Tomato, and its concentrate are important food ingredients with outstanding gastronomic and industrial importance due to their unique organoleptic, dietary, and compositional properties. Various forms of food adulteration are often suspected in the different tomato-based products causing major economic and sometimes even health problems for the farmers, food industry and consumers. Near infrared (NIR) spectroscopy and electronic tongue (e-tongue) have been lauded as advanced, high sensitivity techniques for quality control. The aim of the present research was to detect and predict relatively low concentration of adulterants, such as paprika seed and corn starch (0.5, 1, 2, 5, 10%), sucrose and salt (0.5, 1, 2, 5%), in tomato paste using conventional (soluble solid content, consistency) and advanced analytical techniques (NIR spectroscopy, e-tongue). The results obtained with the conventional methods were analyzed with univariate statistics (ANOVA), while the data obtained with advanced analytical methods were analyzed with multivariate methods (Principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares regression (PLSR). The conventional methods were only able to detect adulteration at higher concentrations (5–10%). For NIRS and e-tongue, good accuracies were obtained, even in identifying minimal adulterant concentrations (0.5%). Comparatively, NIR spectroscopy proved to be easier to implement and more accurate during our evaluations, when the adulterant contents were estimated with R2 above 0.96 and root mean square error (RMSE) below 1%.

Extension of shelf life and preservation of products are both very important for the food industry. However, just as with other processes, speed and higher manufacturing performance are also beneficial. Although microwave heating is utilized in a number of industrial processes, there are many unanswered questions about its effects on foods. Here we analyze whether the effects of microwave heating with continuous flow are equivalent to those of traditional heat transfer methods. In our study, the effects of heating of liquid foods by conventional and continuous flow microwave heating were studied. Among other properties, we compared the stability of the liquid foods between the two heat treatments. Our goal was to determine whether the continuous flow microwave heating and the conventional heating methods have the same effects on the liquid foods, and, therefore, whether microwave heat treatment can effectively replace conventional heat treatments. We have compared the colour, separation phenomena of the samples treated by different methods. For milk, we also monitored the total viable cell count, for orange juice, vitamin C contents in addition to the taste of the product by sensory analysis. The majority of the results indicate that the circulating coil microwave method used here is equivalent to the conventional heating method based on thermal conduction and convection. However, some results in the analysis of the milk samples show clear differences between heat transfer methods. According to our results, the colour parameters (lightness, red-green and blue-yellow values) of the microwave treated samples differed not only from the untreated control, but also from the traditional heat treated samples. The differences are visually undetectable, however, they become evident through analytical measurement with spectrophotometer. This finding suggests that besides thermal effects, microwave-based food treatment can alter product properties in other ways as well.

Near Infrared Hyperspectral Imaging (NIRHSI) is an emerging technology platform that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Two important problems in NIRHSI are those of data load and unserviceable pixels in the NIR sensor. Hyperspectral imaging experiments generate large amounts of data (typically > 50 MB per image), which tend to overwhelm the memory capacity of conventional computer systems. This inhibits the utilisation of NIRHSI for routine online industrial application. In general, approximately 1% of pixels in NIR detectors are unserviceable or ‘dead’, containing no useful information. While this percentage of pixels is insignificant for single wavelength imaging, the problem is amplified in NIRHSI, where > 100 wavelength images are typically acquired. This paper describes an approach for reducing the data load of hyperspectral experiments by using sample-specific vector-to-scalar operators for real time feature extraction and a systematic procedure for compensating for ‘dead’ pixels in the NIR sensor. The feasibility of this approach was tested for prediction of moisture content in carrot tissue.

The need for better quality gluten-free (GF) bread is constantly growing. This can be ascribed to the rising incidence of celiac disease or other gluten-associated allergies and the widespread incorrect public belief, that GF diet is healthier. Although there is a remarkable scientific interest shown to this topic, among the numerous studies only a few deals with commercially available products. The gap between research and commercial reality is already identified and communicated from a nutritional point of view, but up to date texture studies of commercial GF breads are underrepresented. In this study, 9 commercially available GF bread were compared to their wheat-based counterparts from texture and sensory viewpoints. Results showed that among GF loaves products, some performed significantly better at hardness and springiness attributes during the 4-day-long storage test compared to the wheat-based products. Two of GF cob breads performed significantly better or on the same level as the wheat-based cob regarding to hardness and cohesiveness during 3 days. Among sensorial properties mouth-feel, softness and smell were evaluated as significantly better or similarly for some GF versus wheat-based products. Two GF bread had more salty taste which reduced the flavor experience. Both the texture and sensory data of the storage test indicate that the quality of some GF bread products has significantly improved in the recent years; they stayed comparable with their wheat-based counterparts even for a 4-day-long storage period.

Probiotic bacteria have been associated with a unique production of aroma compounds in fermented foods but rapid methods for discriminating between foods containing probiotic, moderately probiotic, or non-probiotic bacteria remain aloof. An electronic nose (e-nose) is a high-sensitivity instrument capable of non-invasive volatile measurements of foods. In our study, we applied the e-nose to differentiate probiotic, moderately probiotic, and non-probiotic Lactobacillus bacteria strains at different fermentation time points (0th, 4th, and 11th) of milk fermentation. The pH of the changing milk medium was monitored with their corresponding increase in microbial cell counts. An e-nose with two gas chromatographic columns was used to develop classification models for the different bacteria groups and time points and to monitor the formation of the aromatic compounds during the fermentation process. Results of the e-nose showed good classification accuracy of the different bacteria groups at the 0th (74.44% for column 1 and 82.78% for column 2), the 4th (89.44% for column 1 and 92.22% for column 2), and the 11th (81.67% for column 1 and 81.67% for column 2) hour of fermentation. The loading vectors of the classification models showed the importance of some specific aroma compounds formed during the fermentation. Results show that aroma monitoring of the fermentation process with the e-nose is a promising and reliable analytical method for the rapid classification of bacteria strains according to their probiotic activity and for the monitoring of aroma changes during the fermentation process.