Rapid Determination Of Freshness And Mechanical Damage Of Strawberry Based On Nondestructive Volatile Compound Detection Techniques

Strawberry is preferred by consumers for its unique flavor and nutritional quality,such as vitamins,anthocyanins,and folic acid.China is an important strawberry producing and consuming country.However,strawberry is highly perishable and vulnerable to mechanical damage during post harvest handling,which cause a high loss rate in supply chain.Rapid detection of freshness and mechanical injuries It is important to improve the efficiency of supply chain,increase information level,reduce strawberry waste and ensure food safety.Traditional non-destructive methods for detecting strawberry quality are mostly optical imaging techniques.These techniques are not suitable for detecting mechanical damage and freshness of strawberry fruit in packaging,especially during storage and transportation.Strawberry contains many volatile compound（VOC）and will change when strawberry is injured or freshness is reduced.Therefore,volatiles compounds can be a potential indicator of changes in strawberry quality.At present,there are few technologies suitable for detecting strawberry freshness and mechanical based on VOC.It is also not clear about how VOC changes in strawberries during storage and after mechanical damage.In this study,Hongyan,the most common strawberry variety in Zhejiang Province,was used.The electronic nose technology was used to detect the VOC signal of strawberry fruit.Chemometrc algorithms,such as feature extraction,pattern recognition and data modeling,were used to analyze the electronic nose data.VOC changes of strawberries with different freshness and mechanical damage levels were analyzed using gas chromatography-surface acoustic sensor(（GC-SAW）and gas chromatography-mass spectrometry（GC-MS）.The results of this study can provide technical support for real-time monitoring of strawberry quality changes in the supply chain.Morever,the representative VOC of quality deterioration of strawberry was analyzed,which is important to study the mechanism of strawberry quality changes during the supply chain,and to develop VOC based quality deterioration sensors for strawberries.The main research content and results are described as below:1.Detection strawberry with different freshness levels based on VOC.Using electronic nose response signals of strawberries with different freshness levels,the partial least squares regression（PLSR）and support vector machine（LS-SVM）models for predicting strawberry freshness were established.The optimal model was the LS-SVM-EN 120 model,which had the highest Rp value of 0.983.GC-SAW technology was used to detect the headspace VOC of strawberries nondestructively,and it was found that the peak areas of the five peaks showed increase or decrease tendencies with the change of strawberry storage time.Non-destructive GC-MS method was also used for analyzing the VOC of strawberries.The results showed that with the decrease of strawberry freshness,the relative content of methyl butyrate and methyl hexanoate gradually decreased,and the relative content of ethyl butyrate and ethyl n-hexanoate gradually increased.2.Detection strawberry with different impact damage levels based on VOC.The electronic nose signals of strawberries,which were dropped from different heights,were acquired.Then the PLSR and LS-SVM models for predicting strawberry impact levels were established,respectively.LS-SVM-EN_All was the best model,which had the Rp value of 0.931,indicating that electronic nose can be used for non-destructive detection of the impact damage levels of strawberry.Moreover,the models to determine impact levels were established based on the electronic nose data of the samples at different time points after impacted.The results were generally better than those of the models based on the samples at all time points.Furthermore,the models for distinguish whether the strawberries had impact damage or not were established.Among them,the average classification accuracy of prediction sample set of the LS-SVM models based on the samples at all time points was 94.12%;and the classification accuracy of the best LS-SVM model based on the samples at different time points was 100%.In addition,a prediction model for the impact time of strawberries was established.The Rp value of the best model,which was LS-SVM-EN_All,was 0.866.The accuracy of the prediction models based on samples with different impact levels was also better than the model based on all samples.Using SPME to collect the VOC of strawberries dropped from different height nondestructively.The results showed that 24 h after the imprct process,the relative content of D-Limonene increased significantly,and the relative content of alcohols also increased.The GC-MS results showed that（E）-2-Hexen-1-ol and trans-2-Heptenal may be the key VOCs that can reflect the impact damage of strawberry.3.Detection strawberry with different vibration damage levels based on VOC.The electronic nose was used to obtain VOC signals of strawberries with different vibration damage levels,and the used to establish the PLSR and LS-SVM models.The average Rp value of the PLSR models based on all samples was only 0.59,and that of the LS-SVM models was 0.83.The latter increased by 40.68%compared with the former.The average Rp value of the models based on samples at different storage time was greater than 0.96,and the results were generally better than the models based on all samples.Among them,the accuracy of the LS-SVM models was higher than that of the PLSR models,and the average accuracy of the prediction set was of the LS-SVM model was more than 90%.The results of models for predicting when strawberries had the vibration damage showed that the best model(LS-SVM-EN_Max)had the Rp value of0.983.Non-destructive GC-MS analysis results show that strawberries contained about30%of olefins after 0.5 h vibration,which was higher than the strawberries after 1 h and 2 h vibration.In GC-MS detection,No VOC was found that can be used to distinguish strawberry from different vibration damage levels...