Chloroplast genomes of seven species of Coryloideae (Betulaceae): structures and comparative analysis.

Coryloideae is a subfamily in the family Betulaceae consisting of four extant genera: Carpinus, Corylus, Ostrya, and Ostryopsis. We sequenced the plastomes of six species of Corylus and one species of Ostryopsis for comparative and phylogenetic analyses. The plastomes are 159-160 kb long and possess typical quadripartite cp architecture. The plastomes show moderate divergence and conserved arrangement. Five mutational hotspots were identified by comparing the plastomes of seven species of Coryloideae: trnG-atpA, trnF-ndhJ, accD-psaI, ndhF-ccsA, and ycf1. We assembled the most complete phylogenomic tree for the family Betulaceae using 68 plastomes. Our cp genomic sequence phylogenetic analyses placed Carpinus, Ostrya, and Ostryopsis in a clade together and left Corylus in a separate clade. Within the genus Corylus, these analyses indicate the existence of five subclades reflecting the phylogeographical relationships among the species. The data offer significant genetic information for the identification of species of the Coryloideae, taxonomic and phylogenetic studies, and molecular breeding.

Electronic Nose-Based Technique for Rapid Detection and Recognition of Moldy Apples.

In this study, the PEN3 electronic nose was used to detect and recognize fresh and moldy apples inoculated with Penicillium expansum and Aspergillus niger, taking Golden Delicious apples as the model subject. Firstly, the apples were divided into two groups: individual apple inoculated only with/without different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then, the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined to simplify the analysis process and improve the accuracy of the results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM), and radial basis function neural network (RBFNN), were applied to analyze the data obtained from the characteristic sensors, aiming at establishing the prediction model of the flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W, and W2W in the PEN3 electronic nose exhibited a strong signal response to the flavor information, indicating most were closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors were used for modeling. The results of the four pattern recognition methods showed that BPNN had the best prediction performance for the training and testing sets for both Groups A and B, with prediction accuracies of 96.3% and 90.0% (Group A), 77.7% and 72.0% (Group B), respectively. Therefore, we demonstrate that the PEN3 electronic nose not only effectively detects and recognizes fresh and moldy apples, but also can distinguish apples inoculated with different molds.

Rapid detection of adulterated lamb meat using near infrared and electronic nose: A F1-score-MRE data fusion approach.

Individual detection techniques cannot guarantee accurate and reliable results when combatting the presence of adulterated lamb meat in the market. Here, we propose an approach combining the electronic nose and near-infrared spectroscopy fusion data with machine learning methods to effectively detect adulterated lamb meat (mixed with duck meat). To comprehensively analyse the data from both techniques, the F1-score-based Model Reliability Estimation (F1-score-MRE) data fusion method was introduced. The obtained results demonstrate the superiority of the F1-score-MRE method, achieving an accuracy rate of 98.58% (F1-score: 0.9855) in detecting adulterated lamb meat. This surpasses the performance of the traditional data fusion and feature concatenation methods. Furthermore, the F1-score-MRE data fusion method exhibited enhanced stability and accuracy compared with the single electronic nose and near-infrared data processed by the self-adaptive BPNN model (accuracy: 94.36%, 93.66%; F1-score: 0.9435, 0.9368). This study offers a promising solution to address concerns regarding adulterated lamb meat.

Research on quantitative detection technology of raccoon-derived ingredient adulteration in sausage products.

This project presents a quantitative detection method to identify raccoon-derived ingredient adulteration in sausage products. The specific copy gene of the raccoon was selected as the target gene. According to the specificity of its primer and probe, the quantitative detection method of raccoon microdrops by droplet digital PCR was established. In addition, the accuracy of the proposed method was verified by artificially mixed samples, and the applicability of this method was tested based on the commercially available products. The experimental results indicate that the raccoon mass (M) and raccoon-extracted DNA concentration have a good linear relationship when the sample content is 5-100 mg, and there is also a significant linear relationship between DNA content and DNA copy number (C) with R 2 = .9982. Therefore, using DNA concentration as the median signal, the conversion equation between raw raccoon mass (M) and DNA copy number (C) could be obtained as follows: M = (C + 177.403)/16.954. The detection of artificially mixed samples and commercial samples shows that the method is accurate and suitable for quantitative adulteration detection of various sausage products in the market.

Development of electronic nose for detection of micro-mechanical damages in strawberries.

A self-developed portable electronic nose and its classification model were designed to detect and differentiate minor mechanical damage to strawberries. The electronic nose utilises four metal oxide sensors and four electrochemical sensors specifically calibrated for strawberry detection. The selected strawberries were subjected to simulated damage using an H2Q-C air bath oscillator at varying speeds and then stored at 4°C to mimic real-life mechanical damage scenarios. Multiple feature extraction methods have been proposed and combined with Principal Component Analysis (PCA) dimensionality reduction for comparative modelling. Following validation with various models such as SVM, KNN, LDA, naive Bayes, and subspace ensemble, the Grid Search-optimised SVM (GS-SVM) method achieved the highest classification accuracy of 0.84 for assessing the degree of strawberry damage. Additionally, the Feature Extraction ensemble classifier achieved the highest classification accuracy (0.89 in determining the time interval of strawberry damage). This experiment demonstrated the feasibility of the self-developed electronic nose for detecting minor mechanical damage in strawberries.

An enzyme-free, ultrasensitive strategy for simultaneous screening of the p-nitrophenyl substituent organophosphorus pesticides.

An enzyme-free, sensitive, and convenient approach was reported for the P-nitrophenyl substituent organophosphorus pesticides (NSOPs) of paraoxon-methyl (PM), paraoxon-ethyl (PE), parathion-methyl (PTM) and parathion-ethyl (PTE)) by indirectly quantification of the 4-nitrophenol (4-NP, hydrolysis product of the NSOPs). NaOH instead of hydrolase/nanozyme was applied, and temperature, pH, ultrasound was investigated to improve the NSOPs hydrolysis. Under the optimized conditions, the hydrolysis efficiencies were up to 99.9 %, 99.9 %, 99.6 %, 96.0 % for PM (10 min), PE (30 min), PTM (90 min) and PTE (120 min), based on which a low detection limits of 0.06 (PM), 0.07 (PE), 0.06 (PTM) and 0.07 (PTE) ppb were calculated with the 4-NP detection limit (0.03 ppb). Furthermore, the method exhibited good performance for the NSOPs with recoveries from 88.87 % to 100.33 % in real samples. This indirect approach offered an ultrasensitive alternative for the NSOPs detection, which holds great potential in practical application for the assessment of food safety and environmental risks.

An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm.

To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization-back-propagation neural network (PSO-BPNN), a gray wolf optimization-backward propagation neural network (GWO-BPNN), and a sparrow search algorithm-backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.40%. This study provides an important reference value for the application of an electronic nose in the non-destructive and rapid detection of fungal infection in apples.

Label-free hairpin-like aptamer and EIS-based practical, biostable sensor for acetamiprid detection.

Acetamiprid (ACE) is a kind of broad-spectrum pesticide that has potential health risk to human beings. Aptamers (Ap-DNA (1)) have a great potential as analytical tools for pesticide detection. In this work, a label-free electrochemical sensing assay for ACE determination is presented by electrochemical impedance spectroscopy (EIS). And the specific binding model between ACE and Ap-DNA (1) was further investigated for the first time. Circular dichroism (CD) spectroscopy and EIS demonstrated that the single strand AP-DNA (1) first formed a loosely secondary structure in Tris-HClO4 (20 mM, pH = 7.4), and then transformed into a more stable hairpin-like structure when incubated in binding buffer (B-buffer). The formed stem-loop bulge provides the specific capturing sites for ACE, forming ACE/AP-DNA (1) complex, and induced the RCT (charge transfer resistance) increase between the solution-based redox probe [Fe(CN)6]3-/4- and the electrode surface. The change of ΔRCT (charge transfer resistance change, ΔRCT = RCT(after)-RCT(before)) is positively related to the ACE level. As a result, the AP-DNA (1) biosensor showed a high sensitivity with the ACE concentration range spanning from 5 nM to 200 mM and a detection limit of 1 nM. The impedimetric AP-DNA (1) sensor also showed good selectivity to ACE over other selected pesticides and exhbited excellent performance in environmental water and orange juice samples analysis, with spiked recoveries in the range of 85.8% to 93.4% in lake water and 83.7% to 89.4% in orange juice. With good performance characteristics of practicality, sensitivity and selectivity, the AP-DNA (1) sensor holds a promising application for the on-site ACE detection.