A novel FTIR discrimination based on genomic DNA for species-specific analysis of meat and bone meal

The Development of a Risk Assessment Model for Inedible Rendering Plants in Canada: Identifying and Selecting Feed Safety-Related Factors

The Canadian Food Inspection Agency (CFIA) is developing an establishment-based risk assessment model to categorize rendering plants that produce livestock feed ingredients (ERA-Renderer model) according to animal and human health risks (i.e., feed safety risks) and help in determining the allocation of inspection resources based on risk. The aim of the present study was to identify and select feed-safety-related factors and assessment criteria for inclusion in the ERA-Renderer model. First, a literature review was performed to identify evidence-based factors that impact the feed safety risk of livestock feed during its rendering processes. Secondly, a refinement process was applied to retain only those that met the inclusion conditions, such as data availability, lack of ambiguity, and measurability. Finally, an expert panel helped in selecting factors and assessment criteria based on their knowledge and experience in the rendering industry. A final list of 32 factors was developed, of which 4 pertained to the inherent risk of a rendering plant, 8 were related to risk mitigation strategies, and 20 referred to the regulatory compliance of a rendering plant. A total of 179 criteria were defined to assess factors based on practices in the Canadian rendering industry. The results of this study will be used in the next step of the model development to estimate the relative risks of the assessment criteria considering their impact on feed safety. Once implemented, the CFIA's ERA-Renderer model will provide an evidence-based, standardized, and transparent approach to help manage the feed safety risks in Canada's rendering sector.

Evaluation of the Quality of Selected White and Red Wines Produced from Moravia Region of Czech Republic Using Physicochemical Analysis, FTIR Infrared Spectroscopy and Chemometric Techniques

The FTIR-ATR method coupled with the multivariate analysis of specific spectral areas of samples was developed to characterize two white grape varieties (Sauvignon Blanc and Hibernal) and two blue grape varieties (André and Cabernet Moravia) of wine planted and harvested in the Moravia region, Czech Republic. Principal component analysis and hierarchical cluster analysis were performed using fingerprint regions of FTIR spectra for all wines. The results obtained by principal component analysis in combination with linear discriminant analysis (PCA-LDA) scores yielded clear separation between the four classes of samples and showed very good discrimination between the wine samples, with a 91.7% overall classification rate for the samples. The conducted FTIR spectroscopy studies coupled with chemometrics allowed for the swift analysis of multiple wine components with minimal sample preparation. These methods can be used in research to improve specific properties of these wines, which will undoubtedly enhance the quality of the final wine samples obtained.

FTIR differentiation based on genomic DNA for species identification of Shigella isolates from stool samples

Shigellosis is one of the major public health concerns in developing and low-income countries caused by four species of Shigella. There is an apparent need to develop rapid, cost-effective, sensitive and specific methods for differentiation of Shigella species to be used in outbreaks and health surveillance systems. We developed a sensitive and specific Fourier-transform infrared spectroscopy (FTIR) based method followed by principal component analysis (PCA) and hierarchical clustering analysis (HCA) assays to differentiate four species of Shigella isolates from stool samples. The FTIR based method was evaluated by differentiation of 91 Shigella species from each other in clinical samples using both gold standards (culture-based and agglutination methods) and developed FTIR assay; eventually, the sensitivity and specificity of the developed method were calculated. In summary, four distinct FTIR spectra associated with four species of Shigella were obtained with wide variations in three definite regions, including 1800–1550 cm⁻¹, 1550–1100 cm⁻¹, and 1100–800 cm⁻¹ distinguish these species from each other. In this study, we found the FTIR method followed by PCA analysis with specificity, sensitivity, differentiation error and correct differentiation rate values of 100, 100, 0 and 100%, respectively, for identification and differentiation of all species of the Shigella in stool samples.

A novel near infrared spectroscopy analytical strategy for meat and bone meal species discrimination based on the insight of fraction composition complexity

This study analyzed the meat and bone meal (MBM) matrix complexity from the perspective of fraction composition diversity and a classification strategy was proposed to accurately and rapidly identify the MBM species based on near infrared spectroscopy (NIRS). Partial Least Squares-Discrimination Analysis (PLS-DA) based on full samples, meat meal (MM), MBM and bone meal (BM) performed with decreasing classification errors of 0.115, 0.079, 0.044 and 0.039 which were partly caused by wide sample range; bone fraction content had positive correlation with most of MBM species differences reflected by principal component scores; and PLS-DA classification errors among MM, MBM and BM were lower than 0.013. To take fully advantage of the above results, a sequential classification strategy was proposed; near infrared spectra were selected (belong to MM, MBM or BM) and then species discrimination analysis was conducted based on the specific PLS-DA model.

FT-IR-based quantitative analysis strategy for target adulterant in fish oil multiply adulterated with terrestrial animal lipid

The interference of nontarget adulterant on FT-IR-based target adulterant quantitative analysis was explored and a sequential strategy was proposed to improve the prediction accuracy of the quantitative analysis model. Based on the FT-IR data of fish oil adulterated with terrestrial animal lipid, PLS and PLS-DA results show that quantitative analysis modeled by multiple and single adulteration data do not apply to each other; quantitative models based on the fusion of single and multiple adulteration data were established and showed a low quantitative analysis precision (higher RSD); and the sensitivity and specificity of discrimination analysis for multiply and singly adulterated fish oils both all exceed 0.910. To enhance the detection accuracy, a sequential strategy was proposed; identifying singly or multiply adulterated fish oil and then quantifying the content of adulterant was considered an efficient approach.