Determination of gossypol content in cottonseeds by near infrared spectroscopy based on Monte Carlo uninformative variable elimination and nonlinear calibration methods

Quantitative analysis of three ingredients in Salvia miltiorrhiza by near infrared spectroscopy combined with hybrid variable selection strategy

Rosmarinic acid (RA), Tanshinone IIA (Tan IIA), and Salvianolic acid B (Sal B) are crucial compounds found in Salvia miltiorrhiza. Quickly predicting these components can aid in ensuring the quality of S. miltiorrhiza. Spectral preprocessing and variable selection are essential processes in quantitative analysis using near infrared spectroscopy (NIR). A novel hybrid variable selection approach utilizing iVISSA was employed in this study to enhance the quantitative measurement of RA, Tan IIA, and Sal B contents in S. miltiorrhiza. The spectra underwent 108 preprocessing approaches, with the optimal method being determined as orthogonal signal correction (OSC). iVISSA was utilized to identify the intervals (feature bands) that were most pertinent to the target chemical. Various methods such as bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable combination population analysis (VCPA), successive projections algorithm (SPA), iteratively variable subset optimization (IVSO), and iteratively retained informative variables (IRIV) were used to identify significant feature variables. PLSR models were created for comparison using the given variables. The results fully demonstrated that iVISSA-SPA calibration model had the best comprehensive performance for Tan IIA, and iVISSA-BOSS had the best comprehensive performance for RA and Sal B, and correlation coefficients of cross-validation (R2cv), root mean square errors of cross-validation (RMSECV), correlation coefficients of prediction (R2p), and root mean square errors of prediction (RMSEP) were 0.9970, 0.0054, 0.9990 and 0.0033, 0.9992, 0.0016, 0.9961 and 0.0034, 0.9998, 0.0138, 0.9875 and 0.1090, respectively. The results suggest that NIR spectroscopy, along with PLSR and a hybrid variable selection method using iVISSA, can be a valuable tool for quickly quantifying RA, Sal B, and Tan IIA in S. miltiorrhiza.

Near-Infrared Spectroscopy Analysis of the Phytic Acid Content in Fuzzy Cottonseed Based on Machine Learning Algorithms

Cottonseed is rich in oil and protein. However, its antinutritional factor content, of phytic acid (PA), has limited its utilization. Near-infrared (NIR) spectroscopy, combined with chemometrics, is an efficient and eco-friendly analytical technique for crop quality analysis. Despite its potential, there are currently no established NIR models for measuring the PA content in fuzzy cottonseeds. In this research, a total of 456 samples of fuzzy cottonseed were used as the experimental materials. Spectral pre-treatments, including first derivative (1D) and standard normal variable transformation (SNV), were applied, and the linear partial least squares (PLS), nonlinear support vector machine (SVM), and random forest (RF) methods were utilized to develop accurate calibration models for predicting the content of PA in fuzzy cottonseed. The results showed that the spectral pre-treatment significantly improved the prediction performance of the models, with the RF model exhibiting the best prediction performance. The RF model had a coefficient of determination in prediction (R2p) of 0.9114, and its residual predictive deviation (RPD) was 3.9828, which indicates its high accuracy in measuring the PA content in fuzzy cottonseed. Additionally, this method avoids the costly and time-consuming delinting and crushing of cottonseeds, making it an economical and environmentally friendly alternative.

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm

Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, blue-green (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R2c, R2p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R2c, R2p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas.

Ultra-low limit of luminescent detection of gossypol by terbium(III)-based metal-organic framework

The widespread use of gossypol-containing animal feed and cottonseed oil poses a great threat to water quality and livestock and human health, and there is an urgent need for a sensor for the rapid detection of trace amounts of gossypol in aqueous solutions and cottonseed oil. As a result, an unprecedented three-dimensional metal-organic framework sensor based on terbium(III) and a flexible ligand 4-(3,5-dicarboxyphenoxy)isophthalic acid (H₄L) was developed. Tb-MOF, {[Tb(H₂O)(HL)]·0.5MeCN·0.25 H₂O}_n, is highly stable in water and polar organic solvents and exhibits terbium-centered luminescence with 44% quantum yield. Suspensions of MOF in water and ethanol demonstrate a luminescence quenching response to cotton phytotoxicant gossypol with an unprecedented low detection limit of 0.76 nM and 1.89 nM, correspondingly, without interference from the components of cottonseed oil and blood plasma, making it suitable for the detection and determination of gossypol in real-life water and oil samples. Significantly, Tb-MOF is the first highly efficient sensor that uses water as a solvent to detect trace amounts of gossypol, and it can visualize and quantify gossypol in edible-grade cottonseed oil as well, which proves its great potential for practical application. In addition, Tb-MOF exhibited a detection limit for Fe³⁺ (0.23 μM) among the lowest reported for lanthanide-based MOFs in aqueous solutions so far.

Intelligent Evaluation of Stone Cell Content of Korla Fragrant Pears by Vis/NIR Reflection Spectroscopy

Stone cells are a distinctive characteristic of pears and their formation negatively affects the quality of the fruit. To evaluate the stone cell content (SCC) of Korla fragrant pears, we developed a Vis/NIR spectroscopy system that allowed for the adjustment of the illuminating angle. The successive projective algorithm (SPA) and the Monte Carlo uninformative variable elimination (MCUVE) based on the sampling algorithm were used to select characteristic wavelengths. The particle swarm optimization (PSO) algorithm was used to optimize the combination of penalty factor C and kernel function parameter g. Support vector regression (SVR) was used to construct the evaluation model of the SCC. The SCC of the calibration set ranged from 0.240% to 0.657% and that of the validation set ranged from 0.315% to 0.652%. The SPA and MCUVE were used to optimize 57 and 83 characteristic wavelengths, respectively. The combinations of C and g were (6.2561, 0.2643) and (2.5133, 0.1128), respectively, when different characteristic wavelengths were used as inputs of SVR, indicating that the first combination had good generalization ability. The correlation coefficients of the SPA-SVR model after pre-processing the standardized normal variate (SNV) for both sets were 0.966 and 0.951, respectively. These results show that the SNV-SPA-SVR model satisfied the requirements of intelligent evaluation of SCC in Korla fragrant pears.

A turn-on NIR fluorescence sensor for gossypol based on Yb-based metal-organic framework

The development of analytical method for selective and sensitive detection of gossypol (Gsp), an extraction from the cotton plants, is important but still challenging in food safety and medical field. Herein, we reported a turn-on near infrared (NIR) fluorescence detection strategy for Gsp based on a metal-organic framework (MOF), QBA-Yb, which was prepared from 4,4'-(quinolone-5, 8-diyl) benzoate with Yb(NO₃)₃·5H₂O by solvothermal synthesis. The Gsp acted as another "antenna" to sensitize the luminescence of Yb³⁺, leading to the turn-on NIR emission upon 467 nm excitation. As Gsp concentration increased, the NIR emission at 973 nm enhanced gradually, thus enabling highly sensitive Gsp detection in a turn-on way. The experiment and theoretical calculation results revealed the presence of strong hydrogen bonds between Gsp molecules and the MOF skeleton. The developed QBA-Yb probe showed excellent characteristics for detection of Gsp molecules, accompanied by wide linear range (5-160 μg/mL), low detection limit (0.65 μg/mL) and short response time (within 10 min). We have further demonstrated that the QBA-Yb probe was successfully applied for the determination of Gsp in real samples of cottonseeds.

Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis

The tuber development and nutrient transportation of potato crops are closely related to canopy photosynthesis dynamics. Chlorophyll fluorescence parameters of photosystem II, especially the maximum quantum yield of primary photochemistry (Fv/Fm), are intrinsic indicators for plant photosynthesis. Rapid detection of Fv/Fm of leaves by spectroscopy method instead of time-consuming pulse amplitude modulation technique could help to indicate potato development dynamics and guide field management. Accordingly, this study aims to extract fluorescence signals from hyperspectral reflectance to detect Fv/Fm. Hyperspectral imaging system and closed chlorophyll fluorescence imaging system were applied to collect the spectral data and values of Fv/Fm of 176 samples. The spectral data were decomposed by continuous wavelet transform (CWT) to obtain wavelet coefficients (WFs). Three mother wavelet functions including second derivative of Gaussian (gaus2), biorthogonal 3.3 (bior3.3) and reverse biorthogonal 3.3 (rbio3.3) were compared and the bior3.3 showed the best correlation with Fv/Fm. Two variable selection algorithms were used to select sensitive WFs of Fv/Fm including Monte Carlo uninformative variables elimination (MC-UVE) algorithm and random frog (RF) algorithm. Then the partial least squares (PLS) regression was used to establish detection models, which were labeled as bior3.3-MC-UVE-PLS and bior3.3-RF-PLS, respectively. The determination coefficients of prediction set of bior3.3-MC-UVE-PLS and bior3.3-RF-PLS were 0.8071 and 0.8218, respectively, and the root mean square errors of prediction set were 0.0181 and 0.0174, respectively. The bior3.3-RF-PLS had the best detection performance and the corresponding WFs were mainly distributed in the bands affected by fluorescence emission (650-800 nm), chlorophyll absorption and reflection. Overall, this study demonstrated the potential of CWT in fluorescence signals extraction and can serve as a guide in the quick detection of chlorophyll fluorescence parameters.

A novel hybrid filter/wrapper method for feature selection in archaeological ceramics classification by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has been appreciated as a valuable analytical tool in the cultural heritage field owing to its unique technological superiority, particularly in combination with chemometric methods. Feature selection (FS) as an indispensable pre-processing step in data optimization, for eliminating the redundant or irrelevant features from high-dimensional data to enhance the predictive capacity and result comprehensibility of multivariate classification based on LIBS technology. In this paper, a novel hybrid filter/wrapper method based on the MI-DBS algorithm was proposed to enhance the qualitative analysis performance of the LIBS technique. The proposed method combines the advantages of the mutual information (MI) algorithm based filter method and bi-directional selection (DBS) algorithm based wrapper method. The MI algorithm is the first to remove the redundant or uncorrelated features so that a simplified input subset can be established. Then, the DBS algorithm is used to further select the retained features and hence to seek an optimal feature subset with good predictive performance. To benefit the above feature selection process, the wavelet transform denoising (WTD) method was used to reduce the noise from LIBS spectra. LIBS experiments were performed using 35 archaeological ceramic samples. Besides, the proposed hybrid filter/wrapper method was implemented through a random forest (RF) based nonlinear multivariate classification method. Through a comparison between several other feature selection methods and the proposed method, it has been seen that the proposed method is the best regarding the predictive performance and number of the selected features. Finally, the MI-DBS algorithm is used to seek the optimal features from the full spectrum (220-720 nm); the corresponding sensitivity, specificity and accuracy acquired through the RF classifier for the test set were 0.9722, 0.9956 and 0.9850. It is shown from the general results that the MI-DBS algorithm is more effective in terms of improving the model performance and decreasing the redundant or uncorrelated features and computational time and serves as a good alternative for FS in multivariate classification.

Development of a novel wavelength selection method VCPA-PLS for robust quantification of soluble solids in tomato by on-line diffuse reflectance NIR

This work was attempted to evaluate the feasibility of a constructed on-line NIR platform coupled with efficient algorithms for rapid and robust quantification of quality parameter in cherry tomato. Specifically, a system was developed based on shortwave NIR spectroscopy for on-line quality inspection of cherry tomatoes. The spectra were recorded in diffuse reflectance mode from 900 to 1700 nm, and the conveyor belt speed was fixed to five samples per second. Three novel methods, namely variable combination population analysis (VCPA), uninformative variable elimination (UVE) and competitive adaptive reweighed sampling algorithm (CARS) were coupled with partial least square (PLS) for selecting optimal dataset, and modeling. The obtained results showed that under the optimal tuning parameters (N = 100, k = 500, ω = 14, σ = 10%), a total of 512 original variables, only 9 variables (1.75%) were extracted by VCPA. Subsequently, VCPA-PLS yielded outstanding performance in predicting soluble solid content in cherry tomatoes, with a higher correlation coefficient (R_P = 0.9053), and lower root mean square errors (RMSEP = 0.382) in prediction set. This methodology demonstrated the versatile potential of the proposed installation coupled with VCPA methods for on-line detection of total soluble solids in cherry tomatoes.

Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.

The method of calibration model transfer by optimizing wavelength combinations based on consistent and stable spectral signals

Basing on the wavelengths with consistent and stable spectral signals between spectrometers, wavelength combinations were screened by different methods to obtain robust and simple near infrared spectra (NIR) calibration models that can be shared by slave spectrometers directly. Firstly, the wavelength set of Usc, at which the spectral signals between spectrometers are consistent and stable, was obtained by the method of screening the wavelengths with consistent and stable signals between spectrometers (SWCSS for short). Then, the wavelength set of Uscr whose spectral responses are correlated with dependent variables strongly was selected from Usc. Basing on Uscr, the methods of uninformative variable elimination (UVE), variable importance in projection (VIP) and selectivity ratio (SR) were applied to further screen optimal wavelength sets to obtain better NIR calibration models. These sets were recorded as Uscr^UVE, Uscr^VIP and Uscr^SR, respectively. The NIR partial least squares (PLS) models for predicting total alkaloids content of tobacco leaves were built on the three optimal wavelength sets, and named as Uscr^UVE-PLS, Uscr^VIP-PLS, Uscr^SR-PLS, respectively. Both Uscr^UVE-PLS and Uscr^VIP-PLS give satisfactory prediction errors for master and slave samples, and work better than the PLS model built on the whole wavelengths (WW-PLS) after piecewise direct standardization (PDS) calibration. The results show that further optimizing wavelength combinations based on consistent and stable spectral information cannot only simplify PLS models and improve the models' efficiency, but also ensure the models' accuracy when they are transferred to slave spectrometers. Wavelength selection based on the whole wavelengths without considering spectra consistency between spectrometers can improve the performance of the calibration models on the master spectrometer but cannot ensure the prediction accuracy of the slave samples.

Luminescence "Turn-On" Detection of Gossypol Using Ln3+-Based Metal-Organic Frameworks and Ln3+ Salts

Gossypol (Gsp), a natural toxin concentrated in cottonseeds, poses great risks to the safe consumption of cottonseed products, which are used extensively throughout the food industry. In this work, we report the first luminescence "turn-on" sensors for Gsp using near-infrared emitting lanthanide (Ln³⁺) materials, including Ln³⁺ MOFs and Ln³⁺ salts. We first demonstrate that the Yb³⁺ photoluminescence of a Yb³⁺ MOF, Yb-NH₂-TPDC, can be employed to selectively detect Gsp with a limit of detection of 25 μg/mL via a "turn-on" response from a completely nonemissive state in the absence of Gsp. The recyclability and stability of Yb-NH₂-TPDC in the presence of Gsp was demonstrated by fluorescence spectroscopy and PXRD analysis, respectively. A variety of background substances present in practical samples that would require Gsp sensing, such as refined cottonseed oil, palmitic acid, linoleic acid, and α-tocopherol, did not interfere with the Yb³⁺ photoluminescence signal. We further identified that the "turn-on" of Yb-NH₂-TPDC photoluminescence was due to the "antenna effect" of Gsp, as evidenced by spectroscopic studies and supported by computational analysis. This is the first report that Gsp can effectively sensitize Yb³⁺ photoluminescence. Leveraging this sensing mechanism, we demonstrate facile, highly sensitive, fast-response detection of Gsp using YbCl₃·6H₂O and NdCl₃·6H₂O solutions. Overall, we show for the first time that Ln³⁺-based materials are promising luminescent sensors for Gsp detection. We envision that the reported sensing approach will be applicable to the detection of a wide variety of aromatic molecules using Ln³⁺ compounds including MOFs, complexes, and salts.

Melatonin enhances cotton immunity to Verticillium wilt via manipulating lignin and gossypol biosynthesis

Plants endure challenging environments in which they are constantly threatened by diverse pathogens. The soil-borne fungus Verticillium dahliae is a devastating pathogen affecting many plant species including cotton, in which it significantly reduces crop yield and fiber quality. Melatonin involvement in plant immunity to pathogens has been reported, but the mechanisms of melatonin-induced plant resistance are unclear. In this study, the role of melatonin in enhancing cotton resistance to V. dahliae was investigated. At the transcriptome level, exogenous melatonin increased the expression of genes in phenylpropanoid, mevalonate (MVA), and gossypol pathways after V. dahliae inoculation. As a result, lignin and gossypol, the products of these metabolic pathways, significantly increased. Silencing the serotonin N-acetyltransferase 1 (GhSNAT1) and caffeic acid O-methyltransferase (GhCOMT) melatonin biosynthesis genes compromised cotton resistance, with reduced lignin and gossypol levels after V. dahliae inoculation. Exogenous melatonin pre-treatment prior to V. dahliae inoculation restored the level of cotton resistance reduced by the above gene silencing effects. Melatonin levels were higher in resistant cotton cultivars than in susceptible cultivars after V. dahliae inoculation. The findings indicate that melatonin affects lignin and gossypol synthesis genes in phenylpropanoid, MVA, and gossypol pathways, thereby enhancing cotton resistance to V. dahliae.

Spectral interval optimization on rapid determination of prohibited addition in pesticide by ATR-FTIR

BACKGROUND

Acetamiprid, as a low-toxicity pesticide, has already been extensively used to increase plant production and quality. Although fipronil has been prohibited, it is usually illicitly added to acetamiprid due to its particular insecticidal action and effect, so it is highly desirable to obtain a rapid and effective method to detect its concentration. Mid-infrared spectroscopy (MIR) combined with two variable selection methods, interval combination optimization (ICO) and interval partial least squares (iPLS), were used to determinate the prohibited addition of fipronil.

RESULTS

The full spectra for both ICO and iPLS were divided into 40 equal-width intervals. Consequently, 45 and 135 characteristic variables were extracted from ICO and iPLS to establish the models. Compared with iPLS, the ICO model acquired a more suitable spectral region and as a result gained a higher prediction accuracy. Specifically, the ICO method selected the characteristic wavelengths ascribed to CF and CN (in five-membered heterocyclics), iPLS chose the intervals associated with CF and SO.

CONCLUSION

Results revealed that MIR combined with ICO could be efficiently used for rapid identification of illegal addition and had great potential to provide on-site pesticide quality control. © 2018 Society of Chemical Industry.

Identification and profiling of upland cotton microRNAs at fiber initiation stage under exogenous IAA application

Background

Cotton is the most essential textile crop worldwide, and phytohormones are critical for cotton fiber development. One example is the role of auxin in fiber initiation, but we know little molecular basis. MicroRNAs (miRNAs) have a significant function in cotton development; nevertheless their role in fiber initiation remains unclear. Here, exogenous IAA was applied to cotton plant before anthesis. Utilizing small RNA sequencing, the mechanism underlying miRNA-mediated regulation of fiber initiation under exogenous IAA treatment was investigated.

Results

With exogenous IAA application, the endogenous IAA and GA contents of IAA treated (IT) ovules were higher than control (CK) ovules at the fiber initiation stage, while endogenous ABA content was lower in IT than CK. Using scanning electron microscopy, we found the fiber number and size were significantly promoted in IT at 0 DPA. Fiber quality analysis showed that fiber length, uniformity, strength, elongation, and micronaire of IT were higher than CK, though not statistically significant, while lint percent was significantly higher in IT. We generated six small RNA libraries using − 3, 0, and 3 DPA ovules of IT and CK, and identified 58 known miRNAs and 83 novel miRNAs together with the target genes. The differential expressed miRNAs number between IT and CK at − 3, 0, 3 DPA was 34, 16 and 24, respectively. Gene ontology and KEGG pathway enrichment analyses for the target genes of the miRNAs expressed in a differential manner showed that they were significantly enriched in 30 terms and 8 pathways. QRT-PCR for those identified miRNAs and the target genes related to phytohormones and fiber development was performed, and results suggested a potential role of these miRNAs in fiber initiation.

Conclusions

The exogenous IAA application affected the relative phytohormone contents in ovule and promoted fiber initiation in cotton. Identification and profiling of miRNAs and their targets at the fiber initiation stage provided insights for miRNAs’ regulation function of fiber initiation. These findings not only shed light on the regulatory network of fiber growth but also offer clues for cotton fiber amelioration strategies in cotton.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5760-8) contains supplementary material, which is available to authorized users.

Highly Sensitive and Selective Fluorescent Detection of Gossypol Based on BSA-Stabilized Copper Nanoclusters

In this paper, fluorescent copper nanoclusters (NCs) are used as a novel probe for the sensitive detection of gossypol for the first time. Based on a fluorescence quenching mechanism induced by interactions between bovine serum albumin (BSA) and gossypol, fluorescent BSA-Cu NCs were seen to exhibit a high sensitivity to gossypol in the range of 0.1–100 µM. The detection limit for gossypol is 25 nM at a signal-to-noise ratio of three, which is approximately 35 times lower than the acceptable limit (0.9 µM) defined by the US Food and Drug Administration for cottonseed products. Moreover, the proposed method for gossypol displays excellent selectivity over many common interfering species. We also demonstrate the application of the present method to the measurement of several real samples with satisfactory recoveries, and the results agree well with those obtained using the high-performance liquid chromatography (HPLC) method. The method based on Cu NCs offers the followings advantages: simplicity of design, facile preparation of nanomaterials, and low experimental cost.

Identification of antibiotic mycelia residue in protein rich feed using on near-infrared microscopy imaging

Antibiotic mycelial residues (AMRs) added to animal feeds easily lead to drug resistance that affects human health and environment. However, there is a lack of effective detection methods, especially a fast and convenient detection technology, to distinguish AMRs from other components in animal feeds. To develop effective detection methods, two types of global Mahalanobis distance (GH) algorithms based on near-infrared microscopy (NIRM) imaging are proposed. The aim of this study is to investigate the feasibility of using NIRM imaging to identify AMRs in soybean meals. We prepared 15 mixed samples containing 5% AMRs using three types of soybean meals and four types of AMRs. The GH algorithm was used to identify non-soybean meals among the mixed samples. The hierarchical cluster analysis was employed to verify the recognition accuracy. The results indicate that use of the GH algorithm could identify soybean meals with AMR at a level as low as 5%.