Olfactory Visualization Technology For In-situ Perception Of Fruit Ripeness Information

Fruits are an important source of vitamins,minerals,amino acids,and dietary fiber in the human diet,and timely information on fruit ripening is critical to safeguard fruit yield and quality.The variety and concentration of volatile organic compounds（VOCs）vary depending on the ripeness of the fruit,so it is scientifically important to realize real-time sensing of the characteristic VOCs in fruit to grade the ripeness of the fruit.As a new non-destructive detecting technique,olfactory visualization has the potential for fruit ripeness detection owing to its reasonable specificity,simplicity,and low cost.The existing olfactory visualization sensors for detecting fruit VOCs have problems of low sensitivity,inability to achieve in-situ real-time detection,complicated data acquisition methods,and low classification accuracy.This paper’s main objective is to attain in-situ non-destructive sensing of fruit ripeness information,which is carried out in four aspects:the investigation of fruit VOCs release pattern,the establishment of highly sensitive sensing method,the construction of flexible wearable sensors,and the research of in-situ non-destructive sensing system.Firstly,we investigated the release pattern of VOCs during the storage period of fruits,followed by the development of a metal-organic framework（MOF）-based gas sensing method with a nano sensitization effect.Then we designed and built a flexible,wearable olfactory visualization sensor to explore its feasibility for the in-situ acquisition of fruit ripeness information.The main findings of this paper are as follows:（1）Investigation of VOCs emission patterns during storage of fruits and construction of a quantitative VOCs gas distribution systemAiming at the diversity of types of VOCs released from fruits at different stages of ripening,Headspace solid-phase microextraction with gas chromatography-tandem mass spectrometry（HS-SPME-GC-MS）was used to investigate the release of VOCs from fruits（banana,mango,peach,and kiwi）at different stages of ripening.Firstly,we analyze the changes in appearance,hardness,and soluble solids content（SSC）during the ripening process in combination with the sensory evaluation results,and then establish a classification method for fruit ripeness.The results show that the fruit can be classified into unripe,ripe,and rotten stages according to the evolution of the appearance,hardness,and SSC during storage.On this basis,HS-SPME-GC-MS and infrared gas detection techniques were used to detect VOCs produced by fruits at the ripening and rotting stages.Trans-2-hexenal,benzaldehyde,3-carene,terpinolene,ethanol,and ethyl acetate were selected as characteristic VOCs for banana,mango,kiwi,and peach at different ripening levels based on relative content and matching factors.Subsequently,the quantitative VOCs gas distribution system was constructed to explore the olfactory visualization sensors based on conventional color-sensitive materials for the sensing of characteristic VOCs.The results show that the sensor has the potential to achieve specific sensing of the characteristic VOCs,laying the foundation for the realization of fruit ripeness information sensing.（2）Construction of a highly sensitive vinyl sensing method based on the nano sensitization effect and its sensing performance studyAiming at low concentrations of ethylene released from fruit,we established a highly sensitive ethylene sensing method with a sensitizing effect.Firstly,Pd²⁺/BB/NH₂-UiO-66composites were prepared using liquid phase adsorption,and the detection performance of Pd²⁺/BB/NH₂-UiO-66 composites for the same concentration ethylene could be improved by 16.8times compared to pure dye.Subsequently,we prepared olfactory visualization sensors by combining various Pd²⁺/BB/NH₂-UiO-66 composites to investigate their performance in sensing ethylene and fruit（banana and peach）ripeness information.The results show that the sensor has a detection limit of about 5 ppm for ethylene,which enables the detection of unripe,slightly ripe,ripe,over-ripe,and rotten bananas and peaches during storage and provides technical support for the in-situ sensing of fruit ripeness information.（3）Construction of a flexible wearable olfactory visualization sensor device and sensing performanceTo address the challenge of uneven fruit surfaces that make it difficult to achieve an effective fit to rigid devices,we designed and prepared a flexible wearable olfactory visualization device that can make good contact with the fruit surface.The olfactory visualization sensor based on dye/NH₂-UiO-66/PAN colorimetric chip was prepared using electrostatic spinning and vacuum filtration techniques,followed by an investigation of its performance in sensing characteristic VOCs.The results show that the sensor can achieve highly sensitive detection of trans-2-hexenal at 5 ppm,benzaldehyde at 12 ppm,terpinolene at 12 ppm,and 3-carene at 21 ppm.On this basis,flexible polydimethylsiloxane（PDMS）material was chosen to combine with the above-mentioned colorimetric chips to construct a flexible wearable olfactory visualization sensor for sensing banana,mango,and kiwi ripeness.Thanks to the enrichment of VOCs in the microchamber between the sensor and the fruit peel,the flexible wearable olfactory visualization sensor can detect the ripeness of banana,mango,and kiwi during the storage period,laying the foundation for the establishment of an in situ non-destructive fruit ripeness sensing system at a later stage.（4）A flexible wearable in-situ non-destructive sensing system for fruit ripeness informationTo address the problems of complex data acquisition and low classification accuracy in fruit ripeness information sensing,a flexible wearable in-situ non-destructive sensing system for fruit ripeness information was constructed by combining the microchannel flexible wearable olfactory visualization sensor and deep convolutional neural network（DCNN）classification method.A camera-based fruit ripeness sensing device was first constructed to investigate the effect of the DCNN method on fruit ripeness classification.The results show that the method was 99.91%accurate in classifying bananas and peaches at unripe,ripe,and rotten stages.To directly access fruit ripeness information,a microchannel flexible wearable olfactory visualization sensor was designed and constructed followed by the exploration of banana,mango,and kiwi ripeness information perception.The results show that the camera can directly acquire the color information of the microchannel olfactory visualization sensor and the combination of the microchannel flexible wearable olfactory visualization sensor and the DCNN method can achieve accurate classification of unripe,ripe,and rotten stages of fruits with an accuracy of 99.76%.It has specific implications for the in-situ non-destructive detection of fruit ripeness and provides a new method and idea for the construction of flexible wearable fruit ripeness in-situ sensing system in the future.