Monitoring Botrytis cinerea Infection in Kiwifruit Using Electronic Nose and Machine Learning Techniques

Haghbin, Najmeh; Bakhshipour, Adel; Mousanejad, S.; Zareiforoush, Hemad

doi:10.1007/s11947-022-02967-1

Cited by 15 publications

(10 citation statements)

References 79 publications

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“…36 The results obtained by E-nose suggested that Bc development led to a greater emission of volatile organic compounds (VOCs) already in the early stages of infection. Similarly, information associated with E-nose-detectable VOCs has recently been obtained for Bc-infected kiwifruit by Haghbin et al 11 Therefore, it may be assumed that VOCs are released early by the berry tissues as a stress response during the penetration phase of the pathogen, as previously reported. 37 This process appeared to diminish before reaching a constant trend.…”

Section: Discussionsupporting

confidence: 61%

“…Obtained results agree with the clear segregation observed for these samples in the PLS‐DA score plot. Haghbin et al ., 11 in kiwifruits, obtained more robust results but using a larger sample set of fruits, which allowed the application of more efficient methods of data computation based on machine learning approaches.…”

Section: Resultsmentioning

confidence: 99%

“…9,10 In particular, E-nose technology has been recently tested for revealing Bc infection in kiwifruit. 11 In the same way, NIR spectroscopy has been increasingly adopted for evaluation of the internal quality in intact fruit and food crops, 12 even for performing a quality assessment of wine grapes and wines. 13 NIR technology has also been tested for featuring fungal infections and Botrytis presence in several horticultural crops.…”

Section: Introductionmentioning

confidence: 99%

“…Its application has already been reported as a proficient tool able to detect pests in plants 9,10 . In particular, E‐nose technology has been recently tested for revealing Bc infection in kiwifruit 11 . In the same way, NIR spectroscopy has been increasingly adopted for evaluation of the internal quality in intact fruit and food crops, 12 even for performing a quality assessment of wine grapes and wines 13 .…”

Section: Introductionmentioning

confidence: 99%

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Destructive and non‐destructive early detection of postharvest noble rot (Botrytis cinerea) in wine grapes aimed at producing high‐quality wines

Modesti,

Alfieri,

Chieffo

et al. 2023

J Sci Food Agric

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BACKGROUNDBotrytis cinerea (Bc) is the causative agent of gray mold disease in wine grape bunches. Under particular climatic and edaphic conditions, typical of some wine regions, the grapes infected by this fungus can develop noble rot, the basic phenomenon for the production of sweet botrytized wines or some high‐quality dry wines, such as Amarone. The possibility of early detection of noble rot on plants and at postharvest is an interesting option for managing botrytized wines.RESULTSThe present work aimed at early detection of noble rot and monitoring its development, at postharvest, on Trebbiano wine grapes by means of destructive and non‐destructive analytical approaches (e.g., electronic nose and near‐infrared spectroscopy). The development of Bc led to substantial modifications in grape composition, including dehydration, biosynthesis, and accumulation of different compounds due to Bc metabolism, grape stress responses, or both. However, these modifications are appreciable, notably at advanced stages of infection. Consequently, a specific focus was to monitor the infection in the first 72 h post inoculation for testing, potentially through non‐destructive technologies, and to identify the real early stages of Bc development.CONCLUSIONThe destructive chemical analyses performed over the 16 monitored days confirmed what is widely reported in the literature regarding the metabolic/compositional changes that occur following the development of Bc. Moreover, non‐destructive technologies allowed us to identify the evolution of Bc, even at early stages of its presence. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Section: Discussionsupporting

confidence: 61%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Destructive and non‐destructive early detection of postharvest noble rot (Botrytis cinerea) in wine grapes aimed at producing high‐quality wines

Modesti,

Alfieri,

Chieffo

et al. 2023

J Sci Food Agric

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“…The e-nose system contained 13 metal oxide semiconductor (MOS) gas sensors, including 8 MQ sensors (Hanwei Electronics Co., Ltd.) and 5 TGS sensors (Figaro Electronic Co., Ltd.). The specifications of the sensors are provided in articles by Haghbin et al [ 50 ] and Mirhoseini-Moghaddam et al [ 51 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems

Asadi,

Ghasemnezhad,

Bakhshipour

et al. 2024

BMC Plant Biol

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The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits.

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Machine Learning Algorithms for Smart Gas Sensor Arrays

Nath,

Bharath,

Dsouza

et al. 2024

Advanced Structured Materials

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Monitoring Botrytis cinerea Infection in Kiwifruit Using Electronic Nose and Machine Learning Techniques

Cited by 15 publications

References 79 publications

Destructive and non‐destructive early detection of postharvest noble rot (Botrytis cinerea) in wine grapes aimed at producing high‐quality wines

Destructive and non‐destructive early detection of postharvest noble rot (Botrytis cinerea) in wine grapes aimed at producing high‐quality wines

Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems

Machine Learning Algorithms for Smart Gas Sensor Arrays

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