Study On The Internal Quality Determination And Maturity Predication Base On The Visble/Near Infrared Spectrosphy And Chromatism Of Sweet Orange Fruit

The fast, accurate non-destructive determination the internal quality and prediction maturity of sweet orange fruit becomes more and more important with people's higher requirements of internal quality of orange fruit. Visible/near infrared spectrum (Vis/NIRS) technology takes advantage of fast accurate non-destructive determinationcompared with traditional chemical analysis, and had been widely applied to quality assessment of agricultural products. In this study, Vis/NIRS was adopted to assess the feasibility of maturity prediction and internal quality determination of sweet orange fruit.. The results were presented as follows:1.A spectrophotometer with a wavelength range of 400-1000 nm was used to measure the spectral reflectance data of fruit peels from different harvest times, and a relationship was established between VNIRS measurements and the major internal quality (total soluble solids (TSS), titratable acid (TA), Vitamin C (Vc) and skin color index (CI)) of Hamlin sweet orange fruit. The data set (absorbance [log 1/R]) was analyzed to build the best predictive model for TSS, TA, Vc and CI using partial least square regression (PLS) with MAS+MSC, SNV+MAS, MSC and MSC+MAS pretreatments, respectively. The TSS, TA, Vc and CI prediction models(Rp2=0.699,0.745,0.962 and 0.995, respectively) resulted in excellent predictive ability. Whilst, the relationship was also established between VNIRS measurements and the major internal quality (total soluble solids (TSS), titratable acid (TA), Vitamin C (Vc) and skin color index (CI)) of Olinda Valencia orange fruit. The data set (absorbance [log 1/R]) was analyzed to build the best predictive model for TSS, TA, Vc and CI using partial least square regression (PLS) with MSC+MAS, MSC+MAS, MSC and MSC+MAS pretreatments, respectively. The TSS, TA, Vc and CI prediction models (Rp2= 0.957,0.975,0.951 and 0.965, respectively) resulted in excellent predictive ability.These results provide fundamental and practical knowledge for the development of a non-destructive, fast, and accurate technology for classifying fruit internal quality (total soluble solids (TSS), titratable acid (TA), Vitamin C (Vc) and skin color index (CI)).2.The prediction models for maturity of sweet orange fruit were established using accumulated temperature and fuit skin colour.(1)The regression model between accumulated temperature (y) and degree of ripeness (TSS (x1), T/A (x2)) of Hamlin orange was established by y=-14493+1915.43x1+665.547x2-57.551x1x2-51.958x12 with R2=0.845, the accumulated temperature difference (Ay) equation that the degree of ripeness from initial (TSS (x1), T/A (x2)) to the harvest standard (TSS (xo), T/A (x)) of Hamlin orange was established by Ay= 1915.43(xo-x1)+665.547(x-x2)-57.551(x0x-x1x2)-51.958(x02-x12),and the linear regression model between fruit growth days (x3) and fruit quality of Hamlin orange was established by x3=214.237(xo-x1) +74.44(x-x2)-6.437(xox-x1x2)-5.81(x02-x12).Then the optimal picking date of Hamlin orange can be predicted by the above-mentioned regression models. For example, the TSS and T/A of fruits picked on Dec 19,2009 were calculated by the Vis/NIRS models for TSS and T/A, and the predicted harvest date was Jan 6,2010. Compared with the actual picking date, there was only five days in advance.Whilst, the regression model between accumulated temperature (y) and degree of ripeness (TSS(x1), T/A (x2)) of Olinda Valencia orange was also established by y=36082.1-6850.3x1-43.156x2+319.413x]2+ 22.0939x22 with R2=0.966, the accumulated temperature difference (Ay) equation that the degree of ripeness from initial (TSS (x1),T/A (x2)) to the harvest standard (TSS (x0), T/A (x)) of Olinda Valencia orange was established byâ–³y=-6850.3(x0-x1)-43.156(x-x2)+319.413(x02-xi2)+22.0939(x2-x22), and the linear regression model between fruit growth days (x3) and fruit quality of Olinda Valencia orange was established by x3=-357.737(x0-x1)-2.254(x-x2)+16.68(x02-x12)+1.154(x02-x22). Then the optimal picking date of Olinda Valencia orange can be predicted by the above-mentioned regression models. For example, the TSS and T/A of fruits picked on Mar 8,2010 were calculated by the Vis/NIRS models for TSS and T/A, and the predicted harvest date was May 15,2010. Compared with the actual picking date, there was only eight days in advance.The results indicated that this method of prediction the maturity of citrus is feasible.(2) The internal quality of orange had a significant correlation with CI, and thhe linear regression model between fruit growth days (x3) and CI (y) of Hamlin orange was established by y=8.9407x3+114.91 with R2=0.9873.According to CI data of Hamlin orange from 2008 to 2009, the number of CI for optimal harvest date was 7.93.Then the optimal picking date of Hamlin orange can be predicted by the above-mentioned regression models. For example, the CI of fruits picked on Dec 19,2009 was calculated, and the predicted harvest date was Jan 15,2010. Compared with the actual picking date, there were twelve days late. It showed that the predictive ability of the model was low. Whilst, due to the phenomena of fruit "turning green" in the process of maturity, this method was not suitable for Olinda Valencia orange to predict the maturity.In all, the harvest date predicted method by accumulated temperaturehad a better performance than by CI of orange fruit, and accumulated temperature combined with Vis/NIRS technology was promising for rapid and reliable to predict the maturity and harvest date of citrus fruit.