عنوان مقاله [English]
Peach, as an edible fruit with an acceptable economic advantage, is mainly produced in the Mediterranean region and Central Asia and consumed all over the world. Flavor is one of the key factors in fruit quality, and it largely depends on the content of soluble sugars and organic acids. Sweetness, which is determined by the level of soluble sugars, is one of the main characteristics that affect consumer satisfaction. In the mature peach fruit, sucrose constitutes more than 54% of the total soluble sugars, which are mainly stored in the vacuole and occupy up to 90% of the total cell. However, the underlying mechanisms of sugar accumulation in peach fruit remain largely unknown.
The complexity of food odor makes it difficult to analyze them with conventional analytical techniques such as gas chromatography. However, sensory analysis by experts is a costly process and requires trained people who can only work for a relatively short period of time. Problems such as the human subjectivity of the response to smell and the variation between people should also be considered. Hence, there is a need for a tool such as an electronic nose with high sensitivity and correlation with human sensory panel data for specific applications in food control. Due to its easy construction, cheapness and the need for little time for analysis, the electronic nose is becoming an automatic non-destructive method to describe the smell of food.
An olfactory machine can recognize the fragrance composition by estimating its concentration or determining some of its intrinsic properties, which the human nose is hardly able to do. In general, the human olfactory system is a five-step process including smelling, receiving the scent, evaluating, detecting and erasing the effect of the scent. The olfactory phenomenon begins with inhaling the intended smell and ends with breathing fresh air to remove the effect of the scent. The human olfactory system, with all its unique capabilities, also has disadvantages that limit its use in quality control processes, including subjectivity, low reproducibility (for example, results depending on time, people's health, analysis before the presence of odor and fatigue is variable), time-consuming, high labor cost, adaptation of people (less sensitivity when exposed to odor for a long time). In addition, it cannot be used to evaluate dangerous odors.
The purpose of this research was to evaluate the ability and accuracy of the electronic nose using chemometrics methods to detect and differentiate peach cultivars using their volatile compounds.
First, 5 varieties of peaches were prepared. After preparing different varieties of peaches, first, the samples were placed in a closed container (sample compartment) for 1 day to saturate the space of the container with the aroma and smell of peach fruit, and then the sample compartments were used for data collection with an odor machine.
In this research, the electronic nose made in the Biosystems Engineering Department of Mohaghegh Ardabili University was used. In this device, 9 metal oxide semiconductor (MOS) sensors with low power consumption are used, which are listed in Table1.
The sample chamber was connected to the electronic nose device and data collection was done. This data collection was done in such a way that first, clean air was passed through the sensor chamber for 100 seconds to clean the sensors from the presence of odors and other gases. Then the smell of the sample was sucked from the sample chamber by the pump for 100 seconds and directed to the sensors, and finally, clean air was injected into the sensor chamber for 100 seconds to prepare the device for repetition and subsequent tests. 30 repetitions were considered for each sample.
The chemometrics method in this research, started with principal component analysis (PCA) to discover the output response of the sensors and reduce the dimension of the data. In the next step, linear discriminant analysis (LDA) was used to classify 5 peach cultivars.
Principal component analysis (PCA) is one of the simplest multivariate methods and is known as an unsupervised technique for clustering data according to groups. It is usually used to reduce the dimensionality of the data and the best results are obtained when the data are highly correlated, positively or negatively.
The scores chart (Figure 2) showed that the total variance of the data is equal to PC-1 (89%) and PC-2 (7%), respectively, and the first two principal components account for 96% of the total variance of the normalized data. When the total variance is higher than 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. So it can be concluded that the e-Nose has a good response to the smell of peaches and it is possible to distinguish peach cultivars, which shows the high accuracy of the electronic nose in identifying the smell of different products.
The LDA method was also used to identify and distinguish peach cultivars based on the output response of the sensors. Unlike the PCA method, the LDA method can extract multi-sensor information to optimize the resolution between classes. Therefore, this method was used to detect 5 varieties of peach based on the output response of the sensors. The results of the identification of cultivars were equal to 90% (Figure 3).
In this research, an olfactory machine with 9 metal oxide sensors was used to identify and differentiate peach cultivars using their scent. Chemometrics methods including PCA and LDA were used for qualitative and quantitative analysis of complex data from the electronic sensor arrays. PCA was used for data reduction and with two principal components PC1 and PC2, it described 96% of the variance of the data set and provided an initial classification, while LDA was able to accurately identify and classify grape cultivars. It became 90%. The scent machine has the ability to be used and exploited as a quick and non-destructive method to identify peach cultivars based on their smell. The use of this method in identifying peach cultivars will be very useful for consumers, especially processing units and food industries, in order to choose suitable cultivars.