Advance in Detection Technique of Lean Meat Powder Residues in Meat Using SERS: A Review.

Food that contains lean meat powder (LMP) can cause human health issues, such as nausea, headaches, and even death for consumers. Traditional methods for detecting LMP residues in meat are often time-consuming and complex and lack sensitivity. This article provides a review of the research progress on the use of surface-enhanced Raman spectroscopy (SERS) technology for detecting residues of LMP in meat. The review also discusses several applications of SERS technology for detecting residues of LMP in meat, including the enhanced detection of LMP residues in meat based on single metal nanoparticles, combining metal nanoparticles with adsorbent materials, combining metal nanoparticles with immunizing and other chemicals, and combining the SERS technology with related techniques. As SERS technology continues to develop and improve, it is expected to become an even more widely used and effective tool for detecting residues of LMP in meat.

Toward commercial applications of LED and laser-induced fluorescence techniques for food identity, quality, and safety monitoring: A review.

The assessment of food safety and quality is a matter of paramount importance, especially considering the challenges posed by climate change. Convenient, eco-friendly, and non-destructive techniques have attracted extensive attention in the food industry because they can retain food safety and quality. Fluorescence radiation, the process by which fluorophore emits light upon the absorption of ultraviolet or visible light, offers the advantages of high sensitivity and selectivity. The use of excitation-emission matrix (EEM) has been extensively explored in the food industry, but on-site detection of EEMs remain a challenge. To address this limitation, laser-induced fluorescence (LIF) and light emitting diode-induced fluorescence (LED-IF) have been implemented in many cases to facilitate the transition of fluorescence measurements from the laboratory to commercial applications. This review provides an overview of the application of commercially available LIF/LED-IF devices for non-destructive food measurement and recent studies that focus on the development of LIF/LED-IF devices for commercial applications. These studies were categorized into two stages: the preliminary exploration stage, which emphasizes the selection of an appropriate excitation wavelength based on the combination of EEM and chemometrics, and the pre-application stage, where experiments were conducted on scouting with specific excitation wavelength. Although commercially available devices have emerged in many research fields, only a limited number have been reported for use in the food industry. Future studies should focus on enhancing the diversity of test samples and parameters that can be measured by a single device, exploring the application of LIF techniques for detecting low-concentration substances in food, investigating more quantitative approaches, and developing embedded computing devices.

A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders.

Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As the offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from the powder and capsule, the SORS spectra for each sample were resolved into pure component spectra using self-modeling mixture analysis (SMA) and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials.

[Identification of Fertilized Chicken Eggs Based on Visible/Near-Infrared Spectrum During Early Stage of Incubation].

In the process of chicken egg hatching, some eggs can not be hatched successfully due to the absence of fertilization. These eggs not only cause a lot of waste, but also infect other normal eggs with bacteria. In the study, the fertilized eggs and clear eggs is identified by using the visible/near-infrared spectrum. It is of great necessity to get the best time of identifying the clear eggs in the early of hatching, so the variation of eggs' quality in the condition of hatching over time is studied. The results show that eggs are fresh after 24 hours' hatching and eggs can not be eaten after 72 hours' hatching while the best time of identification is within 36 hours. Static acquisition system is developed based on visible/near-infrared transmission spectrum for acquiring spectrum. Comparing the effect of the model of the different samples of same breed and samples of different breed, the different part of spectrum among fertilized eggs and clear eggs is deleted which caused by the color of eggshell and yolk, the effective spectral band are 355～590 and 670～1 025 nm. Adopting the pretreatment of PCA and comparing the accuracy of the various mathematical models with different time and the number of principal components decide the best number of principal components. Considering the production efficiency and comparing the different pretreatment methods of spectrum, for examples, SNV, MSC, Derivative correction and PCA, and various mathematical models are combined to establish the most efficient discriminant model. The result shows that the most efficient discriminant model is established with Fisher and based on the pretreatment of PCA after 24 hours' hatching. And the precision rate is 87.18%. The study provides a new way for nondestructive and online identification of the fertilized eggs and clear eggs.

Determination of Tomato's SSC and TS Based on Diffuse Transmittance Spectroscopy.

In order to meet the demands for rapid and safe nondestructive testing of fruit and vegetable quality，tomato detection system with a special circular light source was built based on the visible / near infrared diffuse transmission principle. Taking soluble solids content (SSC) and total sugar (TS) as the internal quality index, the prediction of 58 tomato samples was carried out by using this system. First, we collected the spectral data of four points for each tomato. Second, Savitzky-Golay smooth(SG-Smooth), standard normal variable transformation(SNV), multiplication scattering correction(MSC), first derivative (FD) and other methods were used to process the original spectral curve before the partial least squares regression(PLSR) model was established. Finally, we validated the established model. The results show that the correlation coefficient (r) of calibration and prediction of the SSC prediction model -are 0.995 6 and 0.976 0 when using 10 point SG-smooth, and the root mean square error of calibration and prediction are 0.052 4% and 0.082 3%. The partial least square regression (PLSR)model, with respect to the first derivative (FD) spectra, provides better prediction performance for total sugar of tomato, with correlation coefficient (r) of calibration of 0.969 1 and 0.972 9, and prediction, root mean standard error of 0.423 8% and 0.454 9%. In the experimental verification of the prediction model, the relationship of SSC between predicted and true value is 0.985 5, root mean square error is 0.066 3°Brix, the relationship of TS between predicted and true value is 0.944 9 while root mean square error is 0.571 5%. The results show that the content of soluble solids and total sugar in tomato can be realized by using visible / near infrared diffuse reflectance spectroscopy. It provides a real-time, nondestructive and rapid detection method for the evaluation of the internal quality of tomato, and provides a theoretical basis for its online grading.

[Simultaneous Detection of Multiple Quality Parameters of Pork Based on Fused Dual Band Spectral].

For dual band visible/near infrared spectroscopy system (350~1 100 and 1 000~2 500 nm), there exsits a band overlap and for the same sample the reflectivity data were unlike due to the performance difference between instruments. A band connection and data fusion method was proposed in this paper to make better use of the dual-band data. A dual-band visible/near-infrared spectroscopy system was built in the study to collect 60 pork samples' reflectance spectra. The reflectance spectra of samples were performed with pretreatment methods of Savitzky-Golay (S-G) and standard normal variable transform to eliminate the spectral noise. Then partial least squares regression (PLSR) prediction models of pork quality attributes (color, pH and cooking loss) based on single-band spectrum and dual-band spectrum were established, respectively. For the cross of two band overlap, the data were connected and integrated using the method put forward in this paper and then PLSR models were established based on the integrated data. The PLSR model yielded prediction result with correlation coefficient of validation (R(p)) of 0.948 8, 0.920 0, 0.950 5, 0.930 1 and 0.903 5 for L(*), a(*), b(*), pH value and cooking loss, respectively. To simplify the model, uninformative variables elimination (UVE) was employed to select characteristic variables. The experimental results show that the proposed method was able to achieve a better fusion of the two band spectral data, and it was good for the establishment of a more simplified and better prediction model.

[Detection of Chlorpyrifos on Spinach Based on Surface Enhanced Raman Spectroscopy with Silver Colloids].

In this research, the surface enhanced Raman spectroscopy (SERS) technique is used to develop a nondestructive and fast detecting method for the detection of residual chlorpyrifos on spinach. Silver colloids used for SERS spectroscopy is prepared by the reduction of silver nitrate with hydroxylamine hydrochloride at alkaline pH. The prepared silver colloids are dropped onto spinach samples, then the SERS spectra are collected non-destructively with a self-developed Raman system. This method can be made without physical contact to samples, and rapidly completed without time-consuming sample pre-treatments, and suited to the development of real-time on-line detection methods for trace pesticide residues. SERS signals are collected from 20 points on each spinach sample with 450 mW laser power and 2.5 s exposure time. Chlorpyrifos concentrations in 24 samples are determined with gas chromatography after SERS spectra taken. Savitzky-Golay (SG) smoothing filter and effective peak linear fitting method are used to remove the random noise and the fluorescence background for improving the accuracy of SERS results. The SERS signals are collected from different parts of 50 spinach samples with the same concentration of chlorpyrifos but at different fresh degrees. The relative standard deviation (RSD) of chlorpyrifos' characteristic peak intensities is 13.4%. Although the differences of samples lead to differences in the curves of Raman spectrum, they have little influence on the characteristic peak intensities, which indicates the stability of the proposed detecting method. After the fluorescent background removed, the 20 curves of each sample are averaged. Correlation analysis is done between chlorpyrifos concentration and signal intensity at every Raman shift. Results show that correlation coefficients are higher than 0.85 in the range of 615.5~626.4 cm-1. Signals in this range are used to establish multiple linear regression (MLR) model for the prediction of residual chlorpyrifos. MLR model was developed for chlorpyrifos concentration versus Raman signal intensity at 615.5~626.4 cm-1 for predicting residual chlorpyrifos content in samples, the correlation coefficients of calibration (RC) and validation (RP) are 0.961 and 0.954, which indicate a good linear relationships between them. The minimum detectable threshold for this method is 0.05 mg·kg-1 which is close to the value limited by the national standard of China (0.1 mg·kg-1 for chlorpyrifos in spinach). The proposed practical method is sample, fast, without sample preparation, thus it shows great potential in safety detection of fruits and vegetables.

[A Point Raman Scanning Method for Rapidly Detection of Spinach Quality and Safety Parameters].

According to actual market demand for nondestructive detection of vegetables quality and safety, combined with the heterogeneity of quality and safety parameters such as pesticide residues on leaf vegetables surface and to realize the automatic point scanning for the whole leaf vegetables samples, a suction device based on laboratory (self-designed) Raman spectroscopy hardware and a GUI application software based on the LabVIEW development platform were developed. This system can test the Raman spectroscopy of the whole spinach including the automatic collection, display and storage of the Raman signal of all the scanned points by set up different scan step. A new method to remove the Raman spectrum background was proposed based on data replacement with linear equation at the range of threshold spectrum on both sides of the effective peaks according to the characteristics of spinach original spectra. Its principle is to determine the starting position of linear fitting by judging whether there is trough on both sides of the crest, and then to generate and replace the original spectra data in peak position through the linear fitting equation. Spinach samples were used for the experiment showed that the chlorophyll content and distribution of chlorpyrifos pesticide residue on each scanning point can be obtained after scanning. Therefore, the point scanning Raman system could improve detection accuracy of the quality and safety parameters for the non-uniform samples effectively.

[Research on Identification and Determination of Pesticides in Apples Using Raman Spectroscopy].

Raman spectroscopy combined with chemometric methods has been thought to an efficient method for identification and determination of pesticide residues in fruits and vegetables. In the present research, a rapid and nondestructive method was proposed and testified based on self-developed Raman system for the identification and determination of deltamethrin and acetamiprid remaining in apple. The peaks of Raman spectra at 574 and 843 cm(-1) can be used to identify deltamethrin and acetamiprid, respectively, the characteristic peaks of deltamethrin and acetamiprid were still visible when the concentrations of the two pesticides were 0.78 and 0.15 mg · kg(-1) in apples samples, respectively. Calibration models of pesticide content were developed by partial least square (PLS) algorithm with different spectra pretreatment methods (Savitzky-Golay smoothing, first derivative transformation, second derivative transformation, baseline calibration, standard normal variable transformation). The baseline calibration methods by 8th order polynomial fitting gave the best results. For deltamethrin, the obtained prediction coefficient (Rp) value from PLS model for the results of prediction and gas chromatography measurement was 0.94; and the root mean square error of prediction (RMSEP) was 0.55 mg · kg(-1). The values of Rp and RMSEP were respective 0.85 and 0.12 mg · kg(-1) for acetamiprid. According to the detect performance, applying Raman technology in the nondestructive determination of pesticide residuals in apples is feasible. In consideration of that it needs no pretreatment before spectra collection and causes no damage to sample, this technology can be used in detection department, fruit and vegetable processing enterprises, supermarket, and vegetable market. The result of this research is promising for development of industrially feasible technology for rapid, nondestructive and real time detection of different types of pesticide with its concentration in apples. This supplies a rapid nondestructive and environmentally friendly way for the determination of fruit and vegetable quality and safety.

[Near-infrared hyperspectral imaging combined with CARS algorithm to quantitatively determine soluble solids content in "Ya" pear].

The present study proposed competitive adaptive reweighted sampling (CARS) algorithm to be used to select the key variables from near-infrared hyperspectral imaging data of "Ya" pear. The performance of the developed model was evaluated in terms of the coefficient of determination(r2), and the root mean square error of prediction (RMSEP) and the ratio (RPD) of standard deviation of the validation set to standard error of prediction were used to evaluate the performance of proposed model in the prediction process. The selected key variables were used to build the PLS model, called CARS-PLS model. Comparing results obtained from CARS-PLS model and results obtained from full spectra PLS, it was found that the better results (r(2)pre = 0. 908 2, RMSEP=0. 312 0 and RPD=3. 300 5) were obtained by CARS-PLS model based on only 15. 6% information of full spectra. Moreover, performance of CARS-PLS model was also compared with PLS models built by using variables got by Monte Carlo-uninformative variable elimination (MC-UVE) and genetic algorithms (GA) method. The result found that CARS variable selection algorithm not only can remove the uninformative variables in spectra, but also can reduce the collinear variables from informative variables. Therefore, this method can be used to select the key variables of near-infrared hyperspectral imaging data. This study showed that near-infrared hyperspectral imaging technology combined with CARS-PLS model can quantitatively predict the soluble solids content (SSC) in "Ya" pear. The results presented from this study can provide a reference for predicting other fruits quality by using the near-infrared hyperspectral imaging.

[A method for assessing the total viable count of fresh meat based on hyperspectral scattering technique].

The objective of this study is to develop a hyperspectral imaging system to predict the bacteria total viable count in fresh pork. The hyperspectral scattering data were curvefitted by different fitting methods, and correlation differences of models were compared based on the bacteria total viable count of fresh pork, thus providing modeling basis of device for future study. Total 63 fresh pork samples which was used in the experiment were stored at 4 degrees C in the refrigerator of constant temperature. Experiment was performed everyday for 15 days. 4 or 5 random samples were used each day for the experiment. Hyperspectral scattering images and spectral scattering optical data in the wavelength region of 400 to 1 100 nm were acquired from the surface of all of the pork samples. Lorentz and Gompertz function and the modified function was applied to fit the scattering profiles of pork samples. Different parameters could be obtained by Lorentz and Gompertz fitting and the modified function fitting. The different parameters could represent the optical characteristic of the scattering profiles. The standard values of the bacteria total viable count of pork were obtained by classical microbiological plating methods. Because the standard value of the bacteria total viable count was big, log10 of the bacteria total viable count obtained by classical microbiological plating was used to simplify the calculation. Both individual parameters and integrated parameters were explored to develop the models. The multi-linear regression statistical approach was used to establish the models for predicting pork the bacteria total viable count. Both Lorentz and Gompertz function and the modified function included three and four parameters formula. The results showed that correlation coefficient of the models is higher with Lorentz three parameters combination, Lorentz four parameters combination and Gompertz four parameters combination than the individual parameters and other two or three integrated parameters. The three models' correction set and prediction set correlation coefficients were 0.93, 0.96, 0.96 and 0.90, 0.90, 0.92, and the corresponding standard deviation were 0.47, 0.44, 0.39 and 0.56, 0.46, 0.42. Correlation was best with Gompertz four parameters. The device system will select the best correlation and the best stability of the model as the final model.

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion.

Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization-extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.

A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control.

Tomato is a globally grown vegetable crop with high economic and nutritional values. Tomato production is being threatened by weeds. This effect is more pronounced in the early stages of tomato plant growth. Thus weed management in the early stages of tomato plant growth is very critical. The increasing labor cost of manual weeding and the negative impact on human health and the environment caused by the overuse of herbicides are driving the development of smart weeders. The core task that needs to be addressed in developing a smart weeder is to accurately distinguish vegetable crops from weeds in real time. In this study, a new approach is proposed to locate tomato and pakchoi plants in real time based on an integrated sensing system consisting of camera and color mark sensors. The selection scheme of reference, color, area, and category of plant labels for sensor identification was examined. The impact of the number of sensors and the size of the signal tolerance region on the system recognition accuracy was also evaluated. The experimental results demonstrated that the color mark sensor using the main stem of tomato as the reference exhibited higher performance than that of pakchoi in identifying the plant labels. The scheme of applying white topical markers on the lower main stem of the tomato plant is optimal. The effectiveness of the six sensors used by the system to detect plant labels was demonstrated. The computer vision algorithm proposed in this study was specially developed for the sensing system, yielding the highest overall accuracy of 95.19% for tomato and pakchoi localization. The proposed sensor-based system is highly accurate and reliable for automatic localization of vegetable plants for weed control in real time.

A determination method for clenbuterol residue in pork based on optimal particle size gold colloid using SERS.

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.

Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique.

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 (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 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.

[Rapid nondestructive detection of water content in fresh pork based on spectroscopy technique combined with support vector machine].

Visible near infrared reflectance spectra in the range of 350 nm to 1700 nm were collected from 98 pork samples to develop online, rapid and nondestructive detection system for water content in fresh pork Median smoothing filter (M-filter), multiplication scatter correlation (MSC) and first derivative (FD) were used as compound preprocessing method to reduce noise present in the original spectrum. Seventy four samples were randomly selected to develop training model and remaining 24 samples were used to test the model. The optimal punishment parameters for the support vector machine (SVM) were determined by using cross--validation and grid--search in the training set. SVM prediction model was developed with the radial basis function (RBF) and the developed model was compared with the model developed by partial least squares regression (PLSR) method. SVM prediction model based on RBF had the correlation coefficient and root mean standard error of 0.96 and 0.32 respectively in the training set. The model obtained correlation coefficient of 0.87 and root mean square error of 0.67 in the test set. The result thus obtained demonstrates the applicability of SVM model for rapid, nondestructive detection of water content in pork.

Corrigendum to "Raman enhancement effect of different silver nanoparticles on salbutamol" [Heliyon 8 (6), (June 2022) Article e09576].

[This corrects the article DOI: 10.1016/j.heliyon.2022.e09576.].

Raman enhancement effect of different silver nanoparticles on salbutamol.

Salbutamol is a ß-adrenergic receptor agonist compound which has been abused as an animal growth promoter to improve carcass lean meat percentage. At present, the detection of salbutamol by SERS mostly uses gold colloid as substrate, which is expensive and has a high detection limit. In this report, Raman enhancement signal of salbutamol was compared with concentrated gold and silver colloids. The results show that the concentrated silver colloid prepared by reducing silver nitrate with hydroxylamine hydrochloride had superior performance. Three silver colloids with different particle sizes were synthesized by the same reducing agent and used as substrates for spectra acquisition of salbutamol to explore the enhancement performance of different silver nanoparticles sizes on salbutamol. The results showed that silver nanoparticles with larger particle sizes were more conducive to the adsorption of salbutamol. Finally, under the optimal conditions (Silver colloid A as enhanced substrate, 0.2 mol/L NaOH aqueous solution as aggregating compound), a better linear relationship between the concentration of salbutamol (ranged from 0.2 to 1 mg/L) and SERS intensity. The linear equation between SERS intensity and salbutamol concentration was C = 0.0023∙I-0.079 (mg/L) with a good linearity (R 2 =0.994) and lower root mean square error (RMSE c = 0.022 mg/L), where C (mg/L) was the concentration of salbutamol solution and I was the SERS intensity of salbutamol solution. Validation set correlation coefficient was 0.988 and prediction root mean square error was 0.029 mg/L. This method provides a new idea for further reducing the detection limit of salbutamol. This study is helpful to further develop a simple and low-cost SERS detection method of salbutamol based on silver colloid.

Rapid Detection of Clenbuterol Residues in Pork Using Enhanced Raman Spectroscopy.

Clenbuterol (CB) is a synthetic ß-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.

Detection of nitrofurans residues in honey using surface-enhanced Raman spectroscopy.

Residues of veterinary antibiotics in honey may be damaging to human health. Surface-enhanced Raman scattering spectroscopy (SERS) is an emerging technology widely applied in food safety. SERS has advantages of enabling fingerprint identification and fast detection, as well as does not require complex pretreatment. Considering the overuse of nitrofurans in honeybee breeding, SERS combined with spectral preprocessing was used to detect nitrofurantoin in honey. By using standardized experimental procedures and improved spectral correction methods, the lowest detection limit of nitrofurantoin was 0.1321 mg/kg. A good linear relationship in the partial least squares regression model was found among spiked samples, which allowed prediction of nitrofurantoin content in honey sample ( R C 2 $R_C^2$ = â€¯0.9744; R P 2 $R_P^2$ =  0.976; RMSECV = 1.0353 mg/kg; RMSEP  =  0.9987 mg/kg). Collectively, these results reliably demonstrated that quantification is more accurate when spectral preprocessing is better controlled. Therefore, this study indicates that SERS could be further implemented in fast and onsite detection of nitrofurantoin in honey for improved food safety. PRACTICAL APPLICATION: This article presents a novel SERS-based method for the rapid detection of nitrofurantoin residues in honey. The original spectra were corrected by multiple linear regression based on the fitting baseline. This study aims to develop a rapid onsite detection method for toxic hazardous substance residues in food.