Nahid Aghili nategh; adieh anvar; mohammad jafar dalvand
Abstract
Introduction Sour cherry fruit (Prunus cerasus) is one of the most desirable fruit by the consumer due to its precocity and great quality. Pesticides are considered a basic ingredient of modern agricultural. Pesticides have been widely applied to protect agricultural products against detrimental pests, ...
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Introduction Sour cherry fruit (Prunus cerasus) is one of the most desirable fruit by the consumer due to its precocity and great quality. Pesticides are considered a basic ingredient of modern agricultural. Pesticides have been widely applied to protect agricultural products against detrimental pests, to ameliorate their quality, and increase product efficiency .The evaluation of pesticide residues in fruits has become too much required provisions for consumers, producers and authorities for fruit quality control. Nowadays, monitoring programmes for pesticides in food are carried out worldwide to guarantee consumer health, better management of agricultural resources, and to prohibit economic losses Acetamiprid is the most important pesticides of sour cherry. A possible tactic for defining the pesticide residues, sensing the aromatic volatiles released by fruit using e-nose. The e-noses (Electronic nose) is one of the best non-destructive methods which have shown to be well superseded for conventional methods in food odor detection Materials and Methods For detection the acetamiprid residue in sour cherry, the e-nose machine was designed and fabricated. The e-nose mainly composed of: data acquisition card (USB self-designed), sensor array, three two-way valves normally closed, vacuum pump, air filter (active carbon), GUI (LabVIEW 2014), power supply, laptop and sample chamber. The main stages of electronic nose work consist of three phases: 1- baseline 2- injection of sample odor into the sensor chamber 3- clearing the sensor array. The fractional method was employed in this research for baseline correction. Acetamipridpesticide Sprayed at 1 Liter per 1000 liters of water on cherry trees before pre-bloom in growth stage. This is a critical time for management of pests. Organic and inorganic healthy samples were collected from multiple trees sprayed and non-sprayed cherry trees and divided into four ripeness grades (RG1 = totally ripe, RG2 = close to ripeness, RG3 = intermediate to ripeness and RG4= unripe), according to the criteria used by expert growers (based on physical size and appearance as well as estimated maturity stages) during June2019. One uncontrolled (PCA) and one controlled (LDA) pattern recognition models were used to classify fruit samples. Results and Discussionorganic and inorganic sour cherries have different response patterns. This indicates that their aromatic compounds are different. Generally, in organic sour cherry MQ3sensor and in inorganic sweet cherry TGS2602 sensor had the highest response and role in detecting organic and inorganic sour cherries. PCA analysis described 89% to 96% of the variance in the diagnosis of organic and inorganic sour cherries. The value of variance in the first and second principal components changed from 63% to 91% and 17% to 26%, respectively. Organic and inorganic sour cherry in RG1, RG2, RG3 and RG4 significantly discriminated. To check the association of each sensor in the acetamipride diagnosis, loading plot, were used. In all of RGs TGS2620, TGS2610, MQ9 and TGS2611 have lowest response and sensors MQ3, TGS813, TGS2602 and TGS826 showed the highest contribution in detection acetamipride residue in sour cherry. For detection of ripeness grades of inorganic sour cherry the amount of variance in the first and second principal components was 81% and 10%, respectively. RG1 and RG2 and RG3 and RG4 overlapped. For organic sour cherry PC1 and PC2 described 63% and 26%, respectively, of the variance between samples. RG2 and RG3 overlapped. Also TGS2610, TGS2611 and TGS2620 have lowest response than to other sensors in detection RGs in organic and inorganic sour cherryLDA could specify acetamipride in sour cherry very well. The accuracy of LDA analysis for residual detection of acetamipride at 4 degrees of maturity was 83.3-100%. LDA could specify RGs of inorganic sour cherry well, but RG2 and RG3 and RG3 and RG4 have little overlap. The accuracy of the analysis was 95.83%. For organic sour cherry LDA could to distinguish RGs well, but RG3 and RG4 have little overlap. The accuracy of the analysis was 97.2% Conclusion Each two methods can be detected acetamipride, but LDA with correct classification percentage83.3-100%. are the best methods. According to the study, it can be expressed that the e-nose is a suitable instrument for detecting acetamipride residue of sour cherry and can be used with less time and cost to determine the appropriate harvest time.
