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.

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 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.

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.

An Intelligent Detector for Sensing Pork Freshness In Situ Based on a Multispectral Technique.

Fresh pork is prone to spoilage during storage, transportation, and sale, resulting in reduced freshness. The total viable count (TVC) and total volatile basic nitrogen (TVB-N) content are key indicators for evaluating the freshness of fresh pork, and when they reach unacceptable limits, this seriously threatens dietary safety. To realize the on-site, low-cost, rapid, and non-destructive testing and evaluation of fresh pork freshness, a miniaturized detector was developed based on a cost-effective multi-channel spectral sensor. The partial least squares discriminant analysis (PLS-DA) model was used to distinguish fresh meat from deteriorated meat. The detector consists of microcontroller, light source, multi-channel spectral sensor, heat-dissipation modules, display system, and battery. In this study, the multispectral data of pork samples with different freshness levels were collected by the developed detector, and its ability to distinguish pork freshness was based on different spectral shape features (SSF) (spectral ratio (SR), spectral difference (SD), and normalized spectral intensity difference (NSID)) were compared. The experimental results show that compared with the original multispectral modeling, the performance of the model based on spectral shape features is significantly improved. The model established by optimizing the spectral shape feature variables has the best performance, and the discrimination accuracy of its prediction set is 91.67%. In addition, the validation accuracy of the optimal model was 86.67%, and its sensitivity and variability were 87.50% and 85.71%, respectively. The results show that the detector developed in this study is cost-effective, compact in its structure, stable in its performance, and suitable for the on-site digital rapid non-destructive testing of freshness during the storage, transportation, and sale of fresh pork.

Retinal Examinations Provides Early Warning of Alzheimer's Disease.

Patients with Alzheimer's disease have difficulty maintaining independent living abilities as the disease progresses, causing an increased burden of care on family caregivers and the healthcare system and related financial strain. This patient group is expected to continue to expand as life expectancy climbs. Current diagnostics for Alzheimer's disease are complex, unaffordable, and invasive without regard to diagnosis quality at early stages, which urgently calls for more technical improvements for diagnosis specificity. Optical coherence tomography or tomographic angiography has been shown to identify retinal thickness loss and lower vascular density present earlier than symptom onset in these patients. The retina is an extension of the central nervous system and shares anatomic and functional similarities with the brain. Ophthalmological examinations can be an efficient tool to offer a window into cerebral pathology with the merit of easy operation. In this review, we summarized the latest observations on retinal pathology in Alzheimer's disease and discussed the feasibility of retinal imaging in diagnostic prediction, as well as limitations in current retinal examinations for Alzheimer's disease diagnosis.

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.

Mechanism of Inosine Monophosphate Degradation by Specific Spoilage Organism from Grass Carp in Fish Juice System.

Microbial growth strongly affects the quality and flavor of fish and fish products. This study aimed to explore the role and function of grass carp-borne microorganisms in the degradation of inosine monophosphate (IMP) related compounds in a fish juice system during chill storage (4 °C. Prokaryotic transcriptomic analysis was used to explore the microbial contribution to metabolic pathways and related enzymes. The degree of microbial contribution was verified by the activity of enzymes and metabolite content. Collectively, there were multiple IMP relative product degradation pathways. A. rivipollensis degraded IMP by producing 5'-nucleotidase (5'-NT) while S. putrefaciens degraded IMP mainly by alkaline phosphatase (ALP). Hypoxanthine (Hx) was degraded to uric acid (Ua) induced by P. putida and S. putrefaciens mainly with producing xanthine oxidase (XOD), while A. rivipollensis almost could not produce XOD. This work can used as a guide and provide basic knowledge for the quality and flavor control of aquatic products.

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.

UV-fluorescence imaging for real-time non-destructive monitoring of pork freshness.

This study aimed to develop a cost-effective fluorescence imaging system to rapidly monitor pork freshness indicators during chilled storage. The system acquired fluorescence images of pork and the color features were extracted from these images to establish partial least squares regression (PLSR) models to predict total volatile basic nitrogen (TVB-N), total viable count (TVC), pH for pork. For TVB-N, TVC and pH values, Rp were 0.92, 0.88 and 0.74, residual predictive deviation (RPD) were 2.24, 2.03, and 1.19, respectively. For TVB-N and TVC indicators showed that the predictive ability of this model was largely comparable to that of fluorescence hyperspectral imaging. However, combining fluorescence and color imaging improved the model's predictive ability. For TVB-N, TVC and pH, Rp were 0.94, 0.93 and 0.85, RPD were 2.62, 2.59, and 1.95, respectively. Therefore, this study developed a system with great potential for detecting the value of most pork quality indicators in real-time.

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.

A feasibility quantitative analysis of free fatty acids in polished rice by fourier transform near-infrared spectroscopy and chemometrics.

Free fatty acids (FFAs) are an important indicator of the freshness and quality of rice. In this study, the vibration response of C-H chemical bonds (-CH3 , -CH2 , H-C = C-H) of FFAs in the near-infrared region was determined by analyzing the standard reagent. In addition, the spectral data of different physical forms of rice and chemometrics, such as partial least squares (PLS), synergy interval-PLS, and competitive adaptive reweighted sampling (CARS), were applied to develop an optimal regression model for rice FFAs determination. The performance of the FFAs model established by using the polished rice granule spectrum (PRG) combined with CARS was the best, the correlation coefficients of the calibration set and prediction set were 0.99 (root mean squared errors of the calibration = 2.00 mg/100 g) and 0.98 (root mean squared errors of the prediction = 3.21 mg/100 g), respectively, and the ratio of performance-to-deviation was 4.50. Compared with the rice powder spectral, the PRG spectral can better retain the information of FFAs. The result shows that NIRS can rapidly, non-destructively, and accurately detect FFAs in rice granules, which will help rice business and food regulatory authorities to establish an early warning mechanism of rice aging. PRACTICAL APPLICATION: Free fatty acids (FFAs) in rice are an important indicator for evaluating the freshness of rice, and their high responsiveness to the deterioration of rice quality. The real-time detection of FFAs in rice can timely adjust the parameters of the rice storage environment, which is very meaningful to ensure the quality of rice.

Detection of fraud in high-quality rice by near-infrared spectroscopy.

A key feature of food fraud is the use of a lower value ingredient to imitate an authentic product. This study was based on near-infrared spectroscopy (NIRS) analysis technology, partial least squares discriminant analysis (PLS-DA), and a support vector machine (SVM) to detect whether high-quality rice was mixed with other varieties of rice. As an aid to qualitative discrimination, PLS was used to establish the quantitative analysis model to assist in the recognition of the degree of fraud. Due to the direct correlation between the results of NIRS analysis and the homogeneity of the samples, four groups of samples with different physical forms (full granules, 40 mesh, 70 mesh, and 100 mesh) were prepared, each group consisted of 20 pure samples and 140 mixed samples, and the mixing ratio was between 5% and 50%, with an interval of 5%. Regarding qualitative analysis, the performance of the model has no obvious relationship with the physical state of the sample, the qualitative model of PLS-DA and SVM can detect the fraudulent rice with a 5% detection limit, respectively. Regarding quantitative analysis, the performance of the prediction model was closely related to the particle size of the samples: 100 mesh > 70 mesh > 40 mesh > full grains. The determination coefficient and root mean square errors of the optimal prediction result were 0.96 and 2.93, respectively. These results demonstrate that NIRS analysis technology is a reliable and fast tool to determine whether high-quality rice contains other varieties of rice. PRACTICAL APPLICATION: The work of this article is based on the current background of increasingly serious rice fraud, using near-infrared spectroscopy to quickly identify fraudulent rice, to a certain extent, and effectively alleviate the rice fraud. This technology can serve for the supervision of food regulatory agencies on rice fraud, and can also be used in food factories to ensure the authenticity of raw materials of rice.

Raman and IR spectroscopic modality for authentication of turmeric powder.

Deliberate chemical contamination of food powders has become a major food safety concern worldwide. This study used Raman imaging and FT-IR spectroscopy to detect Sudan Red and white turmeric adulteration in turmeric powder. While Sudan Red Raman spectral peaks were identifiable in turmeric-Sudan Red samples, Sudan Red false positive detection was observed in binary Raman images, limiting effective quantitative detection. In addition, white turmeric Raman spectral peaks were unidentifiable in turmeric-white turmeric mixtures. However, IR spectra of turmeric-Sudan Red and turmeric-white turmeric samples provided discrete identifier peaks for both the adulterants. Partial least squares regression models were developed using IR spectra for each mixture type. The models estimated Sudan Red and white turmeric concentrations with correlation coefficients of 0.97 and 0.95, respectively. Priority should be given to developing an IR imaging system and incorporating it with Raman system to simultaneously measure of food samples for detection of adulterants.

A new scattering correction method of different spectroscopic analysis for assessing complex mixtures.

Physical properties such as particle size distribution and compactness have significant confounding effects on the spectral signals of complex mixtures, which multivariate linear calibration methods such as partial least-squares (PLS) cannot effectively model or correct. Therefore, these effects significantly deteriorate calibration models' predictive abilities for spectral quantitative analysis of complex mixtures. Here, new scattering correction methods were proposed to estimate the additive and multiplicative parameters considering light scattering effects in each spectrum and hence mitigate the detrimental influence of additive and multiplicative effects on quantitative spectroscopic analysis of complex mixtures. Three different correction methods were proposed to estimate the addition coefficient based on two different underlying assumptions, namely, whether this coefficient is related to the wavelength. After addition coefficient elimination, the multiplicative parameter can be eliminated by a simple but very efficient spectral ratio method. Furthermore, linear models are built with key variables, and the predictive performance of these models is verified using the root-mean-square error of prediction datasets. The proposed methods were tested on one apple data set and two publicly available benchmark datasets (i.e., near-infrared spectral data of meat and powder mixture samples) and compared with some existing correction methods. The results showed that (1) additive effects of different types of samples can be eliminated by different methods and (2) these methods can appreciable improve quantitative spectroscopic analysis of complex mixture samples. This study indicates that accurate quantitative spectroscopic analysis of complex mixtures can be achieved through the combination of additive effect elimination and the spectral ratio method.

Research Progress of Near-Infrared Fluorescence Immunoassay.

Near-infrared fluorescence probes (NIFPs) have been widely used in immunoassay, bio-imaging and medical diagnosis. We review the basic principles of near-infrared fluorescence and near-infrared detection technology, and summarize structures, properties and characteristics of NIFPs (i.e., cyanines, xanthenes fluorescent dyes, phthalocyanines, porphyrin derivates, single-walled carbon nanotubes (SWCNTs), quantum dots and rare earth compounds). We next analyze applications of NIFPs in immunoassays, and prospect the application potential of lateral flow assay (LFA) in rapid detection of pathogens. At present, our team intends to establish a new platform that has highly sensitive NIFPs combined with portable and simple immunochromatographic test strips (ICTSs) for rapid detection of food-borne viruses. This will provide technical support for rapid detection on the port.

Detection of Additives and Chemical Contaminants in Turmeric Powder Using FT-IR Spectroscopy.

Yellow turmeric (Curcuma longa) is widely used for culinary and medicinal purposes, and as a dietary supplement. Due to the commercial popularity of C. longa, economic adulteration and contamination with botanical additives and chemical substances has increased. This study used FT-IR spectroscopy for identifying and estimating white turmeric (Curcuma zedoaria), and Sudan Red G dye mixed with yellow turmeric powder. Fifty replicates of yellow turmeric-Sudan Red mixed samples (1%, 5%, 10%, 15%, 20%, 25% Sudan Red, w/w) and fifty replicates of yellow turmeric-white turmeric mixed samples (10%, 20%, 30%, 40%, 50% white turmeric, w/w) were prepared. The IR spectra of the pure compounds and mixtures were analyzed. The 748 cm-1 Sudan Red peak and the 1078 cm-1 white turmeric peak were used as spectral fingerprints. A partial least square regression (PLSR) model was developed for each mixture type to estimate adulteration concentrations. The coefficient of determination (R2v) for the Sudan Red mixture model was 0.97 with a root mean square error of prediction (RMSEP) equal to 1.3%. R2v and RMSEP for the white turmeric model were 0.95 and 3.0%, respectively. Our results indicate that the method developed in this study can be used to identify and quantify yellow turmeric powder adulteration.