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Clenbuterol (CB) is a synthetic β-receptor agonist which can be used to improve carcass leanness in swine, but its residues in pork also pose health risks. In this report, surface-enhanced Raman scattering (SERS) technology was used to achieve rapid detection and identification of clenbuterol hydrochloride (CB) residues. First, the effects of several different organic solvents on the extraction efficiency were compared, and it was found that clenbuterol in pork had a better enhancement effect using ethyl acetate as an extraction agent. Then, SERS signals of clenbuterol in different solvents were compared, and it was found that clenbuterol had a better enhancement effect in an aqueous solution. Therefore, water was chosen as the solvent for clenbuterol detection. Next, enhancement effect was compared using different concentration of sodium chloride solution as the aggregating compound. Finally, pork samples with different clenbuterol content (1, 3, 5, 7, 9, and 10 µg/g) were prepared for quantitative analysis. The SERS spectra of samples were collected with 0.5 mol/L of NaCl solution as aggregating compound and gold colloid as an enhanced substrate. Multiple scattering correction (MSC) and automatic Whittaker filter (AWF) were used for preprocessing, and the fluorescence background contained in the original Raman spectra was removed. A unary linear regression model was established between SERS intensity at 1472 cm-1 and clenbuterol content in pork samples. The model had a better linear relationship with a correlation coefficient R2 of 0.99 and a root mean square error of 0.263 µg/g. This method can be used for rapid screening of pork containing clenbuterol in the market. [ABSTRACT FROM AUTHOR]

Ractopamine, frequently misused to promote lean meat production, raises significant food safety issues. This study introduces a rapid detection approach for ractopamine in pork using size-turnable Au@Ag NPs core-shell nanoparticles as surface-enhanced Raman scatting (SERS) substrates. By changing the diameter of the gold core and the thickness of the silver shell, the effects of them on the SERS enhancement of ractopamine signals were carefully studied. Interestingly, with the increase of the silver shell thickness, the SERS intensity exhibited a pattern of initially increasing, then decreasing, followed by another increase and subsequent decrease. In contrast, the gold core enlargement displayed a simpler trend: the SERS intensity first rose and then diminished. These phenomena reveal the relationship between SERS signal enhancement effect of ractopamine and local surface plasmon resonance (LSPR) and the contact area between ractopamine molecule and substrate. The most effective SERS enhancement was identified using Au@Ag NPs nanoparticles having a 32 nm Au core coupled with an 18 nm Ag shell. Further quantitative analysis showed the impressive regression correlation coefficient (R2) of 0.99, root mean square error (RMSE) of prediction at 0.012 μg/g, and the limit of detection (LOD) of 19 ng/g for ractopamine detection model spanning concentrations from 0.1 to 10 μg/g. [ABSTRACT FROM AUTHOR]

Escherichia coli , Listeria monocytogenes , and Salmonella typhi are three pathogens commonly found in food. Label-free enhanced substrates have limitations in achieving high sensitivity in three bacteria detection. To enable low-concentration detection and differentiation of foodborne pathogens, this research presents an optimized detection strategy using Au @Ag NPs as the enhancing substrate for SERS technology. The impact of the particle size of Au @Ag NPs and the pH of the borate buffer solution on enhancing the Raman signals of these bacteria was investigated through electromagnetic and chemical enhancement mechanisms. By evaluating the intensity of bacterial Raman spectra, and employing chemometric techniques, the concentration and classification of the three bacterial species were predicted and analyzed. The research findings revealed that the optimized detection method was able to detect three pathogens at the concentration lower than 3 lg CFU/mL. Logarithmic fitting of the bacteria enabled prediction correlations above 0.98 and prediction root mean square errors below 0.17. After normalizing, efficient discrimination of low-concentration bacteria was achieved using the PLS-DA, with a classification prediction correlation greater than 0.94. The fabrication process of the proposed enhancement substrate is simple, but the stability of signal detection needs further improvement in subsequent experimental testing steps. [ABSTRACT FROM AUTHOR]

• A sample collection component was designed to detect clenbuterol. • The problem of poor reproducibility of SERS detection was solved. • The optimal particle size of gold colloid for detection of clenbuterol was found. • This method can be used for the rapid crude screening of clenbuterol in pork. Clenbuterol is often used as a feed additive to increase the percentage of lean meat in livestock. Meat containing clenbuterol can cause many illnesses and even death for people. In this paper, the particle growth method was used to prepare gold colloids of different sizes, and the enhanced effectiveness of gold colloids of different sizes on clenbuterol in pork was investigated. The results showed that the gold colloid with the best enhanced effectiveness for clenbuterol had a particle size of approximately 90 nm. Second, a sample collection component was designed to detect clenbuterol from bottom to top, solving the problem of poor reproducibility of Surface-enhanced Raman scattering (SERS) detection caused by different droplet sizes and shapes. Then, the influence of different volumes of samples and concentrations of aggregating compounds on the enhanced effectiveness was optimized. The results showed that, based on the sample collection components designed in this article, 5 µL of enhanced substrate, 7.5 µL of clenbuterol and 3 µL of 1 mol/L mixed detection of NaCl solution had the best enhanced performance. Finally, 88 pork samples (0.5, 1, 1.5,..., 10, 12, 14 µg/g) with different concentrations were divided into correction sets and prediction sets in a ratio of 3:1. Unary linear regression models were established between the concentration of clenbuterol residue in the pork and the intensity of the bands at 390, 648, 1259, 1472, and 1601 cm−1. The results showed that the unary linear regression models at 390, 648, and 1259 cm−1 had lower root mean square errors than those at 1472 and 1601 cm−1. The intensity of the three bands and the concentration of clenbuterol residue in the pork were selected to establish a multiple linear regression model, and the concentration of clenbuterol residue in the pork was predicted. The results showed that the determination coefficients (R2) of the correction set and the prediction set were 0.99 and 0.99, respectively. The root mean square errors (RMSE) of the correction set and the prediction set were 0.169 and 0.184, respectively. The detection limit of clenbuterol in pork by this method is 42 ng/g, which can realize the crude screening of pork containing clenbuterol in the market. [ABSTRACT FROM AUTHOR]

Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (R P) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and R P = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork. [ABSTRACT FROM AUTHOR]

• The quality of apples on the tree was detected by flying method at close range. • A miniature low-cost multispectral sensor was optimized. • An RGB-D camera was used to recognize the position and size of apples. • Compared and analyzed the static and dynamic acquisition results of the spectrum. • The flying detector can be used in smart agriculture and unmanned farms. Quality detection of apples on trees is important for predicting apple maturity, harvest time and crop yield. Based on the multispectral sensor and red green blue-depth (RGB-D) camera carried by micro unmanned aerial vehicles (UAVs), this study designed a close detection system for the determination of the internal and external quality of apples. An RGB-D camera was used to perceive the spatial position of the apple and predict the apple diameter. The multispectral sensor was self-optimized, inexpensive, small and easy to install in the acquisition module for apple soluble solid content (SSC) detection. The coefficient of the correlation between apple size values obtained by automatic and manual measurements of the system is 0.9672. The root mean square error was 2.2624 mm. The apple SSC detection test includes spectrum acquisition experiments under static and dynamic UAV conditions. The results showed that the linear model established by partial least squares regression method performed slightly better than the nonlinear model established by support vector regression method under both conditions. The dynamic modeling results were slightly worse than the static modeling results, but there was little difference between the two sets of modeling results. The correlation coefficient of the prediction set of the model was 0.8568 and the root mean square error was 0.7753 °Brix. Results revealed that the flight sensor system designed in this study has the potential to detect the internal and external quality of apples on trees. [ABSTRACT FROM AUTHOR]

Clenbuterol and ractopamine can be used to improve carcass leanness in swine, but their residues in pork also pose health risks. This study used surface-enhanced Raman scatter (SERS) technology to establish MLR and PLSR prediction models for clenbuterol and ractopamine, respectively, to achieve rapid detection and identification of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues. In this report, the detection of clenbuterol and ractopamine was carried out using three gold colloids with different particle sizes as enhanced substrates. It was found that the larger particle size of gold nanoparticles was, the better enhancement effect was, which indicated that gold nanoparticles with larger particle sizes were more conducive to the adsorption of RAC and CL. Then, the SERS spectra of 0.1–10 µg/mL clenbuterol and ractopamine solution were collected by using NaCl aqueous solution of 1 mol/L as aggregating compound and gold colloid A as enhanced substrate. Automatic Whittaker Filter (AWF) was used for preprocessing to reduce fluorescence background contained in the original Raman spectra collected. Finally, models were established between SERS intensity at each characteristic peak and the concentration of clenbuterol and ractopamine, respectively. The results show that the PLSR model for clenbuterol using six peaks was stable. The correlation coefficient R 2 and the root mean square error of correction set were 0.99 and 0.025 µg/mL, respectively. The correlation coefficient R 2 and the root mean square error of prediction set were 0.99 and 0.026 µg/mL, respectively. The MLR model for ractopamine using six peaks had a higher correlation coefficient and a lower root mean square error (RMSEC) than other models. The correlation coefficient R2 and the root mean square error of correction set were 0.99 and 0.043 µg/mL, respectively. The correlation coefficient R2 and the root mean square error of prediction set were 0.99 and 0.05 µg/mL, respectively. The detection limit (LOD) value of this method for the detection of clenbuterol and ractopamine was 18 ng/mL and 24 ng/mL, respectively. This study is helpful for further development of a simple SERS detection method of clenbuterol and ractopamine based on gold colloid. • Raman enhancement signal of clenbuterol and ractopamine were compared using three gold colloids with different particle sizes. • Gold nanoparticles with larger particle sizes were more conducive to the adsorption of RAC and CL. • The methods can realize clenbuterol and ractopamine at trace concentrations detection. [ABSTRACT FROM AUTHOR]

• Error sources analyzed and improved methods proposed for crop height evaluation. • Need for spatial auxiliary information in height estimation discussed. • Impacts of different ground control point configurations on crop height extraction. • Strategies discussed for height estimation relative to cost and accuracy. Unmanned aerial vehicles (UAVs) have emerged as a promising platform for determining the dynamic phenotypic traits of crops in the field in a rapid and cost-effective manner. Crop height is a common and important phenotypic trait, and its acquisition with high accuracy usually requires spatial auxiliary (SA) information, such as a digital terrain model in the early growing season, digital surface models later in the season, ground control points, and ground truth of crop height. The reasonable selection of SA information involves balancing the cost and accuracy of crop height acquisition, but this problem has not been systematically studied and it needs to be resolved urgently in the agricultural industry. In this study, we compared four rapeseed height estimation methods using UAV images collected at three growth stages based on the structure from motion algorithm, where one method had complete data and the other three had incomplete SA information. To reduce the crop height estimation errors with incomplete data, improved methods were developed to construct the missing SA information. The optimum results were obtained using complete SA information, where R 2 was 0.932 and the root mean square error (RMSE) was 0.026 m. For crop height acquisition using incomplete data, the R 2 values could be controlled above 0.445 and RMSE below 0.146 m. In this study, systematic strategies were developed for selecting appropriate methods to acquire crop height with reasonable accuracy while balancing the cost requirement for use in scientific research and agricultural production. [ABSTRACT FROM AUTHOR]