Genome-wide identification and expression analysis of the MYB transcription factor in Japanese plum (Prunus salicina).

MYB proteins constitute one of the largest transcription factor families in plants, members of which are involved in various plant physiological and biochemical processes. Japanese plum (Prunus salicina) is one of the important stone fruit crops worldwide. To date, no comprehensive study of the MYB family in Japanese plum has been reported. In this study, we performed genome-wide analysis of MYB genes in Japanese plum including the phylogeny, gene structures, protein motifs, chromosomal locations, collinearity and expression patterns analysis. A total of 96 Japanese plum R2R3-MYB (PsMYB) genes were characterized and distributed on 8 chromosomes at various densities. Collinearity analysis indicated that the segmental duplication events played a crucial role in the expansion of PsMYB genes, and the interspecies synteny analysis revealed the orthologous gene pairs between Japanese plum and other four selected Rosaceae species. The 96 PsMYB genes could be classified into 27 subgroups based on phylogenetic topology, as supported by the conserved gene structures and motif compositions. Further comparative phylogenetic analysis revealed the functional divergence of MYB gene family during evolution, and three subgroups which included only Rasaceae MYB genes were identified. Expression analysis revealed the distinct expression profiles of the PsMYB genes, and further functional predictions found some of them might be associated with the plum fruit quality traits. Our researches provide a global insight into the organization, phylogeny, evolution and expression patterns of the PsMYB genes, and contribute to the greater understanding of their functional roles in Japanese plum.

Detection of 1-OHPyr in human urine using SERS with injection under wet liquid-liquid self-assembled films of ß-CD-coated gold nanoparticles and deep learning.

1-Hydroxypyrene (1-OHPyr), a typical hydroxylated polycyclic aromatic hydrocarbon (OH-PAH), has been commonly regarded as a urinary biomarker for assessing human exposure and health risks of PAHs. Herein, a fast and sensitive method was developed for the determination of 1-OHPyr in urine using surface-enhanced Raman spectroscopy (SERS) combined with deep learning (DL). After emulsification, urinary 1-OHPyr was separated using simple liquid-liquid extraction. Gold nanoparticles with ß-cyclodextrin (ß-CD@AuNPs) were synthesized, and homogeneous and ordered ß-CD@AuNP films were prepared through a liquid-liquid interface self-assembly process. The separated 1-OHPyr was injected under wet assembled films for SERS detection. Concentration as low as 0.05 µg mL-1 of 1-OHPyr in urine could still be detected, and the relative standard deviation was 5.5 %, and this was ascribed to the adsorption of ß-CD and the high-probability contact between 1-OHPyr molecules and the nanogap of assembled films under the action of capillary force. Meanwhile, a convolutional neural network (CNN), a classical DL network architecture, was adopted to build the prediction model, and the model was further simplified by genetic algorithm (GA). CNN combined with a GA obtained optimized results with determination coefficient and a root mean square error of prediction sets of 0.9639 and 0.6327, respectively, outperforming other models. Overall, the proposed method achieves fast and accurate detection of 1-OHPyr in urine, improves the assessment human exposure to PAHs and is expected to have applications in the analysis of other OH-PAHs in complex environments.

SERS with Flexible ß-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network.

The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling ß-cyclodextrin-modified gold nanoparticles (ß-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (ß-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 µg/cm2, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the ß-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field.

Surface-enhanced Raman spectroscopy charged probes under inverted superhydrophobic platform for detection of agricultural chemicals residues in rice combined with lightweight deep learning network.

In this study, surface-enhanced Raman spectroscopy (SERS) charged probes and an inverted superhydrophobic platform were used to develop a detection method for agricultural chemicals residues (ACRs) in rice combined with lightweight deep learning network. First, positively and negatively charged probes were prepared to adsorb ACRs molecules to SERS substrate. An inverted superhydrophobic platform was prepared to alleviate the coffee ring effect and induce tight self-assembly of nanoparticles for high sensitivity. Chlormequat chloride of 15.5-0.05 mg/L and acephate of 100.2-0.2 mg/L in rice were measured with the relative standard deviation of 4.15% and 6.25%. SqueezeNet were used to develop regression models for the analysis of chlormequat chloride and acephate. And the excellent performances were obtained with the coefficients of determination of prediction of 0.9836 and 0.9826 and root-mean-square errors of prediction of 0.49 and 4.08. Therefore, the proposed method can realize sensitive and accurate detection of ACRs in rice.

Detection of apple fruit damages through Raman spectroscopy with cascade forest.

Apple fruit damages seriously cause product and economic losses, infringe consumer rights and interests, and have harmful effects on human and livestock health. In this study, Raman spectroscopy (RS) and cascade forest (CForest) were adopted to determine apple fruit damages. First, the RS spectra of healthy, bruised, Rhizopus-infected, and Botrytis-infected apples were measured. Spectral changes and band attribution were analyzed. Different modeling methods were combined with various pre-processing and dimension reduction methods to construct recognition models. Among all models, CForest constructed with full spectra processed by Savitsky-Golay smoothing obtained the best performance with accuracies of 100%, 91.96%, and 92.80% in the training, validation, and test sets (ACCTE). And the modeling time is reduced to 1/3 of the full-spectra model with a similar ACCTE of 91.56% after principal component analysis. Overall, RS and CForest provided a non-destructive, rapid, and accurate identification of apple fruit damages and could be used in disease recognition and safety assurance of other fruits.

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels.

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception-attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception-attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.

Fast and Quantitative Analysis of Ediphenphos Residue in Rice Using Surface-Enhanced Raman Spectroscopy.

Detection of residual farm chemicals in agricultural crops is a hot topic in the field of food safety. In this study, ediphenphos residue in rice was detected using surface-enhanced Raman spectroscopy (SERS) on a portable Raman spectrometer. A simple pretreatment method for rice samples was developed, and uniform gold nanorods were used for SERS measurement. Characteristic signals can still be detected when ediphenphos concentration in rice extraction solution was higher than or equal to 0.1 mg/L. Quantitative analysis of ediphenphos was conducted by regression models developed using partial least-squares regression, random forest and kernel principal component analysis, and root-mean-square error of cross validation, coefficient of determination and relative predicted deviation of optimal model were 0.022 mg/L, 0.9967 and 297.45, which indicated the proposed method can predict ediphenphos concentration with high precision. To validate the feasibility of practical application further, rice samples spiked with 10, 5, 1, 0.5, and 0.1 µg/g ediphenphos residue were analyzed using the above method. The predicted recovery was in the range of 93.4% to 102%, and the predicted error was small for residue of each concentration. These results demonstrated that the presented method could be used for accurate and quantitative detection of ediphenphos residue in rice. PRACTICAL APPLICATION: This study developed a surface-enhanced Raman spectroscopy (SERS) method for detection of ediphenphos in rice coupled with simple extraction protocol and gold nanorods on a portable Raman spectrometer. SERS is a rapid and accurate method which can be applied in agricultural grain safety inspection.

Quantitative Determination of Chlormequat Chloride Residue in Wheat Using Surface-Enhanced Raman Spectroscopy.

A simple and sensitive method for detection of chlormequat chloride residue in wheat was developed using surface-enhanced Raman spectroscopy (SERS) coupled with chemometric methods on a portable Raman spectrometer. Pretreatment of wheat samples was performed using a two-step extraction procedure. Effective and uniform active substrate (gold nanorods) was prepared and mixed with the sample extraction solution for SERS measurement. The limit of detection for chlormequat chloride in wheat extracting solutions and wheat samples was 0.25 mg/L and 0.25 µg/g, which was far below the maximum residual value in wheat of China. Then, support vector regression (SVR) and kernel principal component analysis (KPCA), multiple linear regression, and partial least squares regression were employed to develop the regression models for quantitative analysis of chlormequat chloride residue with spectra around the characteristic peaks at 666, 713, and 853 cm-1. As for the residue in wheat, the predicted recovery of established optimal model was in the range of 94.7% to 104.6%, and the standard deviation was about 0.007 mg/L to 0.066 mg/L. The results demonstrated that SERS, SVR, and KPCA can provide the accurate and quantitative determination for chlormequat chloride residue in wheat.

Fast detection of fenthion on fruit and vegetable peel using dynamic surface-enhanced Raman spectroscopy and random forests with variable selection.

Dynamic surface-enhanced Raman spectroscopy (D-SERS) based on the state change of the substrate not only significantly enhances but also provides a highly reproducible Raman signal. Hence, we develop a fast and accurate method for the detection of fenthion on fruit and vegetable peel using D-SERS and random forests (RF) with variable selection. With uniform Ag nanoparticles, the dynamic spectra of fenthion solution at different concentrations were obtained using D-SERS, and fenthion solution greater than or equal to 0.05mg/L can be detected. Then, the quantitative analysis models of fenthion were developed by RF with variable selection for spectra of different range. The model of best performance is developed by RF and spectra of characteristic range with higher RF importance (top 40%), and the root mean square error of cross-validation is 0.0101mg/L. Moreover, the fenthion residue of tomato, pear, and cabbage peel were extracted by a swab dipped in ethanol and analyzed using the above method to further validate the practical effect. Compared to gas chromatography, the maximal relative deviation is below 12.5%, and the predicted recovery is between 87.5% and 112.5%. Accordingly, D-SERS and RF with variable selection can realize the fast, simple, ultrasensitive, and accurate analysis of fenthion residue on fruit and vegetable peel.