The long-term research goal is to develop rapid, simple, sensitive and specific detection strategies to identify contaminated food products to provide reliable and rapid screening methods for control of compliance, in order to ensure the integrity and safety of processed foods. Our central hypothesis is that the infrared sensor combined with chemometric analysis will provide the fingerprint power, reliability and accuracy of expensive, lab-based instruments in portable, rugged, easy-to-use systems designed for field analysis of foods. The significance of the proposed research is to provide the food industry with a system that will allow for the rapid and specific analysis of food chemical contaminants and provide tools for the reliable assessment of quality and safety, based on infrared sensors. It will enable the food manufacturer to rapidly asses the quality of their food, allowing for timely correction measures during manufacture. We expect this system to be simple to use and require minimal or no sample preparation, reducing the assay time and helping to streamline the analytical procedure so that it is more applicable to higher sample throughput and automation.
Objectives: To generate highly specific vibrational spectroscopic signature profiles of potential food contaminants by using infrared spectroscopy. To develop multivariate classification models for the reliable and reproducible identification and quantification of contaminants, determining the sensitivity and selectivity of the infrared technique based on HPLC as reference method. To evaluate the feasibility of a handheld portable infrared system for rapid screening of potential contaminants in foods and to validate the infrared sensor performance to the specifications required by industry and regulatory agencies. <P>Expected results: We will develop simple quantitation and classification models for contaminants/toxins levels in foods. Combining SPE to separate and concentrate the target toxic compound and infrared to provide spectral signature profiles will permit the chemically-based determination of contaminant levels in foods, if detected. We anticipate developing an easy-to-use, accurate and rapid system that will allow direct determination of contaminants minimizing the confounding effects of other chemical constituents in the food.
NON-TECHNICAL SUMMARY: The contamination of food by chemical and microbial hazards is a worldwide public health concern and a leading cause of trade problems internationally. Rapid and cost-effective techniques for the food industry and food safety agencies are required for effective surveillance. In recent years, the food industry and consumers have experienced several new or unsuspected contamination problems such as acrylamide, phthalates, organic pollutants, Sudan dyes, and recently melamine in dairy products. Rapid, sensitive and cost-effective detection techniques are necessary for surveillance of the food supply to prevent public health situations created by the potentially toxic effects of these hazards. Analysis of chemical food contaminants and toxins requires the development and validation of analytical methods for screening, quantification and identification of contaminants and their implementation as quality control programs and risk management systems by food producers and authorities. Our research will focus in the development of predictive models for the rapid detection, identification and classification of chemical contaminants in foods. Infrared spectral data combined with multivariate analysis techniques have the potential for the determination of food contaminants and changes in food integrity. Infrared spectroscopy allows qualitative or quantitative analysis of samples with little or no sample preparation which greatly speeds sample analysis. Spectral bands arising from functional group vibrations of organic molecules in the mid- infrared (4000-700 cm-1) region can be associated to specific chemical entities allowing for the chemically-based discrimination of organic constituents, producing distinct and reproducible fingerprints. Infrared spectroscopy combined with pattern recognition analysis is uniquely positioned for profiling of complex matrices resulting in reliable, accurate, robust and simple methods for routine analysis of adulterants or hazards in foods involving minimal personnel training and Lab supplies. Due to the use of mid-IR techniques in quality and process control applications, the food industry is familiar with the technology and the potential exists to extend its capabilities to monitor for food tampering. In 2008 Ahura Scientific launched the TruDefenderTMFT, a handheld infrared spectrometer that incorporates the analytical precision of FTIR spectroscopy to field applications, with a spectral resolution equivalent to bench-top instruments.
APPROACH: Standards of chemical contaminants (i.e. acrylamide, melamine, pesticides, rodent bait etc.) and toxins (i.e. mycotoxins) will be obtained from Sigma-Aldrich (St. Louis, MO). Stock solutions will be prepared, serially diluted to various concentrations and assayed in triplicate. Spiked samples will be analyzed to determine recoveries, sensitivity and specificity of the technology. Samples will be analyzed directly or after applied to solid-phase extraction (SPE) or immunoaffinity cartridges. For each matrix tested, a control (no toxin) sample will also be analyzed to determine background signal. An aliquot (5uL) of the samples (direct analysis or SPE/IMS concentrated) will be applied onto an ATR-IR crystal and analyzed by handheld infrared spectrometer and infrared microspectroscopy. The handheld infrared spectrometer incorporates the analytical precision of FTIR spectroscopy to field applications, with a spectral resolution equivalent to bench-top instruments. The application of an infinity corrected microscope coupled to a high performance infrared spectrometer (IRMS) will provide unique capabilities for resolving the desired signature structures. Spectra will be analyzed by using multivariate classification models for identification of potential contamination of foods. Analysis by SIMCA, a supervised method for classification, consists of assigning training sets to classes and then a principal component model is created for each class with different confidence regions. The performance of the models will be evaluated by visualization of clustering among samples by using score plots, model misclassification tests and inter-class distances. Probability clouds (95%) are built around the clusters based on PCA scores, allowing SIMCA to be used as a predictive modeling system. By using literature information on fundamental vibrations of specific functional groups and by using standards we will correlate these signals to unique cellular biomarkers. In addition, classification methods will be used to generate prediction models and model accuracy will be evaluated to predict the identities of unknown species. Quantitative models based on infrared spectra will be generated by Partial Least Squares regression (PLSR) based on the levels inoculated into the food samples. The levels will be confirmed by LC-MS or other reference method, using a standard fitted curve generated using contaminants/toxins at various concentrations. Optimum models are selected based on their lowest SECV, highest coefficient of determination, number of PCs and predictive ability. Validation of the models - Predictive ability: Determination of the performance of the PLSR models will be done with spiked food matrices with selected levels of contaminants/toxins. Recoveries will also be determined for each contaminant/toxin. The predictive ability of the generated PLSR models will be compared to the LC-MS values (reference method).