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Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small thin-peel fruit, although less so for large thick-peel fruit because of the low signal-to-noise ratio of the spectral signal, resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. This research explored a method of spectroscopy for the total soluble solid (TSS) content and acidity detection of ‘Shatian’ pomelo, which are two major parameters of fruit internal flavor. VIS/NIR spectral signal detection of 100 pomelo samples during storage was performed. Detection based on raw data, signal jitter, and scattered light noise removal, feature extraction, and deep learning were performed and combined with modeling detection to achieve an accurate step-by-step detection. Our results showed that 600 W is the optimal light intensity for detecting the internal flavor of pomelo. The TSS content of pomelo is optimally detected using Savitzky-Golay (SG) + multiplicative scatter correction (MSC) + genetic algorithm (GA) + principal component analysis (PCA) + convolutional neural network (CNN) + partial least squares regression (PLSR); however, acidity of pomelo is optimally detected using SG + MSC + GA + PLSR. With the optimal detection method, the coefficient of determination and root mean squared error (RMSE) of the validation set for TSS detection are 0.72 and 0.49, respectively; and for acidity detection are 0.55 and 0.10, respectively. Even though the accuracy is not high, the data are still acceptable and helpful in nondestructive quality grading of large quantities postharvest fruit. Therefore, our results demonstrated that VIS/NIR was feasible for detecting the TSS content and acidity of postharvest pomelo, and for providing a possible method for the nondestructive internal quality detection of other large thick-peel fruit.

The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of &ldquo, Luogang&rdquo, orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky&ndash, Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of &ldquo, orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.

Wireless sensor network (WSN) bidirectional nodes, including the sensing node and the solenoid valve control node, were developed for information collection and micro-irrigation monitoring system in a litchi orchard, aimed at improving the problem of wireless communication barriers and the micro-irrigation management efficiency. The sensing node was composed of an MCU8051, a CC2530 RF chip for communication, a RFX2401for amplification. This node is supposed to collect data from a DHT22 air temperature and humidity sensor, a GY-30 light intensity sensor and a TDR-3 soil moisture sensor. The control node includes an MCU8051, a CC2530 RF chip for communication, and peripheral drive circuits for adapting the bi-stable pulse solenoid valve. The application software and backstage management software were written with these two nodes as the hardware platform, based on ZStack agreement. The maximum effective bidirectional communication distance of the designed nodes reached 1205m in unoccupied regions and 122m in litchi orchards. Within a 30 min working cycle, it could be estimated that two 3.7 V battery with a rated capacity of 3000 mA•h can power the sensing node for time up to 500d. Test results in litchi orchards show that the average packet loss rate is 0.75%. The system was processing smoothly with the above nodes for information acquisition and controlling micro-irrigation in litchi orchards.

The visible/near infrared (VIS/NIR) spectrometer and electronic nose (E-nose) are two commonly used portable and nondestructive detection apparatuses which have a promising application for the quick acquisition of fruit&rsquo, s internal quality in both the orchard and market. However, the accuracy of these instruments is sometimes unsatisfactory, especially for thick peeled fruit like the &lsquo, Aiyuan 38&rsquo, orange, which was investigated in this research. The objective of this research was to find a method to improve the accuracy for the detection of an orange&rsquo, s total soluble solid content (TSS) using a VIS/NIR spectrometer and E-nose. Different spectrum detection positions and conventional feature extraction methods are compared to get the optimal data fusion parameters. The detection model was then built up based on the obtained fusion data under the optimal parameters. Partial least squares regression (PLSR) and mutual information theory (MIT) were applied for feature extraction, and PLSR and principal component analysis (PCA)-back propagation neural network (BPNN) were applied for modeling and detection. PLSR results showed that the sampling reflection spectrum at the position of the calyx results in a better orange TSS detection than other sampling positions. For VIS/NIR reflection spectrum feature extraction, PLSR and MIT results showed that the optimal data process + feature extraction method is Savitzky-Golay + 763 features, when their mutual information values between the feature and TSS value were larger than 0.74. For E-nose feature extraction, PLSR and MIT results showed that the combined feature (combination of 75 s value, average value, average of differential value, integral value, and maximum value) is the optimal feature extraction method, and all features are retained for modeling. The PLSR detection ability of orange TSS based on fusion data is better than the single detection method, with the detection ability of the single detection methods being unsatisfactory. PCA-BPNN has better orange TSS detection ability than PLSR. The R2, RMSE, and slope from the calibration set for PCA-BPNN detection were 0.9695, 0.1895, and 0.9665, respectively, and from the validation set for PCA-BPNN detection were 0.8872, 0.4709, and 1.0871, respectively, indicating that this method can detect orange TSS efficiently.

The orchard irrigation is susceptible significantly to various environmental factor but the approach to predict water demand of irrigation remains an outstanding challenge up to now. In this paper, a prediction model of irrigation based on GA-BP neural network has been proposed in orchards, which selects three environmental factors including air temperature, soil moisture content and light intensity as the input of back. propagation neural network. In order to overcome BP’s disadvantage of being easily stuck in a local minimum, genetic algorithm is used to optimize the weight and threshold of neural network. The results showed that the GA-BP neural network model can express the nonlinear relationship between the water demand of litchi and the main environmental factors more accurately. The mean absolute percentage error (MAPE) is only 0.0283, and the correlation coefficient of the target and output value is 0.9799. Hence, the model can provide a theoretical basis for the further development of the intelligent irrigation decision system of litchi orchards.

This article presents a fruiting branch shaker designed for litchi ( Sonn.) harvesting and field testing results of the mechanical shaker with abscission chemical treatment. During the mature litchi season, mechanical harvesting experiments were conducted on Feizixiao litchi trees with and without applying the abscission chemical (Ethrel, 2-chloroethyl phosphoric acid). Fruit detachment force (FDF) and fruit weight (W) were measured to determine the effect of chemical treatment on the FDF/W ratio. Fruits harvested by different methods were transported to the laboratory in Guangzhou city and stored at 6°C. Testing results showed that more than 88% of litchi fruits untreated with the chemical were removed at a frequency of 26 or 32 Hz and that the harvesting rate was roughly 3.5 times greater than that of hand harvesting. Application of the abscission chemical increased the removal percentage and harvesting rate of litchi fruits by about 10% while showing a decreased damage rate. Storage tests also indicated no significantly different characteristics between untreated fruits harvested by the shaker and by hand. However, there were significant differences between treated fruits and untreated fruit in browning index and total soluble solids content. Keywords: Abscission chemical, Detachment force, Litchi (Litchi chinensis Sonn.), Mechanical harvest, Multi-finger branch shaking.

Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400&ndash, 1100 nm and 900&ndash, 1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) (Table A1) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.

Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small, thin-peeled fruit, though less so for large, thick-peeled fruit due to a weak spectral signal resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. &ldquo, Shatian&rdquo, pomelo is a traditional Chinese large, thick-peeled fruit, and granulation and water loss are two major internal quality factors that influence its storage quality. However, there is no efficient, nondestructive detection method for measuring these factors. Thus, the VIS/NIR spectral signal detection of 120 pomelo samples during storage was performed. Information mining (singular sample elimination, data processing, feature extraction) and modeling were performed in different ways to construct the optimal method for achieving an accurate detection. Our results showed that the water content of postharvest pomelo was optimally detected using the Savitzky&ndash, Golay method (SG) plus the multiplicative scatter correction method (MSC) for data processing, genetic algorithm (GA) for feature extraction, and partial least squares regression (PLSR) for modeling (the coefficient of determination and root mean squared error of the validation set were 0.712 and 0.0488, respectively). Granulation degree was best detected using SG for data processing and PLSR for modeling (the detection accuracy of the validation set was 100%). Additionally, our research showed a weak relationship between the pomelo water content and granulation degree, which provided a reference for the existing debates. Therefore, our results demonstrated that VIS/NIR combined with optimal information mining and modeling methodswas feasible for determining the water content and granulation degree of postharvest pomelo, and for providing references for the nondestructive internal quality detection of other large, thick-peeled fruits.

This study investigated the feasibility of using an hammerhead ribozyme against C II TA, a major regulator of MHC II antigens, to repress the expression of MHC II molecules on Hela cells. A hammerhead ribozyme (Rz464) specific to 463-465 GUC triplet of C II TA and its target gene were transcribed, then mixed up and incubated in vitro. The cleavage products were analyzed by PAGE and silver-staining. Rz464 was then inserted into the pIRES2-EGFP vector (pRz464). Stable transfectants of Hela with pRz464 were tested for class II MHC induction by recombinant human interferon-gamma (IFN-gamma). mRNA of C II TA was measured by RT-PCR. Our results showed that Rz464 could exclusively cleave C II TA RNA. When induced with IFN-gamma, the expression of HLA-DR, -DP, -DQ on pRz464+ Hela was induced, and the mRNA content of C II TA decreased too. It is concluded that Rz464 could inhibit C II TA and thus the family of genes was regulated by C II TA:MHC II molecules. These results provided insight into the future application of Rz464 as a new nucleic acid drug against auto-immune diseases.

We have shown previously that high-efficient gene transfer can be attained in primary hematopoietic cells using liposome-mediated gene transfer strategy. In order to examine the stability of gene expression mediated by this gene transduction protocol, we observed the expression of marker gene in vivo by using bone marrow transplantation (BMT) to engraft lethally irradiated mouse with the genetically modified hematopoietic cells. The results showed that the mouse transplanted with appropriated number of transduced cells remained alive and healthy. The PCR analysis and G418 selection of the spleen colonies and bone marrow cells isolated from lethally irradiated animals 15 days and 30 days after injection of genetically modified bone marrow cells showed that the progeny cells of the transduced hematopoietic stem cells still contained and expressed the transduced genes, suggesting that the hematopoietic system is at least partially re-constructed by the stem cells with marker gene and that the stable expression of foreign genes in vivo can be attained by using this easy and harmless transduction protocol. These findings provide experimental basis for clinician to further investigate the biology of marrow reconstruction and the mechanism of leukemia relapse after BMT.