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Statistical results for the artificial neural networks pattern recognition models. Model 1: microPHAZIRTM for leaves, and Model 2: microPHAZIRTM for berries. Performance is based on means squared error (MSE).

Statistical results for the artificial neural networks pattern recognition models. Model 1: microPHAZIRTM for leaves, and Model 2: microPHAZIRTM for berries. Performance is based on means squared error (MSE).

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Article
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Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination, and subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whe...

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... followed by different letters are statistically significant based on Fisher's least significant difference (LSD) post hoc test (α = 0.05). Table 4 shows the confusion matrices for the two models developed using the spectral readings as inputs and the experimental treatments as targets. Both models displayed high accuracy in classifying the spectral readings according to the treatments, with an overall accuracy of 98% for the microPHAZIR TM leaf model (Model 1) and 97.4% for the microPHAZIR TM berry model (Model 2). ...
Context 2
... ANN models classified leaf and berry readings as a function of smoke exposure with high accuracy. The microPHAZIR TM leaf model (model 1) had the highest positive classification, with 98% accuracy (Table 4). The NIR region selected for use in Model 1 was between 1600-1800 nm in order to minimize any possible interference due to the absorption spectra of water in the region of approximately 1930 nm [69]. ...

Citations

... Not only were previous phases approaching GFP (namely GFP-M and GFP-N) classified as one group, but GFP and GFP-F were also closely associated. The artificial neural network was better at classifying complex data with efficiency and accuracy [23] which showed an ideal classification performance on GFP processed decoction pieces with a prediction rate of 100%. In aggregate, the integration strategy based on the exterior and interior characteristics is expected to provide a reference for the overall quality evaluation of other decoction pieces and monitoring dynamic processes. ...
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The intrinsic chemical components and sensory characteristics of Gardeniae fructus Praeparatus (GFP) directly reflect its quality and subsequently, affect its clinical curative effect. However, there is little research on the correlation between the appearance traits and chemical compositions of GFP during heat processing. In this study, the major components of five typical processed decoction pieces of GFP were determined. With the deepening of processing, the contents of geniposidic acid and 5-HMF gradually increased, while the contents of deacetyl-asperulosidic acid methyl ester, gardenoside, and two pigments declined. Moreover, the electronic eye, electronic tongue, and electronic nose were applied to quantify GFP’s sensory properties. It was found that the chroma values showed a downward trend during the processing of GFP. The results of odor showed that ammonia, alkenes, hydrogen, and aromatic compounds were the material base for aroma characteristics. Complex bitterness in GF was more obvious than that in other GFP processed products. Furthermore, one mathematical model was established to evaluate the correlation between the sensory characteristics and chemical composition of GFP during five different stages. A cluster analysis and neural network analysis contributed to recognizing the processing stage of GFP. This study provided an alternative method for the exterior and interior correlation-based quality evaluation of herbs.
... Much of the smoke taint research undertaken to date has been in response to recurring incidents of vineyard exposure to bushfire/wildfire smoke and has investigated: the physiological responses of grapevines to smoke exposure [6,18,19]; factors that influence the intensity of smoke taint in wine, including the timing and duration of smoke exposure [1,2], fruit maturity at harvest [20] and grape variety [6]; and strategies that mitigate the impact of smoke taint in the vineyard [12,14,[20][21][22][23] or the winery [24][25][26][27]. However, prescribed burning can also result in vineyard exposure to smoke. ...
Article
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It has been well established that bushfire/wildfire smoke can taint grapes (and therefore wine), depending on the timing and duration of exposure, but the risk of smoke contamination from stubble burning (a practice employed by some grain growers to prepare farmland for sowing) has not yet been established. This study exposed excised bunches of grapes to smoke from combustion of barley straw and pea stubble windrows to investigate the potential for stubble burning to elicit smoke taint. Increased levels of volatile phenols (i.e., chemical markers of smoke taint) were detected in grapes exposed to barley straw smoke (relative to control grapes), with smoke density and the duration of smoke exposure influencing grape volatile phenols. However, the sensory panel did not perceive wine made from grapes exposed to low-density smoke to be tainted, despite the presence of low levels of syringol providing compositional evidence of smoke exposure. During the pea stubble burn, grapes positioned amongst the burning windrows or on the edge of the pea paddock were exposed to smoke for ~15–20 and 30–45 min, respectively, but this only resulted in 1 µg/kg differences in the cresol and/or syringol concentrations of smoke-affected grapes (and 1 µg/L differences for wine), relative to controls. A small, but significant increase in the intensity of smoke aroma and burnt rubber flavor of wine made from the grapes positioned amongst the burning pea stubble windrows provided the only sensory evidence of any smoke taint. As such, had vineyards been located immediately downwind from the pea stubble burn, it is unlikely that there would have been any smoke contamination of unharvested grapes.
... Digital technologies using the low-cost e-nose in the crushing and fermentation process coupled with AI models can accurately quantify the levels of smoke-related compounds at different stages of the winemaking process 7,12 . The implementation of NIR and AI also showed high accuracy in detecting and quantifying smoke-related compounds in wine 10,11 . ...
Article
While starting a new 2021-22 grape-growing season in Australia, potential climatic anomalies such as bushfires are in the consciousness of many grape growers and winemakers. Increasing ambient temperatures has resulted in associated climatic anomalies, such as extreme wildfires in Australia, California, Siberia, Greece and Turkey as forecasted and reviewed by the latest Intergovernmental Panel on Climate Change report (IPPC-AR6). The latest IPCC report also claims a “virtually certain” increase in the frequency and intensity of heatwaves due to greenhouse emissions from burning fossil fuels. Many of these wildfires or bushfires occur in Mediterranean countries that cultivate grapes for winemaking. Recent review papers on smoke taint in wines related to mitigation techniques have assessed the state of the art research efforts related to smoke taint and developed methods for mitigation and incorporating some of the latest digital technologies for its assessment. Both reviews agreed that the best practices to remediate smoke taint in wine are activated carbon fining and reverse osmosis treatments. This article focuses on the latest advances to monitor the levels of smoke contamination in grapevines and grapes and smoke taint in wines using novel digital and non-invasive technologies. Much of these latest efforts are the product of research from the Digital Agriculture, Food and Wine group from The University of Melbourne. The latest research has produced tools, and artificial intelligence (AI) models with high accuracy (Table 1) to be deployed in near-real-time using affordable technologies either in the field between veraison to harvest and in the winery that can be accessible to winegrowers and winemakers.
... This special issue focused on the applications of AI to environmental systems related to hazard assessment in Urban, Agriculture and Forestry. A total of ten papers were published in this special issue, with topics ranging from reviewing the current climate-smart agriculture approaches for smart village development [1] to the integration of visible and infrared thermal cameras for automated urban green infrastructure monitoring on top of moving vehicles [2]; the implementation of machine learning to classify contaminant sources for urban water networks [3]; water network contamination assessment using machine learning in the UK [4]; future landscape changes, seismic and hazard assessment tested in Tabriz, Iran assessed using satellite remote sensing [5]; AI applied to a robotic dairy farm to assess milk productivity and quality traits using meteorological and cow data [6]; AI and computer vision from visible and infrared thermal images to obtain non-invasive biometrics from sheep to assess welfare [7]; the assessment of smoke contamination and smoke taint in wines due to bushfires using a low-cost electronic nose and AI [8]; the classification of smoke contaminated grapevine berries and leaves using chemical fingerprinting and machine learning [9]; and the detection of aphid infestation in wheat plants and insect-plant physiological interactions using low-cost electronic noses, chemical fingerprinting and machine learing [10]. ...
... These climatic anomalies have a specific impact on viticulture and winemaking, specifically with bushfires producing smoke contamination on leaves and berries, which are later passed to the wine through the fermentation process. These have been investigated in two studies focused on implementing digital technologies and machine learning modeling using low-cost electronic noses [8] and near-infrared spectroscopy to assess the levels of smoke contamination in berries and smoke taint in wines [9]. The models demonstrated high accuracy, showing the good potential of these approaches as practical options for grape-growers. ...
Article
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Artificial intelligence (AI), together with robotics, sensors, sensor networks, internet of things (IoT) and machine/deep learning modeling have reached the forefront to-wards the goal of increased efficiency in multitude of application and purpose. The development and application of AI requires specific considerations, approaches, and methodologies. This special issue focused on the applications of AI to environmental systems related to hazard assessment in Urban, Agriculture and Forestry. A total of ten papers were published in this special issue, with topics ranging from reviewing the current climate-smart agriculture approaches for smart village development to the integration of visible and infrared thermal cameras for automated urban green infra-structure monitoring on top of moving vehicles; implementation of machine learn-ing to classify contaminant sources for urban water networks; water network con-tamination assessment using machine learning in the UK; future landscape changes, seismic and hazard assessment tested in Tabriz, Iran assessed using satellite remote sensing; AI applied to a robotic dairy farm to assess milk productivity and quality traits using meteorological and cow data; AI and computer vision from visible and infrared thermal images to obtain non-invasive biometrics from sheep to assess welfare; assessment of smoke contamination and smoke taint in wines due to bushfires us-ing a low-cost electronic nose and AI; classification of smoke contaminated grape-vine berries and leaves using chemical fingerprinting and machine learning; and detection of aphid infestation in wheat plants and insect-plant physiological interac-tions using low-cost electronic noses, chemical fingerprinting and machine learing.
... Furthermore, smoke exposure negatively affects grape ripening, such as sugar accumulation. Previous research also showed that high levels of smoke exposure resulted in increased leaf senescence, which can also impact grape maturation and ripening [15,31]. Therefore, these factors may have reduced the aromatic compound potential of the HS and HSM treatment wines [32,33]. ...
... Field trials describing smoke and misting treatments have been previously reported by Szeto et al. [44] and Summerson et al. [31] and consisted of: (i) a control treatment (C; i.e., no smoke exposure or in-canopy misting); (ii) a control treatment with in-canopy misting (CM); (iii) high-density smoke exposure (HS); (iv) high-density smoke exposure with in-canopy misting (HSM); and (v) a low-density smoke exposure (LS). Smoke treatments were applied at approximately seven days post-veraison by pumping smoke derived from the combustion of barley straw into purpose-built tents for one hour. ...
Article
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Wine aroma is an important quality trait in wine influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, in-cluding the water status of grapevines, canopy management and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p<0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = -0.93), deca-noic acid, ethyl ester (r = -0.94) and octanoic acid, 3-methylbutyl ester (r = -0.89). The two models developed in this study may offer winemakers a rapid, cost-effective and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making.
... To date, traditional methods for assessing wine quality and the degree of smoke taint have involved the use of chromatographic techniques for the identification of aroma volatiles and trained panels [4,[39][40][41]. However, there are several drawbacks to these techniques, as they can be time-consuming in terms of sample preparation, as well as training sensory experts, which is expensive, as chromatographic techniques require costly reagents and training, and maintaining trained panels can also be expensive, as well as being destructive in their forms of assessment. ...
... However, there are several drawbacks to these techniques, as they can be time-consuming in terms of sample preparation, as well as training sensory experts, which is expensive, as chromatographic techniques require costly reagents and training, and maintaining trained panels can also be expensive, as well as being destructive in their forms of assessment. Furthermore, the results from sensory evaluations using human panels can be affected by physiological and psychological issues within the individuals, such as fatigue and decreased sensitivity to samples due to prolonged exposure [4,[39][40][41]. Thus, the use of low-cost e-noses may offer a more costeffective and accurate form of quality and smoke taint assessments. ...
Article
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The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke tainted and non-smoke tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke-taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following aging and hydrolysis of glycoconjugates.
... Smoke affecting vineyards can contaminate berries passing these smoke-related compounds to the wine known as smoke taint [35]. Different digital and non-destructive sensors coupled with AI, such as infrared thermography for canopies, have been used to detect smoke contaminated vines and near-infrared spectroscopy to detect smoke-related compounds in grapes and final wine [36][37][38]. Low-cost e-noses have also been developed to detect smoke taint in berries and wine [39]. ...
Conference Paper
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Climate change has posed major risks for viticulture and winemaking around the world, related to increased ambient temperatures, the variability of rain events, higher occurrence and intensity of climatic anomalies, such as frosts and bushfires. These changes have directly impacted grapevine phenology by compressing stages and pushing forward in time harvest to hottest months, producing a dual warming effect. Bushfires also directly impact berry smoke contamination, which can be passed to the wine in the winemaking process producing smoke taint. Due to these events' complexities and their effects on viticulture and winemaking, a smarter approach is required to obtain relevant information and process it efficiently for more appropriate decision-making by different practitioners. In the last 10 years, artificial intelligence has offered various applications to be included in viticultural and winemaking operations, which has rendered important advances and information to deal with climate change adversities.
... Bushfires are a common occurrence globally, in places such as Australia, South Africa, Mediterranean Europe, and North and South America [1][2][3][4][5][6]. Unfortunately, climate change effects, such as increases in temperature, winds, and drought, have led to more favorable bushfire conditions [7][8][9][10][11][12][13]. Recent climate research predicts an increase of 15-70% in the number of days of "very high" or "extreme" fire danger by 2050 and a lengthening of the fire season, resulting in more frequent and intense bushfires [12][13][14][15][16]. ...
... Their results showed that the thicker-skinned grapes absorbed less guaiacol and 4-methylguaiacol than thinner skinned grapes. Ultimately, it is believed that volatile phenols accumulate primarily in grape berries' skin and to a lesser extent in the pulp and seeds [2,5,18,41,53]. ...
... Furthermore, the duration of smoke exposure, the concentration of smoke components, and other plant stressors such as nutrient status, drought, and high temperatures may also affect the physiological response and recovery of grapevines following smoke exposure [21,44]. For example, research by Summerson et al. [5] found that the intensity of smoke exposure affected the extent of stomatal closure and hence g s in Cabernet Sauvignon grapevines. Further studies should, therefore, take all variables into account [21]. ...
Article
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Grapevine smoke exposure and the subsequent development of smoke taint in wine has resulted in significant financial losses for grape growers and winemakers throughout the world. Smoke taint is characterized by objectional smoky aromas such as "ashy", "burning rubber", and "smoked meats", resulting in wine that is unpalatable and hence unprofitable. Unfortunately, current climate change models predict a broadening of the window in which bushfires may occur and a rise in bushfire occurrences and severity in major wine growing regions such as Australia, Mediterranean Europe, North and South America, and South Africa. As such, grapevine smoke exposure and smoke taint in wine are increasing problems for growers and winemakers worldwide. Current recommendations for growers concerned their grapevines have been exposed to smoke are to conduct pre-harvest mini-ferments for sensory assessment and send samples to a commercial laboratory to quantify levels of smoke-derived volatiles in the wine. Significant novel research is being conducted using spectroscopic techniques coupled with machine learning modeling to assess grapevine smoke contamination and taint in grapes and wine, offering growers and winemakers additional tools to monitor grapevine smoke exposure and taint rapidly and non-destructively in grapes and wine.
... There has also been significant research investigating the use of ANNs for the detection of grapevine smoke contamination and smoke taint compounds in wine. Using grapevine berry and leaf NIR readings as inputs, ANN models have been developed to classify the spectral readings according to smoke exposure levels with high accuracy (Summerson et al., 2020). Other ANN models have been developed using readings obtained from a low-cost electronic nose (E-nose) to accurately predict levels of smoke compounds in wines (Fuentes et al., 2020). ...
... Using grapevine berry and leaf NIR readings as inputs, ANN models have been developed to classify the spectral readings according to smoke exposure levels with high accuracy (Summerson et al., 2020). Other ANN models have been developed using readings obtained from a low-cost electronic nose (E-nose) to accurately predict levels of smoke compounds in wines (Fuentes et al., 2020). Furthermore, other research has used NIR spectroscopy within the region of 700-1100 nm to develop a model for quantifying levels of guaiacol glycoconjugates in berries and wine and levels of guaiacol in wine (Fuentes et al., 2019). ...
... The experimental site is located at the University of Adelaide's Waite campus in Urrbrae, South Australia (34°58'S, 138°38'E). The experiment was conducted during the 2018/2019 season, as previously described by Szeto et al. (2020) and Summerson et al. (2020). The grapevines were planted at 2.0 and 3.3 m spacing between vines and rows, respectively, and trained to a bilateral cordon and vertical shoot-positioned trellis system (VSP). ...
Article
Full-text available
The number and intensity of wildfires are increasing worldwide, thereby also raising the risk of smoke contamination of grapevine berries and the development of smoke taint in wine. This study aimed to develop five artificial neural network (ANN) models from berry, must, and wine samples obtained from grapevines with different levels of smoke exposure (i) Control (C), i.e., neither misting nor smoke exposure; (ii) Control with misting (CM), i.e., in-canopy misting but no smoke exposure; (iii) low-density smoke treatment (LS); (iv) high-density smoke treatment (HS) and (v) a high-density smoke treatment with misting (HSM). Models 1, 2 and 3 were developed using the absorbance values of near-infrared (NIR) berry spectra taken one day after smoke exposure to predict levels of 10 volatile phenols (VP) and 18 glycoconjugates in grapes at either one day after smoke exposure (Model 1: R= 0.98; R2= 0.97; b= 1) or at harvest (Model 2: R= 0.98; R2= 0.97; b= 0.97), as well as six VP and 17 glycoconjugates in the final wine (Model 3: R= 0.98; R2= 0.95; b= 0.99). Models 4 and 5 were developed to predict the levels of six VP and 17 glycoconjugates in wine. Model 4 used must NIR absorbance spectra as inputs (R= 0.99; R2= 0.99; b= 1.00), while Model 5 used wine NIR absorbance spectra (R= 0.99; R2= 0.97; b= 0.97). All five models displayed high accuracies and could be used by grape growers and winemakers to non-destructively assess at near real-time the levels of smoke-related compounds in grapes and/or wine in order to make timely decisions around grape harvest and smoke taint mitigation techniques in the winemaking process.