field for a period of 150 d for indoor mixed culture.[Results]Different ages of Korla fragrant pear branches have different effects on soil nutrient content during the simulated return to field decomposition process.Compared with the control in the same period,the treatment of returning to field reached a significant level(P<0.05).Compared with the control,the average values of organic matter,total nitrogen and available phosphorus content in treatment 1 and treatment 2 increased by 2.16 times and 1.93 times,61%and 59%,5.88 times and 6.88 times,respectively;compared with the control,the average increase performance of the alkaline hydrolysis nitrogen content of the two treatments was basically the same,and the treatment 2 was the best;compared with the control,the average total phosphorus content of treatment 1 and treatment 2 increased but not significantly.[Conclusions]The contents of soil organic matter,nitrogen and phosphorus were all increased during the decomposition of pear branches,and the overall improvement effect of 10-year-old trees was better than that of 5-year-old trees.Returning the pruned branches to the field can provide a reliable theoretical basis for solving the problem of organic fertilizer shortage in orchards,and also can ensure technical support for improving soil fertility and improving the rhizosphere micro-environment of pear trees.

Non-destructive detection of the fruit firmness of Korla fragrant pear based on electrical properties

In order to achieve the non-destructive detection of the firmness of Korla fragrant pear during the ripening period,the characteristic variables integrating the parallel equivalent inductance(Lp),quality factor(Q),parallel equivalent capacitance(Cp),dissipation factor(D),parallel equivalent resistance(Rp)and impedance(Z)were formulated through principal component analysis(PCA).Further,based on the characteristic variables,the models were established for predicting the firmness of Korla fragrant pear by using the generalized regression neural network(GRNN)and back-propagation neural network(BPNN).The results showed that firmness has significant correlations with the six electrical parameters.The first two principal components(PCs)were selected as the characteristic variables of the electrical parameters.GRNN exhibited the best performance in predicting firmness(R2=0.9628,RMSE=0.383).The results could provide important references for non-destructive detection of the quality of Korla fragrant pear.

Construction method of quantitative evaluation model for the maturity of Korla fragrant pear

The maturity variation laws of Korla fragrant pears were explored for a quantitative evaluation of harvest maturity to solve the reasonable matching between harvest maturity of Korla fragrant pears and market quality demands.Korla fragrant pears from different harvesting periods were chosen as the research objects.Some quality indexes were chosen as the evaluation indexes per industry standards,including hardness,soluble solid content(SSC),single-fruit weight,fruit longitudinal diameter,fruit equatorial diameter,pericarp color parameters(L*,a*,and b*),and titratable acid.Variation data of these quality indexes with accumulated temperature were collected.Scores of several quality indexes were gained through principal component analysis.A mathematical model of scores and accumulated temperatures was constructed.On this basis,a quantitative maturity model of Korla fragrant pears was constructed.Results demonstrate that SSC,single-fruit weight,fruit longitudinal diameter,fruit equatorial diameter,L*,a*,and b*are significantly and positively correlated with the accumulated temperature.Meanwhile,hardness and titratable acid showed significant negative correlations with the accumulated temperature.Relations between scores of principal components and accumulated temperature conform to the Sigmoidal model.The constructed quantitative maturity model of Korla fragrant pears can quantify the maturity of pears.Research conclusions can provide insight into the harvest periods,evaluate Korla fragrant pears’maturity,and lay a theoretical foundation for quantitative research on fruit maturity.

Prediction method for nutritional quality of Korla pear during storage

It is difficult to control the quality of Korla pear with different degrees of maturity during storage.Here,a method was proposed for predicting the effects of harvest maturity and cold storage time on the quality indices(soluble solid content(SSC)and Vitamin C(Vc)content)of Korla pear.The generalized regression neural network(GRNN)and adaptive neuro-fuzzy inference system(ANFIS)were employed to predict the quality changes of Korla fragrant pear fruit during storage.The results demonstrated that during cold storage the SSC in pears with 10%-70%harvest maturity showed continuous increases in the first 90 d of storage and then a slight decline thereafter,while that in pears with 80%and 90%harvest maturity exhibited slow decreases throughout the storage process.With the extension of storage time,the Vc content of pears with 10%-90%harvest maturity showed continuous decreases.The harvest maturity of Korla pear was extremely positively correlated with SSC and Vc content(p<0.01)in a given storage period.Storage time showed an extremely significant negative correlation with the Vc content(p<0.01)at the 40%-90%harvest maturity and an significant negative correlation with the Vc content(p<0.05)at the 10%-30%harvest maturity.At the 10%-70%harvest maturity,storage time showed a significant positive correlation with the SSC(p<0.05).The trained model could well predict the variation trend of quality indices of pear fruit during storage.The ANFIS with the input membership function of gbellmf had the best performance in predicting the SSC(RMSE=0.175;R2=0.98),and that with the input membership function of trimf exhibited the best performance in predicting Vc content(RMSE=0.075;R2=0.99).The research findings can provide reference for predicting the fruit nutritional quality at delivery and decision-making on the storage time of Korla fragrant pear.