Detection Method Of Producing Area Of Orah Based On Near Infrared Spectroscopy

The main objective of this research project is to analyze the quality of citrus fruit,specifically Orah,using sugar content as the main quality indicator.The study focuses on collecting spectral data,analyzing the data,and determining the internal components of the fruit.The collected spectral data is compared and studied to develop a Partial Least Squares(PLS)model for qualitative and quantitative identification.The conclusions are as follows:1.The study used diffuse transmission near-infrared online detection equipment to obtain near-infrared spectra of different regions and varieties of "Orah" citrus fruits.A comparison of spectral correction models was conducted to select a better model.The model’s accuracy was validated using experimental samples that were not involved in spectral testing,and it was found that the model could relatively accurately predict the origin of "Orah" citrus fruits.In order to reduce the negative effects of some useless interference signals in dynamically collected real-time spectral messages,preprocessing is necessary before modeling diffuse transmission spectra.To compare the effects of different preprocessing methods on spectral information,three methods were used: standard normal variable(SNV),S-G first-order derivative,and multiplicative scatter correction(MSC),and comparative analysis was conducted.The purpose of these preprocessing methods is to eliminate interference signals in spectral data,making modeling more accurate and reliable.By comparing the effects of different preprocessing methods,we can choose the most suitable preprocessing method for our modeling purposes.The impact of three spectral preprocessing methods,standard normal variable(SNV),S-G first-order derivative,and multiplicative scatter correction(MSC),on spectral information was analyzed by comparing experimental results.2.This study explored how to identify the origin and variety of citrus fruit by establishing a spectral model.The research focused on Orah produced in Guangxi,Yunnan,and Sichuan provinces,and established partial least squares regression(PLSR)models for Iyokan 38 and Myojin varieties.The RMSEP values after different pretreatments were 0.449°Brix,0.508°Brix,and 0.448°Brix,respectively,and the prediction accuracies exceeded 80%,indicating that the origin models established using PLS could predict the origin of citrus fruit well.3.This study investigated a general model for Orah detection,using Orah samples from different regions and varieties.PLS models were built for each type of sample,and the results showed that the general model performed well in predicting the Orah content.In addition,when establishing an SSC detection model for Orah,the data processed by single preprocessing method was found to be better than the data processed by mixed preprocessing methods.Furthermore,a general model built by mixing calibration set samples from different regions showed better prediction results than a single-region model.The general model was optimized using the data updating method.4.To select the optimal modeling method,we compared the performance of partial least squares(PLS)modeling under different preprocessing methods.Based on the analysis results,the calibration model built using PLS modeling combined with standard normal variable(SNV)preprocessing method showed the best predictive performance.