Comparison of Cheese Aroma Intensity Measured Using an Electronic Nose (E-Nose) Non-Destructively with the Aroma Intensity Scores of a Sensory Evaluation: A Pilot Study

Cheese aroma is known to affect consumer preference. One of the methods to measure cheese aroma is the use of an electronic nose (e-nose), which has been used to classify cheese types, production areas, and cheese ages. However, few studies have directly compared the aroma intensity scores derived from sensory evaluations with the values of metal oxide semiconductor sensors that can easily measure the aroma intensity. This pilot study aimed to investigate the relationship between sensory evaluation scores and e-nose values with respect to cheese aroma. Five types of processed cheese (two types of normal processed cheese, one type containing aged cheese, and two types containing blue cheese), and one type of natural cheese were used as samples. The sensor values obtained using the electronic nose, which measured sample aroma non-destructively, and five sensory evaluation scores related to aroma (aroma intensity before intake, during mastication, and after swallowing; taste intensity during mastication; and remaining flavor after swallowing (lasting flavor)) determined by six panelists, were compared. The e-nose values of many of the tested cheese types were significantly different, whereas the sensory scores of the one or two types of processed cheese containing blue cheese and those of the natural cheese were significantly different. Significant correlations were observed between the means of e-nose values and the medians of aroma intensity scores derived from the sensory evaluation testing before intake, during mastication, and after swallowing. In particular, the aroma intensity score during mastication was found to have a linear relationship with the e-nose values (Pearson’s R = 0.983). In conclusion, the e-nose values correlated with the sensory scores with respect to cheese aroma intensity and could be helpful in predicting them.

Exploring the potential of polysaccharides or plant proteins as structuring agents to design cheeses with sensory properties focused toward consumers in East and Southeast Asia: a review

The focus of the global cheese industry on accessing new markets for cheese is currently driving a greater need for innovation in cheese products. Research to date suggests that, for example, East Asian consumers prefer cheeses that have a soft texture, with mild and milky flavors. Strategies for achieving such cheese characteristics are reviewed in this article. For example, incorporation of polysaccharides into cheese results in cheese with higher moisture levels and softer textures; this also results in modification of other properties such as adhesiveness, meltability and flavor release. Hydrated polysaccharides may be considered as filler particles within cheese matrices, and therefore filled gel models with suitable filler particles can be used to establish the effect of filler volume, size and surface properties on the fractural and rheological properties of cheese matrices, thus guiding the use of polysaccharides. Addition of plant proteins such as soy and pea protein can also result in cheeses with softer texture. Furthermore, it has been suggested that heat-induced gelation of soy or pea protein with casein results in a gel structure consisting of two independent protein gels, thus facilitating the design of bespoke structures by adjusting the ratio of the two proteins. Finally, it is proposed that incorporation of ingredients with sensory properties familiar to East and Southeast Asian consumers and with the capacity to achieve bespoke textures offer potential for the development of cheese products for consumers in these markets.

Using an optimal CC-PLSR-RBFNN model and NIR spectroscopy for the starch content determination in corn

Corn starch is an important material which has been traditionally used in the fields of food and chemical industry. In order to enhance the rapidness and reliability of the determination for starch content in corn, a methodology is proposed in this work, using an optimal CC-PLSR-RBFNN calibration model and near-infrared (NIR) spectroscopy. The proposed model was developed based on the optimal selection of crucial parameters and the combination of correlation coefficient method (CC), partial least squares regression (PLSR) and radial basis function neural network (RBFNN). To test the performance of the model, a standard NIR spectroscopy data set was introduced, containing spectral information and chemical reference measurements of 80 corn samples. For comparison, several other models based on the identical data set were also briefly discussed. In this process, the root mean square error of prediction (RMSEP) and coefficient of determination (Rp²) in the prediction set were used to make evaluations. As a result, the proposed model presented the best predictive performance with the smallest RMSEP (0.0497%) and the highest Rp² (0.9968). Therefore, the proposed method combining NIR spectroscopy with the optimal CC-PLSR-RBFNN model can be helpful to determine starch content in corn.