A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra

In the study, a new correction method was applied to reduce error during Raman spectral detection on mixed pesticide residue in apples. Combined with self-built pesticide residues detection system by Raman spectroscopy and the application of surface enhancement technology, rapid real-time qualitative and quantitative analysis of deltamethrin and acetamiprid residues in apples could be applied effectively. In quantitative analysis, compared with the intensity value of characteristic peaks of single pesticide with same concentration, the intensity value of characteristic peaks of the two pesticides decreased after mixing the pesticides, which affected the results severely. By comparing the difference in the intensity of characteristic peaks of single and mixed pesticides, a correction method was proposed to eliminate the influence of pesticides mixture. Characteristic peak intensity values of gradient concentration pesticide from 100 mg·kg−1 to 10−3 mg·kg−1 and Lagrangian interpolation were applied in the correction method. And a smooth surface was applied to describe the correction coefficient of characteristic peak intensity. Through detecting the characteristic peak intensity values of the mixed pesticide, correction coefficient would be obtained. Then real values of the peak intensity of pesticides and the content of each component of the mixed pesticide would be acquired by the correction method. Correlation coefficient of model validation exceeded 0.88 generally and Root Mean Square Error also decreased obviously after correction, which proved the reliability of the method

A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation

Raman spectral imaging is an effective method to analyze and evaluate the chemical composition and structure of a sample, and has many applications for food safety and quality research. This study developed a 1064 nm dispersive Raman spectral imaging system for surface and subsurface analysis of food samples. A 1064 nm laser module is used for sample excitation. A bifurcated optical fiber coupled with Raman probe is used to focus excitation laser on the sample and carry scattering signal to the spectrograph. A high throughput volume phase grating disperses the incoming Raman signal. A 512 pixels Indium-Gallium-Arsenide (InGaAs) detector receives the dispersed light signal. A motorized positioning table moves the sample in two-axis directions, accumulating hyperspectral image of the sample by the point-scan method. An interface software was developed in-house for parameterization, data acquisition, and data transfer. The system was spectrally calibrated using naphthalene and polystyrene. It has the Raman shift range of 142 to 1820 cm−1, the spectral resolution of 12 cm−1 at full width half maximum (FWHM). The spatial resolution of the system was evaluated using a standard resolution glass test chart. It has the spatial resolution of 0.1 mm. The application of the system was demonstrated by surface and subsurface detection of metanil yellow contamination in turmeric powder. Results indicate that the 1064 nm dispersive Raman spectral imaging system is a useful tool for food safety and quality evaluation

Detection of Azo Dyes in Curry Powder Using a 1064-nm Dispersive Point-Scan Raman System

Curry powder is extensively used in Southeast Asian dishes. It has been subject to adulteration by azo dyes. This study used a newly developed 1064 nm dispersive point-scan Raman system for detection of metanil yellow and Sudan-I contamination in curry powder. Curry powder was mixed with metanil yellow and (separately) with Sudan-I, at concentration levels of 1%, 3%, 5%, 7%, and 10% (w/w). Each sample was packed into a nickel-plated sample container (25 mm × 25 mm × 1 mm). One Raman spectral image of each sample was acquired across the 25 mm × 25 mm surface area. Intensity threshold value was applied to the spectral images of Sudan-I mixtures (at 1593 cm−1) and metanil yellow mixtures (at 1147 cm−1) to obtain binary detection images. The results show that the number of detected adulterant pixels is linearly correlated with the sample concentration (R2 = 0.99). The Raman system was further used to obtain a Raman spectral image of a curry powder sample mixed together with Sudan-I and metanil yellow, with each contaminant at equal concentration of 5% (w/w). The multi-component spectra of the mixture sample were decomposed using self-modeling mixture analysis (SMA) to extract pure component spectra, which were then identified as matching those of Sudan-I and metanil yellow using spectral information divergence (SID) values. The results show that the 1064 nm dispersive Raman system is a potential tool for rapid and nondestructive detection of multiple chemical contaminants in the complex food matrix

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

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

Continuous Gradient Temperature Raman Spectroscopy of Fish Oils Provides Detailed Vibrational Analysis and Rapid, Nondestructive Graphical Product Authentication

Background: Gradient temperature Raman spectroscopy (GTRS) applies the continuous temperature gradients utilized in differential scanning calorimetry (DSC) to Raman spectroscopy, providing a new means for rapid high throughput material identification and quality control. Methods: Using 20 Mb three-dimensional data arrays with 0.2 °C increments and first/second derivatives allows complete assignment of solid, liquid and transition state vibrational modes. The entire set or any subset of the any of the contour plots, first derivatives or second derivatives can be utilized to create a graphical standard to quickly authenticate a given source. In addition, a temperature range can be specified that maximizes information content. Results: We compared GTRS and DSC data for five commercial fish oils that are excellent sources of docosahexaenoic acid (DHA; 22:6n-3) and eicosapentaenoic acid (EPA; 20:5n-3). Each product has a unique, distinctive response to the thermal gradient, which graphically and spectroscopically differentiates them. We also present detailed Raman data and full vibrational mode assignments for EPA and DHA. Conclusion: Complex lipids with a variety of fatty acids and isomers have three dimensional structures based mainly on how structurally similar sites pack. Any localized non-uniformity in packing results in discrete “fingerprint„ molecular sites due to increased elasticity and decreased torsion

Identification and Evaluation of Composition in Food Powder Using Point-Scan Raman Spectral Imaging

This study used Raman spectral imaging coupled with self-modeling mixture analysis (SMA) for identification of three components mixed into a complex food powder mixture. Vanillin, melamine, and sugar were mixed together at 10 different concentration level (1% to 10%, w/w) into powdered non-dairy creamer. SMA was used to decompose the complex multi-component spectra and extract the pure component spectra and corresponding contribution images. Spectral information divergence (SID) values of the extracted pure component spectra and reference component spectra were computed to identify the components corresponding to the extracted spectra. The contribution images obtained via SMA were used to create Raman chemical images of the mixtures samples, to which threshold values were applied to obtain binary detection images of the components at all concentration levels. The detected numbers of pixels of each component in the binary images was found to be strongly correlated with the actual sample concentrations (correlation coefficient of 0.99 for all components). The results show that this method can be used for simultaneous identification of different components and estimation of their concentrations for authentication or quantitative inspection purposes

Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments