A Simple Surface-Enhanced Raman Spectroscopic Method for on-Site Screening of Tetracycline Residue in Whole Milk.

Therapeutic and subtherapeutic use of veterinary drugs has increased the risk of residue contamination in animal food products. Antibiotics such as tetracycline are used for mastitis treatment of lactating cows. Milk expressed from treated cows before the withdrawal period has elapsed may contain tetracycline residue. This study developed a simple surface-enhanced Raman spectroscopic (SERS) method for on-site screening of tetracycline residue in milk and water. Six batches of silver colloid nanoparticles were prepared for surface enhancement measurement. Milk-tetracycline and water-tetracycline solutions were prepared at seven concentration levels (1000, 500, 100, 10, 1, 0.1, and 0.01 ppm) and spiked with silver colloid nanoparticles. A 785 nm Raman spectroscopic system was used for spectral measurement. Tetracycline vibrational modes were observed at 1285, 1317 and 1632 cm-1 in water-tetracycline solutions and 1322 and 1621 cm-1 (shifted from 1317 and 1632 cm-1, respectively) in milk-tetracycline solutions. Tetracycline residue concentration as low as 0.01 ppm was detected in both the solutions. The peak intensities at 1285 and 1322 cm-1 were used to estimate the tetracycline concentrations in water and milk with correlation coefficients of 0.92 for water and 0.88 for milk. Results indicate that this SERS method is a potential tool that can be used on-site at field production for qualitative and quantitative detection of tetracycline residues.

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.

[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.

Acoustic field and array response uncertainties in stratified ocean media.

The change-of-variables theorem of probability theory is applied to compute acoustic field and array beam power probability density functions (pdfs) in uncertain ocean environments represented by stratified, attenuating ocean waveguide models. Computational studies for one and two-layer waveguides investigate the functional properties of the acoustic field and array beam power pdfs. For the studies, the acoustic parameter uncertainties are represented by parametric pdfs. The field and beam response pdfs are computed directly from the parameter pdfs using the normal-mode representation and the change-of-variables theorem. For two-dimensional acoustic parameter uncertainties of sound speed and attenuation, the field and beam power pdfs exhibit irregular functional behavior and singularities associated with stationary points of the mapping, defined by acoustic propagation, from the parameter space to the field or beam power space. Implications for the assessment of orthogonal polynomial expansion and other methods for computing acoustic field pdfs are discussed.

Chemical compositions and biological activities of the oils from the genus Taxus and factors limiting the regeneration of endangered yews: a review.

The genus Taxus (yews) is the largest genus of the family Taxaceae. It comprises about 24 species with 55 varieties distributed mainly in Asia, Europe, North Africa, and North America. In addition to the taxane diterpenoids and the cancer drug taxol, its species contain many essential oils with actual or potential biological activity. This review covers the chemical constituents as well as biological activities of these oils that have been studied in fourteen countries over 46 years (1975-2021). It also discusses the biotic and abiotic factors that limit the regeneration of these economically and medicinally important plants.

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.

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.

Detection and quantification of adulterants in milk powder using a high-throughput Raman chemical imaging technique.

Milk is a vulnerable target for economically motivated adulteration. In this study, a line-scan high-throughput Raman imaging system was used to authenticate milk powder. A 5 W 785 nm line laser (240 mm long and 1 mm wide) was used as a Raman excitation source. The system was used to acquire hyperspectral Raman images in a wave number range of 103-2881 cm-1 from the skimmed milk powder mixed with two nitrogen-rich adulterants (i.e., melamine and urea) at eight concentrations (w/w) from 50 to 10,000 ppm. The powdered samples were put in sample holders with a surface area of 150 ×100 mm and a depth of 2 mm for push-broom image acquisition. Varying fluorescence signals from the milk powder were removed using a correction method based on adaptive iteratively reweighted penalised least squares. Image classifications were conducted using a simple thresholding method applied to single-band fluorescence-corrected images at unique Raman peaks selected for melamine (673 cm-1) and urea (1009 cm-1). Chemical images were generated by combining individual binary images of melamine and urea to visualise identification, spatial distribution and morphological features of the two adulterant particles in the milk powder. Limits of detection for both melamine and urea were estimated in the order of 50 ppm. High correlations were found between pixel concentrations (i.e., percentages of the adulterant pixels in the chemical images) and mass concentrations of melamine and urea, demonstrating the potential of the high-throughput Raman chemical imaging method for the detection and quantification of adulterants in the milk powder.

Evaluation of Turmeric Powder Adulterated with Metanil Yellow Using FT-Raman and FT-IR Spectroscopy.

Turmeric powder (Curcuma longa L.) is valued both for its medicinal properties and for its popular culinary use, such as being a component in curry powder. Due to its high demand in international trade, turmeric powder has been subject to economically driven, hazardous chemical adulteration. This study utilized Fourier Transform-Raman (FT-Raman) and Fourier Transform-Infra Red (FT-IR) spectroscopy as separate but complementary methods for detecting metanil yellow adulteration of turmeric powder. Sample mixtures of turmeric powder and metanil yellow were prepared at concentrations of 30%, 25%, 20%, 15%, 10%, 5%, 1%, and 0.01% (w/w). FT-Raman and FT-IR spectra were acquired for these mixture samples as well as for pure samples of turmeric powder and metanil yellow. Spectral analysis showed that the FT-IR method in this study could detect the metanil yellow at the 5% concentration, while the FT-Raman method appeared to be more sensitive and could detect the metanil yellow at the 1% concentration. Relationships between metanil yellow spectral peak intensities and metanil yellow concentration were established using representative peaks at FT-Raman 1406 cm-1 and FT-IR 1140 cm-1 with correlation coefficients of 0.93 and 0.95, respectively.

Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique.

A rapid nondestructive method based on hyperspectral scattering technique for simultaneous determination of pork tenderness and Escherichia coli (E. coli) contamination was studied in the research. The hyperspectral scattering images of thirty-one pork samples were collected in 400-1100nm, and the scattering profiles were then fitted by Lorentzian distribution function to give three parameters a (asymptotic value), b (peak value) and c (full width at b/2). The combined parameters of (b-a), (b-a)×c, (b-a)/c and "a&b&c" were used to develop multi-linear regression (MLR) models for prediction of pork tenderness and E. coli contamination. It was shown that MLR models developed using parameters a, b, (b-a) and (b-a)/c can give high correlation coefficients of 0.831, 0.860, 0.856 and 0.930 respectively for pork tenderness prediction. For E. coli contamination of pork, MLR models based on parameters a and "a&b&c" can give high R(CV) of 0.877 and 0.841 respectively.