Nondestructive Detection Of Fruit Internal Quality Based On Visible And Near Infrared Spectroscopy

Fruit is one of the main components of human diet. It provides abundant nutritional elements for human body. China is a big fruit producer, but not powerful. One of the important reasons is the low fruit quality and weak competitiveness in world market caused by low commercialization treatment ability for post-harvest fruit. Therefore, rapid and nondestructive detection and classification of fruit external and internal qualities becomes necessary for fruit industrialization of our country. This study is a further extension of the former research projects done by our group members-fruit external quality detection using machine vision technique and fruit sugar content and acidity detection using near infrared (NIR) spectroscopy, in order to evaluate the fruit quality with multiple quality indices.The research objects are pears and kiwifruits. Detection of pear firmness and kiwifruit vitamin C content were studied using visible/NIR spectroscopy, optic fiber sensor and chemometrics techniques combined with physical-chemical analysis and fruit physiological and pathological knowledge. Quantitative models were established based on visible/NIR spectra for fruit firmness and vitamin C content determination. In this dissertation, pear internal defect discriminant and pear variety classification were also studied using visible/NIR spectroscopy technique and pattern recognition methods. Qualitative models were established for pear internal defect discriminant and variety classification.The main results and conclusions were:1. The influence of spectra acquisition parameters on spectra and modeling results were analyzed. For FT-NIR spectrometer, the setting principal of gain, moving mirror velocity and aperture parameters were explained. The influence of scan number and resolution on spectra and modeling results were analyzed. The results indicated that: model performance was improved with resolution increasing, but the time cost for scanning also increased. Models were relatively better when the scan number was 64. For USB2000/4000 miniature optic fiber spectrometer, the setting principal of integral time and average time were also explained. 2. The curve characteristics of fruit original spectra and spectra after derivative and smoothing pretreatments were analyzed. Main absorption properties on fruit diffuse reflectance spectra were around 970 nm,1190 nm,450 nm,1790 nm and 1940 nm, which were related to O-H and C-H functional groups. The transmittance changes on transmission spectra were much obvious around 680-690 nm and 790-800 nm. With fruit stem-calyx vertical, spectra of pears with internal defect have absorbed more light than sound pears below 750 nm, and absorbed less light than sound pears above 750 nm. With fruit stem-calyx horizontal, the characteristics of defect pear and sound pear were nearly the same, but the threshold moved to 720 nm. The results of variation analysis (ANOVA) of average absorbance and root mean square noise for original spectra and smoothed spectra showed that the spectra characteristics were not influenced by smoothing pretreatments with different points for both FT-NIR and USB2000/4000 spectrometers.3. The difference of spectra acquired from different fruit locations was analyzed. The results of ANOVA of average absorbance and root mean square noise for spectra acquired from three latitudes and three longitudes (nine points) on each fruit shown that the difference of spectra from different latitudes were much more than the difference of spectra from different longitudes.4. Spectra outliers in each sample set were analyzed by Chauvenet testing method. Qualitative models for pear internal defect discriminant and pear variety classification were established after eliminating spectra outliers. Concentration outliers in each sample set were analyzed by leverage and student residual testing method. Samples with relatively larger leverage value or student residual value were considered as outlier and removed from the sample set firstly. And then they were reclaimed to the model one by one to see whether they provided any useful information or not. Quantitative models for pear firmness and kiwifruit vitamin C content detection were established based on both spectra outlier elimination and concentration outlier elimination. 5. Qualitative analysis of pear internal defect: Comparison results of different spectrometers (or detectors) and detection modes indicated that discriminant accuracy of models based on transmission spectra using USB4000 miniature optic fiber spectrometer were much better than the accuracy of models based on diffuse reflectance spectra using FT-NIR spectrometer, which can be concluded that transmission spectra were more suitable for pear internal defect discriminant. Comparison results of different pattern recognition methods of discriminant analysis (DA), soft independent modeling of class analogy (SIMCA), discriminant partial least squares (DPLS), and probabilistic neural networks (PNN) indicated that DPLS and PNN models were not suitable for pear internal defect discriminant, and the discriminant results of SICMA model were a bit better than those of DA model. Comparison results of two fruit placing modes (stem-calyx vertical and stem-calyx horizontal) indicated that discriminant accuracy of the model using spectra acquired with fruit stem-calyx horizontal was much better. Comparison results of different spectra pretreatments and different wavebands showed that:For Xueqing pears after storage, the SIMCA model based on spectra acquired with fruit stem-calyx horizontal in 450-1000 nm after smoothing pretreatment was much better. The discriminant accuracy of calibration and validation were 92.68%and 78.57%, respectively. For Cuiguan pears after storage, the SIMCA model based on spectra acquired with fruit stem-calyx horizontal in 450-1000 nm and using multiplicative signal correction (MSC) pretreatment was much better. The discriminant accuracy of calibration and validation were 96.15%and 88.24%, respectively.6. Qualitative analysis of pear variety: Comparison results of different spectrometers (or detectors) and detection modes indicated that classification correctness of models based on diffuse reflectance spectra were much better than that of models based on transmission spectra. According to the correctness of both calibration and validation, the best model was based on diffuse reflectance spectra acquired by InGaAs detector in the range of 800-2500 nm. Comparison results of models using DA, SIMCA, DPLS, and PNN methods indicated that the classification correctness of DA and SIMCA models were very close, and were much better than that of DPLS model. The calibration results of PNN model were close to those of DA and SIMCA models, but the prediction results were much worse. According to the correctness of both calibration and validation, the performance of DA model was the best. Comparison results of different spectra pretreatments and different wavebands showed that:DA model using diffuse reflectance spectra in 1100-2500 nm and after 25 points smoothing pretreatments turned out the best results, with classification correctness of 99.43%and 99%for calibration and validation, respectively.7. Quantitative analysis of pear firmness: Comparison results of different calibration methods of partial least square regression (PLSR), principal components regression (PCR), multi linear regression (MLR), and least square support vector machines (LS-SVM) showed that the performance of PLSR model was better than PCR model and MLR model. The performance of LS-SVM model using ten principal components was close to that of PLSR model. For Xizilv pears (2005), the correlation coefficients of calibration and validation were 0.870 and 0.849, respectively; standard error of calibration (SEC) and standard error of prediction (SEP) were 2.85 N and 2.78 N, respectively. For Cuiguan pears (2005), the correlation coefficients of calibration and validation were 0.943 and 0.731, respectively; SEC and SEP were 1.15 N and 1.98 N, respectively. For Xueqing pears (2005), the correlation coefficients of calibration and validation were 0.898 and 0.774, respectively; SEC and SEP were 1.78 N and 2.41 N, respectively.Comparison results of different spectra pretreatments and different wavebands showed that:For Xizilv pears (2005), the performance of PLSR model established in 800-1880 nm and 2190-2220 nm after 15 points smoothing pretreatments was much better, the correlation coefficients of calibration and validation were 0.916 and 0.746, respectively; SEC, SEP and standard error of cross validation (SECV) were 2.25 N, 3.26 N and 3.77 N, respectively. For Cuiguan pears (2005), the performance of PLSR model established in 1374-1565 nm,1814-1894 nm and 2017-2217 nm after standard normal variate (SNV) correction was much better, the correlation coefficients of calibration and validation were 0.922 and 0.731, respectively; SEC, SEP and SECV were 1.22 N,2.02 N and 2.18 N, respectively. For Xueqing pears (2005), the performance of PLSR model established in 800-2500 nm after 15 points smoothing pretreatments was much better, the correlation coefficients of calibration and validation were 0.893 and 0.808, respectively; SEC, SEP and SECV were 1.75 N,2.32 N and 2.31 N, respectively.8. Quantitative model modification for pear firmness prediction: The modification results of pear firmness models using slope/incept method indicated that the predictive performance of models after modification was improved obviously compared to that of models before modification, not only for spectra without pretreatments but also for spectra with smoothing, derivative, or scattering correction pretreatments. After modification, SEP decreased distinctively, and the difference distribution of actual and predicted firmness become closer to zero line.9. Quantitative analysis of kiwifruit vitamin C content: Comparison results of different calibration methods of PLSR, PCR, MLR and different spectra pretreatments and different wavebands showed that the performance of PLSR model using original spectra in 800-2500 nm was much better, the correlation coefficients of calibration and validation were 0.932 and 0.616, respectively; SEC, SEP and SECV were 7.95 mg/100g,15.8 mg/100g and 17.5 mg/100g, respectively. PCR model performed worse than PLSR model. The correlation coefficient of MLR calibration using eleven wavelengths was above 0.9, which is close to PLSR calibration performance, and the cross validation performance was better than PSLR model, however, the prediction performance was much worse. The performance of LS-SVM model based on principal component analysis (PCA) was improved with more principal components included, but it was also worse than PLSR model.Using PLS factor loadings instead of principal component scores, the performance of LS-SVM model can be improved and also increased with more factors included. When LS-SVM using the same factors as PLSR model, the correlation coefficients of calibration and validation were 0.926 and 0.907, respectively; SEC and SEP were 8.44 mg/100g and 8.78 mg/100g, respectively. Although, the calibration performance was a bit worse than PLSR model, the prediction performance was improved distinctively.