Sensing and Instrumentation for Food Quality and Safety

Andrographis paniculata Nees (A. paniculata) has been used as herbal medicine for thousands of years in China. In this work, a quick extraction method with microwave-assisted treatment was studied for a complete extraction of active compounds from A. paniculata. Furthermore, the proposed fingerprint method, enhanced fingerprint by HPLC-DAD, has the advantage of efficiency and accuracy. In comparison with common fingerprint at fixed wavelength, enhanced fingerprint compiled additional spectral data and was hence more informative. It could efficiently identify, distinguish and assess A. paniculata. So it could be used to conduct the quality control of this traditional Chinese medicine comprehensively.

Techniques using near infrared (NIR) spectroscopy for quality measurements are becoming more popular in food processing and quality inspection of agricultural commodities. NIR spectroscopy has several advantages over conventional physical and chemical analytical methods of food quality analysis. It is a rapid and non destructive method and provides more information about the components and its structure present in the food products. It can measure more than one parameter simultaneously. The NIR spectrum includes wavelengths from 750 to 3000 nm that follow immediately after the visible region (400–700 nm). Many organic compounds can be well-defined by NIR reflectance, transmittance or diffuse reflectance system. This paper reviews the application of NIR spectroscopy to several oil seeds and examines the feasibility of using this technique for peanut quality analysis. The NIR spectroscopic instrumentation has been explained briefly for a better understanding. Also needs and limitations in use of NIR spectroscopy for peanut quality analysis and grading were explained.

The main goal of this study was to develop an apparatus that makes it possible to measure both, color, refractive index and light scattering (turbidity) of red wines using a single device only. Typically such measurement of red wine quality requires three different measurement devices. As an example of the efficiency of the present device we studied the change of the thermo-optical and time-dependent optical properties of red wine samples. Here we report on change of optical properties of two Merlot wine samples, which were obtained by opening authentic red wine bottles and preserving a set of samples both at room and refrigerator temperature for 26 days, respectively. The interesting feature with both red wine samples was that the light scattering, i.e. the turbidity of the samples, increased as a function of time due to oxidation process. It is suggested that the multifunction spectrophotometer can be applied for optical inspection of red and other wine products.

The presence of sulfonamide residues in muscle foods is an important concern for consumers and regulatory agencies since these residues may pose potential health risks and result in an increase of drug-resistant bacteria. Surface enhanced Raman spectroscopy (SERS) was applied to analyze three sulfa drugs including sulfamerazine, sulfamethazine and sulfamethoxazole with concentrations ranging from 10 ng mL−1 to 5 μg mL−1. Partial least squares regression (PLS) and principal component analysis (PCA) were used for the spectral data analyses. The three sulfa drugs could be detected at concentration levels as low as 10 ng mL−1. For the quantitative analyses, the R 2 values of actual sulfa drug concentrations versus their concentrations predicted by the PLS models ranged from 0.8149 to 0.9009. Plotting of principal components based upon PCA showed clear, separated clusters between different sulfonamides. This study indicated potential for detection and determination of trace amounts of prohibited or restricted drugs with SERS technology.

This research was to study the potential of Fourier transform near-infrared (FT-NIR) diffuse reflectance spectroscopy as a nondestructive way for firmness measurement of “Qinmei” kiwifruit. NIR spectra were acquired from 800 nm to 2500 nm with different combinations of resolution (2, 4, 8, 16, and 32 cm−1) and scan number (32, 64 and 128). Original spectra, along with first and second derivative treatments, were analyzed with and without preprocessing. Two preprocessing methods were investigated, including multiplicative scattering correction (MSC) and standard normal variate (SNV). Partial least square (PLS) model for original spectra of 2 cm−1 resolution and 64 scan number without preprocessing indicated good results for calibration: correlation coefficient (r) of 0.89, root mean square error of calibration (RMSEC) of 4.09 N. The results showed that NIR diffuse reflectance spectroscopy was feasible to predict the firmness of “Qinmei” kiwifruit.

Natural variation of hardness of wheat kernels often results in overlapping hardness indices (HI) distributions between hard and soft classes as measured with the single kernel characterization system (SKCS). This is particularly true for the case of the hard white (HW) and soft white (SW) wheat classes. To address this problem, a color camera was incorporated into the SKCS system so that color and kernel size data could be combined with SKCS measurements for classification purposes. Samples of hard red (HR), soft red (SR), HW, and SW wheat were classified using the SKCS system with and without the camera and results compared. Using the camera system, errors for separating HW from SW classes were reduced to less than 5%, as compared to 17.1% using SKCS alone. Furthermore, improved data processing applied to the low-level data currently produced by the SKCS system led to greater than 50% reduction in classification errors between SW and HR as compared to using HI data alone. Similar improvements in classification accuracies for 300-kernel sample containing mixtures of SW and HW were also achieved. The 300 kernel sample classification is usually what inspectors and grain traders use to determine sample purity rather than individual kernel results. The techniques developed should aid grain inspectors in properly identifying mixtures of these two classes. Unfortunately, for the SR and HR classes, incorporating the camera data decreased classification accuracy while increasing the complexity of the system. However, SR and HR classes can be adequately distinguished with the SKCS in its current form.