نوع مقاله : مقاله پژوهشی
1 عضو هیات علمی دانشگاه محقق اردبیلی
2 گروه مهندسی مکانیک بیوسیستم، دانشگاه رازی، کرمانشاه، ایران
3 دانشگاه محقق اردبیلی، اردبیل، ایران
عنوان مقاله [English]
Walnut is an important economic and commercial product all over the world. The smell can be one of the key factors in determining the ripening time of the fruit and it depends on the content of the chemical compounds of the fruit and its skin. Crunchyness and easy peeling are the main features that affect the level of satisfaction of walnut consumers.
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 of the electronic nose using chemometrics methods to detect the ripening time of walnuts with the help of its volatile compounds during the ripening period.
In each process of investigation (including 5 courses and intervals were determined as one week), premature walnut samples plus its ripe samples (in the last period) from one of the gardens around Ardebil (located in the village September) It was prepared and with an electronic data nose.
In this study, the electronic nose was made in the Biosystem Engineering Department of Mohaghegh Ardebili University. The device uses 9 metal oxide semiconductor sensors (MOS) with low power consumption. The data is that the clean air was first passed through the sensor chamber for 150 seconds to clean the sensors from the smell and other gases. The sample smell was then sucked for 150 seconds by the pump from the sample chamber and directed to the sensors, and finally, clean air was injected into the sensor chamber for 150 seconds to prepare the device for recurring and subsequent tests. 20 repetitions are intended for each sample. During the above steps, the output voltage of the sensors was changed due to exposure to gases emitted from the sample (walnut aroma) and their smell response was collected and recorded by data collection cards.
The Chemometrics method in this study will begin with the analysis of the main components (PCA) to discover the sensor output response and reduce the data dimension. The next step is to classify the time of walnut proceedings using artificial neural network analysis (ANN).
The scores chart (Figure 2) showed the total variance of the total data to PC-1 (98%) and PC-2 (1%), respectively, and the first two main components make up 99%of the total variance of normal data. When the total variance is above 90 %, it means that the first two PCSs are sufficient to explain the total variance. So it can be concluded that E-nose has a good response to peach smell and can be distinguished from peach figures, which indicates the high accuracy of the electronic nose in identifying the smell of different products. These results are highly compatible with the results obtained by XU et al., In a study conducted on class 6 rice digits, the PCA method was 99.5% accurate. The artificial neural network method was also used to identify and differentiate peaches based on the output of the sensors. The results of the diagnosis of walnut proceedings were obtained by 99% (Figure 3), which was the same as the PCA method.
Aimin Li and colleagues, using an electronic nose with GC-MS tests, identified Chinese maca (MacA) at macroscopic and microscopic levels, concluding that there was a direct relationship between the Maca smell and chemical compounds (LI ET AL, 2019). Min Yee Lim and colleagues also achieved good results with the PCA method (Lim et al, 2020). They used the electronic nose to grade the quality of the Chinese commercial mum and were able to classify their quality with 94.3% accuracy, with the results of their PCA method in accordance with our research results. Arun Jana et al. (Jana et al, 2011) also used the olfactory machine with Ann, PCA and LDA to detect aromatic and non-aromatic rice, with the accuracy of the results used for the methods used, respectively: 93%, 96.5% and 80%. The results of our research were far more accurate than this study, which could be due to the presence of different volatile compounds in grape leaves.
In this study, an olfactory machine with 9 metal oxide sensors was used to handle walnuts using their smell. Chemometrics, including PCA and ANN, were used for qualitative and quantitative data analysis of electronic sensor arrays. PCA was used to reduce data and, with two main components of PC1 and PC2, described 99% of the variance of the data set and provided an initial classification, as well as the artificial neural network capable of identifying and accurately classifying grape figures with grape cultivars the accuracy was 99%. The olfactory machine has the ability to use and operate as a rapid and non -destructive way to detect walnuts from their smell. Using this method will be very useful in identifying proper harvesting time for gardeners and manufacturers, especially processing units and food industries.