Huazhong Lu

Quality Detection of Litchi Stored in Different Environments Using an Electronic Nose

The purpose of this paper was to explore the utility of an electronic nose to detect the quality of litchi fruit stored in different environments. In this study, a PEN3 electronic nose was adopted to test the storage time and hardness of litchi that were stored in three different types of environment (room temperature, refrigerator and controlled-atmosphere). After acquiring data about the hardness of the sample and from the electronic nose, linear discriminant analysis (LDA), canonical correlation analysis (CCA), BP neural network (BPNN) and BP neural network-partial least squares regression (BPNN-PLSR), were employed for data processing. The experimental results showed that the hardness of litchi fruits stored in all three environments decreased during storage. The litchi stored at room temperature had the fastest rate of decrease in hardness, followed by those stored in a refrigerator environment and under a controlled-atmosphere. LDA has a poor ability to classify the storage time of the three environments in which litchi was stored. BPNN can effectively recognize the storage time of litchi stored in a refrigerator and a controlled-atmosphere environment. However, the BPNN classification of the effect of room temperature storage on litchi was poor. CCA results show a significant correlation between electronic nose data and hardness data under the room temperature, and the correlation is more obvious for those under the refrigerator environment and controlled-atmosphere environment. The BPNN-PLSR can effectively predict the hardness of litchi under refrigerator storage conditions and a controlled-atmosphere environment. However, the BPNN-PLSR prediction of the effect of room temperature storage on litchi and global environment storage on litchi were poor. Thus, this experiment proved that an electronic nose can detect the quality of litchi under refrigeratored storage and a controlled-atmosphere environment. These results provide a useful reference for future studies on nondestructive and intelligent monitoring of fruit quality.

Improved Algorithms for the Classification of Rough Rice Using a Bionic Electronic Nose Based on PCA and the Wilks Distribution

Principal Component Analysis (PCA) is one of the main methods used for electronic nose pattern recognition. However, poor classification performance is common in classification and recognition when using regular PCA. This paper aims to improve the classification performance of regular PCA based on the existing Wilks Λ-statistic (i.e., combined PCA with the Wilks distribution). The improved algorithms, which combine regular PCA with the Wilks Λ-statistic, were developed after analysing the functionality and defects of PCA. Verification tests were conducted using a PEN3 electronic nose. The collected samples consisted of the volatiles of six varieties of rough rice (Zhongxiang1, Xiangwan13, Yaopingxiang, WufengyouT025, Pin 36, and Youyou122), grown in same area and season. The first two principal components used as analysis vectors cannot perform the rough rice varieties classification task based on a regular PCA. Using the improved algorithms, which combine the regular PCA with the Wilks Λ-statistic, many different principal components were selected as analysis vectors. The set of data points of the Mahalanobis distance between each of the varieties of rough rice was selected to estimate the performance of the classification. The result illustrates that the rough rice varieties classification task is achieved well using the improved algorithm. A Probabilistic Neural Networks (PNN) was also established to test the effectiveness of the improved algorithms. The first two principal components (namely PC1 and PC2) and the first and fifth principal component (namely PC1 and PC5) were selected as the inputs of PNN for the classification of the six rough rice varieties. The results indicate that the classification accuracy based on the improved algorithm was improved by 6.67% compared to the results of the regular method. These results prove the effectiveness of using the Wilks Λ-statistic to improve the classification accuracy of the regular PCA approach. The results also indicate that the electronic nose provides a non-destructive and rapid classification method for rough rice.

Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis

The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.

Detecting and Monitoring the Flavor of Tomato (Solanum lycopersicum) under the Impact of Postharvest Handlings by Physicochemical Parameters and Electronic Nose

The objective of this study was to detect and monitor the flavor of tomatoes, as impacted by different postharvest handlings, including chilling storage (CS) and blanching treatment (BT). CS tomatoes were stored in a refrigerator at 5 °C and tested at storage day 0, 3, and 7. BT tomatoes were dipped in 50 or 100 °C water for 1 min, and tested immediately. The taste, mouth feel, and aroma of tomatoes were evaluated by testing the total soluble solid content (TSS), titratable acidity (TA), ratio of TSS and TA (TSS/TA), firmness, and electronic nose (E-nose) response to tomatoes. The experimental results showed that the CS can prevent taste and firmness loss to a certain extent, but the sensory results indicated that CS accelerated flavor loss due to the TSS/TA of CS tomatoes increasing slower than control. The taste and firmness of tomatoes were impacted slightly by 50 °C BT, and were significantly impacted by 100 °C BT. Based on physicochemical parameters, different postharvest handling treatments for tomatoes could not be classified except for the 100 °C BT treated tomatoes, which were significantly impacted in terms of taste and mouth feel. The E-nose is an efficient way to detect differences in postharvest handling treatments for tomatoes, and indicated significant aroma changes for CS and BT treated tomato fruit. The classification of tomatoes after different postharvest handling treatments, based on comprehensive flavor (physicochemical parameters and E-nose combined data), is better than that based on single physicochemical parameters or E-nose, and the comprehensive flavor of 100 °C BT tomatoes changed the most. Even so, the tomato flavor change during postharvest handlings is suggested to be detected and monitored by single E-nose data. The E-nose has also been proved as a feasible way to predict the TSS and firmness of tomato fruit rather than TA or TSS/TA, during the postharvest handing process.

A modified mean deviation threshold function based on fast Fourier transform and its application in litchi rest storage life recognition using an electronic nose

Since gas sensor drift is a main limitation for the application of an electronic nose, and a reference standard is necessary for shelf management of litchi fruit, a modified mean deviation threshold function based on fast Fourier transform (MDFF–FFT) for electronic nose drift elimination and a new concept the rest storage life (RSL) for litchi fruit shelf situation evaluation have been constructed in this study. Three commonly used threshold acquisition methods, unbiased estimator, fixed threshold, and mini-max principle were evaluated to instead of selecting threshold value randomly for present MDFF–FFT. A PEN3 portable electronic nose was applied to recognize the RSL of litchi during storage across room temperature (RT), refrigerator environment (RE) and controlled-atmosphere (CA) environments. Linear discriminant analysis (LDA), probabilistic neural network (PNN), and partial least squares regression (PLSR) were used to compare the RSL classification effect, recognition accuracy, and predict ability of litchi stored in the three environments based on electronic nose with the drift elimination of different threshold acquisition methods using MDTF–FFT. The results showed that an electronic nose has the potential to recognize the RSL of litchi stored in different environments. Unbiased estimator method can provide better threshold than other threshold acquisition methods for MDTF–FFT. After drift elimination by unbiased estimator method combined with MDTF–FFT, litchi RSL can be classified, recognized and predicted by electronic nose effectively, the accuracy of which was higher than control (no drift elimination) and drift elimination with other methods.

Study of the similarity and recognition between volatiles of brown rice plant hoppers and rice stem based on the electronic nose

Abstract The volatiles of Brown rice plant hopper (BRPH) itself is an important evidence for BRPH electronic nose detection. However, during infestation, BRPH always sucks the juice from the rice stem, therefore, a study on the similarity between BRPH’s volatiles and undamaged rice stem volatiles might help determine whether the volatile contents of BRPH would be influenced by the sucking of the rice stem juice. If so, recognizing BRPH from rice stem should be a crucial step to reduce the misjudgment of BRPH occurrence prediction by using electronic nose, which has not been reported until now. This paper used an electronic nose (PEN3) sample of the volatile of U3IN (under the 3th-instar nymphs), O3IN (over the 3th-instar nymphs) and healthy rice stem. Hierarchical clustering analysis (HCA), Loading analysis (Loadings), principal component analysis (PCA), k-nearest neighbor (KNN), probabilistic neural network (PNN), and support vector machine (SVM) were used for data analysis. HCA, Loadings, and PCA results proved that certain similarities exist between volatiles of rice stem and BRPH, Loadings and PCA results also indicated the volatile similarity between O3IN and rice stem is stronger than the volatile similarity between U3IN and rice stem. To reduce the redundant information and improve computation efficiency, according to Loadings and PCA results, sensor R5 of electronic nose has been be removed, then, the fist four principle components has been kept as the feature values. KNN, PNN and SVM all can recognize rice stem, O3IN, and U3IN effectively, however, KNN and PNN are more fit to solve the problem of rice stem and BRHP recognition than SVM. This experiment results proved that certain similarities exist between volatiles of rice stem and BRPH, also figured out the feasible way to recognize rice stem and BRPH, which could provide a reference for further research of BRPH prediction.

Fruit Surface Color Recognition of Postharvest Litchi during Storage Based on Electronic Nose

An electronic nose and a colorimeter were used to sample post-harvest litchis stored in three different storage environments (room temperature, refrigerator and controlled atmosphere) in order to explore the feasibility of electronic nose for fruit surface color recognition. BP Neural Network (BPNN), Simple Correlation Analysis (SCA), Canonical Correlation Analysis (CCA) and Partial Least Squares Regression (PLSR) were used for data processing. The experimental results demonstrate that with the increasing of storage time, the rate of decrease of color values (L*, a*, b*) is the fastest for litchis stored at the room temperature, followed by litchis stored in a refrigerator environment and a controlled atmosphere environment. During storage, the change in sensors’ response is the fastest for litchis stored at room temperature, followed by litchis stored in a refrigerator environment and litchis stored in a controlled atmosphere environment. The BPNN can effectively classify the storage time of litchis stored in a refrigerator environment and in a controlled atmosphere environment. However, the BPNN classification effect for litchis stored at room temperature is poor. Both of the CCA and the SCA results show that a certain correlations exists between the surface color values of litchi and the electronic nose response of litchi. The PLSR result shows that the prediction effect of surface a* prediction in litchis stored in a refrigerator environment is good. This research demonstrates the feasibility of the electronic nose for fruit surface color recognition, thereby providing a reference for fruit quality monitoring.

Extension and Application of Precision Rice Hill-Drop Drilling Machine

The precision rice hill-drop drilling machine developed by South China Agricultural University (SCAU) can form a ridge, open a drilling furrow on the ridge and a water furrow between the ridges, and hill-drop seeds in the drilling furrow synchronously according to the requirement of rice planting. In comparison with the traditional manual broadcasting, mechanical broadcasting and air broadcasting, precision hill-drop drilling has obvious advantages such as good ventilation and light transmission for orderly seedlings, low risks of the infection by pests and diseases and good lodging resistance, hence a better growth and lower cost can be achieved in later stages of rice production. In order to investigate the extension and application effect of the precision rice hill-drop drilling machines with the difference of soil conditions, rice varieties and growth seasons, large numbers of extension and application experiments were conducted in China, Laos, Burma, Thailand and Vietnam from 2006 to 2011. Results showed that the average yield for precision rice hill-drop drilling reached 8250-9000kg/ha. Compared with manual broadcasting, manual seedling broadcasting and manual transplanting, the yield increased by 10%, 8% and 6%, respectively, and the maximum economic benefit increased individually by RMB 750 yuan/ha, RMB 1,125 yuan/ha, and RMB 1,875 yuan/ha, respectively.