Felix Y.H. Kutsanedzie

Quantifying Total Viable Count in Pork Meat Using Combined Hyperspectral Imaging and Artificial Olfaction Techniques

Total viable count (TVC) of bacteria is one of the most important indexes in evaluation of quality and safety of meat. This study attempts to quantify the TVC content in pork by combining two nondestructive sensing tools of hyperspectral imaging (HSI) and artificial olfaction system based on the colorimetric sensor array. First, data were acquired using HSI system and colorimetric sensors array, respectively. Then, the individual characteristic variables were extracted from each sensor. Next, principal component analysis (PCA) was used to achieve data fusion based on these characteristic variables from two different sensor data for further multivariate analysis. In developing the models, linear (PLS and stepwise MLR) and nonlinear (BPANN and SVMR) pattern recognition methods were comparatively employed, and they were optimized by cross-validation. Compared with other models, the SVMR model achieved the best result, and the optimum results were achieved with the root mean square error of prediction (RMSEP) = 2.9913 and the determination coefficient (Rp) = 0.9055 in the prediction set. The overall results showed that it has the potential in nondestructive detection of TVC content in pork meat by integrating two nondestructive sensing tools of HSI and colorimetric sensors with SVMR pattern recognition tool.

Highly sensitive and label-free determination of thiram residue using surface-enhanced Raman spectroscopy (SERS) coupled with paper-based microfluidics

In this study, a paper-based microfluidic surface-enhanced Raman spectroscopy (SERS) device was employed for the determination of trace level thiram. The paper-based microfluidic device was fabricated by cutting a hydrophilic region which had been printed on the filter paper and then pasting it onto sellotape. The Au@Ag nanoparticles (NPs) were synthesized with a 30 nm Au core and 7 nm Ag shell and used as the SERS probe. The synthesized nanoparticles were dropped in one of the sample adding zones of the paper-based microfluidics and the thiram solution was dropped in another one. The solutions flowed through their own channels by capillary action and mixed together in the reaction chamber. The optimization studies on the use of paper-based microfluidic devices are discussed. In SERS measurements, the intensity of the peak at 1143 cm−1 was highly sensitive, and so it was chosen as an ideal peak for the quantitative analysis of the concentration of thiram solution. The limit of detection (LOD) of thiram was as low as 1.0 × 10−9 mol L−1, and the relative standard deviation (RSD) results analyzed at 10 random spots in the SERS measurement area were all below 10%. The recovery values of thiram in adulterated tea samples were from 95% to 110%. All these results suggest that this proposed method is a prospective candidate for trace level thiram detection.

Feasibility study on nondestructively sensing meat's freshness using light scattering imaging technique

Rich nutrient matrix meat is the first-choice source of animal protein for many people all over the world, but it is also highly susceptible to spoilage due to chemical and microbiological activities. In this work, we attempted the feasibility study of rapidly and nondestructively sensing meats freshness using a light scattering technique. First, we developed the light scattering system for image acquisition. Next, texture analysis was used for extracting characteristic variables from the region of interest (ROI) of a scattering image. Finally, a novel classification algorithm adaptive boosting orthogonal linear discriminant analysis (AdaBoost-OLDA) was proposed for modeling, and compared with two classical classification algorithms linear discriminant analysis (LDA) and support vector machine (SVM). Experimental results showed that classification results by AdaBoost-OLDA algorithm are superior to LDA and SVM algorithms, and eventually achieved 100% classification rate in the calibration and prediction sets. This work demonstrates that the developed light scattering technique has the potential in noninvasively sensing meats freshness.

Rapid Pseudomonas Species Identification from Chicken by Integrating Colorimetric Sensors with Near-Infrared Spectroscopy

Pseudomonas spp. are the dominant spoilage bacteria which can cause chicken spoilage. Some traditional detection methods are often unsuitable for their rapid real-time detection. Thus, in this paper, a fusion strategy based on colorimetric sensors and near-infrared spectroscopy was applied to rapidly identify Pseudomonas spp. in chicken. First, four different species of Pseudomonas—Pseudomonas gessardii, Pseudomonas psychrophila, Pseudomonas fragi, and Pseudomonas fluorescens—were isolated from putrid chicken, and then, the odor and spectral information of the Pseudomonas species and their mixture were obtained by colorimetric sensors and near-infrared spectroscopy, respectively. Thirty-six odor characteristic variables and 33 spectral characteristic variables were extracted from each technique and used for data fusion based on principal component analysis (PCA). Back-propagation artificial neural network (BP-ANN) was used to build identification model for the discrimination of the different Pseudomonas species. The results showed that the discrimination capability of the model based on data fusion was superior to that based on the two techniques independently, and eventually BP-ANN achieved 100% classification rate by cross-validation and 98.75% classification rate in predication set. This work indicates that the combination of colorimetric sensors and near-infrared spectroscopy is promising for the rapid identification of Pseudomonas species in chicken extract, and hence may be applied towards quality monitoring.

Synthesized Au NPs@silica composite as surface-enhanced Raman spectroscopy (SERS) substrate for fast sensing trace contaminant in milk

With increased concerns on milk safety issues, the development of a simple and sensitive method to detect 2,4-dichlorophenoxyacetic acid (2,4-D), a common contaminant in milk, becomes relevant in safeguarding human health threats that results from its consumption. Surface-enhanced Raman spectroscopy (SERS) shows excellent ability for various targets analysis but its usage for rapid and accurate determination of analyte via SERS presents challenges. This study attempted the quantification of 2,4-dichlorophenoxyacetic acid (2,4-D) residue in milk using a novel SERS active substrate- decorated silica films with Au nanoparticles (Au NPs@ silica) coupled to chemometric algorithms. Au NPs@ silica composite was synthesized as a SERS sensor through self-assembly. Thereafter, the SERS spectrum of 2,4-D extract from milk with different concentrations based on the developed SERS sensor was collected and the spectra were analyzed by partial least squares (PLS), and variable selection algorithms - genetic algorithm-PLS (GA-PLS), competitive-adaptive reweighted sampling-PLS (CARS-PLS) and ant colony optimization-PLS (ACO-PLS), to develop quantitative models for 2,4-D prediction. The results obtained showed that the CARS-PLS model gave the optimum result with LOD of 0.01 ng/mL realized and a determination coefficient in the prediction set of (RP) = 0.9836 within a linear range of 10-2 to 106 ng/mL was achieved. Au NPs@ silica SERS sensor combined with CARS-PLS may be employed for rapid quantification of 2,4-D extract from milk towards its quality and safety monitoring.

Prediction of amino acids, caffeine, theaflavins and water extract in black tea using FT-NIR spectroscopy coupled chemometrics algorithms

Fourier transform near-infrared spectroscopy (FT-NIRS), coupled with chemometrics techniques, was performed as a fast analysis technique to assess the quality of various components in black tea. Four PLS models, namely partial least square (PLS), synergy interval PLS (Si-PLS), genetic algorithm PLS (GA-PLS) and backward interval PLS (Bi-PLS), were established as calibration models for the quantitative prediction of amino acids, caffeine, theaflavins and water extract. The results are reported based on the lower root mean square error of cross prediction (RMSEP) and the root mean square error of cross-validation (RMSECV) as well as their correlation coefficient (R2) in the prediction set (RP) and the calibration set (RC). In addition, on the basis of fewer frequency variables, GA-PLS was found to be the best technique for the quantification of amino acids and water extract and Bi-PLS was found to be the best technique for the quantitative analysis of caffeine and theaflavins in this study. It was observed that NIR spectroscopy can be successfully combined with various chemometric techniques for the rapid identification of the chemical composition of black tea. This study demonstrates that FT-NIR spectroscopy, combined with chemometrics (GA-PLS and Bi-PLS), has the best stability and generalization performance for black tea analysis.